Food truck management SaaS

TruckTally

Never Miss a Sale Again

TruckTally streamlines inventory and order management for independent food truck owners with a mobile, offline-ready app. It tracks real-time stock and orders on the go, slashing manual work and order errors, so operators serve every customer quickly—even without internet—while never missing a sale during the busiest shifts.

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TruckTally

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 food truck owners everywhere to thrive with seamless, reliable operations—setting the global standard for mobile food business success.
Long Term Goal
Empower 10,000 independent food trucks worldwide by 2029 to boost sales by 20% and virtually eliminate order errors, setting the benchmark for mobile food business operations.
Impact
Reduces daily inventory tracking time for independent food truck owners by 40%, lowers order errors by 80%, and eliminates lost sales from offline outages, resulting in smoother service, increased order accuracy, and measurable revenue gains during peak hours for on-the-go vendors.

Problem & Solution

Problem Statement
Independent food truck owners waste time juggling paper logs and unreliable restaurant apps to manage inventory and orders, often losing sales during offline periods because existing solutions lack mobile, offline-friendly features tailored for on-the-go vendors.
Solution Overview
TruckTally replaces paper logs with a mobile app that tracks inventory and orders in real time, even offline. Its instant low-stock alerts and automatic order backup ensure food truck owners never miss a sale or lose track in busy, low-connectivity environments.

Details & Audience

Description
TruckTally streamlines inventory and order management for independent food truck owners. Busy operators aged 22-50 get real-time stock tracking and order flow, cutting time spent on manual checks and errors. Its offline-friendly mobile app ensures smooth service and never loses an order—even in dead zones—so every customer gets served and every sale counts.
Target Audience
Independent food truck owners (22-50) needing hassle-free, offline order management for fast-paced service.
Inspiration
Standing in line at a bustling food truck, I watched the owner shuffle handwritten tickets, eyes darting between a smudged inventory log and impatient customers. When the Wi-Fi cut out, confusion and stress filled the air—orders got lost, sales slipped away, and tempers flared. That instant revealed the urgent need for a reliable, offline app to keep food trucks running smoothly, no matter what.

User Personas

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

N

Night Owl Nora

• Age 28, culinary arts graduate from urban Los Angeles • Solo operator of “MoonBites” food truck since 2021 • Generates $50K–$70K annual revenue during off-hours • Specializes in night markets and music festival service • Manages all operations and finances alone

Background

After repeated data losses at late-night events, Nora tested every offline app before selecting TruckTally. A former catering assistant turned solo vendor, she values tools proven under festival lights.

Needs & Pain Points

Needs

1. Offline-capable inventory updates during no-service zones 2. Quick summary sales reports post-event 3. Instant restocking alerts for top-selling items

Pain Points

1. Glitches during offline-to-online data sync 2. Manual stock counts slow peak operations 3. Lost orders amidst crowded festival chaos

Psychographics

• Thrives on high-energy nighttime hustle • Values reliable tech during connectivity gaps • Craves seamless, rapid order processing • Enjoys improvising under time pressure

Channels

1. Instagram Stories quick updates 2. WhatsApp Group restock coordination 3. TikTok short reels promotion 4. SMS stable notifications 5. Vendor-only Facebook community

S

Sustainable Sam

• Age 35, environmental science degree from University of Oregon • Founder of “EcoEats” truck since 2023 • Annual revenue $80K–$100K with 20% eco-premium pricing • Based in Portland’s eco-conscious food scene • Sources 80% ingredients from local organic farms

Background

Raised on a family farm, Sam studied environmental impact at university. After witnessing food waste in commercial kitchens, he launched a low-waste truck and sought tech to quantify every scrap.

Needs & Pain Points

Needs

1. Waste-output tracking tied to inventory data 2. Comparative supplier carbon footprint dashboard 3. Automated alerts for perishable item expiry

Pain Points

1. Inaccurate waste logs require manual audits 2. Hard to compare supplier sustainability metrics 3. Spoilage unnoticed without timely alerts

Psychographics

• Prioritizes sustainability metrics over pure profit • Believes in transparent supply chains • Motivated by community eco-impact scores • Values minimalist, elegant design

Channels

1. LinkedIn Eco Groups discussions 2. Instagram carbon footprint highlights 3. Twitter sustainability threads 4. Email detailed supplier reports 5. Eco-focused podcast interviews

P

Promo Pro Pete

• Age 30, marketing diploma from Full Sail University • Operates “Grill & Thrill” truck since 2022 • Earns ~$60K, spends 10% on digital ads • Based in Chicago’s downtown loop • Runs personal blog and influencer partnerships

Background

A former digital marketer, Pete launched his food truck as a branding experiment. Frustrated by manual data transfers, he adopted TruckTally for real-time social integration.

Needs & Pain Points

Needs

1. Live inventory widget for website embed 2. Exportable sales snippets for social posts 3. Real-time menu status API updates

Pain Points

1. Sold-out items still displayed online 2. Manual screenshotting sales graphs tedious 3. Delayed updates hurt promotional timing

Psychographics

• Seeks real-time data to drive engaging content • Believes social proof boosts foot traffic • Values customizable app integrations • Thrives on data-driven storytelling

Channels

1. Instagram Live menu reveals 2. Twitter rapid sale alerts 3. Facebook paid ads 4. YouTube short recipe videos 5. TikTok trend challenges

C

Catered Cathy

• Age 40, hospitality management degree from Boston University • Runs “PartyPlates” catering truck since 2018 • Annual revenue $120K, 60% from private events • Based in Austin’s suburban event hubs • Hires 5–10 seasonal staff per event

Background

After managing hotel banquets, Cathy launched a catering truck. Facing recurring mix-ups at weddings, she turned to TruckTally for batch order and staff-role clarity.

Needs & Pain Points

Needs

1. Batch order grouping for event menus 2. Guest count-based inventory forecasting 3. Permission-based staff access controls

Pain Points

1. Order mix-ups ruin event timelines 2. Inflexible user roles cause miscommunication 3. Manual guest count adjustments time-consuming

Psychographics

• Demands precision in large-volume orders • Values reliable guest count forecasting • Motivated by flawless client satisfaction • Prefers detailed analytics dashboards

Channels

1. LinkedIn hospitality networks 2. Email formal inquiries 3. Eventbrite vendor listings 4. Facebook business page 5. Instagram portfolio posts

B

Budget-Savvy Ben

• Age 26, community college culinary diploma • Founded “Budget Bites” truck in 2024 • Annual revenue $30K, 15% profit margin • Operates in suburban parking lots • Self-funded with minimal marketing budget

Background

A former line cook, Ben saved for his own truck but struggled with rising costs. He adopted TruckTally for real-time cost visibility and profit tracking.

Needs & Pain Points

Needs

1. Itemized cost-per-item breakdowns 2. Overspending inventory alerts 3. No hidden subscription costs

Pain Points

1. Undetected supplier price hikes surprise budget 2. Monthly app fees shrink tight margins 3. Lack of granular cost insights

Psychographics

• Obsessed with margin optimization and savings • Wary of hidden subscription fees • Motivated by clear cost transparency • Prefers straightforward, no-frills interfaces

Channels

1. Facebook budget-vendors group 2. Reddit r/foodtrucks frugal tips 3. Email cost report alerts 4. SMS price-change notifications 5. YouTube financial tutorials

Product Features

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

Smart Threshold

Dynamically adjusts reorder points based on real-time sales velocity and historical patterns, ensuring thresholds stay optimally tuned to demand and reducing the risk of premature or delayed restocking.

Requirements

Sales Data Collection
"As a food truck owner, I want accurate sales data captured continuously so that the dynamic threshold calculations are based on the latest transactions."
Description

Collect real-time sales events and store them locally, ensuring data integrity in offline mode and syncing automatically when a connection is available.

Acceptance Criteria
Offline Sales Event Capture
Given the user has no network connectivity, When a sale is completed, Then the sale event is saved in local storage with a unique ID, timestamp, product details, and quantity.
Automatic Data Synchronization
Given local sales events exist and network connectivity is restored, When the app detects a stable connection, Then all unsynced events are automatically sent to the server without user intervention, and upon success local entries are flagged as synced.
Data Integrity After Interrupted Sync
Given a sync operation is in progress and the connection drops, When connectivity returns, Then the app retries syncing only the unsynced events without duplicating any previously synced events.
Conflict Resolution for Duplicate Events
Given the same sales event exists locally and on the server with matching IDs, When syncing, Then the app identifies duplicates and prevents multiple entries, ensuring only one record is kept.
Local Storage Persistence Across App Restarts
Given the app is closed or crashes, When it is reopened, Then all unsynced sales events remain intact in local storage and available for synchronization.
Velocity Computation Module
"As an inventory manager, I want the system to compute current sales velocity so that reorder points adjust automatically to real-time demand surges."
Description

Calculate sales velocity over configurable time windows, using sliding-window and exponential smoothing algorithms to detect trends and demand spikes.

Acceptance Criteria
Sales Velocity Computation Over Configured Time Window
Given an admin configures a 6-hour sliding window, when the system processes incoming sales data, then it calculates the total units sold within the last 6 hours and updates the velocity metric every minute.
Exponential Smoothing Trend Detection
Given historical sales data and a smoothing factor α set by the user, when the module applies exponential smoothing, then the smoothed velocity value must reflect recent sales trends with a maximum deviation of 5% from manually calculated reference values.
Demand Spike Identification
Given real-time sales burst occurs (e.g., 50% increase in orders over 10 minutes), when the smoothing algorithm runs, then the system flags a demand spike alert and logs the event with a timestamp and computed velocity change percentage.
Sliding-Window Edge Case with Sparse Data
Given a period of low or zero sales, when the sliding window has fewer data points than its configured length, then the system calculates velocity based on available data without errors and defaults missing intervals to zero.
Configuration Update Effects on Velocity Calculation
Given an operator updates the time window or smoothing factor, when the new settings are saved, then the module immediately recalculates velocity using the updated parameters and displays both previous and updated velocity values for comparison.
Historical Pattern Integrator
"As a food truck operator, I want insights from past sales patterns so that threshold adjustments account for predictable peaks and lulls."
Description

Analyze historical sales data, seasonality, and event-based patterns to generate baseline demand forecasts for each menu item.

Acceptance Criteria
Baseline Forecast Generation for Average Demand Patterns
Given at least six weeks of historical sales data for a menu item When the Historical Pattern Integrator runs the baseline forecast Then the system produces a weekly demand forecast within ±10% of the actual average weekly sales
Seasonal Holiday Demand Forecast
Given two years of sales data covering the same holiday period When generating a demand forecast for an upcoming holiday week Then the forecast reflects seasonal uplift and achieves at least 85% accuracy when validated against prior holiday sales
Event-driven Sales Surge Prediction
Given historical data tagged with local event dates (e.g., festivals) When forecasting demand for a date coinciding with a similar event Then the predicted demand increases by at least 20% above the baseline forecast for that menu item
Low-Volume Item Smoothing Adjustment
Given a menu item with sporadic sales yielding zero in some periods When computing the baseline forecast Then the integrator applies a smoothing algorithm that ensures a minimum forecast of one unit per period
Insufficient Historical Data Handling
Given a menu item with fewer than four weeks of sales data When generating the baseline forecast Then the system flags the item as low-data, applies a default minimum threshold buffer of five units, and logs a data-insufficiency alert
Dynamic Threshold Calculator
"As a truck owner, I want reorder points updated automatically so that I restock at the right time without manual recalculation."
Description

Combine real-time velocity and historical forecasts to compute optimal reorder points per item, adjusting thresholds dynamically to minimize stockouts and overstock.

Acceptance Criteria
Adjust Reorder Point for High-Velocity Items
Given an item’s real-time sales velocity over the past hour exceeds its average by 20%, When the Dynamic Threshold Calculator runs, Then the reorder point for that item increases by at least 15% but no more than 25% of its base threshold.
Historical Forecast Integration for Seasonal Items
Given a seasonal item with historical forecast data indicating a 50% increase in demand for the upcoming week, When the Dynamic Threshold Calculator incorporates the forecast, Then the computed reorder point falls within ±10% of the forecast-driven threshold.
Low-Sales Velocity Threshold Validation
Given an item’s sales velocity drops below 30% of its 30-day moving average, When the Dynamic Threshold Calculator recalculates, Then the reorder point decreases by at least 10% but never below the defined safety stock level.
Offline Mode Threshold Update
Given the app is offline and records five consecutive orders for an item, When connectivity is restored, Then the local sales data sync and the Dynamic Threshold Calculator updates the reorder point to match the threshold calculated by the online service within 5 minutes.
Threshold Stability After Sudden Demand Spike
Given a sudden demand spike where sales double in 10 minutes, When the Dynamic Threshold Calculator processes the spike, Then the new reorder point adjusts but does not fluctuate by more than ±20% between consecutive recalculations.
Threshold Adjustment Notifications
"As a busy operator, I want to be alerted when thresholds shift so that I stay informed and can override adjustments if I anticipate special events."
Description

Notify users via in-app alerts and push notifications when reorder points change significantly, providing context and allowing manual override if needed.

