Food truck management SaaS

Truckly

Profit Soars, Waste Disappears

Truckly equips independent food truck owners with a mobile-first dashboard that tracks inventory and sales in real time—even offline—sends smart restock alerts, and auto-generates vendor lists. It slashes prep time, reduces food waste, and keeps best-sellers stocked, empowering operators to maximize daily profits amid fast-changing, unpredictable demand.

Subscribe to get amazing product ideas like this one delivered daily to your inbox!

Truckly

Product Details

Explore this AI-generated product idea in detail. Each aspect has been thoughtfully created to inspire your next venture.

Vision & Mission

Vision
To empower every food truck owner worldwide to maximize profits and sustainability with intelligent, effortless mobile operations.
Long Term Goal
By 2028, empower 10,000 food trucks worldwide to increase profits by 25% and cut food waste by 30%, establishing Truckly as the global standard for mobile food operations.
Impact
Reduces inventory prep time by 40% and food waste by 25% for independent food truck owners within two months, ensuring top-selling menu items stay in stock and increasing weekly profits by shortening restock cycles and eliminating costly inventory errors.

Problem & Solution

Problem Statement
Independent food truck owners face constant inventory shortages and excess waste due to unpredictable demand and limited storage, while generic restaurant POS systems lack real-time tracking and offline functionality, forcing inefficient, error-prone manual processes that hurt profits.
Solution Overview
Truckly links real-time sales and inventory on a mobile dashboard built for offline use, sending instant restock alerts and auto-generating vendor order lists so food truck owners never run out of top-selling ingredients—even when working from unpredictable locations.

Details & Audience

Description
Truckly streamlines inventory and order management for independent food truck owners, slashing prep time and cutting food waste. Operators gain real-time supply tracking, smart restock alerts, and automatic vendor lists—keeping popular menu items stocked and boosting profits. Unlike generic POS tools, Truckly’s mobile-first dashboard works offline and adapts on the go, matching the fast-paced, unpredictable food truck environment.
Target Audience
Independent food truck owners (25-50) minimizing stockouts and waste, constantly adapting to unpredictable daily demand.
Inspiration
Standing in line at a bustling taco truck, I watched the owner scribble on sticky notes and frantically call suppliers after running out of her best-selling tacos. Each missed sale sparked visible frustration. Seeing her juggle orders, track inventory by hand, and lose profit in real time made me realize food trucks need a management tool as nimble as they are—Truckly was born from that spark.

User Personas

Detailed profiles of the target users who would benefit most from this product.

M

Mobile Maverick Maya

- Age: 28, single entrepreneur - Degree in marketing, self-taught analytics - Rotates across 3 city districts weekly - $45k–$60k annual revenue per truck

Background

After starting as a pop-up café barista, Maya discovered high foot-traffic zones tripled sales overnight. She launched her own roving taco bar, refining routes based on daily customer flows and data-driven experiments.

Needs & Pain Points

Needs

1. Real-time location-based sales data for daily route planning 2. Quick offline stock alerts to prevent mid-shift shortages 3. Automated hotspot heatmaps for high-traffic area identification

Pain Points

1. Wasting time manually mapping daily customer flows in Excel 2. Running out of key ingredients during unplanned location shifts 3. Overordering supplies for low-traffic areas, leading to spoilage

Psychographics

- Adventure-driven risk-taker seeking new hotspots - Metrics-obsessed, thrives on instant feedback - Agile problem-solver, adapts routes daily - Freedom-valuing entrepreneur chasing peak crowds

Channels

1. Instagram Stories: daily location updates 2. Google Maps: live traffic overlays 3. Facebook Groups: local vendor communities 4. WhatsApp: team coordination chats 5. SMS Alerts: instant stock notifications

E

Eco-Efficient Eli

- Age: 35, partnered small-business owner - Environmental science degree, LEED certified - Sells at urban farmers markets - $70k annual revenue reinvested in sustainability

Background

Eli began composting household waste and sourcing local produce in college. Transitioned from a catering job to launching a zero-waste burger truck, trialing biodegradable packaging and donating surplus food to shelters.

Needs & Pain Points

Needs

1. Detailed waste analytics to pinpoint disposal hotspots 2. Eco-friendly supplier recommendations based on data insights 3. Customizable reports on carbon footprint reductions

Pain Points

1. Unpredictable spoilage rates in changing weather conditions 2. High-priced sustainable packaging limits profit margins 3. Difficulty matching supply orders to volatile demand

Psychographics

- Values minimal waste, champions sustainable sourcing - Data-driven, tracks carbon and cost savings - Community-oriented, partners with local farmers - Cost-conscious innovator balancing green and profit

Channels

1. LinkedIn: sustainability professional network 2. Sustainable Food Blogs: research insights 3. Instagram: eco-brand storytelling 4. Local Co-op Newsletters: vendor bulletins 5. Email: detailed performance reports

F

Flash-Sale Felix

- Age: 30, marketing consultant turned vendor - MBA in marketing, digital advertising minor - Central business district location - Peaks $2k per flash-sale event

Background

After leading flash campaigns at startups, Felix launched his own shawarma truck. He perfected timed promotions, testing various discounts and measuring real-time sales impact to optimize clearance.

Needs & Pain Points

Needs

1. Real-time flash promotion scheduling within app 2. Immediate analytics on discount performance impact 3. Easy inventory adjustments for promotional items

Pain Points

1. Delayed sales reports hamper timely deal adjustments 2. Overstock leftover after unsuccessful promotion attempts 3. Lack of dynamic discounting tools in dashboards

Psychographics

- Thrives on creating high-energy sales events - Competitive mind pursuing record-breaking daily deals - Analytically tests pricing strategies rapidly - Enjoys instant feedback through sales spikes

Channels

1. SMS: urgent deal alerts 2. Instagram Live: flash-sale announcements 3. Twitter: quick discount updates 4. Push Notifications: in-app sale pings 5. Email Campaigns: targeted promo blasts

N

Night-Shift Nora

- Age: 42, former restaurant shift manager - Operates 7pm–3am, six nights weekly - Located near nightlife districts - $100k consistent annual sales

Background

Nora honed her skills managing 24-hour diners, mastering late-hour ordering. Transitioned to her own food truck catering bar-goers and travelers, adjusting supply orders based on fluctuating midnight demand.

Needs & Pain Points

Needs

1. Night-mode dashboard for low-light visibility 2. Offline alerts when mobile signals drop 3. Accurate mid-shift inventory checks

Pain Points

1. Glare-heavy screens strain eyes during night shifts 2. Dead zones disrupt real-time data sync 3. Overlooking ingredient shortages until peak hours

Psychographics

- Embraces high-pressure late-night hustle - Relies on disciplined routine after dusk - Prioritizes reliability in unpredictable environments - Values system stability in low connectivity

Channels

1. WhatsApp: team coordination at night 2. SMS: critical stock alerts 3. In-App Night Mode: low-light interface 4. Twitter DMs: quick vendor queries 5. Local Radio Ads: event reminders

S

Seasonal Sam

- Age: 50, small-town fair veteran - Operates June–September fair circuit - Crews up with family teams - $150k peak-season revenue

Background

Sam inherited his carnival food stand, refining bulk-order formulas over decades. He adapts menus each season, balancing limited prep window with fluctuating fair attendance to maximize profits.

Needs & Pain Points

Needs

1. Bulk order forecasting for seasonal surges 2. Quick vendor list generation per event 3. Efficient shutdown inventory reconciliation tools

Pain Points

1. Overstock leftovers post-season cause waste expenses 2. Vendor sourcing delays before event deadlines 3. Manual inventory count errors under time pressure

Psychographics

- Tradition-minded, honors family business legacy - Pragmatic planner for finite selling periods - Risk-averse, avoids overstock before events - Enjoys high-energy festival atmospheres

Channels

1. Facebook Events: fair schedules updates 2. Local Fair Websites: official vendor portals 3. Email Newsletters: pre-season supplier offers 4. WhatsApp Groups: family coordination chats 5. SMS Alerts: urgent restock notifications

Product Features

Key capabilities that make this product valuable to its target users.

Demand Allocator

Automatically distributes inventory across each stop based on historical sales, route-specific trends, and upcoming event data. Ensures high-demand locations receive sufficient stock while minimizing overage at quieter stops, boosting sales and reducing waste.

Requirements

Historical Sales Data Ingestion
"As a food truck operator, I want the system to ingest and preprocess my historical sales data accurately so that the Demand Allocator can make informed stock distribution recommendations."
Description

Ingest and preprocess historical sales data including timestamps, item details, and stop locations to serve as the foundation for demand calculations. Ensure seamless integration with both online and offline data sources, normalize and clean the data for consistency, and store it in a structured format optimized for analytical queries.

Acceptance Criteria
Batch Ingestion from Online Source
Given a valid online sales data source with complete timestamps, item details, and location identifiers When the ingestion pipeline runs Then all records for the past six months are imported into the staging area within 5 minutes with no missing or duplicated entries
Offline Data Synchronization
Given sales data collected offline on the device When the device regains network connectivity Then all offline records are synced to the central repository without duplicates, within 10 minutes, and confirmed via sync logs
Data Normalization and Cleaning
Given raw sales records with inconsistent formats When the preprocessing job executes Then timestamps are converted to ISO 8601 format, item names are matched to the master product list, and stop locations are standardized to predefined codes with invalid entries flagged for review
Structured Storage for Analytical Queries
Given cleaned and normalized sales data When stored in the data warehouse Then data is partitioned by date, indexed by stop location, and total query time for retrieving a day's data does not exceed 2 seconds
Error Logging and Retry Handling
Given a corrupted or malformed sales record When the pipeline encounters the record Then the error is logged with record details to the error log table, the record is skipped, and processing of subsequent records continues without interruption
Route-specific Trend Analysis
"As a food truck operator, I want the system to analyze route-specific sales trends so that I can optimize inventory for each stop based on past performance."
Description

Analyze sales patterns and performance metrics for each designated route and stop, identifying peak periods, slow intervals, and item preference variations. Implement time-series analysis algorithms and visualization tools to highlight trends, enabling granular insights into location-based demand fluctuations.

Acceptance Criteria
Route Trend Dashboard Loading
Given the user navigates to the Route Trend Analysis dashboard for “Downtown Loop”, When the page loads, Then sales volume, waste levels, and restock alerts for the past 30 days are displayed within 5 seconds.
Peak and Slow Periods Identification
Given historical sales data exists for “City Center Route”, When the analysis runs for the selected 7-day window, Then the system highlights at least three peak hours and three slow hours per day in a time-series chart.
Item Preference Variation Report Generation
Given multiple menu items sold on “North Avenue Route”, When the user requests an item variation report, Then the report displays percentage changes in sales volume per item for each stop and flags any item with >20% increase or decrease.
Time-Series Algorithm Accuracy Validation
Given a test dataset with known demand fluctuations, When the time-series analysis algorithm processes the data, Then the output matches expected peak and trough timestamps with ≥95% accuracy.
Visualization Filtering and Interaction
Given the trend chart is displayed for “University Route”, When the user applies date range and item filters, Then the chart updates dynamically within 2 seconds to reflect only the selected parameters.
Event-based Demand Prediction
"As a food truck operator, I want the system to incorporate upcoming events into demand forecasts so that I can anticipate and prepare for spikes in sales."
Description

Integrate external event data—such as local festivals, weather forecasts, and public holidays—via APIs to adjust demand forecasts dynamically. Develop a weighted scoring model that combines historical trends with upcoming event factors to predict spikes or dips in customer demand at specific stops.

Acceptance Criteria
Incorporate Local Festival Data
Given the event API returns a local festival with expected attendance >1000 for Stop A within 48 hours, when the demand forecast recalculation runs, then the forecasted units for Stop A increase by at least 20% compared to the baseline historical average.
Weather-Influenced Demand Adjustment
Given the weather API predicts rain with precipitation probability ≥50% on service day, when recalculating demand, then predicted sales for cold beverages decrease by ≥15% and hot items increase by ≥10%.
Public Holiday Demand Surge
Given an upcoming national public holiday coinciding with Stop B’s service date, when predicting demand, then the total forecast for Stop B increases by 25% compared to the average for the same weekday.
Event Data API Failure Handling
Given the event data API request times out or returns an error, when retrieving event data, then the system falls back to historical-trend-only forecasting and logs an API failure alert.
Weighted Scoring Model Accuracy
Given a validation dataset of historical sales and associated event factors, when evaluating the weighted scoring model, then the mean absolute error (MAE) of demand predictions is ≤10%.
Dynamic Inventory Distribution Engine
"As a food truck operator, I want the system to automatically allocate inventory across my stops so that busy locations receive sufficient stock while minimizing overage at quieter stops."
Description

Automatically calculate optimal inventory splits across multiple stops by balancing predicted demand, truck capacity constraints, and perishability considerations. Generate actionable allocation recommendations and dispatch lists, allowing operators to configure thresholds and adjust allocations manually if needed.

Acceptance Criteria
Peak Lunch Rush Allocation
Given a scheduled lunch route with stops A, B, and C and historical sales data indicating Stop B demand peaks at noon, when the engine runs allocations for a 12:00 route, then the system allocates ≥50% of total sandwich inventory to Stop B, distributes remaining inventory proportionally across A and C, and ensures total allocation does not exceed truck capacity.
Low-Demand Evening Route
Given an evening route with historically low sales at all stops, when the allocation engine runs, then it assigns inventory quantities that do not exceed 120% of each stop’s predicted demand, leaves any surplus in the truck as buffer stock, and alerts the operator if buffer exceeds 10% of total capacity.
Unexpected Event Surge
Given incoming event data showing a 200% demand surge at Stop C two hours before arrival, when the engine recalculates allocations, then it adjusts Stop C’s allocation to cover at least 150% of the surge-predicted demand, reduces allocations to other stops accordingly, and generates a real-time restock alert if total inventory is insufficient.
Manual Adjustment Override
Given the operator sets a custom allocation threshold of 80% demand coverage for Stop A, when the engine applies allocations, then it overrides the default threshold, allocates inventory based on the 80% setting, flags the manual override in the UI, and persists the setting for subsequent runs.
Perishability Constraint Handling
Given perishable items with a 4-hour shelf life on a 6-hour route, when the engine generates allocations, then it schedules perishable item distribution only to the first two stops (within 4 hours), excludes later stops from perishable allocations, and marks any unallocated perishables for return.
Offline Sync and Update Mechanism
"As a food truck operator working in areas with poor connectivity, I want the Demand Allocator to operate offline so that I can still receive and adjust recommendations without internet access."
Description

Ensure the Demand Allocator functions seamlessly without internet connectivity by caching relevant data and calculations locally on the mobile device. Implement a robust sync mechanism that resolves conflicts, updates backend systems when reconnected, and preserves allocation recommendations for offline review and adjustments.

Acceptance Criteria
Caching Demand Allocator Data Offline
Given the mobile app goes offline before running the Demand Allocator When the operator initiates inventory distribution Then the app uses locally cached historical sales, route-specific trends, and upcoming event data to generate allocation recommendations without errors
Local Conflict Resolution on Reconnection
Given the operator made manual adjustments to allocations while offline When the device reconnects to the internet Then the sync mechanism merges local changes with backend data, resolving conflicts by prioritizing the most recent timestamp and logs each resolution
Sync of Backend Updates Post-Offline Session
Given new sales and event data occurred on the backend while the app was offline When the device reconnects Then the app retrieves and applies all backend updates to local cache and confirms data consistency with no data loss
User Review and Adjustment Offline
Given allocation recommendations are displayed offline When the operator modifies quantities or stops Then the app saves these changes locally and flags them for synchronization when online without blocking further offline usage
Data Integrity After Multiple Offline Sessions
Given the operator experiences several offline-to-online cycles with allocation changes each time When the device reconnects the nth time Then the sync process ensures no duplicates or missing entries and the final allocation results reflect all cumulative changes

BufferBoost

Calculates dynamic safety stock levels by analyzing variability in demand and route uncertainty. Adds an adjustable buffer to your restock list to safeguard against unexpected surges or delays, keeping you prepared for any on-the-road fluctuations.

Requirements

Real-time Demand Variability Analysis
"As a food truck operator, I want the system to analyze my sales data in real time so that it can accurately determine demand variability and adjust safety stock levels accordingly."
Description

Implement an analytics engine that processes incoming sales and inventory data in real time to calculate demand variability metrics (e.g., standard deviation, coefficient of variation) for each menu item. This engine must integrate seamlessly with the existing data pipeline, providing continuous updates to the safety stock calculator and ensuring the system adapts instantly to changing sales patterns.

Acceptance Criteria
Initial Variability Metric Computation
Given the analytics engine has historical sales data for each menu item for at least 30 minutes, when the engine starts, then it calculates the standard deviation and coefficient of variation for each item and stores each metric in the database within 5 seconds.
Continuous Real-time Variability Updates
Given an incoming sales or inventory update event, when the event is processed, then the engine recalculates the demand variability metrics for the affected menu item and updates both the in-memory cache and the safety stock calculator within 2 seconds.
Integration Verification with Data Pipeline
Given a stream of 1,000 sales and inventory events published to the Kafka topics, when the analytics engine consumes and processes them, then there is zero event loss or duplication and the engine maintains a processing throughput of at least 500 events per second.
Peak Hour Variability Adaptation
Given a simulated sales rate increase of 200% during peak hours, when the engine processes the increased volume, then the coefficient of variation for each menu item increases by at least 10% and triggers an updated safety stock recommendation.
Error Handling for Data Anomalies
Given malformed or incomplete data events arriving in the pipeline, when the engine encounters these events, then it logs the anomaly, skips the faulty event without crashing, and continues processing subsequent events while recording the error in an audit log.
Route Uncertainty Assessment Module
"As a food truck operator, I want the system to factor in route uncertainties so that my stock buffer reflects potential delays and I’m not caught off guard on the road."
Description

Develop a module that evaluates route-specific uncertainties, including traffic patterns, weather conditions, and historical delivery deviations. This module should score each planned route on its level of risk and feed this score into the buffer calculation, allowing the system to dynamically adjust safety stock based on the likelihood of delays or disruptions.

Acceptance Criteria
Route Risk Scoring Calculation
Given a planned route with available traffic patterns, weather conditions, and historical delivery data, when the module evaluates the route, then it generates a composite risk score as a numeric value between 0 and 1 with precision to two decimal places within 5 seconds.
Traffic Data Integration
Given the real-time traffic feed updates every 5 minutes, when the module ingests the latest traffic data, then it accurately maps congestion levels to each route segment and incorporates this factor into the risk score calculation.
Weather Impact Assessment
Given forecasted weather conditions for the route’s timeframe, when the module retrieves parameters such as precipitation, wind speed, and temperature, then it computes a weather impact factor and integrates it into the overall route risk score.
Historical Delivery Deviation Analysis
Given at least 30 past delivery records for the same route, when the module analyzes these records, then it calculates the standard deviation of delivery times and includes this metric in the composite risk score.
Buffer Adjustment Feedback Loop
Given a computed route risk score, when the BufferBoost feature determines safety stock, then it adjusts the buffer quantity within a configurable range (±20% of the base stock) and logs the adjustment decision in the restock list.
Adjustable Buffer Configuration Interface
"As a food truck operator, I want to customize my buffer settings so that I can balance waste reduction with readiness for unexpected surges."
Description

Design a user interface within the dashboard that allows operators to adjust buffer parameters such as maximum allowed buffer percentage, minimum threshold levels, and item-specific buffer rules. The interface should offer default recommendations based on analytics while permitting manual overrides to tailor safety stock levels to unique business needs.

Acceptance Criteria
Loading Default Buffer Recommendations
Given the user navigates to the Buffer Configuration Interface with no prior adjustments, When the interface loads, Then the default maximum buffer percentage, minimum threshold levels, and item-specific buffer recommendations appear based on analytics.
Manual Override of Buffer Percentage
Given the user views a default buffer recommendation, When the user edits the buffer percentage field to a new value within allowed limits and saves, Then the interface updates to show the overridden value and flags it as manually adjusted.
Defining Item-Specific Buffer Rules
Given the user selects a specific inventory item, When the user sets a custom buffer rule (percentage or quantity) within the global min/max range and confirms, Then the rule is saved and applied exclusively to restock calculations for that item.
Validating Threshold Input Constraints
Given the user enters a buffer percentage or threshold outside the configured global range, When the user attempts to save the invalid input, Then an inline validation message appears describing the allowed range and the save action is blocked.
Persisting and Reflecting Saved Buffer Settings
Given the user has modified buffer parameters and saved changes, When the user closes and reopens the dashboard (online or offline), Then the last saved buffer settings are retrieved and displayed accurately in the configuration interface.
Automated Restock Recommendation Engine
"As a food truck operator, I want the system to provide prioritized restock lists so that I can quickly order the right quantities without manual calculations."
Description

Build an engine that synthesizes demand variability and route uncertainty outputs to auto-generate restock lists with recommended quantities. The engine should rank items by urgency and visually highlight those exceeding buffer thresholds, making restocking decisions faster and more accurate.

Acceptance Criteria
High-Demand Item Ranking During Peak Sales
Given real-time sales data and demand variability inputs, when the engine generates the restock list, then the top 5 items with the highest projected sales growth over the next 2 hours are ranked first.
Buffer Threshold Highlighting
Given calculated buffer thresholds for each item, when an item’s recommended quantity exceeds its buffer, then the item entry in the restock list is visually highlighted in red and an alert notification is generated.
Offline Data Synchronization
Given the device is offline with cached demand and route uncertainty data, when the engine runs, then it generates a restock list using the last known data, queues it locally, and upon network reconnection, synchronizes without data loss.
Route Delay Buffer Adjustment
Given a detected route delay of more than 30 minutes, when the engine recalculates buffers, then buffer quantities increase by 10% for every additional 15 minutes of delay beyond the initial 30 minutes.
Real-Time Inventory Update Integration
Given new sales or restocks recorded in the field, when inventory levels change, then the engine incorporates updated stock levels into its next restock recommendation within one minute of the change.
Offline Safety Stock Calculation
"As a food truck operator operating in remote locations, I want the buffer calculations to work offline so that I can continue restocking decisions without internet."
Description

Enable the safety stock calculation and buffer adjustment logic to run locally when the device is offline, syncing results with the server once connectivity is restored. This capability ensures continuous reliability for operators working in areas with poor or intermittent internet access.

Acceptance Criteria
Offline Calculation Initialization
Given the device is offline and the user triggers a safety stock calculation, when the calculation logic runs locally, then the system computes safety stock using the most recent demand and route data without error and stores the results locally.
Buffer Adjustment Offline
Given the device is offline and variability in demand increases by ≥20%, when the buffer adjustment logic runs locally, then the computed buffer automatically scales according to the new variability rules and is reflected in the restock list.
Offline-to-Online Sync
Given calculations were performed offline, when the device regains connectivity, then the local safety stock results and buffer adjustments automatically sync to the server within 2 minutes and appear in the central dashboard with matching timestamps.
Conflict Resolution on Sync
Given the same safety stock record was updated both locally and on the server before sync, when syncing occurs, then the system merges changes using last-write-wins logic and logs a conflict resolution event accessible in the sync history.
User Notification for Offline Mode
Given the device enters offline mode during a scheduled calculation, when calculations complete locally, then the user receives an in-app notification indicating 'Calculation completed offline; results will sync when online' with no loss of functionality.
Buffer Threshold Alert Notifications
"As a food truck operator, I want to receive alerts when my stock nears buffer limits so that I can restock proactively and avoid stockouts."
Description

Implement a notification system that alerts operators via in-app messages or push notifications when any item's projected stock falls within a configurable buffer threshold. Alerts should include recommended actions and direct links to adjust restock orders or buffer settings.

Acceptance Criteria
Single Item Buffer Threshold Reached Alert
Given an item’s projected stock level falls within its configured buffer threshold When the system performs its hourly inventory projection check Then an in-app message or push notification is sent within 60 seconds containing the item name, current projected stock, buffer threshold, recommended restock quantity, and a direct link to adjust restock orders or buffer settings
Multiple Items Buffer Threshold Notification Summary
Given more than one item’s projected stock levels fall within their configured buffer thresholds When the daily buffer check completes Then a single consolidated notification listing all affected items with individual projected stock, buffer thresholds, and recommended actions is sent to the operator
Deep Link to Adjust Restock Order from Notification
Given an operator taps the direct link in a buffer threshold alert When the app handles the deep link Then the operator is taken directly to the restock order adjustment page with the specific item and recommended quantity pre-populated
Offline Mode Alert Queuing and Delivery
Given the mobile device is offline when an item’s projected stock falls within its buffer threshold When network connectivity is restored Then all queued alert notifications are delivered to the operator in chronological order with correct content and actionable links
Snooze or Dismiss Buffer Threshold Alerts
Given an operator receives a buffer threshold alert When the operator chooses to snooze or dismiss the alert Then the system respects the snooze duration or removes the alert from the active notification feed and logs the action in the alert history

LiveRouteSync

Continuously syncs with your route management tool to adjust restock lists in real time when routes change. Ensures your inventory plan reflects last-minute detours, cancellations, or new stops, eliminating guesswork and wasted trips.

Requirements

Real-time Route Event Listener
"As a food truck operator, I want the system to automatically detect when my scheduled stops change so that I have up-to-date inventory restock needs."
Description

Implement a listener that connects to the route management tool via API/webhooks to detect and capture any route changes (additions, deletions, detours, or cancellations) as they occur. The system should securely authenticate, maintain a persistent subscription, and process incoming events with minimal latency, ensuring the inventory module always receives up-to-date route information.

Acceptance Criteria
Mid-Route Detour Event
Given the route management tool emits a detour event for an active route, when the listener receives the event, then it updates the inventory module with the revised route path within 2 seconds.
New Stop Addition Event
Given a dispatcher adds a new stop to an existing route, when the listener processes the addition event, then the inventory module should include the inventory requirements for the new stop in the next restock list update.
Route Cancellation Event
Given a route is canceled before departure, when the listener captures the cancellation event, then it marks the route as inactive in the inventory module within 1 second and removes related inventory reservations.
Route Deletion Event
Given a route is deleted from the route management tool, when the listener receives the deletion notification, then it purges the route data from the inventory module within 2 seconds and logs the deletion event.
Authentication Token Refresh Event
Given the authentication token for the route tool API expires, when the listener detects an authentication failure, then it automatically requests a new token, re-establishes the subscription, and resumes event processing within 5 seconds.
Dynamic Restock List Generator
"As a food truck operator, I want the restock list to adjust in real time based on route modifications so that I prepare the right items for upcoming stops."
Description

Build a dynamic engine that recalculates and updates the restock list in real time whenever route data changes. The module should consider new stops, removed stops, and detours to adjust quantities of ingredients and products needed, minimizing wastage and ensuring best-sellers remain in stock.

Acceptance Criteria
Route Addition Triggers Restock Increase
Given a new stop is added via the route management tool, When LiveRouteSync runs within 5 seconds, Then the restock list updates to include additional ingredient quantities based on sales history for the new stop, with quantities accurate to within 610% of predicted demand.
Stop Removal Reflects Inventory Reduction
Given an existing stop is removed from the route, When the next sync occurs, Then all ingredients and product quantities associated solely with that stop are removed or reduced, ensuring total restock quantities decrease appropriately.
Detour Adjusts Restock Quantities
Given a detour that extends route distance by more than 10%, When the detour is detected and synced, Then the restock list recalculates to account for increased consumption time and distance, adjusting quantities by at least the percentage increase in route length.
Offline Changes Sync Upon Reconnection
Given the mobile dashboard is offline and route changes occur, When the device reconnects, Then LiveRouteSync reconciles offline route edits and updates the restock list to reflect all additions, removals, and detours in a single sync action.
High-Priority Stop Overrides Standard Allocation
Given a stop is marked as high-priority in the route management tool, When LiveRouteSync processes the route, Then the restock list allocates an extra 20% of best-selling items for that stop, overriding default calculations.
Offline Change Queuing and Reconciliation
"As a food truck operator, I want the app to save route and inventory changes while offline and sync when back online so that I never lose critical updates."
Description

Enable the app to capture route updates and inventory adjustments while offline, queuing events locally and then reconciling them with the server once connectivity is restored. This includes conflict detection and resolution to merge offline changes with real-time route updates without data loss.

Acceptance Criteria
Offline Route Update Queuing
Given the user loses connectivity during route editing, when they make route changes offline, then the app queues these updates locally with timestamps and unique identifiers.
Offline Inventory Adjustment Capture
Given the user updates inventory levels while offline, when they adjust item quantities, then the app records each adjustment event locally and marks it as pending synchronization.
Automatic Synchronization Upon Reconnection
Given the device regains network connectivity, when the app detects the connection, then it automatically initiates a sync process to send all queued offline events to the server without user intervention.
Conflict Detection and Resolution
Given there are conflicting updates between offline changes and server-side route modifications, when the sync occurs, then the app detects conflicts based on timestamps, prompts the user with clear options to merge or override, and resolves conflicts according to the user’s choice.
Data Integrity Verification After Sync
Given the offline changes have been synchronized, when the sync completes, then the app verifies that all queued events have been applied correctly by comparing local and server data hashes and displays a success message or detailed error report if discrepancies exist.
User Alert Dashboard Integration
"As a food truck operator, I want to receive notifications in my dashboard when restock lists change due to route updates so that I can review and confirm adjustments immediately."
Description

Integrate in-app notifications and dashboard alerts that inform users instantly when restock lists change due to route modifications. Alerts should be actionable, allowing operators to review, accept, or override changes directly within the mobile dashboard.

Acceptance Criteria
Real-time Route Change Alert Display
Given the user is on the mobile dashboard When a route modification occurs in the connected route management tool Then an in-app notification appears within 5 seconds with the title 'Restock List Updated' and a summary of changes
Actionable Alert Options
Given an alert banner is visible When the user selects 'Review Changes' Then the restock list details screen opens highlighting added, removed, and adjusted items
Offline Alert Queueing and Sync
Given the device is offline When a route change is detected upstream Then the alert is queued locally and automatically displayed within 10 seconds of reconnecting without duplicates
Dismiss and Override Functionality
Given an alert is displayed When the user taps 'Override' Then the original restock list remains active, the override action is logged, and the alert is cleared
Alert Delivery Performance
Given a route change event is processed When network latency is under 2 seconds Then the alert notification is delivered and rendered on the dashboard within 5 seconds
Vendor Inventory Sync Module
"As a food truck operator, I want the vendor list to update automatically based on new restock requirements so that I can efficiently order the necessary supplies."
Description

Extend the vendor management component to automatically update vendor order suggestions based on the adjusted restock list. The module should match required items to preferred vendors, generate purchase orders, and flag discrepancies, ensuring timely procurement aligned with the latest route plan.

Acceptance Criteria
Restock List Update on Route Change
Given a route change is detected in the route management tool When the restock list is synced Then vendor order suggestions update to reflect adjusted item quantities
Preferred Vendor Matching
Given an updated restock list with required items When generating vendor order suggestions Then each item is assigned to the predefined preferred vendor according to vendor priorities
Purchase Order Generation
Given a restock list matched to vendors When the system creates purchase orders Then each purchase order includes correct item IDs, quantities, vendor IDs, and expected delivery dates in PDF and JSON formats
Discrepancy Flagging
Given an item is unavailable from its preferred vendor When matching items to vendors Then the system flags the item in the order suggestion and provides at least one alternative vendor
Real-Time Sync Under Intermittent Connectivity
Given the device is offline and route changes occur Locally store restock updates When connectivity resumes Then the system synchronizes vendor order suggestions and resolves discrepancies without data loss

VendorLink

Integrates directly with preferred suppliers’ catalogs and pricing, allowing you to place orders from your restock list in one click. Streamlines procurement, compares vendor options, and accelerates turnaround to keep your kitchen always stocked.

Requirements

Real-Time Supplier Catalog Sync
"As a food truck owner, I want Truckly to sync supplier catalogs automatically so that I can always order with accurate pricing and availability."
Description

Automatically integrate and update preferred suppliers’ product catalogs and pricing within Truckly’s dashboard in real time, ensuring that users always see the latest item availability, unit costs, and bulk discounts without manual uploads or data entry.

Acceptance Criteria
New Supplier Integration Sync
Given a user adds a new supplier with valid authentication credentials When the system initiates the first real-time catalog sync Then all products and pricing are imported within 120 seconds And there are no duplicate or missing entries And each item displays correct name, SKU, unit cost, and availability status.
Price Change Propagation
Given a supplier updates the unit cost of an existing product in their catalog When the remote update is pushed to Truckly Then the new unit cost is displayed in the dashboard within 5 minutes And the previous price is replaced And the sync history logs the price change event.
Bulk Discount Update
Given a supplier publishes tiered pricing for bulk quantities When the catalog sync runs Then Truckly shows each discount tier accurately (e.g., 5% off for 100+ units) And the pricing thresholds and percentages match the supplier’s documented values.
Inventory Availability Update
Given a product’s availability changes to “out of stock” or “back in stock” at the supplier When the real-time update arrives Then Truckly marks the item as unavailable or available accordingly And disables or re-enables ordering options for that product.
Offline Sync Recovery
Given the user’s device has been offline for more than 10 minutes When network connectivity is restored Then Truckly automatically syncs all missed catalog updates within 5 minutes And merges remote changes without overwriting local unsaved edits And alerts the user if any conflicts require manual resolution.
One-Click Restock Ordering
"As a food truck operator, I want to place my restock orders in one click so that I can quickly replenish my inventory without navigating multiple screens."
Description

Enable users to place orders from their auto-generated restock list with a single click, transmitting line-item quantities directly to the supplier’s ordering system, reducing clicks and manual entry errors for faster procurement.

Acceptance Criteria
Single Click Ordering from Restock List
Given a user has an auto-generated restock list with items and quantities, when the user clicks the "Place Order" button, then the system transmits each line-item and its quantity to the supplier's ordering API without requiring additional input.
Order Quantity Verification Before Submission
Given the restock list is displayed, when the user hovers or taps on an item, then the system displays the quantity that will be ordered and confirms that it matches the recommended restock level.
Handling Supplier Acknowledgement
Given the order has been transmitted successfully, when the supplier system returns a confirmation response, then the system shows an "Order Confirmed" notification and updates the order status in Truckly.
Error Handling for Order Transmission Failures
Given a failure occurs during transmission to the supplier API, when the system detects a timeout or error response, then it retries up to three times, and if it still fails, displays an error message with retry and manual entry options.
Offline Mode Queuing and Sync
Given the user is offline, when the user clicks "Place Order", then the system queues the order locally, shows a "Queued for Submission" status, and automatically syncs and submits the order when connectivity is restored.
Vendor Comparison Dashboard
"As a food truck manager, I want to compare vendors on price and lead time in one view so that I can choose the best supplier for my budget and schedule."
Description

Provide a side-by-side comparison of vendor options for each item—showing price per unit, minimum order quantities, shipping costs, and lead times—to help operators choose the most cost-effective supplier for each restock need.

Acceptance Criteria
Single Item Vendor Comparison
Given a user selects an individual inventory item to restock, When they open the Vendor Comparison Dashboard, Then the system displays at least three suppliers side by side showing price per unit, minimum order quantity, shipping cost, and lead time, and sorts the suppliers by ascending price per unit.
Bulk Restock Evaluation
Given a user selects multiple items for restocking, When they generate a comparison, Then the dashboard presents aggregated total costs (unit price × quantity + shipping) for each vendor, and highlights the vendor offering the lowest overall cost.
Shipping Cost Assessment
Given vendors with varying shipping costs, When the user applies a shipping cost filter (e.g., free shipping or maximum shipping threshold), Then only vendors meeting the selected shipping criteria are displayed in the comparison view.
Lead Time Prioritization
Given the urgency of restocking, When the user sorts or filters by lead time, Then vendors are ordered from shortest to longest lead time, and the estimated delivery date for each vendor is correctly calculated and displayed.
Minimum Order Quantity Verification
Given a desired restock quantity, When the user enters a quantity below a vendor’s minimum order requirement, Then that vendor is flagged with a clear warning message and excluded from the default selected vendor options.
Order Status Tracking
"As a busy food vendor, I want to see the status of my restock orders at a glance so that I know when my ingredients will arrive."
Description

Track the status of placed orders in real time—showing confirmations, shipment updates, expected delivery dates, and any order exceptions—within Truckly’s dashboard to keep users informed of their inventory pipeline.

Acceptance Criteria
Order Confirmation Display Scenario
Given the user submits an order via VendorLink, when the supplier confirms the order, then the dashboard updates the order status to 'Confirmed' within 2 seconds.
Shipment Status Update Scenario
Given an order is in 'Confirmed' status, when the supplier updates the shipment, then the dashboard displays the new status and update timestamp in the order details.
Expected Delivery Date Display Scenario
Given a shipment is confirmed, when the system receives the expected delivery date from the supplier, then it displays the date in the user's local timezone within the order overview.
Offline Order Status Access Scenario
Given the user is offline, when they view the order tracking page, then the dashboard shows the last known statuses of all orders with an 'Offline' indicator.
Order Exception Alert Scenario
Given a supplier reports an exception on an order, when the exception is logged, then the dashboard highlights the order in red and shows the exception details in a modal notification.
Offline Order Queuing
"As a food truck operator often in areas with poor reception, I want to queue my orders offline so that they submit automatically once I’m back online."
Description

Allow users to queue restock orders while offline—saving selections and order details locally—and automatically synchronize and send them to suppliers when connectivity is restored to ensure uninterrupted ordering.

Acceptance Criteria
Queuing Orders While Offline
Given a user is offline, when they select items and submit a restock order, then the order is saved locally with correct item details and quantities.
Local Storage of Order Details
When an order is queued offline, then the system stores all order details (item IDs, quantities, supplier info, and timestamps) in local persistent storage.
Automatic Order Synchronization on Reconnect
Given connectivity is restored, then queued orders are automatically sent to the supplier without user intervention, and successful confirmations are received and displayed.
Managing Multiple Offline Orders
When multiple orders are queued offline, then the user can view, edit, or remove each queued order in a list labeled 'Pending Orders'.
Conflict Handling During Synchronization
If an item in a queued order is unavailable or price has changed upon synchronization, then the user is notified of the conflict and prompted to accept updated details or modify the order before final submission.

MultiDrop Planner

Optimizes load splits and restock allocations across multiple trucks or stops on the same route. Provides a consolidated packing plan and suggested handoff points, enabling efficient resource sharing and balanced inventory across your fleet.

Requirements

Route Optimization Algorithm
"As a fleet operator, I want the system to automatically compute the optimal multi-stop route so that I can reduce travel time and fuel expenses."
Description

Develop an advanced algorithm that calculates the most efficient route for multiple stops and trucks, accounting for real-time traffic, distance, and estimated stop durations. This requirement ensures minimized travel time, reduced fuel costs, and streamlined operations by dynamically optimizing routes based on changing conditions and multiple delivery points.

Acceptance Criteria
Dynamic Traffic-Aware Routing
Given real-time traffic data is available, when calculating routes for multiple trucks, then the algorithm adjusts the route to avoid congestion, resulting in a maximum deviation of no more than 5 minutes from the optimal travel time.
Multi-Stop Distance Optimization
Given a set of delivery stops and available trucks, when generating the route plan, then the algorithm orders stops to minimize total travel distance, with at least a 10% reduction compared to random assignment.
Estimated Stop Duration Incorporation
Given each stop has an estimated service duration, when computing the route schedule, then the algorithm includes these durations in the total route time within a margin of error of ±2 minutes per stop.
Real-Time Rerouting Under Delay Conditions
Given a truck experiences an unexpected delay of over 10 minutes at a stop, when recalculating the remaining route, then the algorithm updates the sequence and timing of subsequent stops to minimize additional delay, ensuring no subsequent stop is more than 5 minutes later than originally planned.
Scalable Fleet Route Distribution
Given up to 10 trucks and 100 stops, when generating the route optimization, then the algorithm completes the computation within 30 seconds and balances load so that no truck's total route distance exceeds 110% of the fleet average.
Consolidated Packing Plan Generation
"As a food truck owner, I want a single consolidated packing list for all stops so that I can prepare supplies efficiently and avoid overstocking or shortages."
Description

Implement functionality to generate a unified packing plan that consolidates inventory requirements for all trucks and stops on a route. This requirement provides a clear, itemized packing list, balancing load distribution and simplifying preparation by aggregating restock needs across multiple vehicles.

Acceptance Criteria
Multi-Truck Route Planning Initiation
Given a route with multiple trucks and stops, when the operator initiates the packing plan generation, then the system aggregates inventory requirements across all trucks into a single consolidated packing list grouped by item and total quantity.
Real-Time Inventory Aggregation
Given current stock levels for each truck, when generating the packing plan, then the system calculates required restock quantities by subtracting existing inventory from aggregated demand and includes only items with positive restock needs.
Load Balancing Across Fleet
Given truck-specific capacity constraints, when the consolidated packing plan is generated, then the system evenly distributes item quantities across trucks to ensure no individual truck is assigned more than its maximum load capacity.
Packing Plan Export and Download
Given a generated consolidated packing plan, when the operator selects export, then the system provides downloads in CSV and PDF formats that include item name, total quantity, and breakdown of quantities per truck.
Conflict Resolution for Overlapping Demand
Given aggregated item demand exceeds total available stock, when generating the consolidated plan, then the system flags conflicts, suggests alternative handoff points, and prevents finalization until the operator resolves the issue or confirms adjustment.
Handoff Point Suggestion Engine
"As a route manager, I want the system to recommend strategic handoff points so that trucks can share supplies without detours or delays."
Description

Create a module that suggests optimal handoff locations where trucks can transfer inventory mid-route. This requirement enhances resource sharing and prevents stockouts by identifying convenient, vehicle-accessible handoff points based on geographic proximity and inventory levels.

Acceptance Criteria
Real-time Handoff Point Identification
Given a multi-stop route with two or more trucks and current locations, when the user initiates handoff suggestion, then the engine returns a list of at least three candidate handoff points ranked by combined proximity and route feasibility.
Accessibility Verification of Suggested Points
Given a set of candidate handoff points, when the system evaluates each location, then it filters out any points lacking truck accessibility (e.g., max vehicle height < vehicle height, weight restrictions, or lack of safe loading zone).
Inventory Level-Based Handoff Prioritization
Given current inventory levels for each truck and minimum threshold settings, when generating handoff suggestions, then the engine prioritizes locations that facilitate transfers to maintain each truck's inventory above the defined threshold.
Route Optimization with Handoff Constraints
Given an original route plan without handoff stops, when handoff points are added, then the recalculated total route time and distance must not exceed 10% of the original metrics.
Offline Mode Handoff Point Caching
Given previously retrieved handoff point suggestions and map data, when the user loses connectivity mid-route, then the system continues to provide handoff suggestions within a 5 km radius using cached data.
Multi-Truck Load Balancing
"As a fleet supervisor, I want the system to evenly distribute stock across my trucks so that each vehicle remains sufficiently supplied throughout the day."
Description

Design a capability that dynamically allocates and balances inventory loads across multiple trucks, ensuring each vehicle carries the right quantity of items. This requirement maintains consistent product availability, reduces waste, and optimizes space utilization by adjusting allocations in response to demand forecasts.

Acceptance Criteria
Initial Load Allocation Based on Forecasted Demand
Given forecasted demand per stop and truck capacity constraints, When load allocation is calculated, Then each truck's load does not exceed its capacity and covers at least 95% of forecasted demand. Given product priority levels, When allocation is computed, Then high-priority items are allocated first to trucks serving peak-demand stops.
Dynamic Rebalancing During Route in Response to Sales Variance
Given actual sales data mid-route deviating more than 10% from forecast, When rebalancing is triggered, Then redistribution suggestions are generated within 60 seconds. Given real-time traffic and distance data, When rebalancing occurs, Then recommended handoff points minimize detour time by at least 15%.
Handling Offline Mode and Queued Updates
Given a temporary loss of connectivity, When offline mode is activated, Then load balancing adjustments are queued locally and sync within 2 minutes upon reconnection. Given queued updates exist, When reconnection occurs, Then no conflicting allocations occur and inventory levels remain accurate across all trucks.
Manual Override of Suggested Allocations by Operator
Given an operator adjusts allocation of specific SKUs, When override is submitted, Then the system accepts changes and recalculates remaining loads to maintain balanced capacity. Given manual override exceeds capacity constraints, When recalculation occurs, Then an error message is displayed and invalid adjustments are blocked.
Inventory Transfer Handoff Between Trucks
Given two trucks at a common stop with complementary surpluses and deficits, When a transfer is initiated, Then the system generates a transfer plan detailing quantities, locations, and timestamps. Given a completed transfer, When the transaction is logged, Then both trucks' inventories reflect the transfer and sync within 1 minute.
Offline Sync for MultiDrop Data
"As a food truck operator, I want to view and edit my multi-stop plans offline so that I can continue working in areas with limited or no internet access."
Description

Enable offline synchronization for all MultiDrop Planner data, allowing route details, packing plans, and handoff suggestions to be accessed and updated without network connectivity. This requirement ensures uninterrupted functionality in areas with poor reception and automatically syncs changes once connectivity is restored.

Acceptance Criteria
Viewing Route Details Offline
Given a user has previously loaded route details online and is now offline, When the user opens the MultiDrop Planner, Then the stored route details for all planned stops are displayed within 2 seconds without any connectivity error messages.
Updating Packing Plans Offline
Given a user is offline and modifies packing quantities for stops, When the user saves the changes, Then the edits are stored locally and a 'Pending Sync' indicator appears next to the modified packing plan.
Syncing Changes Upon Reconnection
Given a user has made offline edits to route configurations and packing plans, When the device regains network connectivity, Then all locally stored changes are automatically synchronized with the server within 30 seconds and a confirmation notification is displayed.
Conflict Resolution on Offline-Edited Data
Given conflicting updates exist between offline edits and server-side changes for the same route, When synchronization is initiated, Then the system prompts the user within 1 minute to choose between 'Keep Local', 'Use Server', or 'Merge' resolutions.
Handling Large Data Sets in Offline Mode
Given a route contains over 100 stops and detailed packing instructions, When the user switches to offline mode, Then the app loads all relevant data within 3 seconds and allows seamless read/write operations without performance degradation.

Spoilage Sentinel

Continuously monitors inventory to detect early signs of spoilage using temperature, humidity, and sales velocity data. Alerts you to potential waste before it happens, allowing timely interventions to save ingredients and reduce costs.

Requirements

Real-time Sensor Data Ingestion
"As a food truck operator, I want real-time environmental data from my storage units so that spoilage detection algorithms can accurately monitor for potential waste."
Description

Continuously ingest temperature and humidity data from connected storage sensors, processing readings every minute and validating data accuracy. Integrate seamlessly with IoT devices installed in ingredient storage units to provide up-to-date environmental metrics. Ensure reliable data feeds to power spoilage detection algorithms, enabling precise monitoring of storage conditions and early identification of deviations that may lead to spoilage.

Acceptance Criteria
Continuous Data Ingestion at One-Minute Intervals
Given the storage sensors are connected, the system ingests temperature and humidity readings every minute with a tolerance of ±5 seconds and stores each reading in the database without loss.
Data Accuracy Validation
When new sensor readings are ingested, the system cross-checks each value against device calibration parameters; if readings fall outside expected ranges or show anomalies (e.g., sudden spikes), the system flags the data and logs an accuracy warning.
Seamless IoT Device Integration
Given a newly installed or reset sensor, the system automatically discovers and authenticates the device within 60 seconds using pre-shared credentials and begins ingesting data without manual configuration.
Offline Buffering and Recovery
When network connectivity is lost, the system locally buffers incoming sensor data for up to 24 hours and automatically resumes data ingestion to the central database within 2 minutes of network restoration, ensuring no data gaps.
Alert on Feed Interruptions
If the ingestion pipeline fails to receive data from any sensor for more than 2 consecutive minutes, the system generates an alert to the operator dashboard and sends an email notification indicating the affected sensor ID.
Spoilage Risk Assessment Algorithm
"As a food truck operator, I want the system to assess spoilage risk for each ingredient so that I can prioritize ingredients at high risk of waste."
Description

Analyze real-time sensor data combined with sales velocity and inventory age to compute a dynamic spoilage risk score for each ingredient. Weight environmental deviations, usage trends, and stock duration in the algorithm to identify high-risk items. Schedule risk assessments hourly and flag ingredients exceeding risk thresholds, enabling proactive interventions to minimize waste and optimize ingredient usage.

Acceptance Criteria
Real-Time Data Ingestion Verification
Given the system receives temperature, humidity, sales velocity, and inventory age data, when the algorithm executes, then all data points are ingested, timestamped, and stored without loss or delay.
Risk Score Calculation Accuracy
Given a predefined test dataset with known weights, when the algorithm processes the data, then the computed spoilage risk scores match expected values within a tolerance of ±0.5%.
Hourly Assessment Scheduling
Given the system is running, when every hour elapses, then the spoilage risk assessment algorithm triggers automatically without manual intervention.
Threshold-Based Alert Generation
Given an ingredient’s computed risk score exceeds the configured threshold, when the hourly assessment completes, then an alert is generated and the ingredient is flagged in the dashboard within two minutes.
Dashboard Display of Risk Scores
Given the algorithm has computed new risk scores, when a user views the inventory dashboard, then each ingredient displays the latest risk score and flag status in real time.
Adaptive Alert Notification System
"As a food truck operator, I want to receive timely notifications when ingredients are at risk so that I can intervene early and reduce waste."
Description

Generate and deliver alerts via push notifications and email whenever spoilage risk scores exceed user-defined thresholds. Provide customization options for alert thresholds per ingredient category, notification channels, and frequency. Ensure timely delivery of critical alerts, prompting operators to inspect or use at-risk ingredients before spoilage occurs and reducing unnecessary waste.

Acceptance Criteria
User Sets Custom Thresholds for Ingredients
Given a user navigates to the spoilage alert settings and sets a custom threshold of 75% for the 'Produce' category, when the user clicks 'Save', then the system stores the new threshold and applies it to all future spoilage risk calculations for 'Produce'.
Spoilage Risk Exceeds Threshold Triggers Notification
Given an ingredient’s spoilage risk score is calculated as 82% and the user-defined threshold is 80%, when the monitoring service processes inventory data, then the system generates a spoilage alert for that ingredient and logs the event.
Notification Channel Delivery Verification
Given a spoilage alert is generated and the user has enabled both push notification and email channels, when the alert is dispatched, then the system delivers a push notification to the mobile device within 30 seconds and sends a formatted email to the user’s registered address within 60 seconds.
Alert Frequency Configuration Adherence
Given multiple spoilage risk recalculations exceed the threshold for the same ingredient within a 30-minute window and the user has configured alerts to be sent at most once per 30 minutes, when the second and subsequent events occur within that window, then no additional alerts are sent until the 30-minute period has elapsed.
Critical Alert Offline Handling and Retry
Given a critical spoilage alert is generated while the mobile device is offline, when the device reconnects to the network, then the system retries sending the missed push notification automatically and marks the alert as delivered once confirmed.
Interactive Spoilage Monitoring Dashboard
"As a food truck operator, I want an interactive dashboard showing spoilage risk and environmental trends so that I can quickly identify and manage at-risk ingredients."
Description

Offer a mobile-friendly dashboard that visualizes spoilage risk scores with color-coded indicators, environmental condition trends, and a sortable list of at-risk ingredients. Integrate seamlessly with the main inventory dashboard, allowing users to drill down into individual item risk details and historical data. Provide interactive charts and filters to support quick decision-making and actionable insights for spoilage mitigation.

Acceptance Criteria
Mobile Dashboard Overview of Spoilage Risk
Given the user accesses the Spoilage Monitoring Dashboard on a mobile device, When the dashboard loads, Then color-coded risk scores, environmental condition widgets, and a sortable list of at-risk ingredients are displayed without horizontal scrolling or UI clipping.
Drill-Down to Individual Item Risk Details
Given the user taps on an at-risk ingredient in the list, When the tap is registered, Then a detailed panel opens showing that item's risk score history, temperature/humidity trends, and recommended actions.
Sort At-Risk Ingredients by Risk Score
Given the at-risk ingredients list is visible, When the user taps the risk score column header twice, Then the list sorts first in descending order and then in ascending order based on the risk scores.
Filter Ingredients by Category and Risk Level
Given the filter controls are available, When the user selects a specific ingredient category and risk threshold, Then only ingredients matching those filters are shown in the list.
Visualize Environmental Trends for a Selected Ingredient
Given the user selects the environmental trends chart for an ingredient, When the chart is in focus, Then it displays at least the last 30 days of temperature and humidity data with interactive tooltips.
Offline Mode Data Sync
"As a food truck operator working in areas with poor connectivity, I want the spoilage monitoring to function offline and sync later so that I don't miss critical alerts."
Description

Cache environmental readings, inventory updates, and generated alerts locally when network connectivity is unavailable, ensuring continuous spoilage monitoring. Upon reconnection, automatically synchronize cached data with the cloud to maintain historical accuracy and update risk assessments. Enable users to access past alerts and environmental logs in offline mode for uninterrupted monitoring.

Acceptance Criteria
Local Data Caching During Offline Mode
Given the device is offline, when new temperature, humidity, inventory updates, or spoilage alerts occur, then 100% of the data is cached locally without errors and visible in the offline logs.
Automatic Data Synchronization upon Reconnection
Given the device reconnects to a network after an offline period, when synchronization begins, then all locally cached environmental readings, inventory updates, and alerts are uploaded to the cloud within 30 seconds with preserved timestamps.
Offline Data Accessibility
Given the user is offline, when accessing past alerts and environmental logs, then data for at least the previous seven days is displayed without requiring connectivity.
Conflict Resolution on Sync
Given conflicting entries between local cache and cloud data upon synchronization, when a conflict is detected, then the system retains the record with the most recent timestamp and logs the conflict resolution event.
Historical Accuracy Verification Post-Sync
Given that data synchronization has completed, when comparing the cloud database to offline logs, then all environmental readings, inventory updates, and alerts match with 100% accuracy.

WasteTrend Dashboard

Provides an intuitive visual overview of waste patterns over time, highlighting peak waste periods and underperforming products. Enables data-driven decisions to adjust portions, pricing, or promotions for maximum profitability.

Requirements

Dynamic Waste Chart Rendering
"As a food truck operator, I want interactive waste charts so that I can instantly identify patterns and make informed inventory adjustments."
Description

Develop an interactive chart component that visualizes waste quantities over selectable time intervals. The charts should support zooming, panning, and tooltip details for individual data points, enabling operators to quickly interpret waste trends and identify anomalies. It must integrate seamlessly with existing dashboard styling and update in real time when new data is ingested, even in offline mode once connectivity is restored.

Acceptance Criteria
Daily Waste Overview in Online Mode
Given the user is connected to the internet and selects a 24-hour time interval, when the dashboard loads, then the waste chart displays hourly data points matching the waste records for the last 24 hours, each with accurate waste quantity and time labels.
Historical Waste Analysis with Offline Data Recovery
Given the user was offline for 48 hours and has since reconnected, when the application synchronizes, then the chart updates to include the missing waste data points from the offline period without duplication or omission.
Product-Specific Waste Drilldown
Given the user hovers over a specific product line on the chart, when the tooltip appears, then it displays the product name, date, and exact waste quantity for that data point, and the chart highlights the selected data point.
Zooming into Peak Waste Periods
Given the user zooms into a selected timeframe containing high waste levels, when the user applies the zoom gesture or control, then the chart rescales to show only the selected timeframe with correctly interpolated axes and all data points for that period.
Real-Time Chart Updates upon Data Sync
Given new waste data is ingested while the user is viewing the chart, when the data is synchronized (either immediately if online or after reconnection if offline), then the chart updates in real time to reflect the new data without resetting the current zoom or pan state.
Peak Waste Period Highlighting
"As a truck manager, I want peak waste periods highlighted so that I can quickly see when waste is highest and take corrective action."
Description

Implement logic to automatically detect and visually emphasize time periods where waste exceeds defined thresholds or historical averages. Highlighted periods should be color-coded and annotated with contextual notes, enabling operators to pinpoint peak waste episodes. This feature must allow threshold configuration and integrate with notifications to inform stakeholders about critical waste events.

Acceptance Criteria
Detecting Peak Waste Periods Based on Thresholds
Given configured waste thresholds, when the waste amount for a specific time period exceeds the defined threshold, then the period is visually highlighted on the dashboard with the correct color code.
Color-Coding Peak Waste Periods
Given a detected peak waste period, when displayed on the WasteTrend Dashboard, then the period is color-coded according to severity levels (e.g., red for >150% of threshold, amber for 100–150%, green otherwise).
Annotating Waste Events with Contextual Notes
Given a highlighted waste period, when the period is rendered, then an annotation is displayed showing the waste amount, date range, and comparison to historical averages.
Configuring Waste Thresholds
Given operator access to threshold settings, when a new threshold value is entered and submitted, then the value is validated (numeric, within acceptable range), saved, and applied immediately to subsequent waste period detections.
Sending Notifications for Critical Waste Events
Given a waste period exceeding the critical threshold, when the event occurs, then a notification containing period details and waste metrics is sent to all subscribed stakeholders within one minute.
Item-Level Waste Breakdown
"As an operator, I want item-level waste data so that I can identify which menu items are generating the most waste and optimize my offerings."
Description

Create a detailed breakdown view that itemizes waste by product SKU over chosen time frames. The view should include sortable tables and bar charts that compare waste volumes, percentages of total waste, and trends for each menu item. This granular analysis will help owners identify underperforming or overproduced items and adjust portions or promotions accordingly.

Acceptance Criteria
Time Frame Waste Breakdown
Given a food truck owner selects a date range, when the Item-Level Waste Breakdown loads, then the table lists each SKU with total waste volume and percentage of overall waste, and the bar chart visualizes waste volumes per SKU for the chosen period.
Sortable Waste Volume Table
Given the breakdown table is displayed, when the owner clicks the 'Waste Volume' column header, then the table sorts rows in descending order on first click and ascending on second click, updating the bar chart order accordingly.
Percentage-Based Filtering
Given the Item-Level Waste Breakdown view, when the owner applies a minimum waste percentage filter, then only SKUs with waste percentages equal to or above the threshold appear in both the table and the bar chart.
Comparative Trend Analysis
Given the owner selects two distinct time frames, when the comparison mode is activated, then the system displays side-by-side bar charts for each period with labeled percentage change for each SKU’s waste volume.
Export Waste Breakdown Report
Given the detailed waste breakdown is visible, when the owner clicks the 'Export CSV' button, then a CSV file downloads containing SKU, selected time frame, waste volume, and percentage of total waste for all listed items.
Custom Timeframe Selection
"As a user, I want to select custom timeframes so that I can analyze waste data for specific events or promotional periods."
Description

Provide flexible controls for selecting predefined (daily, weekly, monthly) and custom date ranges within the WasteTrend Dashboard. The control should include date pickers, quick-select buttons, and auto-refresh of visualizations based on the selected window. This functionality enables targeted analysis around special events, holidays, or promotional periods.

Acceptance Criteria
Predefined Timeframe Selection
Given the user clicks a predefined timeframe button (daily, weekly, monthly), when the button is activated, then the date pickers update to the correct start and end dates and the WasteTrend visualizations refresh within 2 seconds to display data corresponding to that timeframe.
Custom Date Range Selection
Given the user selects a custom start and end date via the date pickers and clicks Apply, when the date range is valid, then the dashboard updates all visualizations to reflect data only within the selected date range without requiring a page reload.
Invalid Date Range Handling
Given the user enters an end date earlier than the start date, when the user attempts to apply the range, then a validation error message is displayed and the Apply button is disabled until the range is corrected.
Single-Day Range Display
Given the user selects the same start and end date and applies the range, when the range is single-day, then the dashboard correctly displays waste data for that single date with no errors.
Auto-Refresh After Selection
Given any valid timeframe selection (predefined or custom), when the selection is applied, then all WasteTrend visualizations automatically refresh without user-initiated page reload, and updated charts are rendered within 2 seconds.
Waste Spike Notifications
"As a vendor, I want real-time alerts when waste spikes so that I can take immediate corrective actions to reduce food waste."
Description

Develop a notification system that triggers real-time alerts (in-app and optional push/email) when waste levels for a selected period exceed user-defined thresholds. Notifications should include summary metrics and direct links to the relevant dashboard view. This feature ensures timely awareness of waste spikes, allowing operators to investigate causes immediately.

Acceptance Criteria
Threshold Exceeded Notification Trigger
Given a user-defined waste threshold for a selected period, When actual waste exceeds that threshold, Then an in-app notification is generated and displayed within 30 seconds.
Summary Metrics Included in Notification
Given a waste spike notification is triggered, When the notification is displayed or sent, Then it includes total waste amount, the percentage above threshold, and a list of affected products.
Direct Link to WasteTrend Dashboard View
Given a waste spike notification contains a link, When the user taps the link, Then the app opens the WasteTrend Dashboard filtered to the relevant period and products.
Push and Email Notification Delivery
Given the user has enabled push and/or email notifications, When a waste threshold is breached, Then push and/or email notifications are delivered within 60 seconds.
Offline Data and Notification Sync
Given the device was offline during a threshold breach, When network connectivity is restored, Then all missed notifications are generated and delivered, and the in-app notification history is updated.

SmartPortion Planner

Analyzes historical consumption and waste data to recommend optimal portion sizes for each menu item. Helps minimize leftovers and spoilage while maintaining customer satisfaction and consistent quality.

Requirements

Historical Data Integration
"As a food truck operator, I want the system to automatically gather my past sales and waste records so that portion recommendations are based on complete and accurate historical data."
Description

Implement a backend service that aggregates and processes historical consumption and waste data from Truckly’s inventory and sales modules. This service should normalize, clean, and store data in a time-series database to ensure accurate inputs for portion analysis.

Acceptance Criteria
Daily Data Aggregation for Portion Analysis
Given new sales and waste records are available at 23:59 UTC, When the aggregation service runs its daily job, Then all records from the previous day must be aggregated into a single dataset and stored in the time-series database without loss or duplication.
Inconsistent Entry Detection
When the aggregation service processes a batch containing duplicate or malformed entries, Then it must identify, log, and exclude those anomalies according to the defined data cleaning rules and notify the monitoring system.
Time-Series Data Ingestion
Given a cleaned data batch, When the service ingests data into the time-series database, Then each record must be timestamped correctly, assigned to the correct measurement and tags, and retrievable via a standard time-range query within 2 seconds.
Data Normalization Accuracy
Given raw consumption values in varying units (grams, ounces, servings), When the normalization routine executes, Then all values must be converted to grams using configured conversion factors and match the canonical format with no rounding error exceeding 0.1 gram.
Offline Data Access Validation
Given the backend service is temporarily offline, When the mobile dashboard requests historical data, Then it must display the most recently synced dataset without errors and automatically sync pending updates once the service is restored.
Portion Recommendation Engine
"As a food truck operator, I want the system to recommend ideal portion sizes for my menu items so that I minimize leftover waste while meeting customer demand."
Description

Develop an algorithmic engine that analyzes time-series consumption and waste data to calculate optimal portion sizes for each menu item. The engine should account for variables such as day of week, event type, weather, and stock constraints, providing statistically-backed recommendations.

Acceptance Criteria
Weekend Event Forecast Analysis
Given the engine has consumption and waste data for the past 4 weekends and an upcoming weekend event forecast, when the Portion Recommendation Engine runs, then it produces portion sizes within ±10% of the historical weekend average adjusted by event demand factors.
Rainy Day Portion Adjustment
Given a rainy weather forecast for the operating day, when the engine generates recommendations, then portion sizes for items with historically lower rainy-day demand decrease by at least 5% compared to clear-weather baselines.
High-Demand Menu Item Recommendation
Given an item has exceeded typical order volume by over 20% for the last 3 days, when recommendations are computed, then the engine increases that item’s portion size by at least 15% for the next day.
Stock Constraint Handling
Given current ingredient stock levels are below typical daily consumption, when the engine calculates portions, then it proportionally adjusts all item portion sizes so total required ingredients do not exceed available stock and no single item exceeds its stock allocation.
Daily Auto-Generated Recommendation Report
Given end-of-day sales, waste, and forecast data are available, when the daily recommendation report is generated, then it includes portion size recommendations for each menu item with scenario factors and timestamp, delivered within 5 minutes of data availability.
Recommendation Visualization UI
"As a food truck operator, I want to see and adjust portion size recommendations on my phone so that I can easily apply them when preparing ingredients."
Description

Create a mobile-first dashboard interface where operators can view suggested portion sizes for upcoming service periods. The UI should display recommendations in a clear, actionable format, allow manual adjustments, and highlight key factors influencing suggestions.

Acceptance Criteria
Pre-Service Portion Review
Given the operator opens the SmartPortion Planner dashboard before service, when the mobile UI loads, then the recommended portion size for each menu item for the upcoming service period is displayed with suggested values, unit measures, and timestamp of last calculation.
Manual Portion Adjustment
Given the operator taps the adjust icon next to a menu item, when a custom portion size is entered and confirmed, then the UI updates the displayed recommendation, marks the item as manually adjusted, and prompts the operator to save changes.
Highlight Influencing Factors
Given recommendations are displayed, when viewing the details for a menu item, then key influencing factors (historical consumption, waste percentage, and sales velocity) are clearly shown alongside the suggested portion size.
Real-Time Recommendation Updates
Given underlying data such as inventory levels or demand forecasts change, when the operator refreshes or returns to the recommendations screen, then the portion size suggestions update in real time and show a visual indicator of value changes.
Offline Access to Recommendations
Given the operator has no internet connectivity, when opening the SmartPortion Planner dashboard, then the most recently synced portion recommendations are displayed with an offline status indicator.
Deviation Alerting System
"As a food truck operator, I want to be alerted when real-time sales differ greatly from the recommended portions so that I can adjust my prep strategy and avoid stockouts or excessive waste."
Description

Build a monitoring component that compares actual consumption during service to recommended portions and generates alerts when significant deviations occur. Alerts should be delivered in-app and via push notifications to help operators adapt quickly.

Acceptance Criteria
Significant Over-Consumption Detected
Given actual consumption exceeds the recommended portion by more than 20% When service ends for an item Then an in-app alert is generated including item name and deviation percentage And a push notification is sent within 30 seconds
Significant Under-Consumption Detected
Given actual consumption falls below the recommended portion by more than 20% When service ends for an item Then an in-app alert is generated including item name and deviation percentage And a push notification is sent within 30 seconds
Offline Mode Deviation Handling
Given the device is offline and a deviation occurs When the device reconnects to the network Then any stored deviation alerts synchronize automatically And all alerts are delivered in-app and via push without duplication
Alert History Verification
Given one or more deviation alerts have been generated When the user navigates to the alert history dashboard Then each alert appears with timestamp, item name, deviation percentage, and current status
User Acknowledgment Logging
Given a deviation alert is displayed When the user acknowledges the alert Then the system logs the acknowledgment with the user ID and timestamp And the alert status updates to ‘acknowledged’
Feedback Loop & Reporting
"As a food truck operator, I want to review reports on how well the portion recommendations performed so that the system learns from real outcomes and improves future suggestions."
Description

Implement a reporting module that tracks the accuracy and effectiveness of portion recommendations over time. Provide summary reports and analytics to refine the recommendation engine based on operator feedback and observed outcomes.

Acceptance Criteria
Generate Accuracy Trend Report
Given daily portion recommendations and actual consumption data are available, when the report module runs, then an accuracy trend report is generated showing percentage deviation for each menu item over the past 7 days.
Submit Operator Feedback
Given an operator reviews a portion recommendation, when they provide feedback via the dashboard, then the system saves the feedback entry with timestamp, menu item, recommended vs actual portions, and comments.
Adjust Recommendation Algorithm
Given new feedback and consumption data are processed, when the recommendation engine retrains overnight, then updated portion sizes reflect corrections such that deviation for each item is within a 5% threshold.
Weekly Summary Dashboard Update
Given weekly data is aggregated, when the dashboard summary loads, then it displays average recommendation accuracy, total waste reduction, number of feedback entries, and recommendations adjusted.
Export Report Data
Given a user selects export on the reporting module, when the export action is triggered, then a CSV file containing accuracy metrics, feedback logs, and adjustment history is downloaded.
Offline Recommendation Caching
"As a food truck operator working in areas with poor internet, I want to access portion size recommendations offline so that I can rely on the planner even without connectivity."
Description

Enable the recommendation engine and UI to function offline by caching the latest portion suggestions and relevant historical data locally on the device. Ensure seamless data synchronization and conflict resolution once connectivity is restored.