Acceptance Criteria
Reorder Threshold Increased Notification Received
Given a threshold increase of 20% or more is calculated for a product based on real-time sales velocity When the new threshold is applied Then an in-app alert and push notification are delivered to the user within 5 seconds
Reorder Threshold Decreased Notification Received
Given a threshold decrease of 20% or more is identified from historical demand patterns When the adjusted threshold is updated Then the user receives both an in-app alert and a push notification detailing the change
Manual Override of Reorder Threshold after Notification
Given a threshold adjustment notification is displayed When the user selects the manual override option and inputs a custom threshold Then the system saves the user’s threshold override and suppresses automatic adjustments until the next review period
Notification Content Includes Contextual Data
Given a threshold adjustment occurs When the notification is generated Then the notification must include product name, previous threshold value, new threshold value, percentage change, and timestamp
Offline Handling of Threshold Change Notifications
Given the device is offline when a threshold adjustment occurs When connectivity is restored Then all pending threshold adjustment notifications are delivered in-app and as pushes within 30 seconds of reconnection

Instant PO

Automatically generates and sends purchase orders to preferred suppliers the moment an item falls below the defined threshold, cutting manual order creation time and guaranteeing swift replenishment.

Requirements

Real-Time Threshold Monitoring
"As a food truck operator, I want the system to automatically detect when my stock falls below the threshold so that I never run out of essential items during service."
Description

The system continuously monitors inventory levels for each item against predefined reorder thresholds. When an item count falls below its threshold, the system triggers the Instant PO workflow. This functionality integrates with the existing inventory module and ensures timely replenishment by eliminating manual checks. It must handle variable thresholds per item and support dynamic updates. The outcome is accurate, automated detection of low stock, minimizing stockouts and manual oversight.

Acceptance Criteria
Single Item Falls Below Threshold During Peak Service
Given an item’s stock level is updated and falls below its predefined threshold, When the inventory update is processed, Then the system automatically generates and sends a purchase order for that item to the preferred supplier within 30 seconds.
Multiple Items Simultaneously Breach Threshold
Given multiple items’ stock levels drop below their respective thresholds in a single sync event, When the system processes the batch update, Then it generates individual purchase orders for each item and consolidates orders by supplier for a single transmission.
Dynamic Threshold Update Before Reorder Trigger
Given an item’s reorder threshold is updated dynamically before its stock falls below the previous threshold, When the new threshold is saved, Then the system applies the updated threshold for real-time monitoring and triggers the PO based on the new value.
Offline Mode Inventory Update Triggering PO
Given the app is offline and an item’s stock level falls below its threshold during offline use, When the device reconnects, Then the system synchronizes the inventory data and immediately generates and sends any pending purchase orders.
Threshold Adjustment Impact on Pending Purchase Orders
Given a pending purchase order exists for an item and its threshold is raised above current stock before the PO is sent, When the threshold change is applied, Then the system cancels the outdated PO and generates a new purchase order reflecting the updated threshold.
Supplier Assignment Configuration
"As a food truck operator, I want to configure preferred suppliers for each item so that POs are sent to the correct supplier without manual intervention."
Description

Provide an interface for users to assign one or more preferred suppliers to each inventory item, specifying order priority, contact details, and ordering rules. This configuration integrates with the supplier management module, enabling the Instant PO feature to select the correct supplier automatically. It allows fallback mechanisms if the primary supplier is unavailable and ensures that orders are always sent to the right source. It enhances flexibility and reduces manual supplier selection errors.

Acceptance Criteria
Primary Supplier Assignment
Given the user is on the Inventory Item configuration page for Item A When the user assigns Supplier X as the primary supplier and saves the configuration Then Item A’s supplier list shows Supplier X as primary with a priority indicator
Supplier Priority Configuration
Given a list of assigned suppliers for Item A When the user sets Supplier Y’s priority to 2 and Supplier Z’s to 3 Then the supplier list is reordered to reflect priorities 1, 2, and 3 and persists after page reload
Supplier Contact Details Entry
Given the user is adding or editing a supplier for Item A When the user enters valid contact details (name, email, phone) and saves Then the supplier’s contact details are correctly displayed in the UI and stored in the database
Ordering Rules Configuration
Given the user is configuring ordering rules for Supplier X on Item A When the user sets a reorder threshold of 10 units and a minimum order quantity of 20 units and saves Then those values are applied and used by the Instant PO logic to generate orders when stock falls below the threshold
Fallback Supplier Activation
Given the primary supplier for Item A is unavailable When the Instant PO feature triggers an order because stock is below the defined threshold Then the system automatically selects the next available supplier based on configured priority and sends the order to that supplier
Purchase Order Generation Engine
"As a food truck operator, I want the system to generate a complete purchase order automatically when inventory is low so that I can save time and reduce errors."
Description

Implement an engine that automatically compiles purchase orders when triggered, aggregating low stock items into POs per supplier, populating item details, quantities, pricing, and delivery instructions. The engine integrates with product catalog and pricing modules to ensure accurate order content. Generated POs follow a standardized format and support templating. This functionality streamlines order creation, reduces manual errors, and accelerates the replenishment process.

Acceptance Criteria
Low Stock Threshold Trigger
Given an item’s stock level falls below its predefined reorder threshold When the PO Generation Engine runs Then the item is included in a newly created purchase order for its preferred supplier
Supplier Item Aggregation
Given multiple low-stock items belonging to the same supplier When purchase orders are generated Then all items are aggregated into a single PO per supplier without duplicates
Accurate Pricing and Quantity Calculation
Given the product catalog and pricing modules provide up-to-date unit costs and default reorder quantities When the engine compiles a PO Then each line item reflects the correct unit price and calculates the total quantity based on the defined reorder amount
Standardized PO Template Application
Given the templating engine with a standard PO format When a purchase order is generated Then the PO document includes the standard header, supplier address, item lines, pricing details, totals, and delivery instructions in the correct layout
Successful PO Dispatch
Given a generated PO document When it is sent to the supplier Then the supplier receives a valid PO via the configured delivery method (email or EDI) with all required fields populated
Automated PO Transmission
"As a food truck operator, I want generated POs to be sent to suppliers automatically so that I don’t have to email them manually."
Description

Enable automatic transmission of generated purchase orders to suppliers via email or API as soon as they are created. The feature integrates with communication modules, handles connectivity status, retries failed transmissions, and logs delivery reports. It ensures that suppliers receive orders promptly without manual intervention, accelerating the procurement cycle and maintaining consistent stock levels.

Acceptance Criteria
Supplier Receives PO via Email
Given an item falls below its reorder threshold and a PO is generated, when the system sends the PO via email to the supplier, then the supplier receives an email containing correct PO details and the system logs a successful delivery status.
Supplier Receives PO via API
Given the supplier supports API integration and a PO is generated, when the system invokes the supplier’s order endpoint with the correct payload, then the supplier API responds with 200 OK and the system records the order as sent.
Retry Failed Transmission
Given a PO transmission fails due to connectivity or server error, when the system retries sending the PO up to 3 times at 5-minute intervals, then either a successful delivery is logged within retries or, after all retries fail, an error is recorded and an alert is generated.
Handle Offline Mode Queue
Given the mobile app is offline when a PO is generated, when the device regains connectivity, then the system automatically processes and sends all queued POs and logs each transmission result without user intervention.
Delivery Report Logging
Given any PO transmission attempt (success or failure), when the attempt completes, then the system logs the timestamp, delivery status, and error details (if applicable) in the delivery reports accessible via the admin interface.
Offline PO Queueing and Synchronization
"As a food truck operator, I want POs generated offline to be queued and sent when I reconnect so that I never lose orders made during connectivity drops."
Description

Support offline usage by queuing purchase orders generated while offline and automatically synchronizing and transmitting them when connectivity is restored. This capability integrates with the app’s offline data store and sync engine, ensuring no POs are lost. It maintains consistent operation in low-connectivity environments and guarantees that orders created on the go are delivered once the network is available.

Acceptance Criteria
Queuing PO Offline
Given the user is offline and an item stock falls below its reorder threshold When the Instant PO feature automatically triggers Then a new purchase order record is created and stored in the offline PO queue with correct item details, supplier information, quantity, timestamp, and status "Pending Sync"
Automatic Sync on Connectivity Restoration
Given one or more POs exist in the offline queue and the device regains network connectivity When the app detects stable internet access Then all queued POs are transmitted to the respective suppliers without user intervention and the PO statuses are updated to "Sent"
Conflict Resolution for Duplicate POs
Given a PO in the queue matches an existing PO already sent to the supplier When attempting to synchronize queued POs Then the system identifies the duplicate and prevents re-sending, marking the queued PO status as "Duplicate Skipped"
Offline PO Data Persistence Across App Restarts
Given the user has created offline POs and then closes or restarts the app while still offline When the user reopens the app Then all previously queued POs remain intact in the offline queue and display accurate details and statuses
Error Handling and Retry on Partial Sync Failures
Given a network interruption or supplier API error occurs during PO transmission When the synchronization process fails for one or more POs Then the system retries sending each failed PO up to three times with exponential backoff and updates the final status to "Failed" if all retries fail, exposing an error message to the user
PO Status Tracking and Notifications
"As a food truck operator, I want to receive notifications on the status of my POs so that I can track their progress and address issues promptly."
Description

Implement tracking for purchase order statuses including sent, acknowledged, processed, and delivered. Provide real-time notifications to users on PO state changes, transmission failures, and acknowledgment receipts via in-app alerts and push notifications. This feature integrates with supplier response tracking and ensures operators have visibility on order progress, enabling proactive follow-up and improved communication.

Acceptance Criteria
Automatic PO Sent Notification
Given a purchase order is generated and sent to the supplier, when the transmission succeeds, then the user receives an in-app alert and a push notification within 5 seconds indicating the PO status as “Sent.”
PO Acknowledgement Receipt Tracking
Given the supplier sends an acknowledgment receipt, when the receipt is received by TruckTally, then the PO status automatically updates to “Acknowledged” in the user’s order dashboard and a push notification is dispatched.
PO Processing Status Update
Given the supplier processes the PO, when the processing update is received, then the PO status changes to “Processed” in real time on the PO detail screen.
PO Delivery Confirmation
Given the supplier marks the PO as delivered, when the delivery confirmation is received, then the PO status updates to “Delivered” and an in-app notification is shown to the user.
PO Transmission Failure Alert
Given a network or supplier endpoint error occurs during PO transmission, when the system detects the failure, then the user receives an in-app alert with details of the failure and an option to retry sending the PO.

Multi-Site Sync

Consolidates low-stock alerts across all truck locations into a unified dashboard, enabling fleet managers to coordinate bulk restocks and redistribute inventory efficiently to prevent stockouts on any unit.

Requirements

Real-Time Inventory Data Aggregation
"As a fleet manager, I want real-time inventory data from all trucks so that I can make informed restocking decisions without delay."
Description

The system must continuously collect and aggregate inventory levels from all truck locations, updating the unified dashboard at regular intervals. This functionality ensures fleet managers have the latest stock counts across their entire fleet, enabling timely decision-making and minimizing the risk of stock discrepancies due to outdated information.

Acceptance Criteria
Initial Inventory Data Load Scenario
Given the dashboard is accessed for the first time, when the system connects to all truck endpoints, then inventory data from each truck is retrieved and displayed within 60 seconds.
Continuous Sync Interval Scenario
Given normal operation, when five minutes elapse, then the dashboard is automatically updated with the latest aggregated stock counts without requiring manual refresh.
Low-Stock Alert Scenario
Given an item's aggregated stock level drops below its predefined threshold, when the data aggregation runs, then an alert is generated on the dashboard and added to the notifications panel within two minutes.
Offline Data Reconciliation Scenario
Given a truck goes offline for up to 30 minutes, when it reconnects to the network, then all buffered inventory changes during offline mode are synced and accurately reflected in the aggregated dashboard.
Failed Data Retrieval Handling Scenario
Given the system fails to retrieve data from a truck endpoint, when an error occurs, then the dashboard displays a clear error indicator for that location and retries data sync in the next scheduled interval.
Centralized Low-Stock Alert Dashboard
"As a fleet manager, I want a centralized dashboard of low-stock alerts so that I can easily see which trucks need attention and prioritize restocking tasks."
Description

The application must display all low-stock alerts from each truck location in a single dashboard, complete with filtering, sorting, and prioritization options. This allows managers to quickly identify critical shortages, focus on the most urgent items, and coordinate restocking efforts efficiently.

Acceptance Criteria
Viewing All Low-Stock Alerts
Given multiple trucks report low-stock items, when the fleet manager opens the Centralized Low-Stock Alert Dashboard, then all low-stock alerts from each location must display within 2 seconds, showing truck location, item name, current quantity, and threshold level.
Filtering Alerts by Priority
Given alerts are displayed, when the manager selects a priority filter (Critical, Warning, Normal), then only alerts matching the selected priority level appear and the filter remains active until changed.
Sorting Alerts by Stock Deficit
Given alerts are listed, when the manager clicks the 'Stock Deficit' column header, then the alerts sort in descending order of deficit; clicking again toggles to ascending order.
Highlighting Critical Items
Given alerts include items below 10% of threshold, when displayed on the dashboard, then all critical items are highlighted in red and appear at the top of the list by default.
Generating Redistribution Suggestions
Given low-stock alerts exist, when the manager clicks 'Suggest Redistribution', then the system generates recommendations to move stock from trucks with surplus by priority and displays recommended transfer quantities.
Bulk Restock Coordination Workflow
"As a fleet manager, I want to create bulk restock orders for multiple trucks so that I can streamline the ordering process and reduce administrative overhead."
Description

The feature should enable managers to select multiple low-stock items across different trucks and generate a single bulk restock order. It must support specifying quantities, assigning target locations, and linking supplier information, with options for review, modification, and approval before submission.