Acceptance Criteria
Initial Recommendation Cache Initialization
Given the user is online and accesses SmartPortion Planner, when the recommendation engine fetches the latest portion suggestions, then the engine must store all fetched suggestions and relevant historical consumption data locally on the device within 5 seconds.
Offline Retrieval of Cached Suggestions
Given the device has no network connectivity and the user opens SmartPortion Planner, when the user requests portion recommendations, then the system must retrieve and display the most recent cached recommendations without any errors or empty states.
Synchronization and Conflict Resolution Post-Reconnect
Given the user made manual portion adjustments offline and then regains connectivity, when the system initiates synchronization, then it must merge local adjustments with server data using defined conflict resolution rules (e.g., last-write-wins) and ensure no data is lost or duplicated.
Automatic Cache Update After Reconnecting
Given the device reconnects to the network, when the user returns to SmartPortion Planner, then the system must automatically fetch and replace the local cache with the latest recommendations within 10 seconds while preserving any offline user edits.
Local Cache Storage Management
Given continuous usage over time, when the local cache exceeds data for the last 30 days, then the system must automatically purge data older than 30 days, ensuring cache size remains under the configured storage limit.

EcoRecipe Optimizer

Suggests creative recipe adjustments and daily specials that utilize ingredients approaching their expiry dates. Reduces waste by turning near-expiry stock into high-margin dishes, boosting both sustainability and revenue.

Requirements

Expiry Detection Module
"As a food truck operator, I want to see which ingredients are nearing their expiration so that I can use them in recipes before they go bad and reduce waste."
Description

Continuously monitors inventory data to identify ingredients within a configurable time window of their expiration dates. Provides real-time visibility on near-expiry items within the offline-capable dashboard, ensuring operators can proactively address potential waste and optimize stock utilization.

Acceptance Criteria
Identify Near-Expiry Items in Real-Time
Given the inventory contains ingredients with expiration dates, When the Expiry Detection Module runs its scan every 5 minutes, Then all ingredients within the configured expiry window are highlighted in the dashboard.
Configurable Expiry Threshold Alerts
Given an operator sets an expiry threshold (e.g., 3 days), When the module evaluates inventory, Then alerts are generated only for ingredients whose expiration dates fall within that threshold.
Offline Data Synchronization
Given the dashboard enters offline mode, When the connection is lost and later restored, Then all near-expiry data collected offline is synchronized with the server without data loss or duplication.
Dashboard Visualization of Near-Expiry Stock
When an operator opens the dashboard, Then near-expiry items are displayed in a dedicated section, sorted by expiration date, with counts and color-coded urgency indicators.
Automatic Exclusion of Expired Ingredients
Given ingredients have passed their expiration date, When the module performs its scan, Then expired items are flagged as expired and excluded from recipe suggestions and restock alerts.
Recipe Suggestion Engine
"As a food truck operator, I want tailored recipe ideas using soon-to-expire ingredients so that I can increase revenue while minimizing food waste."
Description

Analyzes current near-expiry inventory and historical sales trends to generate optimized recipe adjustments and creative dish suggestions. Integrates with the dashboard to offer high-margin recipe recommendations that balance customer preferences, ingredient availability, and operational feasibility.

Acceptance Criteria
Daily Expiring Inventory Analysis
Given at least three ingredients are within 48 hours of expiry, when the engine runs at 2 AM daily, then it generates the top three recipe suggestions using only those ingredients, each with an estimated profit margin of at least 20%.
Historical Sales Trend-based Recommendations
Given 30 days of historical sales data and near-expiry stock, when generating suggestions, then the engine prioritizes recipes aligned with the top 10% best-selling dishes adjusted for available ingredients.
User Interface Integration for Recipe Display
Given the dashboard is opened, when new suggestions are available, then the EcoRecipe panel displays each recipe’s title, required ingredients, prep time, and projected margin, updated in real time.
Customizable Suggestion Filters
Given user-defined dietary and prep-time filters, when filters are applied, then all displayed recipe suggestions comply with the selected criteria and exclude any non-conforming ingredients.
Fallback Handling for Insufficient Data
Given fewer than three near-expiry ingredients, when the engine runs, then the system displays a ‘No optimized recipes found’ message along with a link to the default recipe library.
Daily Specials Notification
"As a food truck operator, I want to receive daily alerts with suggested specials using near-expiry ingredients so that I can quickly update my menu and maximize profits."
Description

Automatically compiles and sends daily special recommendations based on the EcoRecipe engine’s output. Delivers push notifications or in-app alerts at a configurable time with suggested specials, ingredient lists, and profit estimates to assist decision-making and timely menu updates.

Acceptance Criteria
Scheduled Notification Dispatch
Given the operator has set a notification time of 7:00 AM When the scheduled time arrives Then the system sends the daily specials notification automatically via both push and in-app channels
Notification Payload Integrity
When the EcoRecipe engine compiles daily specials Then the notification payload includes for each special: name, list of ingredients, expiry date, and profit estimate in JSON format
Configurable Notification Channel
Given the user preference is set to 'in-app only' or 'push only' When notifications are dispatched Then they are delivered exclusively via the selected channel
Retry on Failure
If a notification dispatch fails due to network or service error Then the system retries delivery up to three times at one-minute intervals and logs each attempt
User Acknowledgement Logging
When a user taps 'Acknowledge' on a daily specials notification Then the system records the acknowledgment timestamp and user ID in the audit log
Ingredient Substitution Recommendations
"As a food truck operator, I want automatic suggestions for ingredient substitutions when stock is low so that I can maintain menu offerings without disrupting service."
Description

Provides intelligent alternatives for unavailable or low-stock items by suggesting suitable substitutions from inventory. Ensures recipe integrity and consistency in taste, accounting for flavor profiles and cost implications, with real-time updates to inventory levels upon substitution selection.

Acceptance Criteria
Out-of-Stock Ingredient Substitution
Given a recipe contains an ingredient marked as out of stock, When the user views substitution options, Then the system presents at least three viable alternatives based on ingredient type, flavor profile, and cost.
Low-Stock Ingredient Substitution
Given an ingredient stock level falls below the restock threshold, When a recipe requiring that ingredient is accessed, Then the system automatically suggests substitution options ranked by best flavor match and availability.
Inventory Update on Substitution
Given a user selects a substitution for an ingredient, When the substitution is confirmed, Then the system deducts the used quantity from both the original and substitute ingredient inventory in real time.
Recipe Flavor Integrity Check
Given suggested substitutions are displayed, When a substitution is selected, Then each suggestion must include a flavor similarity score ≥ 0.8 and notify the user of any flavor profile differences.
Cost Impact Display
Given a list of substitution suggestions, When displayed to the user, Then each option shows cost per unit and the estimated cost variance compared to the original ingredient.
Waste Reduction Reporting Dashboard
"As a food truck operator, I want to see reports on how using near-expiry ingredients affects my waste and profits so that I can measure the impact of EcoRecipe Optimizer on my business."
Description

Aggregates data on near-expiry ingredient usage, waste reduction metrics, and profitability gains from EcoRecipe-driven adjustments. Visualizes key performance indicators (KPIs) on a dedicated dashboard tab, enabling operators to track sustainability efforts and financial impact over time.

Acceptance Criteria
Display Waste Reduction Trends Over Time
Given the operator selects a date range on the Waste Reduction Reporting Dashboard, When at least one near-expiry ingredient usage record exists within that range, Then the dashboard displays a line chart showing daily percentages of near-expiry stock utilized versus wasted.
Calculate Profitability Gains from EcoRecipe Usage
Given EcoRecipe adjustments have been applied, When the operator views the Profitability Gains KPI, Then the dashboard calculates and displays the total revenue generated from near-expiry ingredient dishes minus baseline revenue from standard recipes.
Offline Data Synchronization Upon Reconnection
Given the operator uses the dashboard offline and ingredient usage data is logged, When network connectivity is restored, Then the dashboard automatically syncs offline records and updates waste reduction and profitability metrics without data loss.
Export Waste Reduction Report
Given the operator clicks the Export button on the Waste Reduction Reporting Dashboard, When a valid date range is selected, Then the system generates and downloads a CSV file containing daily waste reduction metrics, ingredient usage details, and profitability gains.
Customizable KPI Threshold Alerts
Given the operator sets custom thresholds for waste reduction percentage and profitability gains, When the KPI values exceed or drop below these thresholds, Then the dashboard highlights the KPI in red or green and displays an alert icon with a tooltip explaining the threshold breach.

Expiration Alerts

Sends real-time push notifications or in-app reminders when products are nearing their expiration. Ensures timely usage or markdowns, preventing spoilage and avoiding unnecessary write-offs.

Requirements

Configurable Expiration Thresholds
"As a food truck operator, I want to configure personalized expiration thresholds for my inventory items so that I receive alerts appropriate to each product’s shelf life."
Description

Allow users to set customizable expiration thresholds at the product or category level, with sensible default values. Users can define the number of days before expiration when alerts should trigger, ensuring notifications are tailored to each item’s shelf life and operational needs.

Acceptance Criteria
Setting Custom Threshold for Single Product
Given a user is viewing the settings for a specific product, when they enter a custom expiration threshold value in days and save, then the system stores and displays the new threshold for that product in the dashboard.
Setting Threshold for Product Category
Given a user is managing product categories, when they set an expiration threshold for a category and confirm, then all products within that category inherit and display the configured threshold.
Applying Sensible Default Thresholds
Given a new product or category is added without a custom threshold, when it’s saved, then the system assigns and displays the predefined default expiration threshold value.
Validating Threshold Input Limits
Given a user enters an expiration threshold below the minimum or above the maximum allowed range, when they attempt to save, then the system rejects the input and shows a validation error message.
Triggering Expiration Alerts Based on Threshold
Given a product’s remaining shelf life equals its configured threshold, when the system checks inventory, then a real-time push notification or in-app reminder is sent to the user.
Real-Time Push Notification Delivery
"As a food truck owner, I want to receive real-time push notifications when products near expiration so that I can use or discount them before spoilage."
Description

Implement a robust push notification system that delivers alerts in real time when products approach their expiration thresholds. Notifications must work even if the app is running in the background and queue alerts offline to send once connectivity is restored.

Acceptance Criteria
Background Notification Delivery
Given a product reaches its expiration threshold when the app is running in the background and network is available, when the system triggers the expiration alert, then a push notification must be delivered to the device within 5 seconds.
Offline Notification Queuing
Given the device is offline at the moment a product reaches its expiration threshold, when network connectivity is restored, then all queued expiration notifications must be automatically sent without user intervention.
In-App Alert Presentation
Given the user opens the app dashboard while products are near expiration, when the dashboard loads, then an in-app reminder banner must be displayed immediately showing the product name and days until expiration.
Duplicate Notification Prevention
Given a product has already triggered an expiration alert within the past 24 hours, when the expiration threshold persists, then no duplicate push notifications should be sent for that product within the 24-hour window.
Notification Payload Integrity
Given a push notification is received on the device, when the notification is inspected, then its payload must include the product ID, product name, expiration date, and a deep link that navigates directly to the product detail screen in the app.
In-App Reminder Scheduling
"As an operator, I want in-app reminders at specific times so that I can plan inventory usage effectively and reduce waste."
Description

Provide an in-app reminder feature that allows users to schedule timed or recurring reminders for items nearing expiration. Users can choose reminder intervals and notification channels (e.g., banners, badges) to fit their workflow.

Acceptance Criteria
Single Reminder Scheduling
Given an item with a set expiration date, when the user schedules a one-time reminder for a specified interval before expiration, then the system creates the reminder and delivers the notification at the correct time via the selected notification channel.
Recurring Reminder Scheduling
Given an item with an expiration date beyond the current day, when the user configures a daily recurring reminder starting a specified number of days before expiration, then the app schedules and triggers the reminder at the chosen time each day until the item expires.
Notification Channel Preference
Given the user has selected notification channels (e.g., banners, badges), when a reminder is triggered, then notifications are displayed using only the chosen channels.
Offline Reminder Scheduling
Given the user is offline, when they schedule or modify a reminder, then the changes are stored locally and synchronized with the server once connectivity is restored, ensuring no reminders are lost.
Reminder Modification and Deletion
Given an existing reminder, when the user edits its timing or recurrence, then the system updates the reminder accordingly; and when the user deletes a reminder, then it is removed and no further notifications are sent.
Expiration Overview Dashboard
"As a food truck owner, I want a dashboard view highlighting expiring items so that I can quickly identify and act on them."
Description

Add a dedicated dashboard section that lists all inventory items approaching expiration, sortable by date, category, or quantity. The overview highlights high-risk items and provides quick actions for marking down or scheduling usage.

Acceptance Criteria
High-Risk Items Highlight
Given a user opens the Expiration Overview Dashboard, When inventory items are expiring within 3 days, Then those items are highlighted with a red badge and displayed at the top of the list.
Sort by Expiration Date
Given the user selects the “Sort by Expiration Date” option, When the dashboard refreshes, Then all items are ordered ascending by expiration date.
Filter by Category
Given the user applies a category filter on the Expiration Overview Dashboard, When a category is selected, Then only items belonging to that category are displayed.
Quick Markdown Action
Given an item approaching expiration is displayed, When the user taps the “Mark Down” action, Then the item’s price is reduced by 20% and the change is recorded in the activity log.
Schedule Usage Reminder
Given an item nearing expiration is listed, When the user selects “Schedule Usage,” Then a usage reminder is created in the app for the specified date and time and the user receives a confirmation message.
Alert History & Analytics
"As an owner, I want to review historical expiration alerts and outcomes so that I can analyze waste trends and improve my stocking decisions."
Description

Maintain a log of all expiration alerts and user actions taken in response. Provide analytics on alert frequency, items saved versus wasted, and trends over time to help users optimize inventory planning.

Acceptance Criteria
Viewing Expiration Alert History
Given a user is logged into Truckly and navigates to the Alert History page When the page loads Then a chronological list of all expiration alerts displays with timestamps, product names, alert types, and user action taken, and the list loads within 2 seconds
Filtering Alert History by Date Range
Given the Alert History page is displayed When the user applies a start and end date filter Then only alerts within the specified range appear and the total count of filtered alerts updates within 1 second
Generating Alert Frequency Analytics
Given a user accesses the Analytics section When they select the Alert Frequency metric Then a bar chart displays the number of expiration alerts per week for the last 30 days with tooltips showing exact counts
Analyzing Items Saved vs Wasted Trends
Given a user opens the Analytics dashboard When they select the Saved vs Wasted chart Then a line graph displays items saved versus items wasted over time with options to toggle daily, weekly, and monthly views reflecting actual recorded user actions
Exporting Alert History and Analytics Data
Given a user clicks the Export button on either the Alert History or Analytics page When they choose CSV or PDF format Then the system generates and downloads a file containing the filtered data and summary statistics within 5 seconds
Restock Recommendation Integration
"As a food truck operator, I want vendor list suggestions when products near expiration so that I can plan restocks efficiently and maintain optimal inventory levels."
Description

When items near expiration, automatically generate vendor restock suggestions based on historic sales data and current inventory levels. Integrate suggestions into the existing vendor list feature for seamless ordering.

Acceptance Criteria
Near-Expiration Item Detected
Given an inventory item with an expiration date within 3 days, when the system performs its daily expiration check, then a restock recommendation is automatically generated for that item.
Vendor Suggestions Generated Automatically
When an expiration alert is triggered for an item, the system analyzes the last 30 days of sales data and current inventory levels, then produces a ranked list of up to three vendor suggestions with specific order quantities.
Vendor List Updated with Recommendations
When restock suggestions are generated, they appear in the vendor list interface under a 'Restock Recommendations' section, sorted by urgency and including suggested order quantities.
User Reviews Recommendations Offline
Given the user was offline for up to 24 hours during which expiring items were detected, when connectivity is restored, then the system syncs and displays all pending restock recommendations in the vendor list.
Fallback for Items Without Sales History
When the system cannot find any historic sales data for an expiring item, then it suggests a default restock quantity based on the vendor's standard pack size.

Compost & Donate Connector

Integrates with local compost facilities and food donation networks to streamline the disposal of unsellable but safe-to-consume items. Automates pickup requests and tracks environmental impact metrics for your sustainability reports.

Requirements

Integration Setup Wizard
"As a food truck owner, I want an easy-to-follow setup wizard to connect to compost and donation services so that I can start disposing unsellable items sustainably without technical hurdles."
Description

Develop a step-by-step configuration wizard within the Truckly dashboard allowing users to securely connect their account to local compost facilities and food donation networks via API credentials or OAuth. The wizard should guide users through selecting service providers, entering authentication details, and mapping item categories for disposal and donation. It should validate connections in real time and provide clear error messages or success confirmations. By streamlining the integration process, the wizard ensures food truck operators can quickly enable sustainable disposal workflows without manual setup or technical assistance.

Acceptance Criteria
Service Provider Selection
Given the user launches the Integration Setup Wizard, When they reach the service provider selection step, Then they should see a list of available compost facilities and donation networks sorted by proximity, And they can select one or multiple providers to proceed.
Authentication Details Entry
Given a service provider is selected, When the user is prompted to authenticate, Then the wizard should display fields for API Key/Secret or OAuth login, And input validation should enforce required formats before allowing progression.
Real-time Connection Validation
Given authentication details are entered, When the user submits the credentials, Then the wizard should attempt a real-time connection to the provider, And display a success confirmation if valid, Or an error message with actionable guidance if invalid.
Item Category Mapping Completion
Given a successful connection is established, When the user is mapping inventory categories, Then the wizard should display all local item categories, And allow the user to assign each category to either compost or donation, And highlight any unmapped categories before completion.
Error Handling for Invalid Credentials
Given the user submits incorrect credentials, When the provider rejects authentication, Then the wizard should present an error message specifying the failure reason, And allow the user to retry without restarting the wizard.
Automated Pickup Scheduling
"As a food truck operator, I want the system to auto-schedule pickups when my unsellable inventory reaches a threshold so that I don’t have to manually coordinate with each facility."
Description

Implement a scheduling engine that automatically generates and sends pickup requests to connected compost facilities and donation partners based on user-defined criteria (e.g., volume thresholds, time windows, or specific item categories). The system should batch multiple disposal items into single pickup jobs when possible to minimize logistics costs and environmental impact. Users can review, modify, or cancel scheduled pickups before confirmation. The feature reduces manual coordination and ensures timely removal of food waste.

Acceptance Criteria
Volume Threshold Triggered Pickup
Given a user-defined volume threshold for unsellable items is reached When the threshold is exceeded Then the system generates a pickup request and sends it to the appropriate compost facility or donation partner And the pickup request aggregates total volume and item categories And items meeting the threshold within the same period are batched into a single job
Time Window Scheduled Batch
Given a user-specified time window for pickups (e.g., daily at 6 PM) When the system reaches the scheduled time Then it schedules all pending disposal items into one pickup job And validates partner availability hours, deferring to the next available window if outside hours And if no items are pending, no pickup request is issued
Category-Specific Pickup Job
Given a user has defined an item category threshold (e.g., produce at 10 lbs) When items of that category exceed the threshold Then the system generates a pickup request exclusively for that category And excludes items from other categories And supports multiple concurrent category-specific pickup jobs
User Modification Before Confirmation
Given a pickup request is generated but not yet confirmed by the partner When the user edits the pending pickup in the dashboard Then they can modify date, time, and included items And the system updates the request details and resends to the partner And logs all changes with timestamp and user ID
Cancellation of Scheduled Pickup
Given a pickup request is pending and at least two hours remain before scheduled time When the user initiates cancellation Then the system sends a cancellation notice to the partner And removes the job from the schedule And notifies the user via email and in-app notification And prevents cancellation if within two hours of the pickup time, displaying an error
Environmental Impact Reporting
"As a food truck owner, I want to view and export reports on my composting and donation activity so that I can document my sustainability efforts and share impact metrics with partners."
Description

Build a reporting module that aggregates data on the weight and type of items sent to compost facilities and food banks, calculates key sustainability metrics (e.g., CO₂ emissions avoided, landfill waste prevented, meals donated), and displays them in interactive dashboards. Users should be able to filter reports by date range, location, or partner, and export PDF or CSV summaries for their sustainability reports or marketing materials. This requirement enables operators to quantify their environmental and social impact and share achievements with stakeholders.

Acceptance Criteria
Generate Sustainability Metrics Dashboard
Given access to the reporting module, when the user loads the Sustainability Dashboard, then the dashboard displays aggregated weight of items sent to compost and donation, total CO₂ emissions avoided, landfill waste prevented, and meals donated with correct units and labels.
Filter Environmental Reports
Given the reporting dashboard is displayed, when the user applies date range, location, and partner filters, then the displayed metrics and charts update to reflect only the filtered data within 2 seconds.
Export Reports to PDF and CSV
Given the user has filtered reports, when the user selects 'Export' and chooses PDF or CSV, then the system generates and downloads a file in the correct format containing current report data with proper headers and summary.
Calculate CO₂ Emissions Avoided
Given compost and donation weight data, when the report is viewed, then the system calculates CO₂ emissions avoided using the standard factor (e.g., 0.5 kg CO₂ per kg waste diverted) and displays the calculation details.
Track Compost and Donation Weight by Partner
Given multiple compost facilities and donation partners, when the report is filtered by partner, then the system displays total weight sent to each partner over the selected period and includes a bar chart breakdown.
Real-Time Pickup Notifications
"As a food truck operator, I want to receive instant notifications about my compost or donation pickup status so that I can stay informed and make adjustments if needed."
Description

Create a notification system that sends real-time alerts via push notification, SMS, or email when key events occur in the compost & donate workflow—such as successful pickup scheduling, driver en route, pickup completed, or scheduling errors. Notifications should include relevant details (facility name, scheduled time, items included) and offer quick-access links to the dashboard for adjustments. Timely alerts keep operators informed of disposal status, reducing missed pickups and ensuring smooth operations.

Acceptance Criteria
Pickup Scheduled Notification
Given a user successfully schedules a compost pickup, When the scheduling is confirmed by the system, Then a push notification, SMS, or email is sent within 30 seconds to the user’s registered contact method, including facility name, scheduled date and time, items included, and a link to adjust the pickup.
Driver En Route Notification
Given a driver has been assigned and is en route to the pickup location, When the driver’s status is updated to en route, Then the user receives a notification via their preferred channel within 1 minute containing driver ETA, driver name, vehicle information, and a link to view live status.
Pickup Completed Notification
Given the driver marks the pickup as completed, When the system records the completion, Then the user receives a confirmation notification within 30 seconds showing completion time, items collected, and updated environmental impact metrics.
Scheduling Error Notification
Given a pickup scheduling request fails due to facility unavailability or system error, When the failure occurs, Then an error notification is sent immediately via all preferred channels with error details and a direct link to retry scheduling.
Quick-Access Link from Notification
Given any real-time notification is received, When the user taps the notification link, Then the mobile app opens directly to the related dashboard screen (pickup details or scheduling interface) within 5 seconds.
Data Privacy & Compliance
"As a food truck business owner, I want my disposal data to be handled securely and in compliance with privacy laws so that I can protect customer and operational information and avoid legal issues."
Description

Ensure that all communications and stored data comply with relevant privacy regulations (e.g., GDPR, CCPA) and local food disposal policies. Implement data encryption in transit and at rest, consent capture for sharing item-level disposal data with third parties, and configurable data retention policies. Include audit logs for all API calls and user actions related to compost and donation workflows. This requirement protects user information and helps operators adhere to legal and regulatory obligations.

Acceptance Criteria
User Consent Capture for Data Sharing
Given an operator attempts to share item-level disposal data with a third party, When the share action is initiated, Then the system must prompt the operator to provide explicit consent, record the consent with a timestamp, user ID, and data scope, and prevent data sharing without this recorded consent.
Data Encryption Validation
Given any API call or data transaction in the compost & donate connector, When data is transmitted or stored, Then all data in transit must use TLS 1.2 or higher and all data at rest must be encrypted using AES-256, which must be verifiable via automated security scans.
Configurable Data Retention Policy Enforcement
Given an operator configures a data retention period in the dashboard, When the configured period elapses for any compost or donation record, Then the system must automatically delete or archive the record according to policy and generate a retention action log entry.
Audit Logging of Compliance Events
Given any user action or API call related to compost and donation workflows, When the action or call is executed, Then the system must generate an immutable audit log entry containing timestamp, user ID or API key, action details, and payload hash, and make logs accessible to administrators for review.
Data Subject Erasure and Access Requests
Given a user submits a GDPR/CCPA data request for their compost/disposal data, When the request is received, Then the system must process the request within 30 days, either providing the complete data export or permanently erasing the data, and send a confirmation notification to the user.

GeoFence Trigger

Automatically sends push alerts when opted-in customers enter a customized radius around your truck. Ensures timely notifications at peak proximity, increasing the likelihood of foot traffic by targeting users precisely when they’re nearby.

Requirements

Geofence Definition Interface
"As a food truck operator, I want to define and customize geofence areas on an interactive map so that I can target notifications to nearby customers at the right locations."
Description

The system shall provide an intuitive interface within the mobile-first dashboard enabling operators to create, edit, and delete custom geofences on an interactive map. Operators can set radius distances, assign names, and link each geofence to specific trucks or routes. The interface integrates map APIs for accurate placement, stores geofence definitions in the backend, and ensures responsiveness across desktop and mobile devices. By enabling precise geofence management, this requirement allows operators to target customers effectively as they approach the truck’s location, boosting notification relevance and foot traffic.

Acceptance Criteria
New Geofence Creation on Desktop Interface
- Given the operator is logged in on a desktop device and navigates to the Geofence Definition Interface - When the operator clicks 'Create Geofence', draws a circle of 150m radius at a specified location on the interactive map, enters the name 'Peak Zone', assigns it to 'Truck A', and clicks 'Save' - Then the new geofence appears in the geofence list with the correct name, radius, and assignment - And the geofence overlay is displayed accurately on the map - And the backend confirms persistence of the geofence via a successful API response
Editing Existing Geofence on Mobile
- Given the operator is on a mobile device viewing the list of existing geofences - When the operator selects the geofence named 'Lunch Spot', taps 'Edit', changes the radius to 250m, renames it to 'Lunch Hub', and taps 'Save' - Then the geofence list reflects the updated name and radius - And the map overlay updates accordingly - And the backend confirms the update with a success response
Deleting a Geofence
- Given the operator is viewing the geofence list on any device - When the operator selects the geofence 'Test Zone', taps 'Delete', and confirms the deletion - Then 'Test Zone' is removed from the geofence list - And its overlay is removed from the interactive map - And the backend no longer returns the deleted geofence via the API - And a deletion confirmation message is displayed
Assigning Geofence to Specific Truck or Route
- Given the operator is creating or editing a geofence - When the operator selects a specific truck or route from the assignment dropdown before saving - Then the selected truck or route is linked to the geofence - And the geofence details page shows the correct assignment - And the backend stores the assignment relationship successfully
Responsive Layout on Various Screen Sizes
- Given the operator accesses the Geofence Definition Interface on desktop, tablet, and mobile devices - When the screen size or orientation changes - Then the map, controls, and geofence list adjust layout without overlap or loss of functionality - And all interactive elements remain accessible and operational
Customer Opt-In Consent Management
"As a customer, I want to control my subscription to location-based alerts so that I receive notifications only when I allow and avoid unwanted interruptions."
Description

The product shall include a customer-facing consent management module within the mobile app that allows users to grant, modify, or revoke permissions for receiving geofence-based push alerts. This module must present clear information about the benefits of opting in, comply with privacy regulations (e.g., GDPR, CCPA), securely store consent statuses, and sync changes with the backend in real time. By ensuring transparent opt-in workflows and robust data handling, this requirement protects user privacy and ensures only consenting customers receive notifications.

Acceptance Criteria
User Grants Consent for GeoFence Notifications
Given the user opens the Consent Management module, When they tap “Enable GeoFence Alerts” and accept the permission prompt, Then the app must record consent status as “granted,” display a confirmation message, and begin monitoring geofence events.
User Modifies Consent Preferences from Settings
Given the user navigates to Settings > Notifications > GeoFence Alerts, When they toggle the permission on or off, Then the app must update the consent status locally and on the backend within 5 seconds and display a success toast.
User Revokes Consent and No Alerts Are Sent
Given the user revokes GeoFence permission in the Consent Management module or device settings, When a geofence boundary is crossed, Then the system must not send any push notifications to the user.
Consent Status Synced During Offline-to-Online Transition
Given the user changes their consent status while offline, When the device regains network connectivity, Then the app must automatically sync the updated consent status with the backend, receive an acknowledgement, and reflect the current status in the UI.
User Informed of Benefits and Privacy Compliance
Given a first–time user opens the GeoFence consent screen, When the module loads, Then it must display clear benefit statements, links to GDPR and CCPA policies, and a concise explanation of data usage before the user can grant or deny consent.
Real-Time Geofence Trigger Engine
"As a system administrator, I want the system to detect when opted-in customers enter a defined geofence so that the appropriate push alerts are sent immediately."
Description

Implement a scalable backend engine that continuously monitors incoming customer location events against defined geofences and triggers push notifications in real time. The engine should utilize spatial querying to detect entries and exits, manage an event queue to guarantee low-latency processing, and integrate with push notification services (such as APNs and FCM) for reliable delivery. It must include retry logic and deduplication to prevent missed or repeated alerts. This core capability is essential for timely, accurate notifications when customers enter or leave designated zones.

Acceptance Criteria
First-Time Geofence Entry Notification
Given an opted-in customer with a defined geofence for Truckly_id_123 and radius 500 meters, when the customer’s location event crosses into the geofence, then a push notification is sent to the customer within 2 seconds via APNs or FCM with the correct truck name and distance; and the entry event is marked as processed.
Duplicate Notification Prevention
Given the customer remains within the geofence and sends multiple location events within a 10-minute window, when subsequent location events are received for the same geofence entry, then no additional notifications are sent until the customer exits and re-enters the geofence.
Push Notification Retry Logic
Given the push notification service returns a transient error (e.g., HTTP 500 or timeout), when the engine attempts to send a notification, then it retries up to 3 times with exponential backoff intervals (1s, 2s, 4s) and logs the failure if all retries fail.
Offline Event Queue Processing
Given location events are generated while the backend is offline, when connectivity is restored, then all queued events are processed in chronological order within 1 minute, triggering notifications for valid geofence entries and marking processed events accordingly.
Real-Time Processing Under High Load
Given the system receives 1,000 location events per second from multiple customers, when processing begins, then the engine handles all events with an average processing latency under 200 milliseconds per event and zero dropped events.
Customizable Notification Templates
"As a food truck operator, I want to customize the message content of geofence alerts so that I can send relevant promotions and increase customer engagement."
Description

Provide operators with a template editor in the dashboard that allows them to craft, preview, and manage push notification content for geofence-triggered alerts. The editor should support dynamic placeholders (e.g., truck name, special offers), basic rich text formatting, character limits enforcement, and device preview across multiple screen sizes. Templates can be saved, categorized, and scheduled for reuse. By enabling tailored messaging, this requirement helps operators personalize alerts to promote menu items, deals, or events, driving higher engagement.

Acceptance Criteria
Template Editor Launch and Access
When the operator clicks the "New Notification Template" button in the GeoFence Trigger dashboard, the template editor opens within 2 seconds; The editor displays input fields for template name, message body, placeholder dropdown, formatting toolbar, and save controls; The editor interface is responsive and fully functional on mobile, tablet, and desktop viewport widths.
Dynamic Placeholder Rendering
Given a template containing the placeholders {truck_name} and {special_offer}, when the operator selects "Preview", then the placeholders are replaced with live sample data (e.g., "Joe's Tacos", "20% Off Today"); The preview displays the substituted content without any placeholder tokens.
Rich Text Formatting and Character Limit Enforcement
When the operator applies bold, italic, underline, bullet list, or hyperlink formatting options, then the styled text appears correctly in the preview; When the message body content exceeds 160 characters (including formatting codes), then the editor displays a character counter and prevents further input beyond the limit.
Multi-Device Preview Display
Given device size selectors for small, medium, and large screens, when the operator switches between selectors, then the preview pane updates within 1 second to reflect layout and text wrapping for the selected device; The message content remains legible and formatting is preserved across all preview sizes.
Template Management and Scheduling
When the operator saves a template, then they can assign it to one or more custom categories and see it listed under those categories; When an operator selects a saved template, they can schedule it by date/time or relative to geofence entry (e.g., "5 minutes after entry"), and the schedule is saved and displayed in the template list view.
Geo-Alert Analytics Dashboard
"As a food truck operator, I want to view analytics on geofence-triggered notifications so that I can measure their performance and optimize my marketing efforts."
Description

Enhance the Truckly analytics dashboard with a dedicated section for geofence-triggered alert metrics, including number of triggers, notifications delivered, open rates, and conversion metrics such as user check-ins or visits. The dashboard should offer visualizations by time period, location, and campaign, as well as filtering and export capabilities. Backend data aggregation and processing must ensure accurate KPI calculations. This feature provides operators with actionable insights to evaluate the effectiveness of geo-alert campaigns and optimize targeting strategies.

Acceptance Criteria
Overall Geo-Alert Metrics Overview
Given the user navigates to the Geo-Alert Analytics Dashboard, when the section loads, then the dashboard displays total geofence triggers, notifications delivered, open rate percentage, and check-in conversion rate for the default time frame without error.
Time Period Filtering
Given the dashboard is open, when the user selects a custom date range, then all metric values and visualizations update to reflect data exclusively within that range and the selected dates appear in the header.
Location-Based Filtering
Given the dashboard is open, when the user filters by a specific truck location, then the displayed metrics and charts accurately reflect only the data for that location and the location name is shown in the filter badge.
Data Export Functionality
Given a filter or default view is applied, when the user clicks the export button, then a CSV file is generated within 5 seconds containing column headers matching displayed fields and rows matching the current filtered metrics.
KPI Accuracy Validation
Given the backend aggregation process runs, when comparing dashboard metrics against raw event logs for the same period and location, then discrepancies do not exceed 1% for each KPI.

Dynamic Promo Scheduler

Allows scheduling of multiple time-sensitive promotions based on route, time of day, or weather conditions. Automatically activates the most relevant offer when customers approach, maximizing engagement and sales during optimal windows.

Requirements

Promotion Configuration Interface
"As a food truck owner, I want an intuitive interface to configure promotions by route, time, and weather so that I can quickly set up targeted offers without navigating complex settings."
Description

Provide a mobile-first interface that enables truck owners to create, edit, and manage multiple time-sensitive promotions. The interface must support setting promotion parameters such as route(s), time windows, and weather triggers. It should include real-time validation of scheduling conflicts, intuitive controls for adding or removing criteria, and seamless integration with the existing dashboard. Expected outcomes include reduced setup time, improved accuracy in promotion setup, and a unified view of all scheduled offers.

Acceptance Criteria
Scheduling a new promotion
Given the user is on the 'Create Promotion' screen, When they select at least one route, set a valid time window that does not conflict with existing promotions, and choose weather triggers, Then the 'Save' button becomes enabled. When the user clicks 'Save', Then a new promotion is added to the dashboard and visible in the promotions list within 2 seconds. The system shows a confirmation message "Promotion saved successfully."
Editing an existing promotion
Given the user selects an existing promotion from the list, When they modify time windows or routes and click 'Save', Then the changes are saved and immediately reflected in the promotions list. When the user removes all criteria from a promotion except its name, Then the system prompts to add at least one trigger before allowing save.
Detecting conflicting promotion schedules
Given two promotions with overlapping time windows on the same route, When the user attempts to create or update a promotion causing the overlap, Then the interface displays a real-time validation error "Schedule conflict with Promotion X" and disables the 'Save' button. When the conflicting time window or route is adjusted to a non-conflicting set, Then the error disappears and 'Save' re-enables.
Removing a promotion criterion
Given the user views a promotion's criteria list, When they click the remove icon next to a criterion, Then the criterion is removed and the criteria count updates. When the last criterion is removed, Then the system displays a warning "A promotion must have at least one criterion" and disables 'Save'.
Viewing all scheduled promotions
Given the user navigates to the 'Scheduled Promotions' dashboard, Then all promotions are listed with their names, routes, time windows, and triggers. When there are more than 10 promotions, Then pagination controls or infinite scroll load additional items within 1 second of user scroll.
Geofence and Route Management
"As a truck operator, I want to define geofences for each route so that promotions automatically activate when customers approach our current location."
Description

Implement a geofencing engine that allows defining and managing virtual perimeters around standard routes and locations. The system must map truck routes, support uploading or drawing custom route shapes, and associate promotions with specific geofences. It should trigger promotions when a customer’s device enters the defined area. Integration with the map service and offline support must ensure consistent performance even with intermittent connectivity.

Acceptance Criteria
Uploading a Custom Route Shape
Given the user is on the geofence management screen When the user uploads a valid GPX or KML file Then the system renders the custom route accurately on the map And the route is saved in the user’s route library
Defining and Saving a Geofence on a Standard Route
Given a predefined standard route is selected When the user draws a polygon or circle on the map overlay Then the geofence boundary is highlighted and editable And the geofence is saved with a unique identifier
Associating Promotion with Geofence
Given an existing geofence is available in the geofence list When the user creates or edits a promotion and selects the geofence Then the promotion record lists the selected geofence And the promotion-geofence association is persisted in the database
Triggering Promotion on Geofence Entry
Given the customer’s device app is installed with location permissions granted When the device GPS detects entry into a defined geofence area Then the app automatically displays the associated promotion And a trigger event is logged with timestamp and location data
Offline Geofence Activation
Given the device has cached geofence definitions from the last sync When the device enters a geofence area without network connectivity Then the app triggers the associated promotion locally And queues the trigger event to sync with the server when connectivity is restored
Time-based Scheduling Module
"As an independent operator, I want my promotions to activate automatically during specified time periods so that I don’t have to manually toggle offers throughout the day."
Description

Develop a scheduling module that enables precise activation and deactivation of promotions based on time windows, including start and end dates, daily time ranges, and recurring patterns. The module should handle edge cases like daylight saving shifts and crossing midnight, provide timezone awareness, and integrate with the promo configuration interface. Expected outcomes are reliable promo activation, reduced manual oversight, and consistent customer experience.

Acceptance Criteria
Single-Day Promotion Activation
Given a promo with start date 2025-08-10 and daily time window 11:00–14:00, When the system time is between 11:00 and 14:00 on 2025-08-10, Then the promo is activated and visible to the customer.
Recurring Daily Promotion Activation
Given a promo set to recur daily from 09:00–11:00, When the system time enters that window on any day, Then the promo activates without manual intervention.
Cross-Midnight Promotion Activation
Given a promo configured from 22:00–02:00 next day, When the system time falls between 22:00 and 23:59 or 00:00 and 02:00, Then the promo remains active across midnight.
Daylight Saving Time Adjustment
Given a promo spanning a daylight saving transition, When clocks shift forward or backward, Then the promo adjusts and activates/deactivates at the local intended times without manual updates.
Timezone Awareness Validation
Given multiple truck locations in different timezones, When each truck's local time enters a promo window, Then the promo activates based on the truck's local timezone settings.
Promo Configuration Interface Integration
When a user creates or edits a promotion in the configuration interface, Then the time-based scheduling settings save correctly and the preview reflects activation windows.
Weather Condition Integration
"As a food truck owner, I want promotions to activate when specific weather conditions occur so that I can capitalize on demand spikes, such as offering iced drinks on hot days."
Description

Integrate with a real-time weather API to fetch current and forecasted conditions relevant to each truck location. The system must allow promotions to be triggered or disabled based on parameters such as temperature, precipitation, or humidity. Include threshold-based triggers (e.g., temperature above 80°F) and fallback handling for API failures. Expected outcomes include more contextually relevant promotions and improved engagement during weather-driven demand fluctuations.

Acceptance Criteria
High Temperature Trigger
Given the truck’s location reports a temperature above 80°F, when the promo scheduler executes, then the temperature-based promotion is activated within 60 seconds.
Rain-Induced Offer Activation
Given the precipitation probability is 50% or higher at the truck’s GPS coordinates, when a customer is detected within the geofence, then the rain promotion is displayed to the customer in the app.
API Failure Fallback
Given the weather API returns an error or times out, when the scheduler attempts to fetch data, then weather-based promotions are disabled and a default fallback notification is logged.
Forecast-Based Promotion Scheduling
Given the 2-hour forecast predicts temperature above 75°F, when the scheduler builds the next route’s promo plan, then the temperature-sensitive offer is scheduled for the corresponding time window.
Humidity Threshold Pause
Given the humidity level exceeds 90% at the truck’s location, when the scheduler refreshes promotion conditions, then any humidity-based promotions remain inactive until humidity drops below the threshold.
Rule Prioritization and Conflict Handling
"As a truck owner, I want the system to automatically choose the best offer when multiple promotions could apply so that I avoid conflicting discounts and maximize my revenue."
Description

Create a conflict-resolution engine to evaluate multiple active promotion rules and select the most relevant one when triggers overlap. The engine must support customizable prioritization criteria (e.g., revenue potential, margin, or owner-defined ranking), handle tie-breakers, and log decisions for audit. Expected outcomes include consistent rule application, avoidance of conflicting offers, and maximum ROI by surfacing the optimal promotion.

Acceptance Criteria
Overlapping Promotions by Time and Route
Given two promotions active for the same time window and route with revenue potentials of $100 and $150 respectively, when a customer approaches the route during that time, then the promotion with $150 potential is selected.
Tie-Breaker Using Owner-Defined Ranking
Given two active promotions with identical revenue potential and margin, when the owner has assigned a higher priority ranking to Promotion A over Promotion B, then the engine selects Promotion A.
Audit Logging of Promotion Selections
Given any conflict resolution event, when a promotion is selected by the engine, then the system logs the selected promotion ID, conflicting promotion IDs, applied prioritization criteria, timestamp, and resolution outcome, and the log entry is retrievable via the audit API.
Weather-Based Promotion Prioritization
Given two active promotions valid in current time, one triggered by 'Rain' weather condition with a margin of 20% and the other triggered by time window with a margin of 25%, when the weather condition is 'Rain', then the engine selects the weather-based promotion based on a weather relevance weight of 70% over margin weight of 30%.
Fallback Selection When Metrics Are Undefined or Tied
Given two conflicting promotions without defined prioritization metrics or identical scores across all criteria, when the engine cannot resolve the conflict, then it selects the promotion with the earliest creation timestamp and generates a warning in the audit log.

Loyalty Beacon

Targets repeat customers with Tiered Alerts featuring exclusive deals and rewards. Strengthens customer loyalty by delivering personalized offers to your best patrons as they near your location, driving repeat visits and higher spend.

Requirements

Proximity Detection Engine
"As a returning customer, I want to receive a personalized reward notification when I’m nearby so that I’m incentivized to visit the food truck again."
Description

Implement a geofencing system that continuously monitors customers’ locations relative to the food truck. When a repeat customer enters a predefined radius (e.g., 100 meters), the system triggers the Loyalty Beacon. This feature should integrate with the mobile dashboard, support dynamic radius configuration, and minimize battery consumption while offline and online.

Acceptance Criteria
Repeat Customer Enters Geofence
Given a registered repeat customer has location services enabled and has opted in for loyalty alerts, when they cross the 100-meter radius from the food truck on the mobile app, then the Loyalty Beacon trigger fires within 2 seconds and sends the tiered alert to the customer's device.
Adjustable Geofence Radius Configuration
Given an operator updates the geofence radius from the dashboard settings to 150 meters, when the change is saved, then the new radius is used for subsequent proximity checks without requiring an app restart.
Offline Customer Detection
Given the user's device is offline, when the repeat customer enters the configured radius, then the mobile app logs the proximity event locally and queues the Loyalty Beacon trigger to send when connectivity is restored.
Battery Consumption Optimization
Given the app runs continuously, when the geofencing engine is active for 30 minutes, then the average battery usage increase is less than 5% per hour on standard test devices.
Real-Time Dashboard Integration Test
Given a repeat customer triggers a geofence entry, when the Loyalty Beacon fires, then the dashboard displays a new 'Beacon Sent' event within 5 seconds and updates the customer's activity log.
Tiered Customer Segmentation
"As a food truck owner, I want my customers to be segmented into tiers automatically so that I can send exclusive deals to my best patrons."
Description

Automatically segment customers into tiers (e.g., Bronze, Silver, Gold) based on historical purchase frequency and total spend. The system should recalculate tiers in real time after each transaction and store segment data within the offline-capable dashboard for immediate access.

Acceptance Criteria
Initial Tier Assignment
Given a customer with no purchase history When they complete their first transaction Then the system assigns the customer to the Bronze tier in real time And the updated tier is stored within the offline-capable dashboard
Tier Upgrade Upon Spend Threshold
Given a customer currently in the Bronze tier When their cumulative total spend reaches the Silver threshold Then the system upgrades the customer to the Silver tier immediately And the new tier is recorded in the dashboard
Tier Downgrade Upon Return or Refund
Given a customer in the Silver or Gold tier When a return or refund causes their cumulative spend to fall below the current tier threshold Then the system recalculates and downgrades the customer to the appropriate lower tier And the revised tier is saved offline and displayed on the dashboard
Offline Tier Calculation and Sync
Given the dashboard is offline When a transaction occurs Then the system recalculates the customer’s tier locally in real time And synchronizes the updated tier with the server once connectivity is restored
Dashboard Visibility of Updated Tier
Given any tier change occurs When the transaction completes Then the dashboard displays the customer’s new tier without requiring a manual refresh And the tier badge reflects the correct tier color and label
Personalized Offer Configuration
"As a truck operator, I want to configure personalized offers per customer tier so that I can tailor rewards that resonate with different customer segments."
Description

Provide a UI module for operators to define and manage tiered offers and rewards. Operators should be able to assign offer details (discounts, free items, points) and validity periods per tier. The configuration tool must integrate with the Loyalty Beacon engine to ensure correct offer delivery.

Acceptance Criteria
Creating a New Tiered Offer
Given the operator navigates to the Personalized Offer Configuration UI, When they click on 'Add New Tier', Then the system displays a form to input offer name, discount type, value, and validity dates, And the 'Save' button remains disabled until all required fields are populated.
Defining Offer Details and Validity Period
When the operator enters valid discount values (percentage between 1 and 100 or fixed amount greater than 0) and selects a start and end date where the end date is after the start date, Then the 'Save' button becomes enabled, And the entered values are persisted upon saving.
Editing an Existing Tiered Offer
Given a pre-existing tiered offer in the list, When the operator selects 'Edit', Then the form populates with existing details, And upon modifying any field and clicking 'Save', The updated details are reflected in the offer list.
Assigning Reward Items to a Tier
When the operator selects 'Add Reward' for a tier, Then they can choose from available free items and specify quantity, And the selected items appear in the tier's reward list.
Integration with Loyalty Beacon Engine
After saving a tiered offer configuration, When the system sends the configuration payload to the Loyalty Beacon engine via API, Then it receives a 200 OK response confirming successful registration.
Real-time Push Notification Service
"As a returning customer, I want to get timely push alerts about exclusive deals when I’m near the truck so that I don’t miss out on rewards."
Description

Build a notification service that delivers Loyalty Beacon messages to the customer’s mobile device in real time when the proximity trigger is activated. The service must handle retries, support both iOS and Android push protocols, and fallback to SMS or email if push fails.

Acceptance Criteria
Customer Enters Proximity Zone for the First Time
Given a registered user with the Truckly app enters within the defined proximity radius (e.g., 100 meters) of a participating food truck, when the proximity trigger fires, then the system must deliver a push notification containing the correct tiered Loyalty Beacon offer to the user’s device within 2 seconds.
Push Notification Delivery Retry on Failure
Given an initial push notification attempt fails due to network or service error, when the retry mechanism activates, then the service must retry delivery up to 3 times with exponential backoff intervals (e.g., 1s, 2s, 4s) and log each attempt, and the notification must be delivered upon network restoration.
Fallback to SMS When Push Fails
Given all push notification retries have failed for a user on a mobile device, when the fallback threshold (e.g., no delivery within 5 minutes) is reached, then the system must send an SMS with the identical Loyalty Beacon deal message to the user’s registered phone number within 30 seconds of threshold breach.
Fallback to Email When SMS Fails
Given the SMS fallback fails due to invalid number or delivery error, when the email fallback criteria are met, then the system must send an email with the same deal content to the user’s registered email address within 1 minute and record the failure reason in the notification audit log.
Cross-Platform Delivery to iOS and Android Devices
Given users on both iOS and Android platforms are targeted, when a notification is triggered, then the service must send APNs-compliant payloads to iOS devices and FCM-compliant payloads to Android devices, receive a successful delivery acknowledgment (HTTP 200) from each push provider, and surface any errors in the delivery report.
Engagement Analytics Dashboard
"As a food truck owner, I want to view analytics on how customers engage with loyalty offers so that I can optimize my promotions."
Description

Extend the Truckly dashboard to display metrics on loyalty offer performance, including impressions, redemptions, and conversion rates by tier. Reports should refresh in real time and be available offline, syncing when connectivity is restored.

Acceptance Criteria
Real-time Impressions Monitoring
Given the operator is online and viewing the Engagement Analytics Dashboard, when a loyalty beacon is shown to a customer, then the Impressions metric for the corresponding tier updates within 5 seconds without requiring a page refresh.
Redemption Metrics Display
Given the operator is online and navigates to the Redemption Metrics panel, when a customer redeems an offer, then the Redemptions count increments immediately and the redemption timestamp and customer tier appear in the dashboard table.
Conversion Rate Calculation
Given the operator is viewing metrics for a selected time period and tier, when at least one impression and one redemption exist, then the Conversion Rate is calculated as (redemptions ÷ impressions) × 100 and displayed with two decimal places.
Offline Analytics Access
Given the operator loses internet connectivity while on the Engagement Analytics Dashboard, then the dashboard continues to display the last-synced values for Impressions, Redemptions, and Conversion Rates for all tiers.
Data Sync Upon Connectivity Restoration
Given the operator was offline and makes new loyalty offer impressions and redemptions, when connectivity is restored, then the dashboard automatically syncs all offline interactions and updates all metrics to reflect both offline and real-time data.

Social Share Incentive

Integrates social sharing options into alerts, encouraging users to broadcast their proximity-based deals on social media. Rewards shares with bonus discounts or freebies, amplifying reach and attracting new customers through word-of-mouth.

Requirements

Social Share Button Integration
"As a customer receiving deal alerts, I want to share the alert on my social media platforms so that my friends can learn about the deal and I can earn bonus rewards."
Description

This requirement involves embedding social share buttons within proximity-based alert notifications sent to customers. Implementation will include in-app UI components for share actions, enabling customers to share alerts via Facebook, Twitter, and Instagram directly from the notification. Buttons should be mobile-optimized, support offline caching for later posting, and comply with platform UI guidelines. The feature extends the product’s alert system by transforming alerts into shareable content, increasing virality and customer engagement.

Acceptance Criteria
Displaying Social Share Buttons on Proximity Alerts
Given a user receives an in-app proximity alert, when the alert is displayed, then Facebook, Twitter, and Instagram share buttons are visible, tappable, and correctly labeled.
User Shares Alert via Facebook
Given a user taps the Facebook share button on the alert, when the share dialog opens, then the dialog is pre-filled with the alert details and the user can successfully post the content to Facebook.
Offline Share Caching and Deferred Posting
Given no internet connectivity when a user taps a share button, when the share action is initiated, then the share request is cached locally, a notification informs the user of deferred posting, and the share is automatically sent once connectivity is restored.
Mobile-Optimized Button Layout and Platform Compliance
Given the alert UI on a mobile device, when share buttons are rendered, then they conform to each platform’s UI guidelines for size, spacing, and color, meet accessibility contrast standards, and adapt responsively across screen sizes.
Reward Delivery upon Successful Share
Given a user completes a social share, when the platform confirms the successful post via callback, then the system issues a bonus discount code and displays a confirmation message within the app.
Social Media SDK Integration
"As a developer, I want to integrate social media APIs so that the system can post shared deal content reliably and securely on customers’ social profiles."
Description

This requirement covers integrating the dashboard backend with social media APIs and SDKs (Facebook Graph API, Twitter API, Instagram Sharing API). It includes authentication flows, permission requests, error handling, and offline request queuing. The integration ensures that when users initiate a share, the system can post content on their behalf, handle callback responses, and confirm successful shares for reward eligibility. Proper API rate limiting and security protocols must be observed.

Acceptance Criteria
Successful Social Media Authentication
Given the user taps the Facebook share button When the Facebook SDK prompts for login and permissions Then the user can log in, grant necessary permissions, and the system securely stores the access token
Permission Denied Error Handling
Given the user denies permission to post on Twitter When the share request is initiated Then the system displays a clear error message, does not attempt to post, and logs the denial for analytics
Offline Request Queuing
Given the user is offline and initiates an Instagram share When the share action is performed Then the system queues the share request locally and automatically retries the post when connectivity is restored
API Rate Limit Compliance
Given the user triggers multiple social media shares in quick succession When the number of API calls approaches rate limits Then the system throttles requests according to SDK guidelines, enqueues excess calls, and notifies the user of any delays
Share Callback Confirmation
Given a share request is sent to the social media API When a successful callback response is received Then the system confirms the share, awards the bonus discount to the user, and displays a confirmation message
Share Reward Engine
"As a marketing manager, I want the system to automatically grant rewards after customers share deals so that they are incentivized to promote our promotions on social media."
Description

This requirement defines the rules, logic, and data structures to track customers’ social shares and to award bonus discounts or freebies accordingly. It includes defining reward thresholds, validating share actions through callback verification, de-duplicating shares, and updating customer profiles with earned rewards. The engine must integrate with the existing inventory and promotion modules to ensure real-time availability of rewards and to prevent abuse.

Acceptance Criteria
Single Social Share Reward Grant
Given a user shares a deal and the share callback is verified successfully, Then a bonus discount is added to the user profile within 5 seconds Given the bonus discount is added, Then it appears in both the inventory and promotion modules in real time When the user applies the discount at checkout, Then the final price reflects the bonus without errors
Duplicate Share Prevention
Given a user attempts to share the same deal more than once within 24 hours, When a second callback is received, Then the engine recognizes the share as duplicate and does not award an additional reward Given the duplicate share is detected, Then the user receives an in-app notification stating the share has already been counted
Reward Threshold Enforcement
Given the reward threshold is configured as three unique shares for a free item, When the user completes the third unique share, Then the system automatically awards the free item to the user profile Given fewer than three shares are completed, Then no reward is granted and the user sees progress toward the threshold
Offline Share Retry Handling
Given a user attempts to share while offline, When network connectivity is restored and the callback is verified, Then the share is counted once toward rewards Given multiple offline retries for the same share, Then only the first successful callback after reconnection is counted
Real-Time Inventory Integration for Reward Fulfillment
Given the engine is about to grant a reward, When the inventory level for the reward item is below the minimum threshold, Then the reward is withheld and the user is notified of unavailability Given inventory is sufficient, When the reward is granted, Then the inventory count is decremented in real time and the promotion module reflects the change
Reward Redemption Workflow
"As a customer, I want to view and redeem the bonus reward I earned from sharing a deal so that I can enjoy the discount when I make my next purchase."
Description

This requirement outlines the end-to-end workflow for customers to view, access, and redeem the bonus discounts or freebies they earn through sharing. It includes UI components in the customer’s mobile app to display earned rewards, generate unique coupon codes, validate codes at checkout, and update reward status post-redemption. Notifications and reminders should be sent to encourage timely use of rewards.

Acceptance Criteria
User Views Earned Rewards
Given the user is logged in and has earned rewards through social shares, when they navigate to the Rewards section in the app, then the app displays a list of all active rewards with their type, value, expiration date, and current status.
System Generates Unique Coupon Code upon Share
Given the user successfully completes a social media share, when the share action is confirmed, then the system generates a unique coupon code, associates it with the user’s account, sets an expiration date, and displays it in the Rewards section.
User Applies Coupon at Checkout
Given the user enters a coupon code at checkout, when the code is valid, unexpired, and unused, then the system applies the corresponding discount to the order total and displays the updated price; if the code is invalid or expired, an error message is shown.
System Updates Reward Status after Redemption
Given the user completes an order using a valid coupon, when the transaction is successful, then the system marks the associated reward as Redeemed, removes it from the active rewards list, and logs the redemption timestamp.
User Receives Reminder for Unredeemed Reward
Given the user has an active reward nearing expiration, when the reward has 48 hours or less remaining before expiration, then the system sends a push notification reminder with reward details and expiration date.
Share Analytics Dashboard
"As a food truck operator, I want to see analytics on how many customers share our deals and the impact on sales so that I can optimize our promotions strategy."
Description

This requirement involves creating analytics views within the operator’s dashboard to monitor social share activity, including number of shares per deal, top-sharing customers, conversion rates of shared deals to visits/sales, and ROI metrics. The dashboard should provide filters by date, location, and deal type and exportable reports. Visualizations such as charts and tables will help operators gauge the effectiveness of the social share incentive program.

Acceptance Criteria
Deal Share Count Visualization
Given the operator accesses the dashboard and selects a deal type, when the data loads, then the system displays a real-time chart showing total shares per deal type for the selected period.
Top-Sharing Customers Identification
Given the operator filters by date range and location, when viewing the 'Top Sharers' panel, then the system lists the top 10 customers ranked by share count with identifiers and sorted descending.
Share-to-Visit Conversion Rate Analysis
Given the operator selects a deal, when viewing conversion metrics, then the system calculates and displays the percentage and raw counts of social shares that resulted in visits or sales within the same period.
ROI Metrics Calculation and Display
Given the operator chooses ROI metrics for a deal and time frame, when the dashboard renders, then the system displays ROI as ((revenue from shared deals – cost of incentives) / cost of incentives) formatted as a percentage with two decimal places.
Advanced Filtering and Report Export
Given the operator applies filters by date range, location, and deal type, when clicking 'Export Report', then the system generates and downloads a CSV including share counts, top sharers, conversion rates, and ROI metrics matching the filters.

FootTraffic Heatmap

Provides a real-time visual map of opt-in users’ density around your route. Helps you identify high-potential hotspots and adjust your route or alert radius to concentrate efforts where foot traffic is densest, optimizing ad spend and promotions.

Requirements

Real-Time Data Processing
"As a food truck owner, I want the system to aggregate opt-in user location data in real time so that I can see current foot traffic hotspots instantly."
Description

Implement a backend service that ingests and aggregates location pings from opt-in users in real time. The service must process data streams with minimal latency, handle temporary offline data buffering, and output normalized density metrics for geographic grid cells. This functionality ensures the heatmap displays up-to-the-second foot traffic information, enabling data-driven decisions on the fly.

Acceptance Criteria
High-Frequency Location Ping Processing
Given a continuous stream of up to 5,000 location pings per second, when the service ingests new pings, then 95% of pings are processed and aggregated into grid cells within 200 milliseconds of arrival.
Offline Data Buffering and Recovery
Given a mobile client loses network connectivity, when it regains connectivity, then all buffered pings (up to 1,000) are transmitted to the backend in chronological order within 30 seconds.
Normalized Density Metric Generation
Given raw ping counts per geographic grid cell, when the aggregation window closes (every 10 seconds), then the service computes and outputs a normalized density metric for each cell using the defined formula and includes timestamp and cell identifier.
Scalability under Peak Traffic
Given peak traffic of 10,000 concurrent pings per second, when the system is under load, then end-to-end processing latency remains below 300 milliseconds and no data is dropped.
Real-Time Heatmap Update Delivery
Given clients are subscribed to live heatmap updates, when new density metrics are generated, then the system pushes updates to all subscribed clients within 1 second and clients render updated heatmap tiles without error.
Interactive Heatmap Visualization
"As a food truck owner, I want an interactive heatmap that visualizes foot traffic density so that I can easily identify where to position my truck."
Description

Develop a client-side component that renders an interactive heatmap overlay on the map. It should support smooth zoom and pan interactions, color-gradient density indicators, and tooltips showing precise metrics for individual grid cells. Integration with the Truckly dashboard must ensure consistent UX and performance on both mobile and desktop browsers.

Acceptance Criteria
Smooth Zoom and Pan Interaction
Given the heatmap is displayed on the map When the user performs a pinch gesture (mobile) or scroll/drag action (desktop) Then the heatmap zooms in or out smoothly within 200ms, maintains a minimum of 30fps, and renders without visual artifacts
Color Gradient Density Visualization
Given foot traffic data is loaded on the map Then the heatmap uses at least five distinct color gradients that accurately reflect defined density ranges And all gradient colors match the Truckly dashboard design palette
Grid Cell Tooltip Display
Given the user hovers (desktop) or taps (mobile) on a heatmap grid cell When the interaction occurs Then a tooltip appears within 100ms showing precise metrics (user count, timestamp) for that cell And the tooltip hides immediately when the user taps or clicks outside the cell
Mobile Performance Consistency
Given the user accesses the Truckly dashboard on a mobile device with 3G connectivity or offline mode enabled When the heatmap component loads Then the initial render completes within 2 seconds And subsequent interactions (zoom, pan, tooltip) respond within 200ms And memory usage remains below 50MB
Desktop Performance Consistency
Given the user accesses the Truckly dashboard on desktop browsers (Chrome, Firefox, Safari) When the heatmap component loads Then the initial render completes within 1 second And interactions (zoom, pan, tooltip) respond within 150ms And the component sustains at least 30fps during continuous panning or zooming
Dynamic Route Adjustment Suggestions
"As a food truck owner, I want the system to suggest route adjustments based on real-time hotspots so that I can maximize customer reach."
Description

Build a recommendation engine that analyzes real-time foot traffic density and suggests optimal route modifications. The engine should evaluate current route waypoints, compare with density hotspots, and generate alternative route segments or stopping points. Suggestions must be presented in the UI with estimated gains in potential customer reach.

Acceptance Criteria
Route Planning with Emerging Hotspot
Given the operator has a defined route open and new foot traffic data indicates a hotspot within a 2-mile radius, when the recommendation engine processes the data then at least one alternative waypoint must be suggested with an estimated potential reach increase of 10% or more.
Mid-Route Hotspot Appearance
Given the operator is en route and a new density peak is detected within 0.5 miles, when the data update arrives then the UI must display the route adjustment suggestion on the map within 10 seconds.
Visual Display of Estimated Reach Gains
Given one or more route alternatives are generated, when the suggestions panel loads then each suggestion must include an estimated numeric reach gain percentage and distance change, with the highest-gain option visually highlighted.
User-Driven Route Acceptance
Given suggestions are visible, when the operator selects an alternative route segment then the current route map updates to reflect the change and a confirmation notification appears within 5 seconds.
System Response to No Viable Alternatives
Given no density hotspots meet the defined threshold in range, when the engine completes its analysis then the UI must display a message stating "No alternative suggestions available."
Customizable Alert Radius
"As a food truck owner, I want to customize the alert notification radius for opt-in users so that I can target promotions more effectively."
Description

Add a configuration interface allowing owners to set and adjust the geographic radius for push notifications to opt-in users. The system must respect user-defined thresholds, dynamically update notification targets based on live location data, and provide feedback on estimated reach per radius setting.

Acceptance Criteria
Initial Radius Configuration
Given the owner navigates to the alert radius settings, When they enter a value within the allowed range (100m–10km) and tap Save, Then the system stores the value, displays a confirmation message, and applies the radius for future notifications.
Live Notification Target Update
Given the owner adjusts the saved radius value, When live location data changes or the owner reopens the dashboard, Then the system recalculates and displays the updated list of opt-in users within the new radius in under 5 seconds.
Displaying Estimated Reach
Given the owner moves the radius slider on the configuration interface, When the slider position changes, Then the system displays the estimated number of opt-in users in real time with an accuracy of ±10%.
Handling Invalid Radius Input
Given the owner enters a value outside the allowed range or a non-numeric input, When they attempt to save, Then the interface shows a clear inline error message and disables the Save button until the input is corrected.
Offline Radius Adjustment
Given the owner is offline and updates the alert radius value, When they return online, Then the system automatically synchronizes the new radius with the server and confirms the update without data loss.
Historical Traffic Insights
"As a food truck owner, I want to view historical foot traffic trends so that I can plan future routes based on past performance."
Description

Implement data storage and retrieval for foot traffic density snapshots over time. Provide UI features to play back heatmaps for user-selected date and time ranges, generate trend charts, and export insights as reports. This enables owners to analyze patterns, compare performance across days, and make informed planning decisions.

Acceptance Criteria
Date and Time Range Selection
Given the user navigates to Historical Insights and selects a start date of 2025-07-01 and end date of 2025-07-07, When the user clicks 'Load', Then the heatmap display and trend data correspond exactly to the requested date and time range.
Trend Chart Generation
Given the user has loaded a date range, When the user clicks 'Generate Trend Chart', Then the system displays a line chart showing average foot traffic density per time interval for each day in the selected range.
Heatmap Playback Controls
Given the heatmap playback interface is visible, When the user presses 'Play', 'Pause', 'Fast-forward', or 'Rewind', Then the heatmap animation reflects the correct time increments at the selected speed.
Report Export Functionality
Given the user has generated insights for a specific date range, When the user clicks 'Export Report' and selects PDF format, Then the system downloads a report containing snapshots, trend charts, and summary statistics for the chosen period.
Cross-Date Comparison Overlay
Given the user selects two or more dates, When the user toggles overlay mode, Then multiple heatmap layers display with distinct color keys and adjustable opacity for visual comparison.
Privacy and Consent Management
"As a food truck owner, I want data collection and heatmap features to comply with privacy rules and user consents so that I maintain trust and legal compliance."
Description

Establish a compliance framework that manages user consents, anonymizes location data, and adheres to relevant privacy regulations. Include mechanisms for users to opt in or out, audit trails for data usage, and data retention policies. Ensure all heatmap data processing components integrate with consent controls to maintain legal and ethical standards.

Acceptance Criteria
User Opt-In Consent Capture
Given a new user opens the app, when the location-sharing consent prompt is displayed, then the system records the user’s explicit opt-in decision along with timestamp and policy version.
User Opt-Out and Data Removal
Given an existing user revokes consent, when the user submits an opt-out request, then all personally identifiable location data is deleted within 24 hours and only anonymized, aggregated data is retained according to policy.
Location Data Anonymization
Given collected location data, when data is processed for the heatmap, then the system aggregates data into cohorts of at least 100 users and applies random coordinate jitter to ensure individual locations cannot be re-identified.
Consent Audit Trail Logging
Given any user consent action (opt-in, opt-out, policy update), when the action occurs, then the system logs an immutable audit entry capturing user ID, action type, timestamp, and policy version.
Automated Data Retention Enforcement
Given stored location data, when data reaches 30 days of age, then the system automatically purges raw and aggregated datasets beyond retention period and records the purge event in the audit log.

AI Message Personalizer

Leverages customer purchase history and preferences to craft tailored alert messages. Delivers more relevant suggestions—like favorite menu items or complementary upsells—boosting conversion rates by resonating with individual tastes.

Requirements

Customer Data Aggregation
"As a food truck operator, I want the system to aggregate all customer purchase and interaction data into unified profiles so that I can send highly personalized messages based on complete and accurate information."
Description

Collect and consolidate customer purchase history and interaction data from multiple sources—including in-app orders, loyalty sign-ups, and offline sales logs—into a unified profile repository. This ensures the personalizer has a complete view of each customer’s preferences and behaviors, enabling accurate tailoring of message content.

Acceptance Criteria
In-App Order Data Consolidation
Given a customer places an order in the mobile app When the order is recorded Then the purchase history is added to the unified customer profile within 5 seconds
Offline Sales Log Integration
Given offline sales entries are uploaded via CSV When the upload completes without errors Then the system imports all records and associates them with existing customer profiles or creates new ones with 100% data integrity
Loyalty Sign-Up Synchronization
Given a customer signs up for loyalty in-app or on kiosk When the sign-up data is captured Then the system updates the customer profile repository instantly and links past purchase history appropriately
Duplicate Record Conflict Resolution
Given multiple records with the same email or phone number exist When profiles are merged Then the system consolidates purchase history without loss of data and flags duplicates for manual review
Network Reconsolidation After Offline
Given the device was offline and queued transactions exist When network connectivity is restored Then the system automatically syncs all queued data to the central repository without duplicates within 2 minutes
Preference Analysis Engine
"As a food truck operator, I want an analysis engine that identifies each customer’s favorite items and buying patterns so that message suggestions are always relevant and increase conversion."
Description

Implement an analytical engine that processes aggregated customer data to identify ordering patterns, favorite menu items, and optimal upsell opportunities. The engine should support rule-based and machine-learning models to continuously refine understanding of individual tastes and predicted future orders.