Acceptance Criteria
Selecting Multiple Low-Stock Items Across Trucks
Given the manager is on the Multi-Site Sync dashboard, when they select low-stock items from multiple truck locations, then each selected item appears in the bulk restock order list with the correct truck identifiers.
Specifying Quantities and Target Locations
Given items are added to the bulk restock list, when the manager enters restock quantities and target locations for each item, then the system validates quantities against supplier minimum order requirements and displays an error for any invalid entry.
Linking Supplier Information
Given valid restock quantities and locations are entered, when the manager selects an existing supplier or adds new supplier details, then the bulk restock order is linked to the chosen supplier and supplier information is shown correctly in the order summary.
Review and Modification Before Approval
Given a draft bulk restock order is generated, when the manager reviews and modifies item quantities, locations, or supplier details, then the updated information is saved and accurately reflected in the final order preview before submission.
Approval and Submission Workflow
Given the bulk restock order has been reviewed, when the manager submits the order for approval, then an approval notification is sent to designated approvers and upon approval, the order status changes to 'Submitted' and is dispatched to the supplier.
Cross-Location Inventory Redistribution
"As a fleet manager, I want to redistribute stock from overstocked trucks to those running low so that overall fleet availability is balanced and stockouts are prevented."
Description

The system must provide a guided process for transferring inventory items between trucks. It should calculate optimal redistribution plans based on current stock levels, forecasted demand, and geographic proximity, then generate transfer orders with detailed instructions for drivers.

Acceptance Criteria
Guided Redistribution Plan Generation
Given multiple trucks with varying stock levels, when a fleet manager initiates inventory redistribution, then the system calculates an optimal transfer plan considering current stock, forecasted demand, and geographic proximity, and displays the plan within two minutes.
Transfer Order Generation for Drivers
Given a confirmed redistribution plan, when the fleet manager approves it, then the system generates detailed transfer orders including item names, quantities, pickup and delivery locations, estimated transfer times, and route instructions, available as both PDF export and in-app view.
Demand Forecast Adjustment
Given historical sales data for each truck, when computing the redistribution plan, then the system incorporates forecasted demand for the next seven days and adjusts recommended transfer quantities to ensure each location maintains at least a two-day stock buffer.
Manual Override of Suggested Plan
Given a generated redistribution plan, when the fleet manager manually modifies transfer quantities or locations, then the system recalculates remaining transfers in real-time, updates driver instructions, and prevents any adjustments that would cause a location to fall below its minimum stock threshold.
Error Handling for Insufficient Stock
Given insufficient overall stock to fulfill the redistribution plan, when the system detects shortfalls, then it alerts the fleet manager with a list of understocked items and suggests backorder or external restock options.
Offline Sync Conflict Resolution
"As a fleet manager, I want the system to handle offline data changes and resolve conflicts upon reconnection so that my inventory data remains accurate and consistent."
Description

When trucks operate without internet connectivity, the app must queue all inventory and order changes locally and synchronize updates once the connection is restored. It should implement conflict resolution strategies—such as last-write-wins or flagged discrepancies for manual review—to maintain data integrity across the fleet.

Acceptance Criteria
Offline Data Queuing
Given the app is offline and a user updates inventory or creates an order, When the network is unavailable, Then the changes are stored in a local queue with timestamps and unique IDs.
Automatic Sync on Reconnection
Given the device regains connectivity, When the sync process initiates, Then all queued changes are transmitted to the server in the order they were made without data loss.
Conflict Detection and Flagging
Given an inventory item has been modified both locally and on the server, When syncing occurs, Then the system identifies the discrepancy, flags the record for manual review, and notifies the fleet manager.
Last Write Wins Resolution
Given two conflicting updates exist for the same record, When the conflict is resolved automatically, Then the update with the most recent timestamp overwrites the older record on all devices.
Manual Review Workflow
Given a flagged conflict exists, When the fleet manager reviews discrepancies, Then they can accept the local or server version and the selected version is propagated to all devices within 5 minutes.

Priority Alerts

Classifies low-stock warnings into tiered severity levels—critical, warning, informational—so operators can instantly identify and act on the most urgent restocking needs during busy shifts.

Requirements

Tier Threshold Configuration
"As a food truck operator, I want stock levels to be classified into critical, warning, and informational alerts so that I can prioritize restocking tasks effectively during busy shifts."
Description

Define and implement three distinct low-stock severity tiers—critical, warning, informational—based on predefined quantity or percentage thresholds. This requirement specifies the exact numerical boundaries for each tier, integrates with the inventory management module, and ensures consistent classification of items as they approach low-stock levels. The outcome is an automated, standardized alert generation process that clearly categorizes restocking urgency.

Acceptance Criteria
Configuring Tier Thresholds in Settings
Given the admin accesses the Tier Threshold Settings page When they define numeric boundaries for critical, warning, and informational tiers and save changes Then the system stores the new thresholds and displays a confirmation message
Applying Thresholds to Inventory Levels Across Items
Given multiple inventory items with current stock levels When thresholds are configured Then each item is automatically classified as critical if stock ≤ critical threshold, warning if stock ≤ warning threshold and > critical, informational if stock ≤ informational threshold and > warning, and no alert otherwise
Generating Alerts Based on Stock Changes
Given an item's stock level falls into each defined tier When inventory is updated Then the system generates and displays an alert in the Priority Alerts dashboard with the correct severity color and message
Updating Threshold Configuration and Real-time Reflection
Given existing tier thresholds are updated When the admin modifies and saves new tier boundaries Then all item classifications and active alerts refresh immediately to reflect the new thresholds without manual page reload
Validating Default Thresholds for New Items
Given a new inventory item is created without custom tier settings When the item creation is completed Then the system assigns default critical, warning, and informational thresholds of 5, 10, and 20 units respectively
Real-time Stock Monitoring
"As a food truck operator, I want real-time monitoring of my inventory so that low-stock alerts are generated instantly when items run low."
Description

Continuously monitor inventory levels in real time by detecting updates from sales, returns, and manual adjustments. This requirement covers integration with the app’s local database and sync engine to trigger threshold checks immediately after stock changes. The result is instant alert generation without delay, ensuring operators always have up-to-date information.

Acceptance Criteria
Critical Stock Alert During Offline Mode
Given the device is offline and a product's inventory drops below the critical threshold, When the app records the stock change locally, Then the app must immediately generate a critical severity alert visible on the alerts dashboard.
Warning Level Stock Refill Prompt After Sale
Given a sale transaction reduces stock to the warning threshold, When the local database is updated, Then the system triggers a warning severity alert within 1 second of the update.
Informational Alert on Manual Adjustment
Given an operator manually adjusts inventory above the informational threshold, When the adjustment is saved, Then the app generates an informational alert and logs the adjustment event in the audit trail.
Real-time Threshold Check Post Inventory Sync
Given the app completes a background sync with the server, When discrepancies from thresholds are detected, Then all matching severity alerts are generated within 2 seconds of sync completion.
No Duplicate Alerts for Same Threshold Reached
Given a product remains below a threshold after multiple transactions, When subsequent stock changes occur but the product stays within the same severity tier, Then the system must not generate duplicate alerts until the product moves out of and back into the severity tier.
Tiered Alert Notifications
"As a food truck operator, I want visually distinct alerts for critical, warning, and informational stock levels so that I can easily spot and address urgent items."
Description

Design and implement a user interface component that displays tiered alerts using distinct visual indicators—color codes, icons, and banners—for critical, warning, and informational stock levels. Integrate this component into the main dashboard and order entry screens, and ensure accessibility standards are met. The feature enhances visibility of urgent restocking needs.

Acceptance Criteria
Critical Stock Alert Display
Given a product’s stock level drops below the critical threshold, when the user opens the dashboard, then a red banner with the high-severity icon and the label “Critical” appears next to the product.
Warning Stock Alert on Order Entry
Given a product’s stock level falls between the warning and critical thresholds, when the user enters an order for that product on the order entry screen, then a yellow warning icon and background highlight appear beside the product field.
Informational Stock Alert in Offline Mode
Given the app is offline and a product’s stock level reaches the informational threshold, when the user views the dashboard, then a blue informational banner with the info icon and last sync timestamp is displayed for the product.
Accessibility Verification for Alert Components
When using a screen reader on any alert component, then it must announce the severity level, product name, and current stock level, and the visual design must meet WCAG 2.1 AA contrast ratios (≥4.5:1).
Real-Time Alert Update
Given a live stock update crossing a severity threshold, when the stock data is received, then the alert component updates its visual severity indicator within two seconds without manual page refresh.
Offline Alert Persistence
"As a food truck operator, I want low-stock alerts to be available offline so that I can manage inventory effectively even without internet."
Description

Enable caching of alert states in the device’s local storage to ensure low-stock notifications remain visible and actionable during offline usage. This requirement outlines data synchronization logic to update alert status once connectivity is restored, preventing duplication or loss of alerts. The user benefit is uninterrupted visibility into stock issues, regardless of network availability.

Acceptance Criteria
Offline visibility of alerts after app restart
Given the user had low-stock alerts cached before going offline, When the app is restarted without network connectivity, Then all previously saved alerts appear with correct severity levels and timestamps visible in the alert list.
Alert synchronization on reconnection
Given the user acknowledged or dismissed alerts while offline, When network connectivity is restored, Then the app syncs all local alert state changes to the server without creating duplicate alerts or losing any modifications.
Persistence of new alerts offline
Given stock levels drop below defined thresholds while the device is offline, When the low-stock alert is generated, Then the alert is saved locally and displayed immediately with the correct severity tier and timestamp.
No duplication of alerts after sync
Given an alert was generated and cached offline and then synchronized when online, When fetching alerts from the server, Then the app displays each alert only once and does not create duplicate entries.
Data integrity across app updates
Given the app is updated to a new version while offline, When the updated app is launched without connectivity, Then all previously cached alerts are retained and displayed accurately with no missing or corrupt data.
Custom Threshold Settings
"As a food truck operator, I want to customize low-stock thresholds for items so that alerts reflect my unique usage patterns and preferences."
Description

Provide a settings interface for operators to customize low-stock thresholds on a per-item or per-category basis. This requirement includes UI design, data validation, local storage of user preferences, and sync behavior. Customizable thresholds allow operators to tailor alerts to their specific inventory turnover and usage patterns.

Acceptance Criteria
Operator Sets Custom Threshold for Single Item
Given the operator is on the item threshold settings screen, When they enter a valid integer between 1 and 999 and tap Save, Then the new threshold is displayed next to the item in the inventory list and stored locally.
Operator Sets Custom Threshold for Category
Given the operator is on the category threshold settings screen, When they select a category, set a threshold value, and tap Save, Then all items in that category inherit the new threshold unless individually overridden.
Threshold Validation for Invalid Input
Given the operator enters a non-numeric or out-of-range value and taps Save, Then the system displays a validation error message and prevents saving until a valid value is provided.
User Preferences Persist After App Restart
Given the operator has set custom thresholds and restarts the app, When they navigate back to the inventory page, Then the custom thresholds are still applied and no default values are restored.
Custom Thresholds Sync to Cloud
Given the operator is online and changes thresholds, When the sync service runs, Then the updated thresholds are uploaded to the server and reflected on other devices logged into the same account.

Supplier Hub

Provides in-app messaging and order tracking with suppliers directly from the alert interface, streamlining communication, confirming restock timelines, and reducing back-and-forth emails or calls.

Requirements

Real-time In-app Messaging
"As a food truck owner, I want to message my suppliers directly when I receive low-stock alerts so that I can confirm restock timing without having to switch to email or phone calls."
Description

Enable direct, real-time messaging between the food truck operator and suppliers within the app, allowing users to send and receive text messages, images, and attachments instantly. This feature integrates seamlessly into the alert interface so that when stock levels fall below thresholds, users can immediately contact the appropriate supplier without leaving the application. Messages are stored locally for offline access and synchronized automatically when connectivity is restored, ensuring no communication is lost. The system also supports message threading by order, so users can track conversations related to specific restock requests and maintain a clear history of supplier interactions.

Acceptance Criteria
Trigger Messaging Interface on Low Stock Alert
Given a product’s stock falls below the predefined threshold, when the operator taps the “Contact Supplier” button in the stock alert popup, then the in-app messaging interface launches within 2 seconds displaying the correct supplier’s chat thread for that product.
Real-Time Text Messaging When Online
Given both the operator’s and supplier’s devices have internet connectivity, when the operator sends a text message, then the message is delivered to the supplier within 1 second and a delivery confirmation appears in the chat thread.
Offline Message Storage and Access
Given the operator is offline, when the operator composes and sends messages or attachments, then the messages are saved locally on the device, visible in the chat thread with a “Pending” status indicator.
Automatic Message Synchronization Upon Reconnection
Given the operator’s device reconnects to the internet after being offline, when the app detects network availability, then all locally stored messages and attachments are automatically sent to the supplier within 30 seconds and their statuses update to “Sent” with accurate server timestamps.
Order-Specific Message Threading
Given multiple restock requests exist, when the operator selects a specific order from the alert interface, then the messaging view displays only messages and attachments associated with that order and shows the order ID and product name in the thread header.
Order Initiation from Alerts
"As a food truck operator, I want to initiate a restock order directly from a low-stock alert so that I can quickly replenish supplies without navigating through multiple screens."
Description

Allow users to convert low-stock alerts into supplier order requests with a single action. From the alert interface, users can review current stock levels, select quantities for restock, choose the supplier, and submit a purchase order. The system pre-fills order details based on the alert parameters and historical order data, minimizing manual entry. Once submitted, the order is logged in the Supplier Hub and linked to the originating alert for traceability. This streamlines the ordering workflow, reducing the time between identifying low inventory and placing an order.