Acceptance Criteria
Initial Data Ingestion and Pattern Detection
Given a dataset of aggregated customer orders is provided, When the Preference Analysis Engine executes its pattern detection module, Then it should identify at least five distinct ordering patterns and return the results within two seconds.
Favorite Item Identification
Given a single customer's purchase history containing at least 20 transactions, When the engine processes this history, Then it must output the top three most frequently ordered menu items along with correct frequency counts and confidence scores of at least 0.8.
Upsell Opportunity Suggestion
Given a customer's favorite item list and current cart contents, When generating an upsell recommendation, Then the engine should suggest one complementary menu item ranked by a predicted interest score above 0.75.
Rule-Based Model Adjustment
Given a new business rule (e.g., “after three savory items, suggest a dessert”) is added, When the engine applies this rule, Then it should update affected customer profiles and include the new suggestion in its next output cycle within one minute.
Continuous Learning Feedback Loop
Given post-purchase feedback on upsell suggestions is received, When the feedback is fed into the engine, Then the machine learning model should retrain and demonstrate at least a 5% improvement in suggestion accuracy in subsequent validation tests.
Message Template Generation
"As a food truck operator, I want the system to auto-generate message templates filled with personalized content so that I can quickly deploy targeted alerts without manual editing."
Description

Design a dynamic template system that uses the output from the preference analysis engine to generate personalized alert messages. Templates should support placeholders for item names, suggested upsells, time-based triggers, and promotional offers, ensuring messages are both relevant and engaging.

Acceptance Criteria
Dynamic Placeholder Insertion Scenario
• All template placeholders (e.g., {item_name}, {upsell}, {promo_code}) are replaced with corresponding values from the preference analysis engine output. • No placeholder tokens remain in the final generated message. • Final message length does not exceed 160 characters.
Time-Based Trigger Personalization Scenario
• {time_of_day} placeholder is correctly populated based on current system time (morning/afternoon/evening). • Message is generated and queued for delivery within 5 seconds of the trigger event. • If outside defined trigger windows, the message is not sent.
Promotional Offer Injection Scenario
• When an active promotional offer exists, {promo_offer} is replaced with a valid offer code and expiration date. • If no active offer is available, {promo_offer} and related text are omitted without leaving grammar errors or extra whitespace. • Generated message clearly displays the offer details in a single sentence.
Upsell Suggestion Personalization Scenario
• System selects a complementary upsell item from the top 3 correlated products (correlation ≥ 70%). • Only the single highest-correlated upsell is inserted into the message at {upsell} placeholder. • Suggested upsell is relevant to the user’s last purchase and appears as a standalone sentence.
Fallback Messaging Scenario
• If preference engine data is missing (e.g., no upsell or active promo), system uses a default message variant without missing placeholders. • Fallback template maintains correct sentence structure and stays within 160 characters. • No placeholder tokens appear in the fallback message.
Real-time Personalization Delivery
"As a food truck operator, I want personalized messages delivered reliably in real time or after reconnecting from offline so that customers receive timely suggestions regardless of network conditions."
Description

Ensure seamless integration with the existing notification service to deliver personalized messages in real time or based on scheduled triggers. The system must handle offline scenarios gracefully—queuing messages locally and syncing when connectivity is restored—so that no customer misses a timely alert.

Acceptance Criteria
Real-Time Delivery Under Good Connectivity
Given the user has an active internet connection and the notification service is available, when a personalized message is generated, then the message is delivered to the user's device within 5 seconds.
Scheduled Trigger Message
Given a scheduled delivery time is set for 3:00 PM local time, when the scheduled time arrives, then the system generates and delivers the personalized message within 2 minutes of the scheduled time.
Offline Mode Queuing
Given the user's device is offline, when a personalized message is generated, then the system queues the message locally and persists it until connectivity is restored.
Connectivity Restoration Sync
Given the user's device regains internet connectivity, when queued messages exist locally, then the system automatically syncs and delivers all queued messages within 30 seconds.
High Throughput Handling
Given the system processes 1000 concurrent personalized messages, when messages are generated, then at least 99% of messages are delivered successfully without delay exceeding 10 seconds.
Performance Monitoring & Feedback Loop
"As a food truck operator, I want to see how well personalized messages perform and gather feedback so that the system can improve its recommendations over time."
Description

Build a monitoring dashboard that tracks key metrics—open rates, click-through rates, conversion rates—and collects customer feedback on message relevance. Use this data to retrain the analysis engine and refine templates, creating a continuous improvement cycle for personalization accuracy.

Acceptance Criteria
Real-Time Open Rate Monitoring
1. Given the user accesses the monitoring dashboard, when the page loads or is refreshed, then the open rate metric for the selected time period (e.g., last 24 hours) is displayed within 5 seconds; 2. The open rate is calculated as (number of emails opened)/(number of emails delivered) and matches backend data within a 1% margin of error.
Click-Through Rate Analysis
1. Given the user selects a specific campaign, when viewing the detailed metrics, then the click-through rate (CTR) displays correctly as (clicks/impressions)*100; 2. The CTR updates in real time as new clicks are recorded and does not exceed a 2-second lag; 3. CTR values match the raw event log within a 1% variance.
Conversion Rate Tracking
1. Given a recipient completes a tracked conversion action (e.g., purchase), when the dashboard refreshes, then the conversion rate metric updates to reflect the new conversion; 2. Conversion rate is calculated as (conversions/opened messages)*100 and syncs with the commerce backend within 1 minute; 3. Historical conversion trends for the past week are visualized without data gaps.
Customer Feedback Collection
1. Given a recipient responds to a feedback prompt embedded in a message, when the feedback is submitted, then the response is captured in the dashboard's feedback section within 10 seconds; 2. Feedback entries include timestamp, message ID, and sentiment rating; 3. The dashboard aggregates sentiment scores and displays average relevance rating per campaign.
Automated Model Retraining Trigger
1. Given performance metrics (open rate, CTR, conversion rate) for a campaign fall below predefined thresholds for three consecutive days, when this condition is detected, then the system triggers an automated retraining job for the personalization engine; 2. A retraining job initiation event is logged and visible in the dashboard within 5 seconds; 3. The system sends an alert notification to the operations team confirming the retraining trigger.

WeatherWave

Automatically tailors daily menu suggestions based on real-time and forecasted weather—highlighting chilled drinks and salads on hot days or warm soups and wraps when it’s cool, ensuring offerings match customer cravings and boost sales.

Requirements

Real-time Weather Data Integration
"As a food truck owner, I want the system to automatically pull up-to-date weather data so that menu suggestions reflect current conditions without manual input."
Description

Integrate with a reliable weather API to fetch current and forecasted weather data at regular intervals, ensuring accurate and timely inputs for menu suggestions. This data integration will be seamlessly incorporated into the existing dashboard, enabling automatic retrieval even under fluctuating network conditions and providing the foundation for adaptive menu functionality.

Acceptance Criteria
Fetch Current Weather Data at App Launch
Given the dashboard is opened with network connectivity, when the user launches the app, then current weather data is retrieved from the API within 10 seconds and displayed on the dashboard.
Periodic Forecast Data Update
Given the app remains open, when 30 minutes have elapsed since the last fetch, then the system automatically requests forecast data from the API and updates menu suggestions accordingly.
Offline Mode Data Utilization
Given the network connection is lost, when the user accesses the dashboard, then the system displays the last successfully retrieved weather data, shows an offline notification, and queues future requests.
API Error Handling and Retry
Given the API returns an error response or timeout, when a fetch attempt fails, then the system retries up to three times at 5-second intervals, logs the error, and notifies the user if all retries fail.
Weather Data Schema Validation
Given new weather data is received, when the data is parsed, then the system validates temperature, humidity, and condition fields against the predefined schema and flags any invalid entries.
Dynamic Menu Suggestion Algorithm
"As a food truck owner, I want tailored menu recommendations based on weather forecasts so that I can offer items customers are most likely to purchase."
Description

Develop an algorithm that analyzes real-time temperature, precipitation, and seasonal patterns to generate daily menu recommendations. The algorithm will weigh factors such as customer preferences, past sales, and ingredient availability, delivering prioritized menu items optimized for expected weather-driven demand.

Acceptance Criteria
Menu Recommendation on a Hot Day
Given the current temperature is above 85°F and no precipitation, When the algorithm generates the daily menu, Then at least three chilled beverage options and two salad items must appear among the top five suggestions.
Menu Recommendation with Incoming Rain
Given a precipitation forecast greater than 50% for the day, When the algorithm runs its recommendation process, Then at least two warm soup or wrap options must be included in the top five suggested items.
Menu Recommendation in Seasonal Transition
Given the current date falls within the defined transition window between seasons, When generating menu suggestions, Then the algorithm must blend at least two items from both prior and upcoming seasonal menus in its top seven recommendations.
Menu Recommendation with Limited Ingredient Availability
Given updates to ingredient inventory show fewer than 10 units of a key item, When compiling the recommended menu, Then the algorithm must exclude that item and replace it with the next highest-ranked available item.
Menu Recommendation Based on Customer Purchase History
Given access to the last 30 days of sales data, When tailoring menu suggestions, Then the top five recommendations must include at least two items that were among the top 10 best-sellers in the past week.
Offline Data Caching
"As a food truck operator, I want the menu suggestions to work even without internet access so that I can make informed decisions in areas with poor connectivity."
Description

Implement a local caching mechanism to store recent weather data and menu suggestions, ensuring uninterrupted functionality when connectivity is lost. Cached data will automatically sync when the network is restored, maintaining consistency between offline operations and the central dashboard.

Acceptance Criteria
Offline Data Availability During Network Outage
Given network connectivity is lost When the user accesses the WeatherWave feature Then the application displays the last 24 hours of weather data and menu suggestions from local cache without errors And each record shows a timestamp of the last successful sync
Seamless Data Synchronization Post-Reconnection
Given the device reconnects to the network When connection is detected Then the application automatically syncs all locally cached weather data and menu suggestions to the central dashboard within 30 seconds And a ‘Sync Complete’ notification is displayed
Cache Capacity Management
When the local cache exceeds either 100 records or data older than 7 days Then the system automatically prunes the oldest entries first Ensuring total cached records remain under the threshold Without loss of recent data
Data Integrity After Partial Sync Failure
Given a network disruption occurs during synchronization When the sync process is interrupted Then the system rolls back any partial writes Logs the error And retries the sync automatically once the connection is stable Without creating duplicates or data corruption
Offline Operational Performance Under Load
Given at least 200 cached records exist When the user navigates the offline interface Then pages load within 2 seconds And scrolling and filtering operations on cached data respond within 200ms
Weather Alert Notifications
"As a food truck owner, I want to receive alerts for sudden weather shifts so that I can quickly adjust my menu and inventory to meet new demand."
Description

Create a notification system that sends real-time alerts to the mobile dashboard when significant weather changes occur, such as sudden temperature drops or storms. Alerts will include recommended menu adjustments, enabling timely updates to offerings and proactive inventory management.

Acceptance Criteria
Sudden Temperature Drop Alert Trigger
Given the external weather API reports a temperature drop of at least 10°C within 30 minutes at the truck's current location, When the mobile dashboard processes this data, Then it displays a high-priority notification with recommended warm menu items and restock alerts within 2 minutes.
Severe Weather Storm Incoming Alert
Given the forecast predicts a severe storm with wind speeds ≥50 km/h or precipitation >50% within the next 2 hours, When the system receives the alert, Then the dashboard displays a storm warning notification with suggested indoor-friendly menu items within 1 minute.
Next-Day Heatwave Forecast Notification
Given the next-day forecast indicates temperatures above 30°C, When the overnight forecast data sync completes, Then the dashboard sends a morning notification highlighting chilled drinks and salads with restock alerts for ice and cold beverages.
Offline to Online Sync of Weather Alerts
Given weather alerts were generated while the dashboard was offline, When connectivity is restored, Then all pending alerts from the last hour sync and display in priority order with associated menu recommendations.
User Interaction with Weather Alert Recommendation
Given a weather alert notification appears on the dashboard, When the user taps the notification, Then a detailed recommendation view opens showing menu adjustments and an “Apply to Inventory” button that updates inventory thresholds accordingly.
Admin Override and Customization
"As a food truck owner, I want to customize and override suggested menus so that I can incorporate seasonal specials or promotional items as needed."
Description

Provide an interface for operators to override automated suggestions and customize priority items manually. This feature will allow users to set preferences, lock specific menu items, and adjust algorithm parameters, ensuring flexibility and alignment with unique business strategies.

Acceptance Criteria
Override Suggested Menu Items
Given the operator views daily menu suggestions, when they select the “Override” option for an item and choose an alternative, then the dashboard updates to display the new item in place of the original suggestion.
Lock Priority Menu Item
Given the operator selects a menu item to prioritize, when they click “Lock Item,” then the item remains fixed in the daily menu suggestions regardless of weather-based algorithm changes.
Adjust Algorithm Parameter
Given the operator accesses algorithm settings, when they modify the temperature sensitivity slider and save changes, then subsequent menu suggestions reflect the updated sensitivity within the next suggestion cycle.
Save Custom Preferences
Given the operator configures override and lock settings across multiple items, when they save their preferences, then settings persist across sessions and are automatically applied to daily suggestions.
Revert to Automated Suggestions
Given the operator has overridden items, when they select “Reset to Automated,” then all manual overrides and locks are removed and the dashboard displays the original weather-based suggestions.

SurgeSpotter

Monitors hourly sales velocity to identify emerging best-sellers and promotes them as featured specials. By reacting to real-time demand spikes, it maximizes revenue before stock depletes and keeps menus fresh and enticing.

Requirements

Real-Time Data Aggregator
"As a food truck operator, I want sales transactions to be captured and aggregated in real time so that SurgeSpotter can analyze current demand without delay."
Description

Captures and aggregates hourly sales transactions as they occur, ensuring low-latency data collection and seamless integration with both online and offline modes. Guarantees data integrity through transaction queues and conflict resolution strategies, providing SurgeSpotter with accurate inputs for demand analysis.

Acceptance Criteria
Offline Mode Transaction Capture
Given the device is offline, the application records each sale transaction locally within 500ms of completion When offline for up to 8 consecutive hours, no transactions are lost The UI displays the current queue length of pending transactions
Real-Time Online Sales Sync
Given a stable network connection, each completed sale is transmitted to the server within 2 seconds The server sends acknowledgment receipts for each transaction without errors The dashboard UI updates inventory counts and total sales in real time following each successful sync
Conflict Resolution on Reconnect
When the app reconnects after offline mode, queued transactions synchronize without duplication The system resolves conflicting inventory updates using the latest transaction timestamp rule Users receive notifications for any overridden or merged records post-sync
Hourly Aggregation Accuracy
At the top of each hour, the system generates an aggregate sales report within 60 seconds The sum of hourly transactions in the report matches the total of individual transactions with less than 0.1% variance SurgeSpotter successfully retrieves and displays the hourly aggregates via the API endpoint
Data Integrity Under Load
Under a sustained load of 100 transactions per second, no data loss or corruption occurs Average write latency to the aggregator does not exceed 200ms under peak load The system error rate remains below 0.01% during peak transaction volumes
Sales Velocity Analytics
"As a food truck operator, I want to know which menu items are selling rapidly so that I can promote them before stock runs out."
Description

Implements an analytics engine that computes hourly sales velocity metrics per menu item, applies sliding-window algorithms, and smooths out noise to accurately identify emerging best-sellers. Generates detailed reports and flags items exceeding configurable thresholds, forming the core of SurgeSpotter’s demand detection.

Acceptance Criteria
Real-Time Sales Spike Detection
Given real-time sales events are ingested continuously When an item's hourly sales velocity in a sliding one-hour window exceeds the configured threshold Then the system flags the item as an emerging best-seller within 1 minute And the flag appears on the SurgeSpotter dashboard
Sliding Window Calculation Accuracy
Given the system maintains a sliding window of the last 60 minutes of sales data When computing hourly sales velocity for each menu item Then the calculated velocity must match expected results within a ±1% margin compared to baseline test data
Threshold Configuration Update
Given an administrator updates an item's velocity threshold via the admin panel When the updated threshold is saved Then the new threshold is applied to subsequent evaluations within 5 minutes And flagging respects the updated threshold
Noise Smoothing Validation
Given sales data with random fluctuations below 10% deviation When noise smoothing is applied using the configured algorithm Then transient spikes below the noise threshold are not flagged And the smoothed velocity reflects genuine demand trends
Report Generation and Delivery
Given the scheduled generation time at the start of each hour When the hourly velocity report is generated Then the report includes each item's calculated velocity, flagged items, and generation timestamp And it is delivered to the mobile dashboard and emailed to the operator within 5 minutes
Automated Promotion Trigger
"As a food truck operator, I want SurgeSpotter to automatically promote hot-selling items so that I can instantly highlight them to customers without manual intervention."
Description

Defines logic to automatically trigger featured specials for items identified as emerging best-sellers. Pushes notifications to the dashboard and external channels (e.g., social media), with built-in throttling and scheduling controls to prevent customer fatigue and manage frequency.

Acceptance Criteria
Identify Sales Velocity Spike
Given hourly sales data for an item, when its sales velocity increases by more than 30% compared to the previous hour for at least two consecutive hours, then the item is flagged as an emerging bestseller in the system.
Trigger Automated Promotion
Given an item flagged as an emerging bestseller, when no active promotion exists for that item, then the system automatically schedules a featured special notification within 5 minutes of detection.
Throttle Promotion Frequency
Given an item has already had a promotion triggered, when more than three promotions have been sent for the same item within a 24-hour window, then further promotions for that item are suppressed until the window resets.
Schedule External Notification
Given a promotion is triggered, when the promotion details are finalized, then the system publishes the promotion to connected social media channels at the scheduled time with the correct message format and media attachments.
Offline Mode Promotion Queuing
Given the dashboard is offline, when a promotion is triggered, then the promotion details are queued locally and automatically dispatched to both the dashboard and external channels once connectivity is restored, preserving the original scheduling order.
Featured Specials Dashboard UI
"As a food truck operator, I want to see featured specials prominently on my dashboard so that I can quickly understand which items to highlight."
Description

Designs and implements UI components in the mobile-first dashboard to prominently display current featured specials, emerging trends, and quick-action buttons for manual overrides, social sharing, and menu updates. Ensures responsive design for seamless use on tablets and smartphones.

Acceptance Criteria
Viewing Featured Specials on Mobile
Given a food truck owner opens the dashboard on a smartphone, When data loads successfully, Then the current featured specials section displays at least three items with images, names, and real-time sales velocity indicators.
Identifying Emerging Trends on Tablet
Given the dashboard is accessed on a tablet during peak hours, When hourly sales velocity surpasses 20% above average, Then the UI highlights emerging best-sellers with a trend icon and updated ranking.
Manual Override of Featured Special
Given a food truck owner selects manual override on a featured special, When they choose a new item and confirm, Then the dashboard updates instantly to show the overridden item as the current featured special.
Social Sharing Quick Action
Given a featured special is displayed, When the owner taps the social share button, Then share options for Facebook, Instagram, and Twitter appear and the default post text includes item name and photo.
Menu Update via Dashboard
Given a food truck owner updates menu details for a featured special, When they save changes, Then the new item details reflect across the dashboard and persist after app restart.
Offline Sync and Alert Integration
"As a food truck operator, I want SurgeSpotter to queue promotions when I’m offline and sync once I’m back online so that I don’t miss any opportunities to highlight trending items."
Description

Enables caching and queuing of promotion triggers during offline operation and synchronizes them once connectivity is restored. Includes an alert system that notifies operators of any queued promotions awaiting approval, ensuring no loss of opportunity due to offline periods.

Acceptance Criteria
Offline Promotion Queuing
Given the operator is offline, when they mark an emerging best-seller as a featured special, the system stores the promotion trigger in a local offline queue with a unique ID, timestamp, and item details.
Promotion Sync on Reconnection
Given queued offline promotions exist and network connectivity is restored, when the system detects connectivity, then it automatically synchronizes all queued promotion triggers to the server within 30 seconds and confirms successful upload via an acknowledgment message.
Duplicate Prevention Across Sync Cycles
Given promotions have been synchronized, when connectivity fluctuates and triggers are reprocessed, then the system does not resend already synchronized promotions, ensuring no duplicate entries on the server.
Queued Promotions Notification Alert
Given the operator has queued promotions pending approval, when they access the dashboard, then an alert badge showing the count of pending promotions is displayed, and tapping the badge reveals a list of queued promotions with item names and timestamps.
Promotion Queue Management Interface
Given queued promotions are listed, when the operator views the queue, then they can approve, edit, or discard each queued promotion, and the system updates the queue status accordingly in real time.

PairPerfect

AI-driven cross-selling engine that analyzes purchase patterns to suggest complementary add-ons or combo deals alongside top-selling items. Increases average ticket size and enhances guest satisfaction by offering curated pairings.

Requirements

Sales Data Integration
"As a food truck operator, I want the system to automatically sync my sales data from my POS in real time so that the AI engine can generate accurate suggestion pairings based on my latest sales."
Description

Implement a robust data ingestion pipeline that collects real-time sales transactions from the food truck’s POS system, normalizes the data, and stores it in a centralized database. The pipeline should handle varying data formats, ensure data integrity, and support both online and offline scenarios. By integrating up-to-the-minute sales data, the PairPerfect engine can analyze current purchase patterns accurately and generate timely cross-sell suggestions, minimizing latency and maximizing relevance.

Acceptance Criteria
Real-time Online Sales Data Sync
Given the POS system is online, when a sales transaction occurs, then the pipeline ingests, normalizes, and stores the transaction within 5 seconds with accurate timestamps and identifiers.
Offline Data Queuing and Sync Recovery
Given the POS system is offline, when transactions occur, then the pipeline queues data locally and, upon network restoration, synchronizes all queued transactions to the central database without data loss or duplication.
Data Format Normalization for Multiple POS Types
Given incoming transactions from different POS formats, when data is ingested, then all records are mapped to the canonical schema, validated for required fields, and malformed records are rejected with logged errors.
Data Integrity and Duplicate Detection
Given repeated or duplicate transaction IDs from the POS, when ingesting data, then the pipeline identifies and discards duplicates, ensuring each transaction is stored only once and logs any duplication attempts.
Centralized Database Storage and Query Performance Monitoring
Given the central database receives a sustained load of 100 transactions per minute, when the PairPerfect engine queries the data, then query response time remains under 200ms and returned data reflects 100% accuracy of the last 24 hours of sales.
AI Model Training
"As a truck owner, I want the AI engine to learn my customers’ purchase patterns over time so that it can suggest the best complementary items and increase my average ticket size."
Description

Develop and train machine learning models using historical sales data to identify complementary item relationships and popular combo deals. The training pipeline should support incremental learning to incorporate new sales data and adapt to changing customer preferences. Models must be optimized for low-latency inference on mobile devices or edge servers, ensuring quick response times for suggestion generation.

Acceptance Criteria
Historical Data Model Initialization
The model training pipeline ingests at least three months of historical sales data across all menu items and successfully completes an initial training run without errors
Incremental Learning Integration
When new daily sales data is available, the pipeline updates the existing model within one hour and maintains or improves prediction accuracy compared to the previous model
Edge Inference Performance
The trained model, when deployed on a representative mobile device or edge server, returns cross-selling suggestions in under 200 milliseconds per request under typical load
Suggestion Accuracy Validation
On a held-out test set, the model achieves at least 75% precision and 75% recall in identifying complementary item pairs
Faulty Data Resilience
With up to 5% missing or corrupted records in the input dataset, the training pipeline completes without crashing and logs all data anomalies for review
Suggestion Algorithm API
"As a mobile dashboard user, I want to fetch curated combo suggestions via an API so that I can seamlessly display relevant add-ons in my sales interface."
Description

Build a scalable RESTful API that exposes endpoints for requesting AI-driven product pairing suggestions. The API should accept current cart contents or recent sales events as input and return ranked complementary add-on suggestions or combo deals. It must include authentication, rate limiting, and error handling to maintain security and reliability under varying load conditions.

Acceptance Criteria
Generate Suggestions for Valid Cart
Given a valid POST request with current cart contents When the API endpoint /suggestions is called Then the response returns HTTP 200 with a JSON array of at least 3 complementary add-on suggestions sorted by relevance
Process Recent Sales Events
Given a valid POST request with recent sales event data When the API endpoint /suggestions is called Then the response returns HTTP 200 with combo deal suggestions that reflect the input sales trends
Enforce Authentication
Given a request without valid authentication token When the API endpoint is called Then the response returns HTTP 401 Unauthorized with an appropriate error message
Apply Rate Limiting
Given more than 100 requests from a single client within one minute When additional requests are made Then the API returns HTTP 429 Too Many Requests with a retry-after header
Handle Invalid Input Gracefully
Given a POST request with malformed JSON or invalid product IDs When the API endpoint /suggestions is called Then the response returns HTTP 400 Bad Request with detailed validation error messages
Maintain Performance Under Load
Given 1000 concurrent valid requests When the API is under peak load Then 95% of responses return within 200ms and no errors are returned
Recommendation UI Component
"As an operator using Truckly on my tablet, I want to see visually appealing add-on suggestions next to my order so that I can quickly upsell complementary items."
Description

Design and implement a mobile-first UI component that displays AI-generated cross-sell suggestions alongside the current order. The component should support rich media (images and descriptions), allow one-click addition to the cart, and adapt fluidly to offline usage by queuing requests until connectivity is restored. This UI must blend seamlessly with the existing Truckly dashboard style and workflows.

Acceptance Criteria
Displaying AI-Generated Cross-Sell Suggestions Online
Given the user is online and viewing an active order When they open the PairPerfect suggestion panel Then at least three AI-generated cross-sell suggestions appear with thumbnail image, item name, brief description, price, and an enabled “Add” button
Displaying AI-Generated Cross-Sell Suggestions Offline
Given the user has lost network connectivity When they access the suggestion panel Then previously cached suggestions display with images and details, and an “Add (Offline)” button is shown for each suggestion
Queued Add-to-Cart Requests Synchronization
Given the user has added suggestions to the cart while offline When connectivity is restored Then all queued add-to-cart requests are automatically submitted in the order they were queued and the cart UI updates to reflect all added items without duplicates
Rich Media Rendering Consistent with Dashboard Style
Given the PairPerfect component is rendered within the Truckly dashboard When displayed on a mobile device Then the suggestion cards match the Truckly color palette, typography, spacing, and button styles exactly
One-Click Addition to Cart Functionality
Given a suggestion card is visible When the user taps the “Add” button Then the suggested item is immediately added to the cart, the cart item count increments, and a confirmation toast appears
Admin Configuration Panel
"As a food truck owner, I want to configure which items are eligible for cross-sell and customize combo deals so that the suggestions align with my business strategy."
Description

Create an admin interface within the Truckly dashboard that allows business users to configure recommendation rules, set up combo deal parameters, and adjust AI model sensitivity. Features should include toggling cross-sell on/off for specific items, defining promotional bundles, and viewing real-time model performance metrics to fine-tune pairing logic.

Acceptance Criteria
Toggle Cross-Sell for Specific Item
Given an admin views the item list in the Configuration Panel When they toggle the cross-sell switch for a specific item Then the switch visually updates to reflect On or Off And the change is persisted and the item’s cross-sell status returns consistently on page reload
Define and Save Promotional Bundle
Given an admin accesses the bundle creation section When they enter bundle name, select base item(s), add complementary add-ons, and set discount parameters And click Save Then the new bundle appears in the Promotional Bundles list with correct details
Adjust AI Model Sensitivity
Given an admin opens the AI Sensitivity settings When they move the sensitivity slider or enter a numerical value within allowed range (0–100) And click Apply Then the system updates the sensitivity level And subsequent pairing suggestions reflect the new sensitivity without errors
View Real-Time Model Performance Metrics
Given an admin navigates to the Performance Metrics tab When they load the page Then key metrics (accuracy, average deal uplift, model response time) display with timestamps And metrics refresh every minute without requiring a page reload
Analytics & Reporting
"As a business owner, I want to see reports on how cross-sell suggestions impact my sales so that I can understand ROI and refine my offerings."
Description

Implement analytics capabilities to track the performance of cross-sell suggestions, including conversion rates, revenue uplift, and average ticket size impact. Provide visual dashboards and exportable reports that enable operators to measure the effectiveness of PairPerfect, identify top-performing combos, and uncover opportunities for menu optimization.

Acceptance Criteria
Daily Conversion Rate Tracking
Given the operator selects a specific date range on the PairPerfect performance dashboard When the range is applied Then the system displays the conversion rate of cross-sell suggestions per day and the overall conversion rate for the period.
Revenue Uplift Report Generation
Given the operator navigates to the revenue reports section When the operator requests a report for the last 30 days Then the system generates a summary showing total revenue uplift attributed to cross-sell suggestions and breaks it down by item.
Average Ticket Size Impact Visualization
Given the operator views the PairPerfect analytics dashboard When a date filter is applied Then the system displays a chart comparing average ticket size with and without cross-sell suggestions over the selected period.
Exportable Performance Reports
Given the operator clicks the export button on any dashboard view When a file format (CSV or PDF) is selected Then the system downloads a correctly formatted report containing all visible metrics and charts for external analysis.
Top-Performing Combos Identification
Given the operator accesses the combos performance tab When the last 60 days filter is active Then the system lists the top 10 combos by conversion rate and total revenue, sorted in descending order.

InventoryInnovator

Aligns menu tweaks with on-hand inventory and expiry data, crafting high-margin daily specials using surplus or near-expiry ingredients. Reduces waste while driving revenue through value-packed, sustainable offerings.

Requirements

Surplus Ingredient Analysis
"As a food truck operator, I want the dashboard to automatically identify surplus or soon-to-expire ingredients so that I can plan specials that reduce waste."
Description

The system must analyze real-time inventory levels and expiry dates to automatically detect surplus or near-expiry ingredients. It should integrate inventory data with expiry metadata, run daily batch processes to flag items that exceed predefined thresholds or are within a set number of days from expiration, and surface these insights in the dashboard.

Acceptance Criteria
Daily Surplus Batch Execution
Given the system has up-to-date inventory and expiry metadata, when the daily batch process runs at the scheduled time, then all ingredients exceeding their predefined surplus quantity thresholds or within the configured expiry window are flagged in the system.
Expiry-Based Surplus Detection
Given an ingredient’s expiry date is within the next X days, when the daily analysis runs, then the ingredient is identified as near-expiry and marked for special promotion or usage.
Threshold Configuration Application
Given an operator updates surplus quantity or expiry-day thresholds in settings, when the next batch process executes, then the new thresholds are applied and only ingredients meeting the updated criteria are flagged.
Dashboard Flagged Item Display
Given ingredients have been flagged by the batch process, when a user opens the InventoryInnovator dashboard, then a dedicated section displays each flagged item with its name, current quantity, days to expiry, and a clear surplus indicator.
Performance of Batch Processing
Given the inventory dataset size typical for a food truck, when the daily batch process runs, then it completes its analysis and flags items within 5 minutes without errors or timeouts.
Expiry Prediction Engine
"As a food truck operator, I want the system to predict which ingredients will expire soon so that I can proactively include them in specials."
Description

Leverage historical usage trends and current inventory data to forecast which ingredients are most likely to expire soon. The engine should account for factors such as average sales velocity, seasonal demand shifts, and environmental data where available, providing confidence scores for each prediction.

Acceptance Criteria
Imminent Expiry Forecast for Dairy Ingredients
Given at least 6 months of sales and inventory data for dairy products, when the expiry prediction engine runs, then it generates a list of dairy ingredients predicted to expire within the next 3 days with confidence scores, and 85% of predictions must be accurate within ±1 day.
Seasonal Demand Adjustment for Fresh Produce
Given the current month and historical seasonal demand data for fresh produce, when predicting expiry for tomatoes, then the engine adjusts consumption velocity by at least 15% to reflect seasonal variation and provides a confidence score above 75%.
Offline Data Synchronization and Prediction
Given 24 hours of offline sales and inventory updates on the mobile dashboard, when connectivity is restored, then the engine processes batched data and updates expiry predictions and confidence scores for all items within 2 minutes.
Environmental Factor Impact Analysis
Given external temperature and humidity sensor data indicating a change of ≥5°C or ≥10% humidity, when running expiry predictions, then the engine adjusts predicted expiry dates for produce by at least 5% and records the environmental factor impact in prediction metadata.
Confidence Score Accuracy Validation
Given ground truth data of actual expirations over the previous quarter, when comparing against engine predictions, then at least 90% of items with confidence scores ≥80% must expire within their predicted windows.
Daily Special Suggestion Module
"As a food truck operator, I want automated suggestions for daily specials based on my surplus stock so that I can increase revenue and minimize spoilage."
Description

Generate daily special recommendations by matching surplus and near-expiry ingredients with menu templates and pricing rules. The module should optimize for highest potential margins and customer appeal, and present a ranked list of specials each morning.

Acceptance Criteria
Morning Recommendation Generation
Given the dashboard has synchronized inventory and menu templates, When the operator opens the Daily Special Suggestion Module at 6:00 AM, Then the system displays a ranked list of at least three specials using only surplus or near-expiry ingredients, sorted by descending projected margin.
Ingredient Expiry Prioritization
Given ingredients are within 48 hours of expiry, When the module evaluates potential specials, Then these ingredients must be prioritized so that at least 60% of suggested specials include near-expiry items.
Pricing Rule Compliance
Given predefined pricing rules and minimum margin thresholds, When specials are generated, Then each special’s price is calculated automatically and must meet or exceed the configured minimum margin without exceeding maximum markup limits.
Customer Appeal Integration
Given historical sales data indicating popular ingredients, When ranking specials, Then ingredients with higher popularity scores must receive a weighted boost so that they appear in at least the top two suggested specials if available.
Offline Generation Capability
Given the system is offline but has a local cache of the last inventory sync, When the operator accesses the module, Then the module must generate and display specials within 30 seconds using cached data, and notify the operator that suggestions are based on the last sync.
Margin Optimization Configuration
"As a food truck operator, I want to configure margin and cost rules for specials so that I maintain profitability while reducing waste."
Description

Provide a configuration interface where operators can set minimum margin thresholds, target profit percentages, and cost constraints for special recipes. The system should validate these settings and preview their impact on supplier ordering and expected revenue.

Acceptance Criteria
Margin Threshold Configuration Entry
Given the operator is on the Margin Optimization Configuration interface, When they input a minimum margin threshold of 20%, a target profit percentage of 35%, and a cost constraint of $150 per recipe, Then the system accepts the inputs, stores them in draft configuration, and displays all three values correctly in the configuration summary.
Configuration Impact Preview
Given the operator has entered valid margin and cost settings, When they click the 'Preview Impact' button, Then the system calculates and displays the estimated changes to supplier order quantities and projected revenue impact in a summary panel.
Validation of Invalid Inputs
Given the operator enters a margin threshold below 0% or above 100%, When they attempt to save the settings, Then the system displays an inline error message 'Margin must be between 0% and 100%' and disables the 'Save' button until corrected.
Updating Existing Configuration
Given an existing margin configuration is saved, When the operator modifies the target profit percentage from 30% to 40% and clicks 'Save', Then the system updates the stored configuration, reflects the new percentage in the summary, and maintains other parameters unchanged.
Confirmation of Configuration Saving
Given the operator has provided all valid settings, When they click 'Save', Then the system shows a persistent success notification 'Configuration saved successfully', writes the settings to the database, and the 'Save' button becomes disabled until further edits.
Specials Publishing and Sync
"As a food truck operator, I want to instantly update my POS and online menu with new specials even offline so that customers always see accurate offerings."
Description

Allow operators to publish selected daily specials directly to the mobile POS and customer-facing channels with one action. The feature must support offline operation by queuing updates locally and synchronizing changes when connectivity is restored.

Acceptance Criteria
Online Specials Publishing
Given the operator is connected to the internet and has selected daily specials, When they tap the ‘Publish’ button, Then the specials are immediately visible in the mobile POS and on all configured customer-facing channels within 5 seconds.
Offline Queue Activation
Given the operator is offline and has selected daily specials, When they tap the ‘Publish’ button, Then the specials are added to the local queue with a timestamp and status ‘Pending Sync’.
Automatic Sync on Reconnect
Given the operator’s device regains connectivity and there are queued specials, When the device connects to a stable network, Then all pending specials are automatically published to the POS and customer channels in the order they were queued, and the local queue is cleared.
Sync Error Handling and Retries
Given a queued special fails to publish due to a server error during synchronization, When the synchronization process runs, Then the system retries the publishing up to three times at 30-second intervals and logs any failures in the sync history with error details.
Duplicate Publishing Prevention
Given a previously published special exists in the POS or customer channels, When the operator attempts to publish the same special again, Then the system prevents duplication by displaying a warning message and does not re-publish the special.
Restock Alert Integration
"As a food truck operator, I want restock alerts to adjust based on specials usage so that I avoid stockouts after running special promotions."
Description

Integrate the special generation output with the existing restock alert system so that when surplus ingredients are used in specials, reorder suggestions and vendor lists update accordingly. This ensures inventory levels remain balanced post-special execution.

Acceptance Criteria
Reorder Suggestion Adjustment
Given a special is created using 10 units of surplus potatoes, when the special is saved, then the restock alert system decreases the reorder quantity for potatoes by 10 units.
Vendor List Auto-Update
Given a special uses ingredients from multiple vendors, when the special is confirmed, then the vendor list prioritizes affected vendors and updates product quantities based on reduced inventory levels.
Near-Expiry Ingredient Handling
Given ingredients within 48 hours of expiry are included in a special, when the special is finalized, then the system flags these items and adjusts restock suggestions to prevent overstocking soon-to-expire products.
Inventory Threshold Maintenance
Given a special execution reduces inventory below predefined thresholds, when restock alerts are generated, then the system ensures suggested reorder quantities maintain inventory at or above threshold levels.
Offline Restock Alert Sync
Given the mobile dashboard operates offline, when a special is created using surplus inventory, then restock alert adjustments are queued locally and synchronized with the server once connectivity is restored.

EventAlign

Integrates local event and holiday calendars with weather insights to propose themed menu items—like cool mocktails for summer festivals or hearty stews for rainy weekends—capitalizing on regional foot traffic opportunities.

Requirements

Event Calendar Aggregation
"As a food truck operator, I want to see a consolidated list of local events and holidays so that I can plan themed menu items aligned with regional foot traffic opportunities."
Description

The system must integrate and consolidate event data from multiple local and regional calendar sources (e.g., city event boards, ticketing platforms, community calendars) to provide real-time listings of upcoming events and holidays relevant to each food truck’s location. It should automatically fetch event metadata—including date, time, location, and expected attendance—and update the Truckly dashboard to ensure operators have the latest information for menu planning.

Acceptance Criteria
Event Data Synchronization for New Locations
Given a food truck sets its operating location in the dashboard, When the system performs an event data fetch, Then events from city boards, ticketing platforms, and community calendars within a 50-mile radius are consolidated, and each event entry includes non-null metadata fields: date, time, location, and expected attendance.
Real-Time Dashboard Refresh After Offline Period
Given the dashboard was offline for up to 2 hours, When network connectivity is restored, Then the system automatically fetches and updates new events within 5 minutes and displays a confirmation message.
Graceful Handling of Event Source Failures
Given an external calendar API returns an error or timeout, When the system retries up to 3 times with exponential backoff and still fails, Then it logs the error, continues processing other sources, and displays a non-blocking warning notification on the dashboard.
Radius-Based Event Filtering
Given a truck’s set radius of 25 miles, When events are fetched, Then only events whose geolocation coordinates fall within 25 miles of the truck’s location are displayed, validated against test cases with at least 95% accuracy.
Scheduled Event Export to CSV
Given the operator selects the export function, When they request upcoming events export, Then the system generates a CSV file containing event name, date, time, location, and expected attendance, and initiates download within 10 seconds.
Weather Insights Integration
"As a food truck operator, I want weather forecasts integrated into my dashboard so that I can tailor my menu to match ambient conditions and guest preferences."
Description

Implement a reliable weather forecasting API integration that retrieves current and short-term weather predictions (temperature, precipitation, conditions) for each truck’s location. This data should feed into the EventAlign feature in real time, allowing the system to adjust menu suggestions based on environmental conditions (e.g., suggesting cool drinks for hot days or warm soups for rainy weather).

Acceptance Criteria
Weather API Data Retrieval
Given the truck’s geolocation is set, when the system requests current weather data, then the API returns temperature, precipitation probability, and weather conditions within 2 seconds and with a 200 OK response.
Menu Suggestion Adjustment on Hot Days
Given the retrieved temperature is ≥30°C, when generating EventAlign menu recommendations, then the top three suggested items must include at least one cool beverage or chilled menu item.
Menu Suggestion Adjustment on Rainy Conditions
Given precipitation probability is ≥50% or weather condition indicates rain, when creating menu suggestions, then the system recommends at least two warm food or beverage items (e.g., soups or hot drinks) in the top three suggestions.
API Failure Handling
Given the weather API returns an error or times out, when the system fails to retrieve new data, then it retries up to three times within 10 seconds, logs the error if all retries fail, and alerts the operator of the outage via the dashboard notification area.
Offline Mode Data Caching
Given the device is offline, when attempting to fetch weather data, then the system uses the most recent cached weather information (no older than 24 hours) and displays a notice indicating that cached data is in use.
Themed Menu Suggestion Engine
"As a food truck operator, I want automated themed menu recommendations based on upcoming events and weather forecasts so that I can quickly adjust my offerings to maximize sales."
Description

Develop an intelligent algorithm that analyzes combined event and weather data to generate context-aware menu suggestions. The engine should match event types (e.g., music festivals, sports games) and weather conditions (e.g., heat, rain) against a configurable library of themed recipes and ingredients, ranking suggestions by relevance and potential profitability.

Acceptance Criteria
Festival-Based Cold Drink Recommendations
Given a hot outdoor music festival and temperature above 80°F, when the engine generates suggestions, then it includes at least three cold beverage-themed recipes ranked by relevance and profitability.
Rainy Day Comfort Food Suggestions
Given forecasted rain on a weekend community market day, when suggestions are fetched, then the engine recommends at least three warm, hearty recipes (e.g., stews, soups) with top relevance scores.
Sports Event Snack Pairings
Given a local sports game event, when generating menu suggestions, then the engine provides five snack or finger-food options that match the event type, each accompanied by an expected profit margin, sorted by highest margin first.
Holiday-Themed Seasonal Specials
Given an upcoming local holiday (e.g., Halloween) detected two days in advance, when suggestions are requested, then the engine generates at least five themed menu items aligned with holiday motifs, each tagged with estimated profitability.
Low Demand Weather Compensation
Given a forecast of low foot traffic due to inclement weather, when generating recommendations, then the engine suggests at least three promotional combos or discounted deals designed to boost sales, each with clear discount percentages and projected uplift.
Suggestion Review and Approval UI
"As a food truck operator, I want a clear interface to review and approve suggested menu items so that I can efficiently update my inventory and prep plans."
Description

Design and implement a user-friendly interface within the mobile dashboard where operators can view, filter, and accept or reject the themed menu suggestions generated by EventAlign. The UI should display key details (suggestion rationale, ingredients needed, estimated profit uplift) and allow one-click approval to integrate items into the current inventory and prep lists.

Acceptance Criteria
Viewing Suggested Menu Items
Given the operator navigates to the 'EventAlign Suggestions' screen, When suggestions are generated, Then each suggestion entry displays the item name, suggestion rationale, required ingredients, and estimated profit uplift within the mobile dashboard.
Filtering Suggestions by Event or Holiday
Given the operator taps the filter icon, When the operator selects an event or holiday category, Then the suggestion list updates to show only menu items relevant to the chosen event or holiday.
Approving a Suggested Menu Item
Given a suggestion is displayed, When the operator taps the 'Approve' button for that suggestion, Then the item is added to the current inventory and prep lists, and a confirmation message is shown.
Rejecting a Suggested Menu Item
Given a suggestion is displayed, When the operator taps the 'Reject' button and confirms rejection, Then the suggestion is removed from the list and not added to inventory.
Offline Access to Suggestions
Given the operator's device is offline, When the operator opens the 'EventAlign Suggestions' screen, Then the last-synced list of suggestions is available for viewing, filtering, and taking action.
Offline Cache and Sync
"As a food truck operator, I want to access event-aligned menu suggestions even when offline so that I can make informed decisions in areas with limited connectivity."
Description

Enable offline functionality by caching event, weather, and suggestion data locally on the device. The system should allow operators to view the latest fetched suggestions even without connectivity and automatically synchronize any approvals or changes when the device reconnects to the internet.

Acceptance Criteria
Viewing Cached Suggestions Offline
Given the operator has previously fetched EventAlign suggestions while online, When the operator opens the mobile dashboard without internet connectivity, Then the dashboard displays the most recently cached event, weather, and menu suggestions along with their fetch timestamps.
Queuing Offline Approvals
Given the operator is offline, When the operator approves or modifies a suggested menu item, Then the action is stored in a local queue and visibly marked as pending synchronization.
Automatic Sync Upon Reconnection
Given there are pending offline actions in the local queue, When the device regains internet connectivity, Then all queued approvals and modifications are automatically synchronized with the server and a success confirmation is displayed to the operator.
Conflict Resolution During Sync
Given a locally queued change conflicts with updated server data, When synchronization occurs, Then the operator is notified of the conflict and presented with options to retain the local change, accept the server version, or merge the differences.
Cache Expiration Warning
Given the cached suggestions are older than 24 hours, When the operator accesses the dashboard while offline, Then a warning message is displayed indicating the data may be outdated and prompting the operator to reconnect for fresh suggestions.

SmartSettle Contracts

Automatically executes payment settlements through blockchain-based smart contracts once predefined conditions are met (e.g., invoice approval and delivery confirmation). Eliminates manual intervention, accelerates cash flow, and builds trust by ensuring on-time vendor payments.

Requirements

Contract Template Builder
"As a food truck operator, I want to create and customize smart contract templates so that I can quickly set up consistent payment agreements with different vendors without manual coding."
Description

Provide an interface for food truck operators to define and customize smart contract templates with predefined settlement conditions (e.g., invoice approval, delivery confirmation) and associated payment terms. This requirement ensures that contracts can be tailored to various vendor agreements, streamlining setup and maintaining consistency across transactions.

Acceptance Criteria
New Contract Template Creation
Given the operator accesses the Contract Template Builder, When they enter a unique template name, add at least one settlement condition and define payment terms, Then the system saves the template, assigns it a unique ID, and displays it in the template list.
Existing Template Modification
Given an existing contract template in the list, When the operator edits settlement conditions or payment terms and clicks Save, Then the system updates the template, records the change in version history, and notifies the operator of a successful update.
Default Settlement Condition Application
Given the operator selects the Use Default Conditions option, When they confirm the selection, Then the builder auto-populates standard settlement triggers (invoice approval, delivery confirmation) and default payment terms, and allows further customization.
Mandatory Field Validation on Save
Given the operator attempts to save a template with missing required fields (e.g., template name, at least one settlement condition, payment term), When they click Save, Then the system prevents saving, highlights each missing field, and displays clear error messages.
Contract Template Preview Rendering
Given a completed contract template, When the operator clicks Preview, Then the builder generates a formatted mock contract showing all defined conditions, payment terms, and operator/vendor details, matching the production layout.
Settlement Condition Monitor
"As a food truck operator, I want the system to automatically detect when my invoice is approved and delivery is confirmed so that payments are executed immediately without manual intervention."
Description

Implement a real-time monitoring system that listens for on-chain and off-chain events—such as invoice approval status and delivery confirmations—and evaluates these against the smart contract conditions. This ensures automatic detection of fulfillment criteria and triggers the settlement process without manual checks.

Acceptance Criteria
On-Chain Invoice Approval Detection
Given a new invoice approval event is recorded on the blockchain When the monitoring system receives the event Then it must parse the invoice ID, timestamp, and approval status and mark the invoice as approved in the tracking database within 1 second.
Off-Chain Delivery Confirmation Detection
Given a delivery confirmation is posted in the off-chain logistics platform When the system polls or receives the delivery event Then it must verify the delivery ID and status and update the corresponding invoice record in the monitoring database within 2 seconds.
Combined Condition Fulfillment Trigger
Given an invoice approval and a delivery confirmation exist for the same invoice When both events are validated Then the system must automatically trigger the smart contract settlement function within 500ms of the second event.
Event Processing Error Handling
Given a failure occurs while processing an on-chain or off-chain event When an event processing error is detected Then the system must log the error, retry the event fetch up to three times at 1-minute intervals, and escalate to an alert dashboard if all retries fail.
Latency Performance for Event Processing
Given the system is processing high volumes of events When 1,000 mixed on-chain and off-chain events arrive in a 10-second window Then the system must process and evaluate all events against settlement conditions with an average latency of under 200ms per event.
Blockchain Network Connector
"As a developer, I want a robust integration with our blockchain network so that smart contracts can be deployed and transactions executed securely and reliably."
Description

Establish secure, scalable integration with the chosen blockchain network (e.g., Ethereum, Hyperledger) using industry-standard APIs and SDKs. This connector must handle transaction creation, signing, and broadcasting, ensuring reliable communication between Truckly and the blockchain.

Acceptance Criteria
Secure Blockchain Network Authentication
Given the connector has valid blockchain network credentials stored securely When it attempts to authenticate with the blockchain node Then it establishes a TLS-encrypted session and returns a successful auth token within 5 seconds
Transaction Creation and Signing
Given a validated settlement payload When the connector creates a blockchain transaction Then it signs the transaction with the private key using industry-standard cryptography and the signature is verifiable by the network
Transaction Broadcasting and Confirmation
Given a signed transaction When the connector broadcasts it to the blockchain network Then the network responds with a transaction hash and the connector confirms the transaction is mined with at least 1 block confirmation within 2 minutes
Error Handling and Retry Mechanism
Given a network timeout or node error occurs during broadcast When the connector detects the failure Then it retries the broadcast automatically up to 3 times with exponential backoff and logs each attempt
Scalability Under High Throughput
Given 100 concurrent settlement transactions When the connector processes these transactions Then it queues, signs, and broadcasts all within a 5-second average latency per transaction without failures
Automated Payment Executor
"As a food truck operator, I want payments to be automatically sent to my vendors once contract conditions are satisfied so that I can speed up cash flow and reduce administrative overhead."
Description

Develop the engine that automatically executes payment settlements via the deployed smart contracts once all predefined conditions are met. This includes constructing the transaction, managing gas fees, and ensuring funds are transferred to the vendor’s wallet address.

Acceptance Criteria
Invoice Approved and Delivery Confirmed
Given an approved invoice and confirmed delivery in the system, when all smart contract conditions are met, then the engine constructs a valid blockchain transaction, estimates and reserves sufficient gas fees, and broadcasts the transaction to settle payment within 5 seconds.
Insufficient Gas Fee Reservation
Given the estimated gas fee exceeds the reserved amount, when the transaction is being prepared, then the engine logs a warning, aborts execution, and notifies the finance team to fund additional gas.
Network Congestion Handling
Given the blockchain network is experiencing congestion, when the engine submits the transaction, then it retries up to three times with incremental gas price adjustments and logs each attempt outcome.
Failed Transaction Retry Mechanism
Given a submitted transaction fails due to temporary errors, when the failure is detected, then the engine automatically retries the transaction up to two additional attempts and records success or final failure in the audit log.
Vendor Wallet Update Verification
Given a payment transaction is confirmed on-chain, when the transaction receipt is received, then the engine updates the vendor’s wallet balance record in the dashboard, and sends a confirmation notification to the vendor.
Settlement Audit Trail
"As a food truck operator, I want to view a detailed history of all contract settlements so that I can verify transactions and maintain accurate financial records."
Description

Create a dashboard that provides a transparent view of all smart contract settlements, including contract terms, event triggers, executed transactions, timestamps, and on-chain transaction hashes. This audit trail ensures traceability and builds trust with vendors.

Acceptance Criteria
Audit Trail Dashboard Overview
Given a user with audit trail access navigates to the Settlement Audit Trail dashboard, When the page loads, Then they see a searchable, sortable list of all settlements displaying Contract ID, Terms Summary, Trigger Event, Execution Timestamp, and Transaction Hash, sorted by newest first.
Settlement Detail Drill-Down
Given a user clicks on a settlement entry from the dashboard, When the detail view opens, Then the user can view full smart contract terms, event trigger logs, executed transaction payload, and on-chain transaction details including block number and gas used.
Date Range Filtering
Given a user selects a start date and end date in the filter controls, When they apply the filter, Then the dashboard displays only settlements executed within the selected date range and the total count updates accordingly.
Transaction Hash Link-Out
Given a user views a settlement entry, When they click the Transaction Hash value, Then a new browser tab opens to the correct transaction detail page on the configured blockchain explorer.
Export Audit Data
Given a user clicks the Export button, When they select CSV or JSON format, Then the system generates and downloads a file containing all currently displayed settlements with columns for Contract ID, Terms Summary, Trigger Event, Execution Timestamp, and Transaction Hash in the chosen format.
User Permissions Enforcement
Given a user without audit trail permissions navigates to the Settlement Audit Trail dashboard, When the page loads, Then the user is presented with an access denied message and no audit data is visible.
Settlement Failure Alerts
"As a food truck operator, I want to receive alerts if a payment settlement fails so that I can address issues quickly and ensure vendors are paid on time."
Description

Implement a notification system that detects and reports any failures or delays in contract execution (e.g., insufficient gas, network issues) and provides actionable error details. Alerts should be delivered via in-app notifications and email to enable prompt resolution.

Acceptance Criteria
Insufficient Gas Alert Trigger
Given a smart contract execution is initiated When available gas falls below the predefined threshold Then an in-app notification and an email containing the error code and current gas details are sent within one minute
Network Connectivity Failure Notification
Given a settlement transaction is broadcast When a network timeout or connectivity error occurs Then the system retries the broadcast up to three times and upon final failure sends a high-priority in-app alert and email with retry logs
Delayed Settlement Confirmation
Given a contract settlement transaction is pending When confirmation is not received within five minutes Then an alert is sent via in-app notification and email specifying elapsed time and recommended next steps
Payment Reversion Failure Alert
Given an automatic payment reversion is triggered When the reversal transaction fails Then the system logs the reversal error, triggers an in-app notification, and sends an email with detailed error information
Aggregate Failure Reporting
Given multiple settlement failures occur When three or more failures are detected within a one-hour window Then the system compiles a failure summary report and delivers it via email and updates the dashboard widget

Real-Time Ledger Viewer

Provides an always-up-to-date, read-only dashboard of shared credit records on the blockchain. Grants both vendors and operators transparent access to payment histories, outstanding balances, and transaction timestamps, fostering accountability and boosting supplier trust.

Requirements

Immutable Transaction Ledger
"As a food truck operator, I want to view an immutable record of all credit transactions so that I can verify payments and trust the accuracy of the ledger."
Description

Display a read-only, chronologically ordered ledger of all credit transactions with cryptographic verification. Each entry should include a timestamp, transaction ID, vendor and operator identifiers, and blockchain hash to ensure data integrity and non-repudiation. The ledger integrates seamlessly into the existing dashboard, providing auditors and stakeholders with transparent, tamper-proof payment histories.

Acceptance Criteria
Real-Time Ledger Dashboard Access
Given a logged-in user with dashboard access When the user navigates to the Ledger Viewer Then the ledger displays all credit transactions in read-only mode with timestamp, transaction ID, vendor and operator identifiers, and blockchain hash
Transaction Entry Cryptographic Verification
Given a loaded ledger entry When the system displays transaction details Then each entry’s displayed blockchain hash matches the recomputed hash confirming data integrity
Offline Mode Ledger Accessibility
Given the user loses network connectivity When accessing the Ledger Viewer Then the system displays the last successfully synced entries up to 5 minutes prior and indicates data may be stale
Chronological Order Enforcement
Given multiple transactions across different timestamps When viewing the ledger Then entries are ordered from newest to oldest based on timestamp without exceptions
Dashboard Integration Performance
Given the ledger is embedded in the mobile dashboard When loading the dashboard Then the ledger section loads within 2 seconds and is responsive across devices with no layout issues
Role-Based Access Control
"As a supplier, I want to securely access the shared ledger with appropriate permissions so that I can see relevant transactions without risking data tampering."
Description

Implement secure, role-based permissions allowing vendors and operators to access the real-time ledger in read-only mode. Integrate with Truckly’s authentication system to ensure only authorized users can view relevant transaction data. This feature enhances privacy and security by restricting data access according to user roles.

Acceptance Criteria
Vendor Access to Own Ledger
Given a vendor user is authenticated with the 'vendor' role; When the vendor navigates to the Real-Time Ledger Viewer; Then they can view only their own transaction history in read-only mode with accurate balances and timestamps.
Operator Access to Vendor Ledgers
Given an operator user is authenticated with the 'operator' role; When the operator accesses the Real-Time Ledger Viewer; Then they can view all vendors’ transaction histories in read-only mode without the ability to modify entries.
Unauthorized User Denied Access
Given a user without 'vendor' or 'operator' role attempts to access the Real-Time Ledger Viewer; When they request ledger data via the UI or API; Then the system responds with a 403 Forbidden error and displays no transaction information.
Role Enforcement on Data Visibility
Given users with different roles (vendor and operator) are logged in simultaneously; When each user opens the Real-Time Ledger Viewer; Then each sees only the transaction records permitted by their role (vendor sees own, operator sees all).
Session Expiry and Reauthentication
Given a user's authentication token has expired; When they attempt to view the Real-Time Ledger Viewer; Then they are redirected to the login screen and must successfully reauthenticate before ledger data is displayed.
Search and Filter Transactions
"As a food truck operator, I want to search and filter credit transactions so that I can quickly locate specific records when auditing."
Description

Provide advanced search and filtering capabilities within the ledger view, including date ranges, transaction IDs, vendor names, amounts, and status filters. Enable quick retrieval of specific records for auditing and reconciliation, improving usability and reducing time spent locating entries.

Acceptance Criteria
Search by Date Range
Given the ledger view is open, when a user selects a valid start and end date and applies the filter, then only transactions with timestamps within the specified range are displayed, and the total count matches the expected number of records.
Search by Transaction ID
Given the ledger view is open, when a user enters a complete or partial transaction ID and triggers the search, then the list displays all matching transactions, and no unrelated records appear.
Filter by Vendor Name
Given the ledger view is open, when a user selects one or multiple vendor names from the vendor filter dropdown and applies the filter, then only transactions associated with the selected vendors are displayed.
Filter by Amount Range
Given the ledger view is open, when a user specifies a minimum and maximum amount and applies the filter, then only transactions whose amounts fall within the specified range are shown, and the sum of displayed amounts matches the calculated total for those entries.
Filter by Transaction Status
Given the ledger view is open, when a user selects one or more statuses (e.g., Paid, Pending, Overdue) and applies the filter, then only transactions matching the selected statuses are displayed.
Real-Time Update Streaming
"As a vendor, I want to see new transactions appear instantly in my ledger so that I remain up to date with credits and balances."
Description

Utilize WebSocket connections or blockchain event subscriptions to push new transactions instantly to the ledger view without requiring manual refresh. Ensure updates are reflected in milliseconds, maintaining an always-current dashboard that boosts operational responsiveness and supplier trust.

Acceptance Criteria
Display New Transaction via WebSocket
Given the user has an active WebSocket connection to the ledger service, When a new transaction is committed on the blockchain, Then the dashboard must display the transaction entry within 200 milliseconds without requiring a manual refresh.
Handle Blockchain Event Subscription Updates
Given the user is subscribed to blockchain event notifications, When a transaction event is emitted by the blockchain network, Then the ledger view must update immediately to reflect the transaction details and timestamp.
Maintain UI Responsiveness Under High Transaction Volume
Given there are more than 100 transactions per second incoming, When the transactions stream to the dashboard, Then the UI should remain responsive with update latency not exceeding 300 milliseconds and no message loss.
Re-establish Connection After Disruption
Given the WebSocket connection or event subscription is temporarily lost, When the connection is restored, Then the dashboard must automatically reconnect and reconcile missed transactions so the ledger is current without duplicates.
Update Ledger After Offline Recovery
Given the user goes offline and then regains connectivity, When the application re-establishes the blockchain subscription, Then the dashboard must fetch and display any transactions missed during offline mode in chronological order within 500 milliseconds.
Offline Access and Data Sync
"As an operator working in areas with poor connectivity, I want to access the ledger offline and have changes synced when online so that I can maintain records at all times."
Description

Enable local caching of ledger data on the mobile dashboard for offline viewing. Implement background synchronization to reconcile and update any missing transactions once connectivity is restored, ensuring continuous access to accurate records even in low-network environments.

Acceptance Criteria
Viewing Cached Ledger Data Offline
Given the mobile dashboard is opened without network connectivity, when the user navigates to the ledger viewer, then the locally cached transactions load within 2 seconds, display correct balances and timestamps, and include all entries synced during the last successful connection.
Automatic Background Synchronization Upon Connectivity Restoration
Given the device was offline with pending local changes, when the network connection is restored, then the system automatically synchronizes missing transactions within 60 seconds without user intervention, confirming completion via an updated last-synced timestamp.
Conflict Resolution for Duplicate Transactions
Given a transaction with identical IDs exists locally and on the blockchain, when synchronization occurs, then the system identifies duplicates, retains the most recent version based on timestamp, removes or archives the redundant entry, and logs the conflict resolution outcome.
Data Consistency Verification After Sync
Given the synchronization process has completed, when the user compares local ledger data to the blockchain ledger, then the number of transactions, balances, and timestamps align exactly, and no discrepancies are reported.
User Notification of Sync Status
Given any change in network connectivity affects sync operations, when synchronization succeeds or fails, then the user receives a clear notification within 5 seconds indicating success or failure, along with a brief description of any errors.

Multi-Sign Approval

Enables customizable multi-party approval workflows for high-value transactions. Requires digital signatures from designated stakeholders before payments are released, reducing fraud risk and ensuring compliance with internal policies and vendor agreements.

Requirements

Role-Based Stakeholder Management
"As an operations manager, I want to define stakeholder roles and assign approval permissions so that only authorized personnel can sign off on high-value transactions."
Description

Provide functionality to define and manage stakeholder roles, assign digital approval authority, set signature requirements, and link roles to vendor agreements and internal policies. Allows administrators to create, edit, and remove stakeholder profiles, specifying required signing thresholds and order, ensuring only authorized personnel can approve high-value transactions.

Acceptance Criteria
Stakeholder Role Creation
Given an administrator is on the 'Create Stakeholder Role' page, enters a unique role name, selects digital approval authority, sets a signature threshold of at least 1, defines signing order, and clicks 'Save'; When the form is submitted with valid data; Then the new stakeholder role appears in the roles list with correct name, authority, threshold, and order.
Stakeholder Role Modification
Given an existing stakeholder role in the roles list; When the administrator edits its name, approval authority, signature threshold, or signing order and clicks 'Save'; Then the changes are persisted, the updated attributes display correctly in the roles list, and no validation errors occur.
Stakeholder Role Removal
Given an administrator views the stakeholder roles list and selects 'Delete' on a role not linked to active transactions; When the administrator confirms the deletion action; Then the role is removed from the list, cannot be selected in any approval workflows, and a confirmation message is displayed.
Role Assignment to Vendor Agreement
Given an administrator edits a vendor agreement; When they assign one or more stakeholder roles with defined signature requirements to the agreement and click 'Save'; Then the agreement displays the linked roles with their thresholds and order, and transactions under this agreement enforce those signature requirements.
Signature Order Enforcement
Given a high-value transaction requiring signatures from multiple stakeholder roles in a specific sequence; When the first stakeholder submits their digital signature; Then the system notifies the next stakeholder in the defined order, locks the transaction from further signatures out of sequence, and upon final signature, marks the transaction as fully approved.
Customizable Approval Workflow Builder
"As a finance director, I want to configure multi-stage approval workflows with conditional routing so that high-value transactions follow the correct sign-off path."
Description

Enable users to define multi-stage approval sequences with conditional routing, specifying the order of signatures, threshold amounts for auto-routing, and parallel or sequential approvals. Integrates with the dashboard to visualize workflow states and facilitates adjustments without code changes.

Acceptance Criteria
Define Sequential Approval Workflow
Given an administrator accesses the workflow builder, When they add three approval stages in sequence, Then the system saves the stages in the specified order and displays them in the builder.
Configure Conditional Routing by Amount
Given a predefined approval workflow, When the admin sets a threshold of $5,000 for auto-routing to Finance and $10,000 to Legal, Then any transaction amount triggers the correct route based on the threshold.
Set Up Parallel Approvals
Given a workflow requiring parallel approvals, When the admin marks two stages as parallel and assigns different stakeholders, Then both stakeholders receive simultaneous signature requests and the workflow proceeds once all signatures are collected.
Visualize Workflow States on Dashboard
Given a workflow is in progress, When a stakeholder views the dashboard, Then they see a real-time state diagram highlighting completed, pending, and failed approval stages.
Edit Workflow Without Code Changes
Given an existing approval workflow, When an admin modifies stage order or adds a new approver, Then the system updates the workflow instantly without requiring a redeploy or developer intervention.
Secure Digital Signature Capture
"As a compliance officer, I want to capture secure digital signatures for each approval so that transactions are legally binding and tamper-proof."
Description

Implement a secure digital signature mechanism that captures hand-drawn or typed signatures, timestamps each sign-off, encrypts signature data at rest and in transit, and binds the signature to transaction details, ensuring non-repudiation and compliance with legal standards.

Acceptance Criteria
Offline Hand-Drawn Signature Capture
Given the user is offline and initiates a signature request, when the user draws their signature, then the system stores the signature locally with a timestamp and marks it for synchronization upon network reconnection.
Online Typed Signature Encryption in Transit
Given the user enters a typed signature while online, when the signature is submitted, then the client encrypts the signature data using AES-256 before transmission and verifies a secure TLS connection is used.
Encrypted Signature at Rest
Given a signature has been captured, when it is stored in the database, then the signature data must be encrypted using an industry-standard algorithm and only accessible by authorized service accounts.
Signature-Transaction Binding Verification
Given a stored signature record, when retrieving the record, then the system verifies that the signature hash is bound to the correct transaction ID and timestamp, ensuring non-repudiation.
Audit Trail Tamper Detection
Given an audit log entry for a signed transaction, when the log is accessed, then any attempt to modify the signature or transaction details is detected, and the system flags the entry as tampered.
Real-time Approval Notifications
"As an approver, I want to receive real-time alerts when my signature is required so that I can approve transactions promptly and keep operations moving."
Description

Send automated notifications and reminders via email, SMS, or in-app alerts to designated approvers when their signature is required, providing direct links to pending requests and status updates. Ensures timely sign-offs and reduces approval delays.

Acceptance Criteria
Email Notification Delivery
Given a transaction requiring a signature is submitted When the request is generated Then an email notification is sent to each designated approver within 1 minute containing the requester’s name, transaction details, and a secure link to the pending request
SMS Notification Delivery
Given an approver has a valid mobile number When a signature is required Then an SMS message is sent within 1 minute with the transaction ID, amount, and a clickable link to the approval page
In-App Alert Display
Given an approver is logged into the Truckly dashboard When there are pending signature requests Then a visible notification badge appears on the approvals tab showing the count of pending requests and listing each request with direct navigation links
Reminder Scheduling
Given a pending approval request remains unsigned for 24 hours When the 24-hour period elapses Then the system automatically sends a reminder via email and SMS to each outstanding approver
Approval Status Update Notification
Given one approver completes their signature When the signature is captured Then remaining approvers receive an updated notification indicating the current sign-off count and listing outstanding signers
Comprehensive Audit Trail Logging
"As an auditor, I want to access a complete audit trail of all approval activities so that I can verify compliance and resolve any discrepancies efficiently."
Description

Maintain a detailed, immutable audit log for all approval actions, recording user IDs, timestamps, signature versions, workflow changes, and comments. Provide searchable and filterable logs to support compliance audits and dispute resolution.