Acceptance Criteria
Single Alert Order Creation
Given a low-stock alert for one item When the user clicks the “Order Now” button Then the restock order form opens with the alert’s item and suggested quantity pre-filled And the user can modify the quantity before submission
Batch Alert Order Creation
Given multiple low-stock alerts displayed When the user selects multiple alerts and clicks “Order Selected” Then the order form opens listing all selected items with their suggested quantities And the user can review and adjust each item’s quantity before placing the order
Prefilled Order Details Accuracy
Given historical order data exists for the alert item When the order form is displayed Then the supplier, unit price, and reorder quantity fields are pre-filled based on the most recent applicable order data And the user can confirm or override these values
Supplier Selection and Validation
Given multiple approved suppliers for an item When the user views the order form Then the supplier dropdown displays only active suppliers And selecting a supplier updates estimated delivery date based on supplier lead time
Order Logging and Traceability
Given a purchase order is submitted from the alert interface When submission completes successfully Then the new order is recorded in Supplier Hub And the originating alert shows a link to the created order ID for traceability
Order Status Tracking
"As a food truck owner, I want to track the real-time status of my restock orders so that I can plan my inventory and service schedule accurately."
Description

Provide visibility into the lifecycle of supplier orders by displaying status updates (e.g., Pending, Confirmed, Shipped, Delivered) in the app. Suppliers can update order statuses via the Supplier Hub interface, and users receive push or in-app notifications when statuses change. Each status update includes timestamps and optional notes from the supplier, enabling operators to adjust their schedules and inventory plans. This feature ensures transparency in the restocking process and reduces uncertainty about delivery timelines.

Acceptance Criteria
Order Status Change Notification
Given a supplier updates an order status, When the status is saved, Then the user receives a push and in-app notification within 5 seconds showing the new status, timestamp, and supplier note.
Order Lifecycle Timeline View
Given an order exists with multiple status updates, When the user opens the order details screen, Then all status updates display in chronological order with timestamps, status labels (Pending, Confirmed, Shipped, Delivered), and optional notes.
Supplier Note Display
Given a supplier adds a note to a status update, When the user views the status update, Then the note appears below the status label and timestamp, and can be expanded or collapsed.
Offline Status Sync
Given the app is offline and a supplier updates an order status via the Supplier Hub, When connectivity is restored, Then the app syncs the update automatically and notifies the user without duplicate entries.
Timezone Correct Timestamps
Given users and suppliers operate in different timezones, When a status update occurs, Then the timestamp displays in the user’s local timezone format and matches the supplier’s original update time adjusted accordingly.
Automated Restock Notifications
"As a busy food truck operator, I want to receive notifications when suppliers respond or confirm orders so that I stay up-to-date on restock progress without constantly checking the app."
Description

Implement configurable notifications that alert users of key events in the supplier communication and order process. Users can opt in to receive push notifications, SMS, or email alerts for events such as message replies, order confirmations, shipping updates, and delivery completions. Notification preferences are managed in the settings panel, and default alerts are based on critical order milestones. This keeps operators informed on the go and reduces the risk of missed communications.

Acceptance Criteria
Receiving a Supplier Message Reply
Given the user has enabled push notifications for message replies and is online, When a supplier replies to a message in the app, Then the user receives a push notification within 60 seconds containing the supplier’s name and a message preview; If the user is offline, Then the notification is queued and delivered within 5 minutes of reconnection; If the user opted for SMS, Then an SMS with the supplier’s name and message excerpt is sent within 2 minutes.
Order Confirmation from Supplier
Given the user has enabled email notifications for order confirmations, When a supplier confirms an order, Then an email detailing order ID, items, quantities, and confirmation timestamp is sent within 60 seconds; Given the user has enabled push notifications for order confirmations, Then a push notification with order summary is delivered within 60 seconds.
Shipping Status Update
Given the user has enabled notifications for shipping updates, When a supplier updates an order status to 'Shipped', Then the user receives a notification (push/SMS/email based on preferences) within 2 minutes containing tracking ID and expected delivery date; If multiple shipping updates occur within a 10-minute window, Then they are consolidated into a single notification listing all status changes.
Delivery Completion Acknowledgment
Given the user has enabled delivery completion notifications, When a supplier marks an order as delivered, Then the user receives a notification within 60 seconds confirming delivery and timestamp; And the notification includes a link to view delivery details and report issues if needed.
Configuring Notification Preferences
Given the user is on the notification settings panel, When the user toggles notification channels (push/SMS/email) for each event type, Then the changes are saved immediately and applied to all subsequent notifications; And the settings UI reflects the current saved preferences on reload or new session.
Supplier Performance Insights
"As a food truck owner, I want to view performance metrics for each supplier so that I can choose the most reliable partners and improve my restocking efficiency."
Description

Aggregate and visualize key supplier performance metrics, such as average order confirmation time, on-time delivery rate, and order accuracy, in a dashboard within the Supplier Hub. The system collects data from each transaction and communication event to calculate performance scores and trends over time. Users can filter insights by supplier, date range, and product category, enabling informed decisions when selecting or retaining suppliers. This analytics feature fosters accountability and helps operators optimize their supply chain.

Acceptance Criteria
Filter Performance Insights by Supplier and Date Range
Given the user is on the Supplier Performance Insights dashboard, When the user selects a specific supplier and date range, Then the dashboard updates to display only the performance metrics for that supplier within the selected date range.
Visualize Average Order Confirmation Time
Given performance data exists for a supplier, When the user views the dashboard, Then the average order confirmation time metric is calculated correctly and displayed as a line chart over the selected period.
Track On-Time Delivery Rate Trend
Given delivery records are available, When the user applies any valid filters, Then the on-time delivery rate is computed accurately and shown as a trend graph.
Display Order Accuracy by Product Category
Given order accuracy data is recorded per product category, When the user filters by category, Then the dashboard shows the percentage of orders delivered accurately for that category.
Export Supplier Performance Report
Given the user has applied filters on the dashboard, When the user clicks the export button, Then a CSV file containing the filtered supplier performance metrics is downloaded.

Reorder Analytics

Visualizes past reorder patterns, lead times, and supplier performance in easy-to-read charts, empowering vendors to fine-tune thresholds and choose the fastest, most reliable replenishment partners.

Requirements

Reorder Trend Visualization
"As a food truck operator, I want to view past reorder volume trends so that I can predict future inventory needs accurately."
Description

Provide interactive charts displaying historical reorder quantities over time for each inventory item, enabling users to identify seasonal demand and consumption patterns. These visualizations should integrate within the Reorder Analytics dashboard, support smooth offline access, and automatically update with synchronized data when connectivity is restored. By visualizing trends, operators can make data-driven decisions and optimize inventory levels.

Acceptance Criteria
Viewing Historical Reorder Trends Offline
Given the user is offline and opens the Reorder Analytics dashboard When the user selects an inventory item Then the app displays the cached interactive reorder trend chart for the past 12 months within 2 seconds
Sync and Update Charts on Connectivity Restoration
Given the user was offline and the local cache contains outdated reorder data When connectivity is restored Then the app automatically synchronizes historical data and updates all reorder trend charts within 5 seconds without creating duplicate entries
Interactive Data Point Details
Given the user views any reorder trend chart When the user taps or hovers over a data point Then a tooltip appears showing the exact date, reorder quantity, and supplier lead time for that point
Seasonal Demand Highlight
Given at least one inventory item has monthly reorder quantities 30% above its 12-month moving average When viewing its reorder trend chart Then the chart visually highlights those months with a distinct color or marker
Performance under Large Data Sets
Given the inventory contains over 100 items with two years of reorder history When loading the Reorder Analytics dashboard Then all reorder trend charts render within 3 seconds and the UI remains responsive
Supplier Performance Metrics
"As a food truck operator, I want to compare supplier lead times and reliability so that I can select the most efficient and dependable replenishment partner."
Description

Display comprehensive metrics on supplier lead times, fill rates, and delivery reliability in easy-to-read charts and scorecards. The system should calculate average lead time, on-time delivery percentage, and order fulfillment rates per supplier. These insights will integrate into the analytics dashboard to help operators evaluate and compare supplier performance over custom periods.

Acceptance Criteria
Viewing Supplier Lead Time Metrics
Given the user is on the Supplier Performance dashboard, when the page loads, then the system displays a bar chart of average lead times for each supplier with values in days to two decimal places
Filtering Metrics by Custom Date Range
Given the user selects a start and end date in the date range filter, when the filter is applied, then all supplier performance metrics (lead time, fill rate, on-time delivery) update to include only orders within the specified range
Comparing Supplier Fill Rates
Given the user views the fill rate table, when the data loads, then the system calculates fill rate as (fulfilled orders ÷ total orders) × 100 and displays it as a percentage for each supplier
Visualizing On-Time Delivery Percentage Trends
Given the user switches to trend view, when the trend is displayed, then a line chart shows on-time delivery percentage per week with tooltips indicating exact percentages and on-time definition based on deliveries on or before expected date
Displaying Supplier Performance Scorecards
Given the user selects a supplier from the dashboard, when the scorecard loads, then it shows average lead time, on-time delivery percentage, and fill rate with color-coded badges (green ≥95%, yellow 85–94.99%, red <85%)
Custom Reorder Threshold Configuration
"As a food truck operator, I want to adjust reorder thresholds based on demand patterns so that I minimize stockouts and overstock."
Description

Enable users to set and adjust reorder thresholds for each item based on historical demand patterns and supplier performance data. The configuration interface should suggest optimal threshold values derived from analytics while allowing manual fine-tuning. Updated thresholds will directly influence reorder recommendations and alerts.

Acceptance Criteria
Setting Optimal Threshold for High-Demand Item
Given a user accesses the threshold configuration for an item with sufficient historical demand and supplier performance data, When the interface loads, Then the system displays a suggested reorder threshold value with a brief analytics summary explaining the suggestion.
Manual Override of Suggested Threshold
Given the system presents a suggested reorder threshold, When the user manually enters a new threshold value and confirms, Then the system accepts the custom value, overwrites the suggestion, and displays a confirmation message.
Threshold Adjustment Using Supplier Lead Time Data
Given supplier lead time metrics are available for an item, When the user views the suggestion details, Then the system adjusts the recommended threshold to account for average lead time and displays the adjusted value.
Preventing Threshold Below Minimum Stock Level
Given the system defines a minimum allowable stock level for each item, When a user attempts to set a reorder threshold below this minimum, Then the system rejects the input, highlights the field with an error, and displays a validation message.
Real-Time Update of Threshold Influence on Alerts
Given the user has updated a reorder threshold, When the change is saved, Then the reorder recommendation engine immediately recalculates upcoming alerts and displays updated reorder notifications based on the new threshold.
Data Filtering and Segmentation
"As a food truck operator, I want to filter analytics by date range and item so that I can focus on specific time periods or products when reviewing reorder patterns."
Description

Provide robust filtering and segmentation controls, allowing users to refine analytics by date range, item category, specific inventory items, and supplier. Filtered views should dynamically update all charts and metrics, enabling operators to perform granular analysis and focus on relevant data subsets.

Acceptance Criteria
Date Range Filtering
Given the user selects a valid start and end date in the date range picker, when the filter is applied, then all analytics charts and metrics display data exclusively from within the selected date range.
Category Segmentation
Given the user chooses one or more item categories from the category dropdown, when the filter is applied, then only inventory items belonging to the selected categories are reflected in every chart and metric.
Individual Item Filtering
Given the user searches for or selects specific inventory items by name or SKU, when the filter is applied, then analytics update to show data solely for the chosen items across all visualizations.
Supplier Performance Filter
Given the user selects one or more suppliers from the supplier list, when the filter is applied, then charts and metrics display reorder patterns, lead times, and performance data exclusively for those suppliers.
Combined Filters Application
Given the user applies multiple filters (date range, category, item, and supplier) simultaneously, when the filters are applied, then all charts and metrics dynamically update to represent the intersection of all selected criteria.
Offline-enabled Analytics Sync
"As a food truck operator who may experience connectivity loss, I want to access reorder analytics offline so that I can make informed ordering decisions during events or remote locations."
Description

Ensure the analytics dashboard remains accessible in offline mode by caching recent data locally. Any analytics-specific actions or threshold changes made while offline should queue and automatically sync when connectivity is restored. This guarantees uninterrupted access to reorder insights during mobile or remote operations.

Acceptance Criteria
Offline Data Caching on App Launch
Given the user opens the analytics dashboard while online, When the user navigates to the reorder analytics view, Then the system caches the latest analytics data locally for offline access.
Offline Analytics UI Availability
Given the user is offline and accesses the analytics dashboard, When the dashboard loads, Then the system displays the most recently cached reorder patterns, supplier performance, and lead time charts without errors.
Reorder Threshold Change Queued Offline
Given the user adjusts reorder thresholds while offline, When the user saves the changes, Then the system stores the modifications in a local queue and indicates that they are pending synchronization.
Queued Actions Synchronization on Connectivity Restoration
Given there are pending offline analytics actions, When the device regains internet connectivity, Then the system automatically sends all queued actions to the server and confirms successful synchronization to the user.
Conflict Resolution for Stale Offline Data
Given the user makes analytics-specific changes offline that conflict with updates made elsewhere, When the system processes the queued actions, Then it prompts the user to resolve conflicts or applies last-write-wins, logging each resolution.

SpeedTap

Activate one-tap contactless payments instantly from the home screen. SpeedTap launches the NFC reader in milliseconds, cutting checkout interactions to a single motion and reducing queue times during peak hours.

Requirements

Instant NFC Activation
"As a food truck operator, I want the NFC reader to activate instantly from the home screen so that I can process payments quickly during peak hours."
Description

Initialize and launch the NFC reader within 200 milliseconds of the tap action from the home screen, allowing contactless payment initiation without delays. Ensure compatibility with both iOS and Android native modules, handle device sleep states, and provide clear visual or haptic feedback upon activation.

Acceptance Criteria
Android Home Screen NFC Tap Timing
Given an Android device on the home screen, when the user taps the NFC payment icon, then the NFC reader must be initialized and ready to read within 200 milliseconds.
iOS Home Screen NFC Tap Timing
Given an iOS device on the home screen, when the user taps the NFC payment icon, then the NFC reader must be initialized and ready to read within 200 milliseconds.
NFC Activation from Lock Screen Widget
Given the device is locked and displaying an NFC lock screen widget, when the user taps the widget, then the app must wake, initialize the NFC reader, and present the NFC interface within 200 milliseconds.
Visual Feedback on NFC Activation
Given the NFC reader has been successfully initialized, when the reader interface appears, then a visual indicator confirming activation must be displayed within 100 milliseconds.
Haptic Feedback on NFC Activation
Given the NFC reader has been successfully initialized, when the reader interface appears, then a haptic feedback pulse must be triggered within 100 milliseconds.
Home Screen Payment Shortcut
"As a food truck operator, I want a home screen shortcut for contactless payments so that I can reduce the steps needed to start a sale."
Description

Provide a one-tap shortcut icon on the device home screen that directly triggers the SpeedTap feature, bypassing in-app navigation. The shortcut must persist across sessions, be customizable in appearance, and align with the brand’s design guidelines to facilitate rapid payment initiation.

Acceptance Criteria
Shortcut Icon Installation
Given the user has installed TruckTally on their device, When the user selects “Add SpeedTap Shortcut” from the app settings, Then a one-tap shortcut icon appears on the device home screen without requiring additional permissions or manual configuration.
Launching SpeedTap Feature
Given the shortcut icon is present on the home screen, When the user taps the SpeedTap shortcut, Then the app must launch the NFC reader screen within 200 milliseconds and be ready to accept payment.
Persistence Across Sessions
Given the SpeedTap shortcut has been added, When the user closes and reopens the device or the app, Then the shortcut icon must still be visible and functional on the home screen without user reconfiguration.
Customization of Shortcut Appearance
Given the user accesses the home screen shortcut customization menu, When the user selects an alternate icon style or color from the provided theme options, Then the shortcut icon updates immediately and displays the chosen appearance on the home screen.
Brand Compliance
Given the design guidelines are provided, When the shortcut icon is rendered on various device resolutions and themes (light/dark), Then the icon’s size, color, and branding elements must match the brand’s style guide specifications exactly.
Seamless Contactless Payment Processing
"As a food truck operator, I want the app to process contactless payments end-to-end seamlessly so that I can ensure reliable and fast checkout for customers."
Description

Handle the complete payment flow from NFC handshake and tokenization to communication with the payment gateway and display of confirmation messages in under two seconds. Integrate a PCI DSS–compliant SDK, ensure reliability across varying network conditions, and log transaction details for auditing and reporting.

Acceptance Criteria
Immediate NFC Handshake
Given the user taps their payment card or device on the home screen, when the device is within NFC range, then the NFC handshake completes in under 500ms and the payment flow proceeds to tokenization.
Fast Payment Tokenization and Gateway Communication
Given a successful NFC handshake, when the token is generated, then authorization is received from the payment gateway and a confirmation is displayed within 2 seconds of the initial tap.
Offline Transaction Queuing
Given no internet connectivity at payment time, when a payment is initiated, then the transaction is securely stored locally with PCI DSS encryption and automatically transmitted upon connectivity restoration.
PCI DSS SDK Compliance
Given the PCI DSS–compliant SDK is integrated, when processing a payment, then all sensitive card data is handled exclusively within the SDK and no unencrypted payment data is logged or stored outside its secure environment.
Transaction Audit Logging
Given completion or queuing of any payment, when the transaction is finalized, then the app logs the transaction ID, timestamp, amount, payment status, and user identifier to the audit log for reporting.
Offline Transaction Queuing
"As a food truck operator, I want payments made offline to be queued and transmitted later so that I never miss a sale even in poor connectivity."
Description

Queue payment transactions locally in an encrypted store when network connectivity is unavailable and automatically submit them once connectivity is restored. Notify the operator of queued transactions and confirm successful replay to ensure no sales are lost during offline periods.

Acceptance Criteria
Offline Transaction Storage Upon Network Loss
Given the app detects no network connectivity when an operator completes a payment, When the operator confirms the payment, Then the transaction is encrypted and stored locally in the queue and the operator sees a 'Transaction Queued' notification.
Automatic Replay on Connectivity Restoration
Given there are queued transactions stored locally, When the app regains network connectivity, Then all queued transactions are automatically submitted in the order they were recorded without additional operator intervention.
Queued Transaction Count Notification
Given one or more transactions are queued locally, When the operator returns to the home screen, Then a badge indicator displays the total number of queued transactions pending upload.
Successful Replay Confirmation
Given queued transactions have been submitted after connectivity restoration, When each transaction is confirmed by the server, Then the operator receives a confirmation message for each transaction and the local queue is cleared accordingly.
Encrypted Local Storage Validation
Given transactions are queued locally, When inspecting the device storage, Then all queued transaction records are stored in an encrypted format that cannot be read by unauthorized apps or users.
Secure Authentication for SpeedTap
"As a food truck operator, I want to ensure that only authorized staff can initiate contactless payments so that I maintain transaction security."
Description

Enforce operator authentication via biometric or PIN when SpeedTap is first activated after device lock. Maintain a session timeout after five minutes of inactivity and require re-authentication thereafter. Log all access attempts and authentication events for security monitoring.

Acceptance Criteria
First Activation After Lock
Given the device was locked and SpeedTap is launched When the operator taps SpeedTap for the first time Then the app prompts for biometric authentication or PIN fallback within 100ms and only proceeds to payment upon successful authentication
Session Timeout After Inactivity
Given the operator has been inactive in SpeedTap for over five minutes When the operator taps SpeedTap again Then the app displays the authentication prompt and blocks payment initiation until successful authentication
Failed Authentication Handling
Given an operator submits invalid biometric data When authentication fails Then the app displays the PIN entry screen, logs the failed attempt with timestamp, and after three consecutive failures locks SpeedTap for 30 seconds
Successful Authentication Grants Access
Given the operator provides valid biometric or PIN credentials When authentication succeeds Then SpeedTap launches the NFC reader within 50ms and logs the successful authentication event with timestamp and method
Authentication Event Logging Integrity
Given any authentication attempt (successful or failed) When the attempt occurs Then the system records an audit log entry containing operator ID, authentication method, outcome, timestamp, and device ID in the secure log store

SplitTender

Enable customers to divide their bill across multiple payment methods—mixing cards, wallets, or cash in one transaction. SplitTender ensures flexible checkout options, minimizes payment friction, and accommodates group orders effortlessly.

Requirements

Multi-Method Payment Interface
"As a food truck operator, I want to select and combine different payment methods in one checkout so that I can process group orders seamlessly and accommodate customer preferences."
Description

Develop a user interface within the checkout flow that enables selection and entry of multiple payment methods—such as credit/debit cards, digital wallets, and cash—in a single transaction. The interface should clearly display the remaining balance as payment methods are added, support adding or removing methods dynamically, and provide real-time validation of input fields. This component must integrate seamlessly with the existing TruckTally UI, ensuring a consistent look and feel, and must be accessible on both online and offline modes.

Acceptance Criteria
Adding Multiple Payment Methods
Given an order total of $50, when the user selects a credit card payment and enters $30, the UI displays a remaining balance of $20; when the user adds a second payment method (e.g., cash) and applies the full remaining balance, the UI displays a remaining balance of $0 and enables the final confirmation button.
Removing a Payment Method
Given two payment methods applied in a transaction, when the user removes one method, then the UI recalculates and displays the updated remaining balance reflecting only the remaining payment methods and disables the confirmation button if the total applied amount is less than the order total.
Real-Time Balance Update
Given the user is entering amounts for each payment method, as soon as an amount is entered or adjusted, the system immediately updates and displays the new remaining balance without needing a page refresh or additional clicks.
Input Validation for Payment Amounts
Given the user inputs an amount for a selected payment method, if the entered amount is non-numeric, negative, or exceeds the current remaining balance, then the system displays an inline validation error and prevents adding that payment method to the transaction.
Offline Mode Payment Support
Given the device is offline and the total order amount is $40, when the user adds payment methods and inputs valid amounts summing to $40, then the UI behaves identically to online mode—displaying correct remaining balance, allowing payment confirmation, and storing the transaction to sync when connection is restored.
Split Calculation Engine
"As a cashier, I want the system to automatically compute each payment method’s share so that I can quickly finalize split payments without manual calculations."
Description

Implement the backend logic to calculate the exact amount to be charged on each selected payment method based on customer input. The engine must handle arbitrary splits—equal, percentage-based, and custom amounts—while ensuring that the total of all splits equals the order total. It should apply rounding rules consistent with currency precision, prevent over- or under-charging, and expose an API for the UI to request recalculations whenever a split is modified.

Acceptance Criteria
Equal Split Between Two Cards
Given an order total of $100.00, when the customer selects 'Equal Split' between two card payments, then the system calculates $50.00 per card, and the sum of the splits equals $100.00.
Percentage-Based Split Across Three Payment Methods
Given an order total of $200.00, when the customer specifies splits of 50%, 30%, and 20% across card, digital wallet, and cash, then the system calculates $100.00, $60.00, and $40.00 respectively, ensuring the sum equals $200.00.
Custom Amount Split with Cash and Card
Given an order total of $85.75, when the customer enters custom amounts of $45.75 for card and $40.00 for cash, then the system accepts the entries because their sum equals $85.75.
Rounding Behavior on Currency Precision
Given an order total of $100.01, when the customer selects an equal split between two payments, then the system assigns $50.01 to the first payment and $50.00 to the second payment, applying standard rounding rules.
API Recalculation Upon Modification of Split
Given an existing split configuration, when a payment amount or method is modified, then calling the recalculation API returns updated split amounts within 200ms, maintaining the total order value.
Secure Payment Authorization
"As a food truck operator, I want each payment method to be securely authorized so that I can trust the split transaction without risking fraud or data breaches."
Description

Integrate with existing payment gateway modules to process authorizations for each split payment method in sequence or parallel, as supported by the gateway. Ensure that sensitive payment data is handled securely following PCI-DSS guidelines, using tokenization for card and wallet transactions. Provide clear success and failure callbacks for each authorization, and aggregate the results to present a unified transaction outcome to the user.

Acceptance Criteria
Single Card and Cash Split Transaction
Given a bill of $50 split into $30 card and $20 cash When the user confirms payment Then the card transaction is authorized successfully and the cash payment is recorded locally and the user sees a unified success message
Sequential Card Payment Authorization
Given a $100 bill split across two credit cards When the system processes authorizations in sequence Then each card authorization returns a success callback and the final transaction shows combined successful status
Parallel Wallet and Card Authorization
Given the payment gateway supports parallel processing and a $75 bill split into wallet and card When the user submits the split payment Then both wallet and card authorizations are initiated concurrently and the user receives a unified success message only after both succeed
Partial Authorization Failure Handling
Given a split payment where one method fails When the failed authorization callback is received Then the system prompts the user to retry or choose an alternate payment and does not mark the transaction as complete until all splits are authorized
PCI-DSS Tokenization Verification
Given a card or wallet payment method When processing authorization Then sensitive payment data is tokenized before transmission, no raw card data is stored or logged, and PCI-DSS compliance audit logs confirm token usage
Offline Transaction Storage
"As a food truck operator, I want to record and queue split payments offline so that I never lose a sale during periods of no internet connectivity."
Description

Create a robust local storage mechanism to queue split payment transactions when the device is offline. Ensure the full split payment data—including method types and amounts—is preserved and automatically synced when connectivity is restored. Handle conflict resolution and retry logic to prevent duplicate charges, and notify the operator of sync success or failure.