Acceptance Criteria
Recording of Signature Actions
Given a stakeholder submits a digital signature for a high-value transaction, When the signature is confirmed, Then the system logs an immutable entry containing the user ID, exact timestamp, signature version, and transaction ID.
Audit Log Search and Filter Functionality
Given an auditor accesses the audit trail interface, When they apply filters for transaction ID, date range, or user ID, Then the system returns only the matching log entries within 2 seconds.
Immutable Audit Log Storage
Given any user attempts to modify or delete an existing audit log entry, When the action is initiated, Then the system denies the request, retains the original entry unchanged, and logs the attempted alteration with user ID and timestamp.
Workflow Change History Logging
Given an administrator updates a multi-sign approval workflow, When the update is published, Then the audit log captures the change event, including the user ID, timestamp, previous and updated workflow configurations.
Audit Log Export Capability
Given a compliance officer requests an export of audit logs, When they specify a date range and click export, Then the system generates a CSV file containing all relevant log fields (user ID, timestamp, action type, comments) and prompts the user to download it.
Offline Signature Synchronization
"As a field operator, I want to sign off on approval requests offline so that I can complete tasks in areas with limited connectivity without delaying transactions."
Description

Allow approvers to review and sign approval requests within the mobile dashboard even when offline, securely storing signatures locally and synchronizing automatically once connectivity is restored, ensuring uninterrupted operations in low-signal environments.

Acceptance Criteria
Offline Signature Capture
Given the user is offline and viewing a pending approval request in the mobile dashboard, when the user provides a digital signature, then the signature is securely saved locally with the correct request ID and timestamp.
Local Storage Encryption
Given an offline signature has been captured, when the data is stored on the device, then it must be encrypted using AES-256 and only accessible to the Truckly app.
Duplicate Signature Prevention
Given the user has already signed a specific approval request offline, when the user attempts to sign the same request again, then the app rejects the second signature attempt and notifies the user that a signature already exists.
Automatic Synchronization Upon Reconnection
Given one or more signatures are stored offline, when the device reconnects to the internet, then all pending signatures are automatically uploaded within 30 seconds and marked as submitted on both device and server.
Conflict Detection and Resolution
Given an offline signature was created but the approval request was already processed by another approver on the server, when synchronization occurs, then the app flags the conflict and prompts the user to resolve or cancel the upload.

Vendor TrustScore

Generates a dynamic reliability score for each supplier based on payment timeliness, order accuracy, and dispute resolution records. Helps operators identify top-performing partners and negotiate better terms, while vendors gain incentives to maintain high ratings.

Requirements

Payment Timeliness Data Capture
"As a food truck operator, I want the system to track when I owe vendors and when payments clear so that my TrustScore reflects accurate payment timeliness."
Description

Automatically record payment due dates and actual payment receipt dates for each vendor by integrating with existing accounting and payment gateways. Calculate payment delay durations in real time, flag late settlements, and store historical payment data to feed into the TrustScore calculation engine.

Acceptance Criteria
Recording Payment Dates Upon Receipt
Given a vendor payment is successfully processed through an integrated payment gateway When the payment confirmation is received Then the system automatically records the actual payment received date matching the transaction timestamp
Detecting Late Payments in Real-Time
Given a stored payment due date for a vendor When the current date surpasses the due date without a matching actual payment date Then the system flags the payment as late and triggers a late-payment alert
Calculating Payment Delay Durations
Given recorded due and actual payment dates When both dates exist in the system Then the system calculates the delay duration in days and stores this value in the payment history record
Integrating with Accounting Gateways
Given a new invoice is created in the linked accounting software When the invoice is synced to Truckly Then the payment due date from the invoice is accurately imported and stored in the vendor’s payment record
Archiving Historical Payment Data
Given completed and pending payments for vendors in the system When a payment record’s lifecycle ends Then the system retains all historical due dates, actual payment dates, and delay durations for reporting and TrustScore calculations
Order Accuracy Recording
"As a food truck operator, I want to see discrepancies between what I ordered and what I received so that my TrustScore reflects each vendor’s fulfillment reliability."
Description

Track and compare ordered versus delivered quantities and items for each vendor by capturing order details at the time of request and recording actual delivery data. Log discrepancies automatically, categorize variance types, and maintain a history of order accuracy metrics for score computation.

Acceptance Criteria
Order Request Capture
Given a user submits a vendor order request with item IDs and quantities, when the request is confirmed, then the system stores the SKU, quantity, and timestamp accurately in the order database.
Delivery Details Logging
Given a delivery is completed, when the user inputs actual delivered item IDs and quantities, then the system logs these delivery details with accurate timestamps and associates them with the original order.
Variance Identification
Given stored order and delivery records exist, when there is a mismatch in item or quantity, then the system automatically flags the discrepancy, categorizes it as over-delivery, under-delivery, or wrong item, and creates a discrepancy record.
Order Accuracy History Update
Given a discrepancy record is created, when the record is saved, then the system updates the vendor’s order accuracy history and recalculates their accuracy metrics for use in TrustScore computation.
Dispute Resolution Logging
"As a food truck operator, I want to record and track any disputes with my vendors so that the TrustScore accounts for their dispute resolution performance."
Description

Provide a module to log disputes raised with vendors, including issue type, date raised, resolution date, and outcome. Enable both operators and vendors to update dispute status, capture resolution timeframes, and store detailed notes to inform the TrustScore algorithm and future performance analyses.

Acceptance Criteria
Operator logs a new dispute
Given an operator selects 'Log Dispute' for a vendor, When they enter the issue type, date raised, and initial notes, Then the system creates a new dispute record with a unique ID, status 'Open', and the provided details stored.
Vendor updates dispute status
Given a vendor accesses an existing dispute, When they change the status to 'In Progress' or 'Resolved' and enter the resolution date and outcome, Then the system updates the dispute record with the new status, timestamp, and outcome details.
System calculates resolution timeframe
Given a dispute record contains both a date raised and a resolution date, Then the system automatically calculates the resolution timeframe in days, stores it as a field on the dispute record, and displays it in the dispute details view.
Detailed notes captured for a dispute
Given an operator or vendor views a dispute record, When they add or edit detailed notes, Then the system saves each note with the author’s identity, timestamp, and appends it to the dispute’s history log.
Dispute data feeds into TrustScore algorithm
Given a dispute record is resolved, Then the system sends the dispute’s outcome, resolution timeframe, and related data to the TrustScore module in real time, ensuring the supplier’s TrustScore is updated accordingly.
TrustScore Algorithm Engine
"As a product manager, I want an algorithm that computes vendor reliability scores based on multiple metrics so that the TrustScore dynamically reflects performance."
Description

Develop a configurable scoring engine that weights metrics for payment timeliness, order accuracy, and dispute resolution. Calculate a dynamic TrustScore for each vendor in real time, allow adjustment of metric weightings by administrators, and ensure scores update automatically as new data arrives.

Acceptance Criteria
Real-time Score Calculation Upon Order Completion
Given that an order is marked complete with payment and delivery details When the system receives the order data Then the TrustScore engine recalculates the vendor's score within 5 seconds and updates the displayed TrustScore on the dashboard accordingly.
Administrator Adjusts Metric Weightings
Given an administrator accesses the configuration panel When they modify the weightings for payment timeliness, order accuracy, and dispute resolution and save changes Then the new weightings are stored and applied to all subsequent TrustScore calculations within 2 minutes.
Historical Data Reprocessing After Weighting Change
Given that metric weightings have been updated When the administrator triggers historical data reprocessing Then the engine recalculates TrustScores for all vendors based on the new weightings and confirms completion with a summary report.
Automated Dispute Resolution Metric Update
Given a dispute is resolved with a recorded outcome When the resolution is logged in the system Then the dispute resolution metric for the vendor is updated and the TrustScore reflects the change in real time.
Offline Data Sync and TrustScore Update
Given that offline order and payment events are stored locally When connectivity is restored Then the system syncs all stored events and updates TrustScores accurately, ensuring no data loss and timestamp consistency.
TrustScore Dashboard Integration
"As a food truck operator, I want to view and compare vendor TrustScores in my dashboard so that I can identify top performers and negotiate terms effectively."
Description

Integrate the TrustScore feature into the existing vendor management dashboard by displaying current scores, historical trends, and metric breakdowns. Provide filtering and sorting capabilities, score change visualizations, and drill-down details for each underlying performance metric.

Acceptance Criteria
Display Current TrustScore and Metrics
Given a vendor is selected on the dashboard When the vendor details pane loads Then the current TrustScore is displayed next to the vendor name And each performance metric (payment timeliness, order accuracy, dispute resolution) is shown with percentage values And the displayed TrustScore matches the backend value within a 5% tolerance
Filter Vendors by TrustScore Range
Given the vendor list is displayed When the user sets a minimum and maximum TrustScore filter and applies it Then only vendors with TrustScores within the specified range are shown And the displayed vendor count reflects the number of filtered results
Sort Vendors by TrustScore
Given the vendor list is displayed unsorted When the user clicks the TrustScore column header once Then the list is sorted in descending order by TrustScore When the user clicks the TrustScore column header again Then the list is sorted in ascending order by TrustScore And a sort icon indicates the current sort direction
Visualize TrustScore Trend Over Time
Given a vendor is selected When the user navigates to the TrustScore Trend tab Then a line chart displays the vendor’s TrustScore for the past 12 weeks And hovering over any data point shows the exact TrustScore value and date
Drill-Down into Metric Details
Given the performance metrics breakdown is visible When the user clicks on a specific metric bar (e.g., order accuracy) Then a modal appears listing individual records with date, status, and accuracy percentage And records are sorted by date in descending order
Highlight Score Changes
Given the TrustScore was previously loaded When the current TrustScore differs from the last loaded value Then the TrustScore display is highlighted in green if increased or red if decreased And a tooltip on the score shows the previous TrustScore value
Automated Score Alerts
"As a food truck operator, I want to receive notifications when my vendors’ TrustScores change significantly so that I can take timely action and maintain supply reliability."
Description

Implement a notification system that alerts operators when a vendor’s TrustScore falls below or rises above predefined thresholds. Allow customization of alert conditions and channels (email, in-app push), and log alert history for audit and follow-up.

Acceptance Criteria
Low TrustScore Alert Setup
Given an operator has set a lower threshold of TrustScore below 70 When a vendor’s TrustScore drops to 68 or below Then the system sends a notification to the operator via the configured channels within 5 minutes
High TrustScore Congratulatory Notification
Given an operator has set an upper threshold of TrustScore above 90 When a vendor’s TrustScore rises to 91 or above Then the system sends a celebratory notification to the operator via the configured channels within 5 minutes
Alert Channel Customization
Given an operator has selected email and in-app push as alert channels When a TrustScore threshold is breached Then the system sends the notification simultaneously via email and in-app push
Alert History Logging
Given the system triggers any TrustScore alert When the alert is sent Then the system logs an entry including timestamp, vendor ID, previous score, new score, threshold, and operator ID
Real-time Threshold Adjustment
Given an operator updates a TrustScore threshold in the dashboard When the update is saved Then any subsequent TrustScore breaches use the new threshold immediately

DisputeChain Resolver

Implements an on-chain dispute management module where operators and vendors can log discrepancies, attach evidence, and negotiate resolutions. Leverages immutable records to simplify audits, speed up conflict resolution, and maintain clear accountability.

Requirements

Dispute Initiation Interface
"As a food truck operator, I want to initiate a dispute for a mismatched shipment so that I can formally record the issue and seek compensation or correction."
Description

This requirement enables operators and vendors to initiate a dispute directly from their Truckly dashboard or vendor portal. Users can select the transaction in question, specify the discrepancy type, and provide initial details to launch a formal dispute. This interface must be intuitive on mobile and desktop, guiding users through required fields, validating inputs, and ensuring a seamless start-to-end flow. It integrates with existing order and inventory modules to pre-populate data, reducing manual entry and user error.

Acceptance Criteria
Initiating a Dispute from Transaction History
Given the user is viewing their transaction history, when they select a transaction and tap 'Dispute', then the dispute initiation form opens with the transaction ID, date, and amount pre-filled.
Validating Required Fields on Dispute Form
Given the dispute form is open, when the user attempts to submit without selecting a discrepancy type or entering a description, then inline error messages appear and the form cannot be submitted until all mandatory fields are completed.
Mobile Responsive Dispute Form Display
Given the operator accesses the dispute form on a mobile device, when the form loads, then all fields, dropdowns, and buttons are fully visible without horizontal scrolling and inputs are easily tappable.
Integration with Inventory Module for Data Pre-Population
Given the dispute form is launched for a particular transaction, when the form initializes, then the item names, quantities, order date, and vendor details are automatically populated from the inventory module.
Successful Dispute Submission and Notification
Given the user has completed all required fields and attached any evidence, when they submit the dispute, then a confirmation message appears, the dispute is recorded on-chain, and the vendor receives an in-app notification.
Evidence Attachment Mechanism
"As a vendor, I want to upload delivery receipts and photos of received goods so that I can substantiate my claim during a dispute."
Description

This requirement allows users to attach supporting evidence—such as photos, invoices, delivery receipts, or chat logs—to a dispute record. The mechanism must support multiple file types (images, PDFs, text), enforce size limits, and allow in-app capture (camera upload). Files are to be hashed and stored on-chain or in a linked decentralized storage, ensuring immutability. The UI must display thumbnails and metadata, enable preview, and allow removal or replacement before submission.

Acceptance Criteria
Uploading Multiple Files
Given a dispute record form is open, when the user selects multiple valid files (images, PDFs, text) within size limits, then all selected files are uploaded, hashed, and linked to the dispute record with individual thumbnails displayed.
Enforcing File Type and Size Restrictions
Given the user selects a file that exceeds the maximum size or is of an unsupported type, when the user attempts to attach the file, then the system rejects the upload and displays a clear error message indicating allowed file types and maximum size.
In-App Camera Capture Integration
Given the user is on a mobile device and initiates evidence capture, when the user uses the in-app camera function, then the app captures the photo, hashes the image, and attaches it to the dispute record with a thumbnail preview.
Preview, Removal, and Replacement
Given attachments are displayed as thumbnails with metadata, when the user clicks a thumbnail, then a preview modal opens; and when the user chooses to remove or replace an attachment, then the system updates the list of attachments accordingly before final submission.
Immutable Storage and Verification
Given attachments have been submitted, when a user views the dispute record later, then each file’s hash is displayed and matches the content stored on-chain or in decentralized storage, proving immutability and integrity.
On-Chain Dispute Logging
"As an auditor, I want every dispute event recorded on-chain so that I can verify the historical integrity of all actions without relying on centralized logs."
Description

This requirement logs every dispute action—creation, evidence uploads, status changes, messages—onto a blockchain ledger. Each entry must include a timestamp, user identifier, and cryptographic proof of integrity. The implementation should leverage a permissioned smart contract to record events, ensuring data immutability and auditability. Integration with the blockchain network must handle transaction signing, gas fee management, and error handling, while keeping latency within acceptable bounds for a responsive user experience.

Acceptance Criteria
Dispute Creation Recording
Given an operator submits a new dispute, when the smart contract transaction is executed, then a blockchain entry containing a timestamp, user identifier, and cryptographic proof of integrity must be recorded within 5 seconds and return a transaction hash.
Evidence Upload Logging
Given a vendor attaches evidence to an existing dispute, when the evidence hash is sent to the smart contract, then an on-chain event with the evidence hash, timestamp, and uploader ID is logged and visible in the audit trail.
Status Change Recording
Given a dispute status is updated (e.g., from ‘Open’ to ‘In Review’), when the status change transaction is confirmed, then both the previous and new status, along with timestamp and user identifier, are immutably recorded on-chain.
Message Thread Logging
Given any party sends a message within the dispute conversation, when the message payload is hashed and submitted, then the blockchain logs an event with the message hash, sender ID, timestamp, and reference to the dispute ID.
Transaction Failure Handling
Given a transaction fails due to gas or network errors, when the error occurs, then the system must notify the user within 3 seconds, log the failure reason off-chain, and retry the transaction up to 2 times automatically.
Resolution Negotiation Module
"As a food truck operator, I want to negotiate resolution terms with a vendor so that we can settle discrepancies quickly without external intervention."
Description

This requirement provides an in-app negotiation interface where disputing parties can exchange proposals and counteroffers. The module includes threaded messaging, predefined resolution templates (refund, replacement, credit), and escalation triggers. It must integrate with on-chain logging, updating the dispute record with each negotiation step. Notifications (push, email) inform users of new messages or proposals. The design should facilitate clear communication and guide parties toward agreement before escalation to third-party arbitration.

Acceptance Criteria
Initiate Negotiation Through Threaded Messaging
Given a registered operator and vendor in a dispute, when either party sends a message in the negotiation interface, then the message appears in a threaded view with correct sender, timestamp, and in chronological order.
Apply Predefined Resolution Template
Given a dispute negotiation in progress, when a party selects a resolution template (refund, replacement, credit), then the template content is inserted into the conversation as a proposal ready for review or modification.
Log Negotiation Step On-chain
Given a negotiation message or proposal is sent, when the message is dispatched, then an on-chain transaction logs the step including dispute ID, sender ID, content hash, and timestamp, confirmed before UI update.
Trigger Automatic Escalation After Timeout
Given no agreement after 7 days or 5 proposal exchanges, when the timeout or exchange limit is reached, then the dispute status updates to escalated, triggers third-party arbitration workflow, and notifies both parties.
Notify Parties Of New Messages
Given a new negotiation message or proposal is logged, when the recipient is online or offline, then they receive a push notification in-app and an email notification within one minute of the event.
Offline Message Queueing And Sync
Given a user is offline, when they send a negotiation message or proposal, then the system queues the message locally and automatically syncs and sends it in correct order once connectivity is restored.
Audit Trail Reporting
"As a compliance officer, I want to generate a detailed audit report of a dispute so that I can demonstrate accountability and transparency during audits."
Description

This requirement enables users to generate comprehensive audit trail reports for any dispute. Reports include chronological entries, evidence thumbnails, negotiation transcripts, and final resolution details. Users can export reports in PDF or CSV formats for regulatory compliance or internal review. The reporting tool must allow filtering by date range, dispute status, involved parties, and evidence type. It integrates with the on-chain data layer to pull verified records and ensures reports are tamper-proof via embedded checksums.

Acceptance Criteria
Generate PDF Audit Report for Single Dispute
Given a valid dispute ID exists When the user selects 'Export as PDF' Then a PDF is generated that includes chronological entries, evidence thumbnails, negotiation transcripts, final resolution details, and is downloaded successfully.
Filter Audit Reports by Status
Given multiple disputes with various statuses exist When the user applies a filter for a specific dispute status Then the report output lists only disputes matching that status in chronological order.
Export CSV Audit Reports for Date Range
Given disputes exist within the selected date range When the user exports the audit report as CSV Then the downloaded CSV includes columns for dispute ID, date/time, event type, involved parties, evidence references, resolution status, and matches the filtered date range.
Validate Embedded Checksums in Audit Reports
Given an exported report (PDF or CSV) When the user verifies the checksum Then the checksum matches the on-chain computed value, confirming the report is tamper-proof.
Include Evidence Thumbnails and Transcripts in Audit Reports
Given disputes have attached evidence and transcripts When the report is generated Then the output embeds evidence thumbnails at 150x150px resolution and full negotiation transcripts in a searchable text section.

DemandHeatmap

Visualize hourly demand predictions on an interactive heatmap, allowing you to spot peak sales windows and slow periods at a glance. By clearly mapping predicted demand intensity, you can optimize staff scheduling, menu rotations, and promotional timing to maximize revenue and reduce idle stock.

Requirements

Interactive Heatmap UI
"As a food truck operator, I want to view predicted hourly demand on an interactive heatmap so that I can quickly spot peak sales windows and adjust my operations accordingly."
Description

The system shall provide an interactive heatmap display that visualizes hourly demand predictions in a color-coded grid format. Users can hover over each cell to view exact demand values, and zoom or pan to focus on specific time ranges or days. This requirement ensures intuitive, at-a-glance identification of peak sales windows and slow periods, enhancing operators’ ability to make data-driven scheduling and inventory decisions. Integrated seamlessly within the Truckly dashboard, the heatmap must respect both mobile and offline-first constraints, caching data for offline use and synchronizing updates when connectivity resumes.

Acceptance Criteria
Mobile Offline Heatmap Access
Given the user is offline, when the Truckly dashboard heatmap is opened, then the most recently cached heatmap data loads within 5 seconds and displays correctly without errors.
Zoom and Pan Heatmap Interaction
Given the interactive heatmap view, when the user performs pinch-to-zoom or drag-to-pan gestures, then the heatmap zooms and pans smoothly, maintaining accurate grid alignment and readable labels.
Cell Hover Demand Tooltip
Given the user hovers (or long-presses on mobile) over any heatmap cell, then a tooltip appears within 300ms showing the exact hourly demand prediction value for that cell.
Post-Connectivity Data Synchronization
Given the user regains internet connectivity after offline use, when the dashboard sync process runs, then the heatmap data updates automatically within 10 seconds, reflecting any new demand predictions without user intervention.
Demand Intensity Color-Coding Accuracy
Given a defined demand-to-color mapping scale, when the heatmap renders, then each cell's color accurately corresponds to its demand prediction value according to the scale with no more than one color-step deviation.
Real-time Demand Data Processing
"As a food truck operator, I want the demand predictions to refresh in real time so that my heatmap reflects current trends and I can act on the latest information."
Description

The system shall ingest live sales and inventory data, apply predictive algorithms to forecast demand for each hour of the day, and update the heatmap in near real time. This includes handling data synchronization in offline mode and resolving conflicts upon reconnection. By processing data in real time, the feature provides the most current predictions, enabling operators to adapt menu offerings and staffing levels proactively. The processing engine integrates with existing analytics modules and adheres to Truckly’s performance and reliability standards.

Acceptance Criteria
Live Data Ingestion under Online Conditions
Given the system is online and sales and inventory data streams are active, when a new sales transaction or inventory update occurs, then the system ingests and processes the event within 2 seconds, updating the demand heatmap without data loss.
Offline Mode Data Collection
Given the mobile dashboard is in offline mode, when sales and inventory events occur on the device, then the system caches each event locally with accurate timestamps and unique identifiers, ensuring no events are dropped or overwritten.
Data Synchronization and Conflict Resolution
Given the device reconnects to the network after offline operation, when cached events are synced with the server, then conflicts are resolved by merging based on the latest timestamp, no duplicate records exist, and both client and server data sets align, with the heatmap reflecting the merged data.
Predictive Algorithm Performance under Load
Given a simulated peak load of 1000 events per minute, when the predictive algorithm processes incoming data, then it generates updated hourly demand forecasts in under 5 seconds and CPU usage remains below 70% to meet performance standards.
Integration with Existing Analytics Modules
When the real-time demand processing engine outputs forecast data, then the existing analytics module ingests the data via the defined API endpoint, verifies schema conformity, and displays the updated heatmap within 3 seconds, with 100% data accuracy.
Historical Demand Overlay
"As a food truck operator, I want to compare predicted demand with historical sales data so that I can gauge forecast accuracy and refine my planning."
Description

The system shall allow users to overlay historical actual sales data onto the predictive heatmap for selected past dates, comparing predicted versus actual demand. Users can toggle this overlay on and off and select multiple date ranges for comparison. This requirement aids in validating prediction accuracy, refining forecasting models, and providing context for decision-making based on historical performance. The implementation must integrate with Truckly’s data archive and maintain visual clarity in the combined display.

Acceptance Criteria
Overlay Toggle Functionality
Given the historical overlay is disabled, when the user clicks the 'Enable Overlay' button, then the historical sales data overlay appears correctly aligned with the time axis; Given the overlay is enabled, when the user clicks 'Disable Overlay', then the overlay is entirely removed; The overlay state persists across heatmap navigation and page refreshes.
Multiple Date Range Comparison
Given the user opens the date selector, when they select two or more non-overlapping past date ranges, then the heatmap displays distinct colored overlays for each range; A legend clearly maps each color to its date range; When a date range is deselected, its overlay is removed and the legend updates accordingly.
Data Alignment and Accuracy
Given archived sales data for selected dates, when overlaid, the historical heatmap aligns exactly with corresponding hourly slots of the predictive heatmap; For each hour segment, the displayed historical intensity matches archived sales values within a 0.1% tolerance.
Visual Clarity and Accessibility
Overlay layers use distinguishable, high-contrast color palettes meeting WCAG AA guidelines; Combined displays maintain a minimum 50% opacity level for each layer to prevent data obscuration; A clearly labeled legend with ARIA attributes explains overlay colors and intensities.
Performance and Error Handling
When a user requests an overlay for a valid date range, data loads and renders within 3 seconds; If the data archive service is unreachable, an error message 'Historical data unavailable' displays; Requests for dates with no archived data result in an empty overlay and a warning notice.
Time Window Filtering
"As a food truck operator, I want to filter the demand heatmap by specific time windows and date ranges so that I can focus on the periods that matter most to my operations."
Description

The system shall provide filtering controls that let users specify custom time windows (e.g., morning, lunch, evening shifts) and date ranges to adjust the heatmap view accordingly. Filters must support presets (e.g., weekdays vs weekends) and custom ranges, updating the heatmap display in real time. This capability empowers operators to focus on relevant operational windows and conduct targeted analysis for staffing and menu rotations. The filters integrate with Truckly’s UI framework, ensuring consistent behavior across devices.

Acceptance Criteria
Morning Shift Preset Selection
Given the user selects the 'Morning' preset filter, when applied, then the heatmap displays data from 06:00 to 11:00 for the selected date range.
Custom Time Window Filtering
Given the user defines a custom time window from 14:30 to 17:45 and a date range, when applied, then the heatmap updates to show only data within that custom period.
Weekday vs Weekend Preset Toggle
Given the user toggles between 'Weekdays' and 'Weekends' presets, when selected, then the heatmap reloads to reflect only the respective days' data for the chosen date range.
Heatmap Real-time Update on Filter Change
Given the user adjusts any filter control, when the filter value changes, then the heatmap updates within 2 seconds without requiring a full page reload.
Cross-Device Filter Consistency
Given the user applies a filter on one device, when the same user logs in on another device, then the previously applied filter settings persist and reflect identically on the heatmap.
Invalid Date Range Handling
Given the user enters an end date earlier than the start date, when attempting to apply filters, then the system displays an inline validation error and prevents the heatmap from updating.
Peak Demand Alerts
"As a food truck operator, I want to receive alerts when predicted demand is unusually high or low so that I can adjust my staffing and stock levels in advance."
Description

The system shall monitor forecasted demand levels and generate configurable alerts when predicted demand exceeds or falls below user-defined thresholds for specific hours. Alerts can be delivered via push notifications in the mobile dashboard or via email, and include actionable recommendations for staffing or inventory adjustments. This requirement ensures proactive management, preventing under- or over-staffing and optimizing stock levels around critical demand periods. Alert settings integrate with the existing notification engine and follow user preferences.

Acceptance Criteria
Morning Peak Threshold Exceeded
Given the user has set a demand threshold of 100 orders for 8 AM–10 AM, when the system’s forecast predicts 110 orders within that window, then the system sends a push notification and an email alert containing staffing and inventory recommendations.
Afternoon Low Demand Alert
Given the user has configured a low-demand threshold of 20 orders for 3 PM–5 PM, when the forecasted demand falls to 15 orders during that period, then the system issues a push notification and email suggesting staff reduction and inventory adjustments.
Notification Preference Compliance
Given the user’s notification preference is set to email-only, when any threshold breach occurs, then the system sends only an email alert and does not send a push notification.
Actionable Recommendation Inclusion
For every generated alert, verify that the alert message includes at least one staffing recommendation and one inventory adjustment recommendation relevant to the specific hour and threshold type.
Hourly Threshold Configuration
Given the user has configured distinct demand thresholds for multiple hours of the day, when forecasted demand crosses any of those thresholds in their respective hours, then the system generates separate alerts for each hour according to its configured threshold.

SurgeAlerts

Receive real-time push notifications when forecasted demand rises above or falls below customizable thresholds. This feature keeps you proactive—launch flash promotions during sudden demand spikes or adjust prep plans when slower periods are predicted—ensuring you never miss opportunities or overcommit resources.

Requirements

Custom Threshold Configuration
"As a food truck operator, I want to set custom demand thresholds for my best-selling items so that I receive notifications aligned with my unique sales goals and operational constraints."
Description

Allow users to define and adjust upper and lower demand thresholds for specific products or time windows within the SurgeAlerts feature. The interface will provide preset recommendations based on historical sales patterns while enabling manual overrides for precise control. This configuration seamlessly integrates with the mobile dashboard, ensuring threshold settings persist across devices and sessions.

Acceptance Criteria
Preset Recommendations Applied
Given a user opens the Custom Threshold Configuration for a selected product and time window, when the UI loads, then the system displays recommended upper and lower thresholds based on the last 30 days of sales and auto-populates the threshold fields with those values.
Manual Threshold Override
Given recommended thresholds are populated, when the user edits the upper and/or lower threshold values and clicks Save, then the system validates the inputs, saves the new values, and uses them instead of recommendations for triggering SurgeAlerts.
Threshold Persistence Across Sessions
Given the user has saved custom thresholds, when they log out and log back in to the mobile dashboard, then the previously configured thresholds are retrieved from the server and displayed in the configuration UI.
Threshold Synchronization Across Devices
Given the user saves custom thresholds on one device, when the user opens the mobile dashboard on another device, then the dashboard fetches and displays the same threshold settings without manual reconfiguration.
Invalid Threshold Input Handling
Given the user enters an upper threshold that is lower than the lower threshold or non-numeric values, when they attempt to save, then the system displays a clear error message, prevents saving, and highlights the invalid fields.
Real-Time Demand Monitoring
"As a food truck operator, I want the system to monitor and compare live sales data to my thresholds so that I can react immediately to unexpected surges or slowdowns in demand."
Description

Implement a backend service that continuously analyzes incoming sales and inventory data against forecast models to detect demand fluctuations. This service must process data in sub-minute intervals, leveraging edge computing for offline scenarios and syncing updates when connectivity is restored. Upon crossing configured thresholds, it triggers the notification pipeline and logs events for auditing.

Acceptance Criteria
Edge Processing During Offline Operation
Given the device is offline and records a sale When the edge service processes the new data Then it updates local demand metrics within 60 seconds without errors or data loss
Sub-Minute Data Processing Interval
Given continuous incoming sales and inventory data When the backend service ingests data Then it completes analysis and threshold evaluation within 60 seconds for each new data batch
Threshold Breach Notification Triggering
Given a configured upper or lower demand threshold When forecasted demand crosses the threshold Then the system sends a push notification to the user within 30 seconds
Audit Logging of Demand Fluctuations
Given a threshold breach event occurs When the service triggers a notification Then it logs an audit entry containing event timestamp, threshold details, data snapshot, and notification status
Sync Resilience on Connectivity Restoration
Given the device was offline and has queued multiple events When connectivity is restored Then the system syncs all queued events to the central backend within 2 minutes and prevents duplicate entries
Push Notification Engine
"As a food truck operator, I want to receive timely push notifications about demand changes on my phone so that I can quickly respond even when I’m away from the dashboard."
Description

Develop a robust push notification engine that delivers SurgeAlerts to both iOS and Android devices, supporting background delivery and handling intermittent connectivity. The engine must retry failed deliveries, respect user notification preferences (e.g., do-not-disturb hours), and present actionable notifications with deep links back to the dashboard. All notifications are timestamped and stored locally for offline review.

Acceptance Criteria
Intermittent Connectivity Handling
Given the user’s device experiences no connectivity, When a SurgeAlert is triggered, Then the engine queues the notification and delivers it automatically within 30 seconds of connectivity restoration.
Do-Not-Disturb Hours Enforcement
Given user-defined DND hours between 22:00 and 07:00, When a SurgeAlert is generated during this period, Then the engine suppresses immediate delivery and schedules it at 07:00 the same day.
Actionable Deep Link from Notification to Dashboard
Given a received SurgeAlert notification, When the user taps the 'View Details' action, Then the app opens the dashboard at the SurgeAlert details page within 2 seconds.
Offline Storage and Timestamp Accuracy
Given no network connectivity and receipt of multiple queued notifications, When the user opens the app, Then all notifications are displayed in chronological order with accurate timestamps matching server send times.
Retry Mechanism for Failed Deliveries
Given a push notification delivery failure due to an expired token, When the engine retries up to three attempts with exponential backoff, Then the notification is successfully delivered or logs the final failure after 3 attempts.
Alert Management Dashboard
"As a food truck operator, I want a centralized view of all past and current surge alerts so that I can track trends and manage notifications efficiently."
Description

Add a dedicated Alerts tab in the mobile dashboard where operators can view, acknowledge, snooze, or delete SurgeAlerts. The UI will display alert history, severity levels, and associated products along with timestamps. Operators can filter and search past alerts, configure snooze durations, and access quick actions to modify thresholds directly from the alert entry.

Acceptance Criteria
Viewing Alert History
Given the operator opens the Alerts tab, When there are existing SurgeAlerts, Then the alert list displays each alert with its severity level, associated product, and timestamp in descending order.
Acknowledging an Alert
Given an unacknowledged alert in the list, When the operator taps the acknowledge icon, Then the alert’s status updates to acknowledged and the entry is visually marked as read.
Snoozing an Alert
Given an active alert, When the operator selects the snooze action and chooses a duration, Then the alert is hidden from the list for the specified period and reappears with a snoozed badge after the duration elapses.
Deleting an Alert
Given any alert entry, When the operator confirms deletion, Then the alert is permanently removed from the history and no longer appears in searches or filters.
Filtering and Searching Alerts
Given multiple alerts exist, When the operator applies filters or enters a keyword, Then only alerts matching the selected severity, product, date range, or search term are displayed in real time.
Modifying Threshold from Alert
Given an alert entry, When the operator uses the quick action to adjust the alert threshold, Then the new threshold is saved immediately and applied to future demand forecasts.
Flash Promotion Trigger
"As a food truck operator, I want to trigger a flash promotion directly from a surge alert so that I can capitalize on high demand without navigating through multiple screens."
Description

Provide an optional action within SurgeAlerts notifications that allows operators to launch predefined flash promotions with a single tap. The feature integrates with the promotions module to apply discounts, update menu prices, and create limited-time offers. It generates promotion performance reports and auto-adjusts stock recommendations based on expected uplift.