Acceptance Criteria
Queuing Split Payments Offline
Given the device is offline and a split payment is initiated, when the operator confirms payment, then the full split payment data is stored in local storage with a timestamp and unique transaction ID. The stored transaction count increases by one. The app shows an "Offline: Transaction Queued" confirmation to the operator.
Automatic Sync on Connectivity Restoration
Given the device regains internet connectivity, when there are queued transactions, then the app automatically uploads each queued transaction to the server within 10 seconds. The transaction status on the server matches the locally stored split payment data. Successfully synced transactions are removed from local storage.
Retry Logic for Failed Sync
Given a queued transaction fails to sync due to network or server error, when retry attempts are scheduled, then the system retries up to 3 times with exponential backoff. Failed sync attempts are logged with timestamps. If retries exceed the limit, the transaction remains queued and an operator notification is triggered.
Duplicate Charge Prevention
Given a queued transaction is retried, when the server acknowledges the transaction with the same unique ID, then the app does not resend the transaction or duplicate it. Server responses with a 409 Conflict for duplicate IDs are handled gracefully, marking the local transaction as synced.
Operator Notification on Sync Outcome
Given a sync operation completes, when all queued transactions are processed, then the operator receives a notification summarizing the number of successful and failed syncs. For any failed transactions, the notification provides actionable details including transaction IDs and error reasons.
Split Receipt Generation
"As a food truck operator, I want receipts that itemize each payment method so that customers receive clear documentation and I maintain accurate records."
Description

Extend the receipt module to display detailed breakdowns of split payments, listing each payment method, its amount, and authorization status. Ensure both digital (PDF/email) and printed receipts clearly show the split information. Integrate with the reporting dashboard so that split transactions appear correctly in daily sales summaries and can be filtered by payment type.

Acceptance Criteria
Receipt Displays Split Payment Breakdown in POS
Given an order paid with multiple methods, when generating the receipt in the POS app, then the receipt lists each payment method with its amount and authorization status in a clear, itemized format.
Digital PDF Receipt Includes Split Payment Details
Given email receipt is enabled and a split payment transaction occurs, when sending the PDF receipt to the customer, then the PDF contains a distinct section showing each payment method, the corresponding amount, and approval codes.
Printed Receipt Shows Split Transactions
Given a connected receipt printer and a split payment order, when the receipt is printed, then the paper receipt displays a breakdown of each payment method, amount paid, and authorization confirmation.
Dashboard Reflects Split Payment Totals in Daily Summary
Given the reporting dashboard’s daily sales view, when split payment transactions are processed, then the summary correctly aggregates total revenue by summing all partial payments from split transactions.
Dashboard Filters Transactions by Payment Method in Splits
Given the dashboard filter options, when filtering by a specific payment type (e.g., cash or card), then the list shows only transactions that include that payment method, with split amounts highlighted accordingly.
Error Handling and Rollback
"As a food truck operator, I want the system to automatically undo partial payments if one method fails so that I can retry without manual corrections."
Description

Implement comprehensive error detection and rollback procedures for split tender operations. If any payment method in the split fails—due to authorization decline or system error—the system must reverse any successful authorizations and restore the order state to allow retries. Provide clear error messages and guided recovery steps for the operator.

Acceptance Criteria
Authorization Decline on Secondary Payment
Given a split tender with primary and secondary cards When primary payment is authorized successfully and secondary card is declined Then the system voids the primary authorization And reverts the order to pending state And displays “Secondary payment declined. Transaction canceled, please retry.”
System Error During Digital Wallet Authorization
Given a split tender with a credit card followed by a digital wallet When card authorization succeeds and digital wallet authorization returns a system error Then the system reverses the card authorization And restores the order to its original state And displays “Payment processing error. All charges reversed, please try again.”
Network Interruption During Split Tender
Given an order using two card payments When network connection is lost after the first authorization and before the second Then the system automatically rolls back the first authorization upon reconnection And resets the order status to pending And notifies the operator “Network error detected. Payments have been rolled back.”
Cash Tender After Card Decline
Given a split tender sequence starting with card payment then cash When the card authorization fails Then no cash payment is collected And the order remains open for new payment attempts And displays “Card declined. Please choose another payment method.”
Successful Multi-Method Payment
Given a split tender with multiple payment methods When all authorizations and cash tenders succeed Then the system confirms the order and updates inventory And displays “Payment successful. Thank you!”

TallyTip

Prompt and customize tip suggestions directly within the payment flow. TallyTip offers configurable percentages, round-up prompts, or custom amounts, helping vendors boost gratuities and customers easily reward service without extra steps.

Requirements

Tip Configuration Dashboard
"As a food truck owner, I want to easily configure tip suggestions and messaging in one central dashboard so that I can customize the customer experience and optimize gratuity collection without technical support."
Description

Implement a dedicated dashboard within the app settings where vendors can define default tip percentages, toggle round-up suggestions, and set custom prompt messages. The dashboard should offer an intuitive interface for easy configuration, immediately reflecting changes in the payment flow. It integrates with existing settings modules and ensures configurations persist offline and sync upon reconnecting, empowering vendors to control tip prompts without developer intervention.

Acceptance Criteria
Initial Dashboard Access
Given the vendor is logged in and opens the app, when they navigate to the Tip Configuration Dashboard, then the dashboard loads within 2 seconds and displays the current tip settings.
Update Default Tip Percentages
Given the vendor is on the Tip Configuration Dashboard, when they modify the default tip percentages to new values and tap 'Save', then the updated percentages are reflected immediately in both the UI and the payment flow.
Toggle Round-Up Suggestions
Given the vendor is on the Tip Configuration Dashboard, when they disable or enable the round-up suggestion toggle and save, then the change is applied immediately in the payment flow and persists across app restarts.
Set Custom Prompt Messages
Given the vendor enters a custom tip prompt message in the dashboard and saves, then the new message appears in the payment flow in place of the default upon the next transaction.
Offline Configuration Sync
Given the vendor updates tip settings while the app is offline, when connectivity is restored, then the offline changes sync to the server within 5 minutes and the dashboard shows a 'Synced' status.
Predefined Tip Options
"As a customer, I want to quickly select from common tip percentages so that I can add a gratuity without manually entering an amount."
Description

Provide a set of default tip percentage buttons (e.g., 10%, 15%, 20%) that appear dynamically during payment. These options should be configurable by the vendor and clearly displayed to the customer with local currency formatting. The feature must work offline by caching the options and ensure accurate tip calculation upon transaction completion.

Acceptance Criteria
Vendor Configures Default Tip Options
Given the vendor opens the TallyTip settings page, when the vendor sets default tip percentages (e.g., 10%, 15%, 20%), then the app saves these values and displays the corresponding tip buttons in the payment flow.
Customer Views Tip Options During Online Payment
Given a customer initiates payment with an active internet connection, when the payment screen loads, then the configured default tip percentage buttons appear dynamically with the correct local currency formatting.
Customer Views Tip Options During Offline Payment
Given a customer initiates payment without internet connectivity, when the payment screen loads, then the previously cached default tip percentage buttons appear with local currency formatting.
Accurate Tip Calculation Upon Transaction Completion
Given a customer selects a predefined tip percentage or enters a custom tip amount, when the transaction is completed, then the tip is correctly calculated and added to the total, and the receipt displays the accurate tip and grand total.
Currency Formatting Consistency Across Locales
Given the app locale is set to a specific region, when tip options are displayed, then tip amounts and percentage labels use the correct currency symbol, decimal separators, and formatting conventions for that locale.
Custom Tip Entry Field
"As a customer, I want to enter my own tip amount so that I can reward service at the level I choose even if predefined options don’t match."
Description

Allow customers to enter a custom tip amount during checkout. The field should support numeric input with validation against negative values and optional minimum or maximum limits set by the vendor. It must adapt to both tablet and phone form factors and operate reliably offline by storing input and syncing once online.

Acceptance Criteria
Online Custom Tip Within Configured Limits
Given vendor has configured a minimum tip of $2 and a maximum tip of $20, when the customer enters a tip amount between $2 and $20, then the input field accepts the value, updates the order total correctly, and enables the payment button.
Online Custom Tip Outside Configured Limits
Given vendor has configured a minimum tip of $2 and a maximum tip of $20, when the customer enters a tip amount less than $2 or greater than $20, then the input field displays a validation error message and prevents the order from being submitted.
Offline Tip Entry and Sync
Given the device is offline, when the customer enters a custom tip amount and completes checkout, then the app stores the tip locally and synchronizes it with the server automatically once the device regains connectivity.
Device Form Factor Adaptability
When the app is displayed on both phone and tablet form factors, then the custom tip entry field adjusts its layout and size appropriately, ensuring it is fully visible, accessible, and functional on both devices.
Numeric Input Validation
When the customer interacts with the tip entry field, then it only accepts numeric input, rejects any non-numeric or negative characters in real time, and displays an inline error if invalid input is attempted.
Offline Tip Queuing
"As a food truck owner, I want all customer tip selections to be recorded even without internet so that I never lose gratuity data during busy outdoor events."
Description

Design a mechanism to queue tip selections and custom entries when the device is offline, storing them locally and automatically syncing with the server when connectivity returns. Ensure data integrity by handling merge conflicts and providing status feedback to the vendor once tips are successfully transmitted.

Acceptance Criteria
Offline Tip Queued Locally
Given the device is offline, when the vendor selects a preset tip percentage or enters a custom tip amount, then the tip selection is stored in a local queue with order identifier and timestamp without any loss or modification of data.
Tip Sync Upon Connectivity Restoration
Given the device regains internet connectivity, when the app detects the connection, then all locally queued tip entries are automatically transmitted to the server within 30 seconds and removed from the local queue after successful acknowledgment.
Merge Conflict Resolution for Tip Entries
Given a tip entry for an order already exists on the server, when syncing the same order’s local tip entry, then the system merges changes to avoid duplicate or conflicting tips, logs any discrepancies, and retains the highest tip amount.
Feedback Display for Successful Tip Sync
Given a queued tip entry has been successfully sent to the server, then the app displays a confirmation message or icon next to the corresponding order in the vendor’s transaction list indicating 'Tip Synced'.
Local Storage Integrity During Offline Sessions
Given the vendor uses the app offline across multiple sessions or after app restarts, then all queued tip entries persist in local storage and remain intact until successfully synced, with no data loss or duplication.
Tip Round-Up Prompt
"As a customer, I want the option to round up my bill so that my tip feels effortless and convenient."
Description

Offer an optional ‘round-up’ suggestion that prompts customers to round their total to the nearest dollar or predefined increment. Vendors can enable or disable this option and set rounding rules. The prompt must clearly display the rounding difference as the tip amount and work seamlessly offline.

Acceptance Criteria
Vendor Enables Round-Up Option
Given the vendor is in the tipping settings and has enabled the round-up option with a selected increment, when the configuration is saved, then the payment flow displays a round-up suggestion showing the exact tip amount.
Customer Accepts Round-Up Suggestion
Given a customer reaches the payment screen with a total of $10.25 and nearest-dollar rounding is enabled, when the customer selects the round-up suggestion, then the total updates to $11.00 and the tip amount of $0.75 is clearly displayed in the confirmation.
Customer Declines Round-Up Prompt
Given the round-up suggestion appears on the payment screen, when the customer chooses to decline or skip the prompt, then the transaction proceeds with the original total amount and no tip is added.
Offline Payment Round-Up
Given the device is offline and the vendor has enabled round-up, when a payment is initiated, then the app retrieves the rounding increment from local storage, displays the correct tip suggestion, and stores the tip locally for later synchronization.
Custom Rounding Increment
Given the vendor sets a custom rounding increment of $0.50, when a transaction total of $10.30 is processed, then the prompt suggests rounding up to $10.50 and indicates a tip amount of $0.20.
Tip Analytics Dashboard
"As a food truck owner, I want to view tip performance analytics so that I can understand customer tipping behavior and adjust my offerings accordingly."
Description

Develop a reporting interface that aggregates tip data by date, location, and option type. The dashboard should display total tips, average tip percentage, and trends over time, exporting data in CSV format. It must pull in offline-synced tip records and integrate with existing analytics modules to give vendors actionable insights.

Acceptance Criteria
Date Range Tip Aggregation
Given a vendor selects a start and end date on the Tip Analytics Dashboard When the date range is applied Then the dashboard displays the total tip amount equal to the sum of all tips within that range And shows the average tip percentage calculated correctly And updates the trend chart to reflect data points within the selected period
Location-based Tip Reporting
Given a vendor filters tips by a specific location When the location filter is applied Then the dashboard displays only tip data from that location And recalculates total tips and average tip percentage accordingly And updates the trend visualization to show location-specific entries
Tip Option Type Breakdown
Given a vendor selects a tip option type (percentage, round-up, or custom) When the option type filter is applied Then the dashboard displays metrics (total tips, average percentage, trend) only for the selected tip option type
Offline-Synced Tip Record Integration
Given a vendor uses the app offline to record tips When the device reconnects to the internet Then all offline tip records are automatically synced to the analytics module without duplication And the dashboard reflects these records in totals and trends
CSV Export of Tip Data
Given a vendor has applied optional filters (date range, location, tip type) When the vendor clicks the Export CSV button Then the system generates and downloads a CSV file containing columns for date, location, tip amount, tip percentage, and tip option type And the number of rows in the file matches the number of records displayed on the dashboard

OfflineBuffer

Securely cache tap-to-pay transactions when connectivity drops and automatically process them once the network returns. OfflineBuffer guarantees no sales are lost in offline mode, providing peace of mind during remote events or dead zones.

Requirements

Offline Transaction Queue
"As a food truck owner, I want my tap-to-pay transactions to be queued when I lose connectivity so that I never miss a sale or inconvenience a customer during busy or remote events."
Description

Implement a robust queueing system that securely caches tap-to-pay transactions when the device is offline. This module should store transaction data in chronological order, maintain data integrity, and ensure no transaction is lost. The queue must seamlessly integrate with existing order workflows and prepare transactions for automatic processing once connectivity is restored.

Acceptance Criteria
Offline Transaction Queuing during Connectivity Loss
Given the device is offline, When a tap-to-pay transaction is initiated, Then the transaction is securely stored in the local offline queue without any data loss.
Automatic Processing upon Connectivity Restoration
Given one or more transactions are present in the offline queue, When network connectivity is restored, Then the system automatically processes each queued transaction in the order received and updates their status to 'Completed'.
Data Integrity Validation in Offline Queue
Given a transaction stored in the offline queue, When the system prepares it for processing, Then it verifies a checksum or digital signature to ensure transaction data has not been corrupted.
Chronological Order Maintenance of Cached Transactions
Given multiple transactions queued offline, When connectivity returns, Then the system processes them sequentially based on their original timestamp to preserve chronological order.
Seamless Integration with Order Workflow
Given an offline-queued transaction is processed, When the order history or reporting module is accessed, Then the transaction appears with accurate details and a 'Processed Offline' label consistent with online transactions.
Secure Local Storage
"As a food truck operator, I want my offline transaction data to be securely stored on my device so that sensitive payment details remain protected even if my device is lost or compromised."
Description

Ensure all cached transaction data is encrypted at rest and access-controlled on the device. The storage solution should comply with industry security standards (e.g., PCI DSS) and protect sensitive payment information until the transaction is processed. Integration with the app’s user authentication should prevent unauthorized access to the locally stored data.

Acceptance Criteria
Payment Captured Offline Then Encrypted Locally
Given the app loses connectivity during a tap-to-pay transaction, When the transaction completes offline, Then the transaction data is encrypted with AES-256 before writing to local storage; And the encryption keys are derived from the user's authenticated session.
Unauthorized Access Attempt to Local Transaction Data
Given a user who is not authenticated attempts to access local transaction data, When they request read or write operations on the storage, Then the operation is denied and logged; And no sensitive data is exposed.
Data Persistence Across App Restart
Given the app is restarted, When previously cached transactions exist, Then all transaction data remains encrypted at rest and is retrievable only after successful user authentication.
Encrypted Data Transfer Upon Connectivity Restoration
Given network connectivity is restored, When cached transactions are sent to the server, Then each transaction is decrypted in-memory, transmitted over HTTPS, and securely wiped from local storage upon confirmation of successful processing.
Compliance Audit Logging for Stored Transactions
Given an internal security compliance audit, When examining local storage, Then all transaction entries are encrypted, audit logs record encryption and decryption events, and storage meets PCI DSS requirements.
Connectivity Monitoring & Retry
"As a user, I want the app to automatically detect when I’m back online and retry sending my queued transactions so that I don’t have to manually intervene or check network status."
Description

Develop continuous network monitoring that detects changes in connectivity status in real time. When offline, the system should trigger retry logic with exponential backoff to attempt reconnection. Once the network is available, it should automatically initiate the sync process. This component must handle intermittent connections gracefully and log retry attempts for audit purposes.

Acceptance Criteria
Network Connectivity Loss Detection
Given the app is online, When the device loses network connectivity, Then the monitoring component logs an offline event and transitions to offline state within 5 seconds.
Exponential Backoff Retry Trigger
Given the system is offline, When a reconnection attempt is made, Then retries occur with exponential backoff delays doubling each attempt up to a maximum of 5 attempts.
Automatic Synchronization on Reconnection
Given the device regains network connectivity, When the network is available, Then cached transactions are automatically synchronized within 10 seconds and marked as processed.
Graceful Handling of Flapping Connectivity
Given intermittent connectivity with rapid disconnects and reconnects, When connectivity flaps, Then the system queues only one sync per stable connection period of at least 15 seconds to avoid redundant operations.
Retry Attempt Logging for Audit
Given each retry attempt, When a retry is executed, Then the system logs the timestamp, attempt count, and outcome to a persistent audit log.
Automatic Sync & Reconciliation
"As a truck owner, I want the app to automatically sync my offline transactions and reconcile them with our backend so that my sales records stay accurate without extra work from me."
Description

Build an automated synchronization engine that processes queued transactions upon reconnection. This engine must reconcile transaction statuses with the server, handle duplicates or conflicts, and update the local order history accordingly. Error-handling mechanisms should retry failed transactions and notify the user if manual resolution is required.

Acceptance Criteria
Offline Transaction Queue Processing
Given the device was offline and the user completed transactions; When the network is restored; Then the system automatically syncs all queued transactions to the server within 2 minutes without manual intervention.
Duplicate Transaction Detection
Given a transaction is already processed on the server; When the same transaction is re-synced; Then the system detects the duplicate by transaction ID and ignores it without creating a new sale record.
Conflict Resolution Alert
Given a synced transaction conflicts with server data; When conflict is detected; Then the system retries reconciliation twice and if still unresolved, notifies the user to manually review the transaction in the app dashboard.
Automatic Retry of Failed Sync Attempts
Given a transaction sync fails due to a temporary server error; When the retry mechanism triggers; Then the system retries the sync up to 3 times with exponential backoff before marking it as failed and notifying the user.
Order History Consistency After Sync
Given pending offline transactions are synced; When final reconciliation completes; Then the local order history reflects all transactions with matching statuses and timestamps as on the server.
User Notifications & Feedback
"As an operator, I want real-time feedback on my offline transactions’ status so that I am confident all payments are captured and know when any issues require my attention."
Description

Provide clear in-app notifications and visual indicators informing users of offline status, queued transactions, sync progress, and any errors encountered. Notifications should guide users on next steps when manual intervention is needed and confirm successful processing of offline transactions once synced.

Acceptance Criteria
Offline Mode Indicator Display
When network connectivity is lost, the app displays a persistent visual indicator (e.g., a red banner) within 2 seconds to inform the user that they are offline.
Queued Transactions Count Notification
When transactions are queued offline, the app shows an updated count badge on the "Transactions" icon reflecting the exact number of pending transactions.
Sync Progress Visualization
During automatic sync, a progress bar appears on the dashboard indicating the percentage of transactions uploaded, updating in real time until 100% completion.
Sync Failure Error Notification
If a queued transaction fails to process after network restoration, the app displays a clear error message with the failed transaction ID and guidance to retry manually.
Successful Sync Confirmation
Once all queued transactions are processed successfully, the app shows a toast notification confirming "All offline transactions synced successfully."
Manual Retry Prompt
If any offline transaction remains in queue for longer than 5 minutes after connectivity returns, the app prompts the user with an option to retry synchronization manually.

ReceiptVault

Deliver digital receipts via SMS, email, or QR code at the tap-to-pay confirmation screen. ReceiptVault reduces paper waste, streamlines record-keeping, and gives customers instant proof of purchase for returns or loyalty tracking.

Requirements

Multi-Channel Receipt Delivery
"As a food truck operator, I want to send digital receipts via SMS, email, or QR code at checkout so that my customers receive instant proof of purchase without using paper."
Description

Implement functionality to send digital receipts via SMS, email, or QR code directly from the tap-to-pay confirmation screen. The system must integrate with SMS and email gateways and generate scannable QR codes linked to receipt data. This feature reduces paper waste, accelerates record-keeping, and provides customers instant proof of purchase for returns or loyalty tracking.

Acceptance Criteria
SMS Receipt Delivery
Given a completed tap-to-pay transaction, when the user selects SMS and enters a valid phone number, then the system sends the digital receipt via SMS within 5 seconds and displays a confirmation message.
Email Receipt Delivery
Given a completed tap-to-pay transaction, when the user selects Email and enters a valid email address, then the system sends the digital receipt via email within 5 seconds and displays a confirmation message.
QR Code Receipt Generation
Given a completed tap-to-pay transaction, when the user selects QR Code, then the system generates and displays a scannable QR code within 2 seconds linking to the receipt data and stores the code for future retrieval.
SMS Gateway Failure Handling
Given the SMS gateway is unavailable, when the system attempts to send an SMS receipt, then it displays an error message allowing retry or alternative channel selection and logs the failure with timestamp.
Email Gateway Failure Handling
Given the email gateway returns an error, when the system attempts to send an email receipt, then it displays an error message allowing retry or alternative channel selection and logs the failure with timestamp.
Offline QR Code Operation
Given no network connectivity, when the user selects QR Code receipt, then the system generates the QR code offline using local transaction data, displays it immediately, and syncs the receipt to the server when connectivity is restored.
Customer Contact Validation
"As a food truck operator, I want to validate the customer’s phone number or email before sending a receipt so that digital receipts are reliably delivered without errors."
Description

Provide a user interface on the payment screen to capture and validate customer contact details (phone number or email). Implement client-side and server-side validation rules to ensure accurate delivery addresses. Invalid inputs should prompt clear error messages to guide the operator, ensuring high delivery success rates.

Acceptance Criteria
Valid Email Address Entry
Given the operator is on the payment screen and enters a valid email address in the contact field, when they tap the confirm button, then the system accepts the input, proceeds to the payment confirmation screen, and sends the digital receipt via email within 5 seconds.
Valid Phone Number Entry
Given the operator is on the payment screen and enters a valid phone number in E.164 format in the contact field, when they tap the confirm button, then the system accepts the input, proceeds to the payment confirmation screen, and sends the digital receipt via SMS within 5 seconds.
Invalid Email Format Detection
Given the operator enters an email address without the '@' symbol or domain extension, when they tap the confirm button, then the system displays an inline error message 'Please enter a valid email address' and disables the confirm button until the input is corrected.
Invalid Phone Format Detection
Given the operator enters a phone number with fewer than 10 digits or non-numeric characters, when they tap the confirm button, then the system displays an inline error message 'Please enter a valid phone number' and disables the confirm button until the input is corrected.
Server-Side Validation Failure Handling
Given the operator enters contact details that pass client-side validation, when the details are sent to the server and the server returns a validation error, then the system displays the server's error message to the operator and prevents proceeding to the payment confirmation screen.
Offline Queuing and Synchronization
"As a food truck operator, I want the system to queue and automatically send receipts once back online so that I never miss sending a receipt even in offline conditions."
Description

Enable the app to queue digital receipt requests when offline and automatically synchronize and transmit them when connectivity is restored. The queued receipts must persist across app restarts and gracefully handle partial failures, ensuring no receipts are lost during connectivity outages.

Acceptance Criteria
Offline Receipt Request Queuing
Given the user taps 'Send Receipt' while offline, When the request is processed, Then the receipt is added to the local queue with all transaction details stored successfully; Given the app is offline, When multiple receipts are sent, Then each receipt is queued in the order they were created.
Persistence Across App Restarts
Given one or more receipts are queued offline, When the user closes and reopens the app, Then the queued receipts remain intact and are available for synchronization.
Partial Failure Handling
Given connectivity is restored and multiple queued receipts are syncing, When one or more receipts fail to upload, Then successfully uploaded receipts are removed from the queue, failed receipts remain with an error flag, and retry attempts are scheduled.
Offline to Online Synchronization Trigger
Given the device regains network connectivity, When the network status changes to online, Then the app automatically initiates synchronization of all queued receipts within 10 seconds and displays a sync-in-progress indicator.
Queue Capacity and Overflow Handling
Given the local queue reaches its maximum threshold of 1,000 receipts, When the user attempts to send another receipt offline, Then the app displays a descriptive error message preventing additional queuing until space is freed.
Receipt Template Customization
"As a food truck operator, I want to customize the digital receipt template so that it reflects my brand identity and includes relevant order and loyalty details."
Description

Allow operators to customize receipt templates with branding elements (logo, color scheme), itemized order details, tax breakdown, and loyalty program information. The customization interface should be accessible in settings, with real-time preview of changes before saving and deploying.

Acceptance Criteria
Logo Upload and Placement Customization
Given the operator navigates to the receipt settings interface and selects the logo upload option When they upload a valid image file Then the new logo appears in the preview within 2 seconds and retains its original aspect ratio and quality.
Color Scheme Selection and Preview
Given the operator selects a primary and secondary color from the color picker When they change either color Then the receipt preview updates immediately to reflect the new color scheme and maintains at least WCAG AA contrast ratios.
Itemized Order Details Formatting
Given the operator toggles the itemized details option When itemization is enabled Then each line item, quantity, unit price, and subtotal displays correctly formatted, aligned, and visible in the preview.
Tax Breakdown Display Toggle
Given the operator chooses to include tax breakdown When the receipt contains taxable items Then the preview shows individual tax rates, tax amounts per item, and the total tax correctly calculated.
Loyalty Program Information Inclusion
Given the operator adds loyalty program details When a customer has loyalty data attached Then the receipt preview displays the loyalty ID, points earned, and any redemption messages according to the template settings.
Save and Deploy Template Validation
Given the operator has customized all template elements and clicks 'Save and Deploy' When deployment completes Then the system displays a confirmation message and all subsequently generated receipts reflect the updated template.
Delivery Confirmation and Retry Logic
"As a food truck operator, I want visibility into receipt delivery status and automatic retries so that I can ensure all customers receive their digital receipts without manual intervention."
Description

Implement end-to-end delivery status tracking for each digital receipt. Display delivery confirmations in the operator’s dashboard and automatically retry failed sends using exponential backoff. Notify operators of persistent failures and provide options for manual resend.