Acceptance Criteria
Flash Promotion Button in Surge Alert Notification
Given a SurgeAlert notification for a demand spike, when the operator taps the 'Launch Flash Promotion' action, then the system triggers the predefined flash promotion, updates menu prices accordingly, displays a success confirmation, and logs the promotion in the event history.
Predefined Promotion Parameter Validation
Given an operator has predefined flash promotion settings configured, when the promotion is launched via SurgeAlerts, then the system applies the correct discount levels, start/end times, and product scope as defined in the settings, without requiring additional manual input.
Promotion Performance Report Generation
Given a flash promotion has ended, when the operator navigates to the promotion reports section, then the system generates a report including key metrics (sales uplift percentage, units sold, revenue change), accessible within 5 seconds, and the report data matches actual sales records.
Automatic Stock Recommendation Adjustment
Given a flash promotion is active, when the system forecasts uplift in demand, then it updates the stock recommendation list to increase suggested inventory for promoted items by the uplift percentage and notifies the operator within 1 minute after promotion launch.
Promotion Trigger Failure Handling
Given an error occurs during promotion launch (e.g., network failure or API error), when the operator taps 'Launch Flash Promotion', then the system displays an error message with retry option, logs the error details, and ensures no partial menu price changes are applied.

SmartOrder Guide

Automatically convert hourly demand forecasts into precise restock suggestions, generating optimal order quantities for each menu item. By aligning procurement with predicted needs, you minimize overstock, cut waste, and streamline vendor orders, freeing up time and budget for value-added tasks.

Requirements

Forecast Data Integration
"As a food truck operator, I want the system to automatically import and validate hourly demand forecasts so that my restock suggestions are always based on accurate, real-time data."
Description

The system must automatically import hourly demand forecasts from the existing demand forecasting module, validate the data for completeness and accuracy, and normalize it for use by the restock suggestion engine. This ensures that restock recommendations are based on the most up-to-date and reliable demand predictions, improving order precision and reducing waste.

Acceptance Criteria
Hourly Forecast Import Initiation
Given the forecasting module API endpoint and valid credentials, when the hourly import job executes, then the system retrieves and stores the forecast payload within 5 seconds with correct timestamp metadata.
Forecast Data Validation
Given raw forecast data for a specific hour, when the validation routine processes the data, then each record contains non-null item_id, forecast_quantity as a non-negative integer, and valid timestamp, otherwise the record is moved to the error queue with a descriptive error code.
Forecast Data Normalization
Given validated forecast records, when the normalization process runs, then forecast_quantity is converted to standardized units, all timestamps are converted to UTC ISO8601 format, and item_ids are mapped to internal SKU codes based on the product catalog.
Error Handling for Forecast Data
Given invalid or incomplete forecast data, when import or validation fails, then the system logs an error with a unique identifier, sends a notification to the operator dashboard within 1 minute, and retries the process up to three times with exponential backoff.
Real-Time Forecast Update Integration
Given new normalized forecast data is available, when the restock suggestion engine triggers, then it consumes the latest data and generates restock suggestions within 2 minutes for all menu items.
Restock Suggestion Engine
"As a food truck operator, I want the system to calculate optimal restock quantities based on forecasts and business rules so that I can order just enough stock and minimize waste."
Description

The system must process normalized demand forecasts and apply business rules—like minimum stock levels, menu item lead times, and perishability thresholds—to calculate precise restock quantities for each menu item. The engine should support dynamic rule adjustments and account for sales variability to refine suggestion accuracy.

Acceptance Criteria
Standard Restock Calculation
Given normalized hourly demand forecasts and active business rules including minimum stock levels, lead times, and perishability thresholds When the Restock Suggestion Engine processes the data Then it outputs restock quantities for each menu item that satisfy all applied business rules
Perishable Item Prioritization
Given a menu item whose perishability threshold is shorter than its lead time and whose forecasted demand is below the minimum stock level When the Restock Suggestion Engine calculates suggestions Then it recommends zero restock quantity for that item to prevent spoilage
Dynamic Rule Adjustment
Given a change to a menu item’s minimum stock level or lead time via the admin interface When the Restock Suggestion Engine recalculates suggestions in real time Then the new restock quantities immediately reflect the updated rule without requiring a system restart
Sales Variability Handling
Given forecast data exhibiting sales variability above the defined threshold When the Restock Suggestion Engine applies variability adjustment rules Then it adds the correct buffer percentage to the calculated restock quantities according to the variability rules
Offline Calculation Mode
Given the mobile device is offline but has cached demand forecasts and business rules When the Restock Suggestion Engine runs locally Then it calculates accurate restock suggestions and queues results to sync automatically when the connection is restored
Manual Override and Review
"As a food truck operator, I want to review and adjust restock suggestions before ordering so that I can incorporate my experience and special considerations."
Description

The system must provide an interface allowing operators to review, adjust, and approve suggested order quantities before finalizing vendor orders. It should display key forecasting metrics and highlight high-variance items requiring attention, ensuring operators retain control over restock decisions.

Acceptance Criteria
Operator Accesses Suggested Order Review Screen
Given the operator is logged in and navigates to the SmartOrder Guide, When they open the Suggested Orders page, Then the system displays all menu items with their forecasted demand, suggested order quantities, and variance metrics.
Operator Adjusts Order Quantity
Given a suggested order quantity is displayed, When the operator modifies the quantity and clicks Save, Then the updated quantity is persisted in the order summary and reflected in the pending order.
Operator Approves Final Order
Given the operator has reviewed and adjusted quantities, When they click the Approve Orders button, Then the system locks in the quantities, generates the purchase order, and sends it to the selected vendors.
High-Variance Item Highlighting
Given an item’s forecast variance exceeds the predefined threshold, Then it is visually highlighted in the list and includes a tooltip explaining the variance percentage and its impact.
Offline Mode Adjustments
Given the operator is offline and opens the review interface, When they adjust order quantities and save changes, Then the system queues the updates locally and synchronizes them automatically when connectivity is restored.
Vendor Order Consolidation
"As a food truck operator, I want consolidated vendor orders generated automatically so that I can quickly send accurate purchase orders and save time."
Description

The system must automatically compile approved restock quantities into vendor-specific order documents in standard formats (CSV, PDF), grouping items by supplier, applying packaging and unit conversion rules, and scheduling orders based on vendor lead times. This streamlines procurement and reduces manual order assembly.

Acceptance Criteria
Single Vendor Order Document Generation
Given a restock list containing items from Vendor A only, when the user initiates vendor order consolidation, then the system generates a single CSV and PDF document named with Vendor A’s ID and order date, containing item lines with SKU, description, approved quantity, unit price, and line total.
Supplier Grouping Verification
Given a mixed restock list with items from multiple vendors, when consolidation runs, then the system creates separate order documents for each vendor, each listing only its approved items and excluding others.
Unit and Packaging Conversion
Given restock quantities in base units and vendor packaging rules (e.g., packs of 12), when consolidation processes quantities, then the system converts base units to the correct number of packages by rounding up to the nearest full pack and reflects both pack count and total base units.
Lead Time Scheduling
Given vendor lead times configured per supplier, when creating the order schedule, then the system sets the recommended order date to current date plus the vendor’s lead time and displays this date in the order document header.
Document Format Export
Given a consolidated order ready for export, when the user selects an export format, then the system successfully exports the document in the chosen format (CSV or PDF) with correct file encoding, formatting, and complete order details.
Restock Notification and Alerts
"As a food truck operator, I want to receive notifications when restock suggestions are ready or require my approval so that I can act promptly and avoid running out of ingredients."
Description

The system must send real-time notifications to operators when new restock suggestions are available, when manual approval is pending, or if forecast data fails validation. Notifications should be delivered via mobile push, email, or in-app alerts, ensuring timely responses and avoiding stockouts.

Acceptance Criteria
New Restock Suggestion Available
Given hourly demand forecasts are processed and a new restock suggestion is generated, when the suggestion becomes available, then the system must send a push notification to the operator's mobile device within 2 minutes and an email within 5 minutes, and display an in-app alert immediately.
Manual Approval Pending Alert
Given a restock suggestion requires manual approval, when the suggestion is pending approval for more than 10 minutes, then the system must send a reminder notification via all enabled channels and escalate to high-priority email if no action is taken after 30 minutes.
Forecast Data Validation Failure
Given the forecast data fails validation checks, when validation errors are detected, then the system must send an error notification labeled 'Forecast Data Failure' via push, email, and in-app channels within 1 minute.
Offline Operator Notification Delivery
Given the operator's device is offline when a notification is triggered, when the device reconnects to the network, then the system must deliver the queued notification exactly once to each enabled channel within 5 minutes of reconnection.
Notification Channel Preference Honor
Given an operator has configured notification preferences, when a restock suggestion event occurs, then notifications must be sent only through the channels the operator has enabled (push, email, in-app) and suppressed on disabled channels.

Accuracy Analyzer

Compare historical forecast data against actual sales to compute forecast accuracy metrics and visualize variance trends. Gain insights into recurring deviations and receive tailored recommendations to calibrate prediction algorithms, continuously improving forecast reliability and boosting confidence in your planning decisions.

Requirements

Automated Data Ingestion
"As a food truck operator, I want the system to automatically ingest my forecast and actual sales data so that I don't have to manually upload files and can trust the data is synchronized accurately."
Description

Implement a robust pipeline that automatically imports and synchronizes historical forecast data and actual sales records from the Truckly system, handling offline data capture, resolving discrepancies, and ensuring accurate time-series alignment for analysis.

Acceptance Criteria
Connectivity Restoration Sync
When the device regains network connectivity, the pipeline must import all offline-captured forecast and sales records within 5 minutes, deduplicate any duplicate entries, and align timestamps to within ±1 minute of the original record time.
Real-time Peak Hour Synchronization
The ingestion pipeline must synchronize new sales and forecast data from Truckly in under 2 seconds per record during periods of up to 10 concurrent data streams without data loss or timeout errors.
Discrepancy Resolution and Alerting
For any imported record with missing or mismatched fields, the pipeline must flag the discrepancy, log the record ID, and generate an alert to the system administrator within 1 minute of detection.
Time-Series Continuity Validation
Imported data points must be correctly ordered by timestamp; if the pipeline detects a gap larger than 1 hour in continuous data streams, it must either generate a placeholder record or send an alert indicating the missing interval.
Bulk Historical Ingestion Performance
The pipeline must import a 6-month historical dataset of at least 100,000 records in under 30 minutes with an error rate below 0.1% and an overall success rate above 99.9%.
Forecast vs Actual Dashboard
"As an operator, I want to compare forecasted inventory needs with actual sales in a clear dashboard so that I can quickly see where forecasts differed from reality."
Description

Develop an interactive dashboard component that displays side-by-side comparisons of forecasted quantities versus actual sales, allowing users to filter by date range, location, and product, and to drill down into daily and hourly levels for in-depth analysis.

Acceptance Criteria
Apply Date Range Filter
Given the Forecast vs Actual dashboard is loaded When the user selects a valid start date and end date in the date range picker and applies the filter Then the dashboard updates to display only forecast and actual sales data within the selected date range across all components
Apply Location Filter
Given the Forecast vs Actual dashboard is loaded When the user selects one or more locations from the location filter dropdown and applies the filter Then the dashboard displays only the forecast and actual sales data for the selected locations in all charts and tables
Apply Product Filter
Given the Forecast vs Actual dashboard is loaded When the user selects one or more products from the product filter dropdown and applies the filter Then the dashboard displays only the forecast and actual sales data for the selected products in all visualizations
Drill Down to Daily Level
Given the dashboard has data displayed When the user clicks on a specific aggregated point in the comparison chart Then the dashboard drills down to show a detailed daily breakdown of forecast versus actual sales for the selected period
Drill Down to Hourly Level
Given the dashboard is in daily breakdown view When the user clicks on a specific day Then the dashboard further drills down to display an hourly forecast versus actual sales breakdown for that selected day
Side-by-Side Forecast vs Actual Display
Given filters are applied and data is loaded When the dashboard renders Then it displays forecasted and actual sales quantities side by side for each time interval and calculates and shows the variance percentage next to each comparison
Variance Metrics Calculation
"As a business owner, I want to understand how accurate my forecasts are by seeing metrics like MAPE and bias so that I can assess the reliability of the predictions."
Description

Calculate key forecast accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and bias for selected time intervals, and provide summarized and detailed views to help operators quantify forecast performance.

Acceptance Criteria
MAPE Calculation for Selected Interval
Given a selected time interval with actual and forecast data, when the system computes MAPE, then the resulting value equals the sum of absolute errors divided by actuals, multiplied by 100%, accurate to two decimal places.
MSE Calculation under Data Variance
Given forecast and actual values for each period in the interval, when calculating MSE, then the system outputs the average of the squared differences between forecast and actual values, accurate to two decimal places.
Bias Determination in Forecast Data
Given actual and forecast values across the interval, when computing bias, then the system displays the mean error (forecast minus actual) with positive values indicating overestimation and negative values indicating underestimation, accurate to two decimal places.
Summarized Metrics Dashboard View
Given computed MAPE, MSE, and bias metrics, when the user views the summary dashboard, then all three metrics are presented with clear labels, correct values, and units of measure.
Detailed Variance Trend Visualization
Given historical accuracy metrics for multiple intervals, when the user selects the detailed view, then the system renders a time-series chart showing MAPE, MSE, and bias trends with appropriate legends and axis labels.
Dynamic Calibration Recommendations
"As a data-driven operator, I want actionable recommendations on how to calibrate forecast models so that I can continuously refine predictions without deep technical expertise."
Description

Provide automated recommendations to adjust forecasting parameters based on recurring deviation patterns, leveraging machine learning to suggest tuning actions such as smoothing factors or model weighting adjustments to improve future forecast accuracy.

Acceptance Criteria
Recurring Deviation Pattern Detection
Given historical forecast and actual sales data for the past 12 weeks, when the system analyzes deviations, then it identifies recurring deviation patterns that occur in at least three consecutive weeks and flags the most frequent deviation type.
Automated Smoothing Factor Adjustment Suggestion
Given identified positive or negative deviation trends, when calibration recommendations are generated, then the system suggests a precise smoothing factor adjustment (e.g., increase or decrease by X%) with a confidence score of at least 80%.
Model Weighting Optimization Recommendation
Given multiple forecasting models in use, when one model underperforms by more than 10% over a rolling four-week period, then the system recommends adjusting that model’s weight in the ensemble by a specified percentage and projects the expected accuracy gain.
User Response Logging for Calibration Recommendations
Given a calibration recommendation displayed in the dashboard, when a user accepts or rejects the recommendation within five days, then the system logs the decision, applies the adjustment immediately if accepted, and records the rejection reason if declined.
Post-Calibration Accuracy Verification
Given that calibration adjustments have been applied, when new forecasts are produced and compared against actual sales over the next two weeks, then the system confirms at least a 5% improvement in forecast accuracy and generates a verification report.
Variance Trend Visualization
"As a food truck manager, I want to see trends in forecast errors over time so that I can identify patterns and plan inventory more effectively."
Description

Create a visual timeline that illustrates variance trends over weeks or months, highlighting periods of over- or under-forecasting, seasonal effects, and recurring deviations to support strategic planning and model refinement.

Acceptance Criteria
Weekly Variance Trend Chart
Given the user selects an 8-week date range When the variance trend timeline loads Then weekly variance values are plotted correctly, connected by a continuous line, and match the underlying data source
Monthly Seasonal Variance Highlight
Given the user filters the timeline by month When the chart renders Then months with variance above +20% are highlighted in red, variance below –20% in green, and all percentage values display on hover
Over-Under Forecast Period Identification
Given the variance timeline is visible When the user hovers or clicks any data point Then a tooltip appears showing period name, forecasted value, actual sales, and calculated variance percentage
Recurring Deviation Pattern Detection
Given a minimum of one year of historical data When the system analyzes trends Then any quarterly or seasonal periods with variance deviations exceeding 15% in two or more consecutive years are flagged and listed in a summary panel
Timeline Zoom and Pan Functionality
Given the timeline spans more than 12 months When the user zooms in or pans the chart Then new data points load dynamically and render within 2 seconds without loss of accuracy or UI responsiveness

Scenario Simulator

Run ‘what-if’ analyses by adjusting variables such as weather conditions, local events, or promotional campaigns to project their impact on hourly demand. Use these simulated scenarios to prepare for special occasions, fine-tune marketing strategies, and ensure your inventory and staffing plans stay ahead of any demand curve.

Requirements

Scenario Variable Input
"As a food truck operator, I want to input and adjust scenario variables like weather forecasts and event schedules so that I can simulate different demand scenarios easily."
Description

Provide a user interface allowing operators to define and adjust scenario variables such as weather conditions (temperature, precipitation), upcoming local events, promotional discounts, and custom parameters. Integrate this module seamlessly with the existing data model so that inputs are validated, stored, and accessible by the projection engine. Enable ease of use through intuitive controls, default presets for common scenarios, and real-time feedback on variable ranges and impacts.

Acceptance Criteria
Validating Weather Variable Input
Given an operator enters temperature and precipitation values when the values fall outside predefined limits then the system displays a clear validation message and prohibits saving the scenario
Adding Local Event Parameter
Given an operator selects a local event from the list when the selection is confirmed then the event is saved and visible in the scenario input module
Configuring Promotional Discounts
Given an operator specifies a promotional discount percentage when the discount is within 0–100% then the system accepts the input and shows projected demand impact in real time
Using Custom Parameter Presets
Given an operator chooses a default preset when the preset is applied then all related variables populate with preset values and remain editable
Real-Time Feedback on Variable Ranges
Given an operator adjusts any scenario variable when the input is valid then the system provides immediate feedback on how the change affects projected demand
Persisting Scenario Inputs to Database
Given the operator has entered valid scenario variables when they save the scenario then all inputs are persisted in the data model and retrievable by the projection engine
Demand Projection Engine
"As a truckly user, I want the system to generate demand forecasts based on my input variables so that I can anticipate peak sales and adjust my preparations."
Description

Implement a scalable projection engine that processes historical sales, inventory levels, and user-defined scenario variables to predict hourly demand. Utilize statistical models and machine learning algorithms to analyze correlations and forecast demand under simulated conditions. Ensure the engine operates efficiently both online and offline, caching results locally and syncing with the server when connectivity is restored.

Acceptance Criteria
Historical Data Upload for Online Projection
Given the user is online and supplies at least 90 days of historical sales and inventory data, When the demand projection engine runs, Then it must process and return an hourly demand forecast for the next 7 days within 30 seconds, And display a confidence score of at least 85%, And log any data gaps or anomalies.
Offline Data Caching and Sync
Given the user loses network connectivity, When running a projection with available local data, Then the engine must generate and store hourly forecasts locally within 10 seconds, And queue the results for automatic sync when connectivity is restored, Without data loss or duplication.
Variable Scenario Prediction Generation
Given the user defines custom variables (e.g., weather='rainy', event='music festival', promo='20% off'), When the scenario simulator is executed, Then the engine must produce separate hourly demand projections for each variable combination, And highlight projected changes relative to baseline in a tabular view.
Scale Performance Under Load
Given a historical dataset of over 1 million transaction records, When the projection engine is executed on a standard mobile device, Then the engine must complete computation within 2 minutes, And maintain peak memory usage below 500MB, And not block the UI thread.
Data Consistency After Sync
Given offline-generated projections queued for sync, When network connectivity is restored, Then the engine must upload all pending forecast results to the server without duplicates, And reconcile any server-side updates so that local and remote projection data are identical.
Scenario Visualization Dashboard
"As a food truck operator, I want to visualize what-if scenarios in an intuitive dashboard so that I can quickly compare outcomes and make informed decisions."
Description

Develop an interactive dashboard that visualizes simulation results through charts, graphs, and heatmaps, showing projected demand by hour, day, or item category. Include filters and comparison features to overlay multiple scenarios, highlight key differences, and drill down into detailed insights. Integrate seamlessly with the mobile-first interface to maintain responsiveness and clarity on smaller screens.

Acceptance Criteria
Hourly Demand Overview
Given a selected simulation, when the dashboard loads, then a line chart displays projected demand for each hour with accurate tooltips for values
Scenario Comparison Overlay
Given two or more saved simulations, when the user toggles comparison mode, then the charts overlay multiple scenarios with distinct colors and a legend identifying each
Item Category Drill-Down
Given a specific time range, when the user selects an item category filter, then the dashboard updates to show demand projections broken down by sub-categories in a bar chart
Mobile Responsiveness
Given the dashboard on a device with a screen width under 480px, when the user interacts with charts, then all visualizations adjust layout and remain legible with touch-friendly controls
Heatmap Detail Insights
Given a heatmap view of demand by day and hour, when the user hovers or taps on a cell, then a tooltip shows the exact projected demand number and percentage change versus baseline
Scenario Comparison Tool
"As a user, I want to compare different simulated scenarios side by side so that I can choose the optimal plan for my food truck."
Description

Build functionality to save, compare, and manage multiple scenario simulations side by side. Allow users to select any two or more saved scenarios and display comparative metrics such as expected sales, inventory shortages, and staffing needs. Provide summary tables and difference indicators to facilitate rapid decision-making.

Acceptance Criteria
Selecting and Comparing Two Saved Scenarios
Given the user is on the Scenario Comparison page and has at least two saved scenarios, When the user selects exactly two scenarios and clicks the 'Compare' button, Then the system displays both scenarios side by side within 3 seconds without errors.
Comparing Multiple Saved Scenarios
Given the user has saved more than two scenarios, When the user selects three or more scenarios and clicks 'Compare', Then the system displays all selected scenarios in a responsive grid layout, clearly labeled and scrollable if necessary.
Displaying Comparative Metrics
Given two or more scenarios are being compared, Then the system displays expected sales, inventory shortages, and staffing needs for each scenario in clearly labeled columns with correct values matching underlying data.
Showing Summary Tables
Given scenarios are compared, When the comparison view loads, Then a summary table is present showing aggregated metrics (total expected sales, total inventory shortage, total staffing hours) above the detailed scenario views.
Highlighting Difference Indicators
Given at least two scenarios are displayed, Then the system highlights metric differences between scenarios using color-coded indicators (green for increases, red for decreases) with tooltips explaining each change on hover.
Reporting and Export
"As an operator, I want to export scenario reports so that I can share planning insights with my staff and stakeholders."
Description

Enable exporting of scenario simulation results and comparisons into PDF and CSV formats. Include customizable templates for reports that highlight key metrics, visualizations, and actionable recommendations. Ensure exports are generated on-device when offline and synchronized when online, providing users with shareable and presentation-ready documents.

Acceptance Criteria
Export Scenario Report as PDF using Default Template
Given a completed scenario simulation, when the user selects “Export to PDF” with the default template, then the device generates a PDF report within 10 seconds that includes all key metrics, visual charts, and actionable recommendations formatted according to the default template.
Generate Scenario Data as CSV
Given any scenario simulation results, when the user selects “Export to CSV,” then a CSV file is created containing raw data for each variable (e.g., hour, projected demand, weather, event) and is saved to the device’s export folder.
Customize Report Template for Export
Given the export interface, when the user selects a custom template and adjusts header, footer, and chart layout options, then the exported PDF or CSV reflects those template settings accurately, including custom branding, titles, and selected data fields.
Offline PDF and CSV Export Capability
Given the device is offline, when the user attempts to export scenario results to PDF or CSV, then the export is completed and stored locally without errors, and a timestamped record of the export action is maintained.
Automatic Sync of Exports Upon Reconnection
Given a report export performed while offline, when the device reconnects to the internet, then all locally stored exports are automatically synchronized to the user’s cloud account within 30 seconds, and upload status is indicated in the export history.

Product Ideas

Innovative concepts that could enhance this product's value proposition.

RouteReady Reorders

Auto-generates restock lists from planned routes and past sales, ensuring optimal stock per stop.

Idea

WasteGuard Insights

Tracks real-time waste metrics and flags spoilage trends, helping you cut waste by pinpointing inefficient products.

Idea

CrowdCall Alerts

Sends push alerts to loyal customers when your truck nears their location, boosting foot traffic with timed promos.

Idea

MenuMorph AI

Analyzes sales and weather data to suggest daily menu tweaks that boost top-selling items.

Idea

VendorSync Ledger

Creates shared vendor credit records via blockchain, streamlining payments and building trusted supplier relationships.

Idea

FlashForecast

Delivers hourly demand predictions per menu item using live sales and weather inputs, reducing overstock.

Idea

Press Coverage

Imagined press coverage for this groundbreaking product concept.

P

Truckly Launches Groundbreaking Mobile Dashboard to Empower Food Truck Operators with Real-Time Inventory and Sales Insights

Imagined Press Article

SAN FRANCISCO, CA — 2025-08-07 — Truckly, the trailblazing SaaS provider for mobile food service operators, today announces the official launch of its flagship mobile-first dashboard designed to revolutionize inventory and sales management for food truck owners. By combining real-time tracking, intelligent restock alerts, and offline functionality, the Truckly dashboard equips independent operators with the tools they need to slash prep time, minimize waste, and maximize daily profits—even when connectivity is limited. Addressing the fast-paced, unpredictable nature of food truck operations, Truckly’s mobile dashboard offers an intuitive interface that displays live sales velocity, on-hand inventory levels, and ingredient expiration alerts. Operators receive smart restock notifications based on dynamic safety stocks and historical demand patterns, while auto-generated vendor lists streamline procurement in just one click. “Independent food truck operators face razor-thin margins and shifting customer demand,” said Priya Patel, CEO and co-founder of Truckly. “Our new mobile dashboard removes the guesswork by delivering actionable insights in real time—online or off. With Truckly, operators can stay focused on serving great food instead of battling spreadsheets or scrambling to restock.” Key Features and Benefits: • Real-Time Inventory & Sales Tracking: Monitor stock levels and sales velocity as transactions occur, with support for offline mode that syncs automatically once connection is restored. • Smart Restock Alerts: Receive push notifications when stock drops below dynamic thresholds calculated by the BufferBoost® algorithm, ensuring best-selling ingredients are always available. • Auto-Generated Vendor Lists: Leverage VendorLink® integration to compile and compare supplier catalogs, pricing, and delivery times in one consolidated list for instant ordering. • Demand Allocator: Distribute inventory across multiple stops based on route-specific trends and upcoming events, guaranteeing high-demand locations are well stocked. • Analytical Reporting: Dive into detailed sales and waste metrics to identify peak periods, underperforming items, and optimization opportunities. Dishcrafters, an early adopter and multi-unit operator in the Bay Area, reported a 25% reduction in food waste within the first month of using the dashboard in beta. “Truckly’s smart restock alerts and live sales insights have transformed our daily prep routines,” said Juan Martinez, founder of Dishcrafters. “We no longer overestimate our requirements—waste is down, profits are up, and our team can spend less time counting stock and more time serving customers.” Product Availability and Pricing: Embedding seamlessly into existing workflows, Truckly’s mobile dashboard is available today on iOS and Android devices. Subscription tiers start at $49 per truck per month, with enterprise packages for multi-unit fleets and catering operations offering additional analytics modules and prioritized support. Implementation and Onboarding: Truckly provides guided setup for newcomers, including personalized training sessions and a dedicated support hotline. Expansion Entrepreneurs planning to add new trucks or catering lines benefit from advanced forecasting capabilities to manage growth efficiently. Event Specialists can leverage route-based restock planning and surge spotting to navigate unpredictable demand at festivals and pop-ups. About Truckly: Truckly equips independent food truck owners with a mobile-first dashboard that tracks inventory and sales in real time—even offline—sends smart restock alerts, and auto-generates vendor lists. It slashes prep time, reduces food waste, and keeps best-sellers stocked, empowering operators to maximize daily profits amid fast-changing, unpredictable demand. Founded in 2024 and headquartered in San Francisco, Truckly serves thousands of operators across North America and Europe. Media Contact: Emily Chan, Director of Communications press@truckly.com | +1 (415) 555-1020 www.truckly.com

P

Truckly Unveils EcoRecipe Optimizer to Transform Food Waste into High-Margin Specials for Sustainable Profit Growth

Imagined Press Article

CHICAGO, IL — 2025-08-08 — Truckly, the innovative inventory and sales management platform for mobile food operators, today introduces EcoRecipe Optimizer, an AI-driven feature that converts near-expiry ingredients into creative, high-margin menu specials. The new capability integrates seamlessly into the Truckly dashboard, empowering vendors to reduce spoilage, boost sustainability, and drive incremental revenue through intelligent recipe suggestions and targeted promotions. Food waste remains a persistent challenge for independent operators, representing up to 15% of total ingredient costs in a typical week. EcoRecipe Optimizer analyzes real-time inventory levels, expiration alerts, and sales trends to recommend daily specials that harness ingredients approaching their best-before date. By automatically drafting recipe ideas and integrating with the Dynamic Promo Scheduler, operators can activate time-sensitive offers that move surplus stock before spoilage occurs. “At Truckly, we’re committed to helping food truck operators achieve both profitability and sustainability,” said Dr. Lena Holt, Chief Product Officer at Truckly. “EcoRecipe Optimizer tackles the dual pain points of waste and margin compression. Our AI doesn’t just identify at-risk ingredients—it turns them into on-trend menu specials that excite customers and keep profits flowing.” How EcoRecipe Optimizer Works: • Inventory & Expiration Analysis: Harnessing real-time data from Spoilage Sentinel® and Expiration Alerts, the feature flags ingredients nearing expiry and quantifies their usage urgency. • Smart Recipe Generation: Leverages historical sales patterns, menu configurations, and seasonal preferences to propose creative recipe adjustments and daily specials. Each suggestion includes cost breakdowns, ingredient lists, and prep instructions. • Integrated Promotion Activation: Syncs with Dynamic Promo Scheduler and GeoFence Trigger to deploy targeted discounts when customer density is high, maximizing the chance to clear surplus stock with minimal margin sacrifice. • WasteImpact Reporting: Tracks waste reduction metrics and incremental revenue generated by EcoRecipe specials, providing operators with clear ROI and sustainability impact statements. Early adopters report substantial gains: Green Wheels, a single-unit operator in Portland, cut weekly spoilage by 40% and increased average ticket size by 12% within two weeks of enabling EcoRecipe Optimizer. “The AI suggestions were spot-on,” noted owner Sara Wong. “We turned wilted greens into a seasonal slaw special that flew off the counter. Our eco-conscious customers loved it, and we saw a tangible lift in profits.” EcoRecipe Optimizer is available now to all Premium and Enterprise subscribers at no additional cost. Operators interested in trialing the feature can sign up for a 30-day pilot program, including one-on-one consulting sessions with Truckly’s resident sustainability experts. About Truckly: Truckly equips independent food truck owners with a mobile-first dashboard that tracks inventory and sales in real time—even offline—sends smart restock alerts, and auto-generates vendor lists. It slashes prep time, reduces food waste, and keeps best-sellers stocked, empowering operators to maximize daily profits amid fast-changing, unpredictable demand. Since 2024, Truckly has served thousands of operators across North America and Europe. Media Contact: Karl Ramirez, Senior Communications Manager press@truckly.com | +1 (312) 555-4782 www.truckly.com

P

Truckly Partners with Top Suppliers Through VendorLink Integration to Streamline Procurement for Multi-Unit Food Truck Fleets

Imagined Press Article

NEW YORK, NY — 2025-08-09 — Truckly, the leader in inventory and sales intelligence for food trucks, today announces a strategic partnership program that integrates its VendorLink® feature directly with a curated network of preferred suppliers. The collaboration delivers a seamless procurement experience for multi-unit managers and expansion entrepreneurs, combining dynamic restock lists with real-time catalog pricing, vendor reliability insights, and one-click ordering. For operators overseeing multiple trucks or planning fleet expansion, procurement complexity can be a major bottleneck. Disparate catalogs, inconsistent pricing, and manual order workflows all contribute to wasted time and lost revenue. Truckly’s enhanced VendorLink integration addresses these challenges by unifying supplier data, automating order placement, and providing actionable reliability scores through the new Vendor TrustScore™ module. “Scaling a food truck operation requires rigorous vendor coordination,” explained Marcus Li, Chief Operating Officer at Truckly. “Our extended VendorLink partnerships remove friction from procurement. Operators can build restock lists based on forecasted demand, compare real-time prices across suppliers, and execute orders with a single click—all from within the Truckly app.” VendorLink Integration Highlights: • Unified Supplier Catalogs: Access up-to-date item lists, pricing, and minimum order quantities for all partnered vendors in one centralized interface. • One-Click Ordering: Place orders directly from your smart restock list; VendorLink automatically sends purchase orders and confirms delivery timelines. • Vendor TrustScore™: Evaluate supplier performance based on historical delivery punctuality, pricing consistency, and dispute resolution records. • SmartSettle Contracts: Utilize blockchain-based smart contracts to automate payment settlements upon delivery confirmation, reducing payment processing time and building supplier trust. • Real-Time Ledger Viewer: Grant vendors view-only access to payment histories and outstanding balances on a transparent, immutable ledger. SOCHEF Catering, a fast-growing brand with six trucks across three states, adopted the enhanced VendorLink integration during its preliminary rollout. “Vendor coordination used to be our Achilles’ heel,” shared CEO Amelia Roberts. “Now we generate restock lists in minutes, compare supplier offers instantly, and finalize orders without chasing emails. VendorLink has saved us over 20 hours per week in procurement tasks.” To celebrate the partnership launch, Truckly is offering multi-unit and enterprise customers complimentary access to VendorLink premium features through the end of the year. New users can schedule a demo to explore how VendorLink streamlines procurement and improves supplier relationships. About Truckly: Truckly equips independent food truck owners with a mobile-first dashboard that tracks inventory and sales in real time—even offline—sends smart restock alerts, and auto-generates vendor lists. It slashes prep time, reduces food waste, and keeps best-sellers stocked, empowering operators to maximize daily profits amid fast-changing, unpredictable demand. Established in 2024 and based in San Francisco, Truckly serves a global roster of operators across North America and Europe. Media Contact: Liam O’Connor, Head of Public Relations press@truckly.com | +1 (212) 555-3301 www.truckly.com

Want More Amazing Product Ideas?

Subscribe to receive a fresh, AI-generated product idea in your inbox every day. It's completely free, and you might just discover your next big thing!

Product team collaborating

Transform ideas into products

Full.CX effortlessly brings product visions to life.

This product was entirely generated using our AI and advanced algorithms. When you upgrade, you'll gain access to detailed product requirements, user personas, and feature specifications just like what you see below.

Start creating for free