Acceptance Criteria
Successful Delivery Acknowledgment
Given a digital receipt is sent via SMS, email, or QR code and the delivery API returns a success response, When the operator views the dashboard, Then the receipt status is displayed as 'Delivered' with a timestamp.
Automatic Retry upon Send Failure
Given an initial send attempt fails, When the system detects a non-success response code or a timeout, Then it automatically schedules a retry for that receipt delivery.
Exponential Backoff Implementation
Given consecutive send failures for the same receipt, When each retry is executed, Then the delay between retries follows exponential backoff (e.g., 1 min, 2 min, 4 min) up to a configurable maximum interval.
Operator Notification on Persistent Failure
Given the system has attempted the maximum number of retries without success, When the last retry fails, Then the dashboard displays an alert notifying the operator of persistent delivery failure for that receipt.
Manual Resend Option Available
Given a receipt is in a persistent failure state, When the operator selects the 'Resend Receipt' action from the dashboard, Then the system initiates a new send attempt and updates the status accordingly.

WalletSync

Seamlessly integrate popular mobile wallets (Apple Pay, Google Pay, Samsung Pay) in-app with auto-detection of available payment methods. WalletSync ensures compatibility with every customer’s preferred digital wallet, maximizing acceptance rates.

Requirements

Payment Method Auto-Detection
"As a food truck customer, I want the app to automatically show which digital wallet I can use so that I can complete my payment quickly without exploring unsupported options."
Description

Implement functionality to automatically detect and display the available mobile wallet options (Apple Pay, Google Pay, Samsung Pay) supported on the user’s device. The system should query the device’s wallet APIs at app startup and dynamically enable or disable payment options in the UI based on compatibility and user preferences. This auto-detection ensures customers only see valid payment methods, reducing confusion and checkout friction.

Acceptance Criteria
Apple Pay Supported Device Startup
Given an iOS device with Apple Pay configured, when the app starts, then the Apple Pay option is visible and enabled in the payment methods list with no other wallet options available.
No Wallet Supported Device Startup
Given an Android device without any mobile wallet apps installed, when the app starts, then no wallet payment options are displayed in the payment methods screen.
Multiple Wallets Detected on Android Device
Given an Android device with both Google Pay and Samsung Pay configured, when the app starts, then both Google Pay and Samsung Pay options are visible and enabled and Apple Pay is hidden.
User Preference Disables a Detected Wallet
Given a user has disabled Samsung Pay in app settings on a device where it’s supported, when the app starts, then Samsung Pay is not displayed among the available payment methods despite device compatibility.
Offline Startup on Device with Configured Wallets
Given a device with Google Pay configured but no internet connection, when the app starts offline, then Google Pay is detected locally and presented as an enabled payment option.
Apple Pay Integration
"As an Apple Pay user, I want to pay for my order with a single tap so that I can complete the transaction quickly and securely."
Description

Integrate Apple’s In-App Payments API to enable customers to pay using Apple Pay within the app. This includes setting up merchant identifiers, certificates, and handling Apple Pay sessions securely, as well as tokenization of payment information. The implementation must provide a seamless one-tap checkout flow for users with Apple Pay configured on their device, improving conversion rates.

Acceptance Criteria
Successful Apple Pay Setup
Given the merchant identifier and Apple Pay certificates are correctly configured When the user accesses the payment option in the app Then the Apple Pay payment sheet is displayed without errors
One-Tap Checkout Flow
Given the user has at least one card configured in Apple Wallet When the user taps the Apple Pay button on the checkout screen Then the app completes the payment with a single tap and returns a confirmation within 3 seconds
Payment Token Generation and Tokenization
Given the user authorizes the payment via Face ID or Touch ID When the Apple Pay session returns a payment token Then the app securely tokenizes the payment data and sends it to the backend API over HTTPS
Payment Authorization and Confirmation
Given a valid payment token is received by the backend When the backend processes the payment with the payment provider Then the user receives an in-app confirmation message with transaction details including amount, date, and transaction ID
Transaction Record Logging
Given a successful payment is processed When the transaction completes Then the app logs the transaction locally and syncs it to the inventory system with a unique transaction identifier
Error Handling for Failed Transactions
Given the payment authorization fails due to insufficient funds or network issues When the error is returned by Apple Pay session Then the app displays an appropriate error message to the user and allows retry
Google Pay Integration
"As a Google Pay user, I want to use my saved payment methods to checkout quickly so that I can get back to enjoying my meal faster."
Description

Implement Google Pay API integration to allow Android users to complete payments using their Google Pay accounts. The integration should handle payment data encryption, merchant validation, and tokenization, ensuring secure transactions. Provide a streamlined checkout button in the app to launch Google Pay with pre-filled order details, reducing checkout time.

Acceptance Criteria
Google Pay Button Visibility
Given the Android user has a Google Pay account configured, when they navigate to the checkout screen, then the Google Pay button is displayed prominently above other payment options.
Successful Payment Flow
Given a valid order and the user taps the Google Pay button, when the user completes authentication, then the app receives a payment token and marks the order as paid within 2 seconds.
Payment Data Encryption
Given the app sends the payment token and order details to the server, when the data is transmitted, then all payment information is encrypted using AES-256 and TLS.
Merchant Validation
Given the app initiates a Google Pay session, when merchant credentials are sent, then Google Pay returns a valid merchant validation token within 3 seconds.
Error Handling on Payment Failure
Given Google Pay returns an error code during tokenization, when the error occurs, then the app displays a clear error message and provides an alternative payment method option.
Samsung Pay Integration
"As a Samsung Pay user, I want to use Samsung Pay in the app so that I can leverage my preferred payment method without hassle."
Description

Enable Samsung Pay support via Samsung’s Payment SDK, allowing users with Samsung devices to pay seamlessly. Ensure proper handling of magnetic secure transmission (MST) and NFC payment flows where supported. Present Samsung Pay as an integrated checkout option with full tokenization and secure handshake.

Acceptance Criteria
Samsung Pay Option Visibility
Given a Samsung device with Samsung Pay configured, when the user navigates to the checkout screen, then the “Pay with Samsung Pay” option is displayed among the payment methods.
Samsung Pay NFC Transaction
Given a Samsung device that supports NFC and Samsung Pay, when the user selects Samsung Pay and taps the device near the NFC-enabled payment terminal, then the transaction completes successfully within 5 seconds and a success confirmation is shown.
Samsung Pay MST Transaction
Given a Samsung device that supports MST and Samsung Pay, when the user selects Samsung Pay and holds the device near a magnetic stripe reader, then the payment is processed securely and a success confirmation is displayed.
Samsung Pay Tokenization and Security
When initiating a Samsung Pay transaction, then the app uses Samsung’s SDK to tokenize payment data, transmits tokens over HTTPS, and does not store or log actual card details.
Samsung Pay Failure Handling
Given a Samsung Pay transaction failure (e.g., insufficient funds or network timeout), when the failure occurs, then the app displays a clear error message with the failure reason and offers options to retry or select another payment method.
Unified Payment UI
"As a customer, I want to see all my available payment options in a clear and consistent interface so that I can easily select my preferred method."
Description

Design and implement a unified payment UI component that adapts to the detected available wallets, displaying them in a consistent style. The UI should support dynamic ordering of options based on popularity or user preference, include clear branding for each wallet, and provide accessibility compliance. This ensures a cohesive experience regardless of payment method.

Acceptance Criteria
Display Available Wallets Upon Payment Screen Load
Given the user opens the payment interface When the app detects supported mobile wallets Then all available wallets are shown in a unified UI component with consistent styling
Dynamic Ordering Based on User Preference
Given the user has prior payment history When the payment UI loads Then wallets are ordered by the user’s most frequent selections
Accessibility Compliance for Wallet Selection
Given the payment UI is displayed When a user navigates via screen reader or keyboard Then each wallet option exposes proper ARIA labels and can be selected without touch input
Clear Branding for Each Wallet Option
Given the list of detected wallets When rendered in the UI Then each wallet displays its official logo and brand color within the unified component
Offline Fallback for Wallet Options
Given the app is offline When the user opens the payment UI Then only locally cached wallet options are shown and selection attempts prompt a retry once connectivity is restored
PCI DSS Compliance Handling
"As a food truck operator, I want the payment process to meet security standards so that my customers’ data is protected and I remain compliant."
Description

Ensure the WalletSync feature adheres to PCI DSS standards by handling all payment data securely. Implement tokenization, data encryption in transit and at rest, and ensure no sensitive card data is stored on-device. Integrate audit logging and secure storage of merchant credentials, verifying compliance through periodic security scans and certifications.

Acceptance Criteria
Card Data Tokenization Scenario
Given a user inputs card payment details in WalletSync, When the payment is processed, Then the system replaces the card data with a PCI-compliant token generated by the payment gateway and discards the original card data immediately.
Data Encryption in Transit Scenario
Given WalletSync transmits payment data over networks, When data is sent to the payment processor, Then the data is encrypted using TLS 1.2 or higher and certificate pinning is enforced.
Data Encryption at Rest Scenario
Given tokenized payment information is stored on the server, When data is persisted in the database, Then it is encrypted with AES-256 and encryption keys are managed via an HSM or secure key management service.
On-Device Storage Restriction Scenario
Given the WalletSync feature runs offline, When caching payment data for retries, Then no sensitive card data (PAN, CVV) is stored on the device; only tokens and transaction identifiers are retained.
Audit Logging and Credential Security Scenario
Given the application performs payment transactions, When actions involve merchant credential access or tokenization events, Then all events are logged with timestamp, user ID, and action details in an immutable audit log, and merchant credentials are stored encrypted with access restricted by role.

Demand Radar

Harnesses historical sales, event calendars, and trend analytics to forecast supply needs for each route, ensuring accurate restock quantities and reducing stockouts by anticipating demand surges.

Requirements

Historical Data Aggregation
"As a Food Truck Operator, I want to aggregate and view my past sales data by location and time period so that I can understand historical performance and inform future supply needs."
Description

The system must collect, consolidate, and store past sales records by route, time period, and item. It integrates with existing order logs and data sources to create a unified dataset. This enables accurate pattern recognition, trend analysis, and reliable inputs for forecasting algorithms. The module supports data normalization, error checking, and archival management to ensure historical data quality and accessibility.

Acceptance Criteria
Route-Based Data Aggregation
Given multiple historical sales logs from different routes and time periods when the aggregation process runs then all records are consolidated into a unified dataset without duplicates and with complete route, date, item, and quantity fields populated
Data Normalization Validation
Given raw sales records with inconsistent date formats, missing values, or variant item names when normalization is applied then all dates conform to ISO 8601, missing quantities are flagged for review, and item names map to a standardized master list with zero unmapped entries
Error Checking Mechanism
Given incoming sales data containing invalid entries such as negative quantities or unrecognized route codes when the error-checking module processes the data then all invalid records are isolated in an error queue, detailed error logs are generated, and only valid records proceed to consolidation
Archival Management and Retrieval
Given historical sales data older than the configured archival threshold when the monthly archival job executes then all qualifying records are moved to archive storage, remain queryable via API, and retrieval of any archived record completes within 5 seconds
External Order Log Integration
Given new order logs available in external POS systems when the integration connector runs then it ingests, transforms, and merges all new logs into the unified dataset within 10 minutes of availability and generates a success audit record
Event Calendar Integration
"As a Food Truck Operator, I want the system to integrate local event schedules into demand analysis so that I can anticipate and prepare for peak crowd events."
Description

The application must integrate external event calendars (local festivals, sporting events, holidays) and internal schedules to identify potential demand surges. Events are tagged by date and geographic area, normalized into a standard format, and linked to routes. This integration allows the forecasting engine to adjust predictions based on upcoming activities that historically drive higher sales.

Acceptance Criteria
Calendar Event Import for Local Events
Given the user has granted access to external event calendars, when the system performs a calendar sync, then all events within a 50-mile radius and within the next 90 days are imported, tagged by source, and stored in the internal event database.
Event Data Normalized to Standard Format
Given imported events with varying date, time, and location formats, when the normalization process runs, then all events are converted to ISO 8601 date-time, standardized geographic coordinates, and consistent event tags.
Events Linked to Relevant Routes
Given normalized events and defined routes, when route matching executes, then each event is linked to every route that serves the event’s geographic area and falls within the event’s date range, and unmatched events generate a review alert.
Forecast Adjustment Triggers
Given upcoming events linked to routes, when the forecasting engine calculates demand, then it applies a historical uplift factor for each event type and adjusts restock recommendations by at least the average percentage increase observed in past similar events.
External Calendar API Error Handling
Given multiple external calendar API calls, when an API call fails or times out, then the system retries up to three times, logs detailed error information, notifies the user of partial sync failures, and continues processing other integrations without halting.
Trend Analysis Module
"As a Food Truck Operator, I want to identify and visualize sales trends and seasonal patterns so that I can adjust inventory planning for recurring demand cycles."
Description

Implement a statistical and machine learning-based module to detect weekly, monthly, and seasonal sales trends and anomalies. The module processes historical data to highlight recurring demand patterns, outliers, and emerging trends. It provides configurable parameters for sensitivity and visualization dashboards for stakeholders to validate insights before feeding them into the forecasting engine.

Acceptance Criteria
Weekly Sales Trend Identification
Given at least four weeks of historical sales data is available, when the module runs weekly trend detection, then it identifies consistent upward or downward sales patterns with at least 80% accuracy.
Seasonal Trend Recognition
Given a full year of sales records is provided, when the module analyzes monthly and seasonal data, then it correctly identifies recurring peaks and troughs corresponding to known seasonal periods.
Anomaly Detection Alerts
Given incoming daily sales entries, when the module processes the data, then any daily sales value deviating more than two standard deviations from the weekly average is flagged and an alert is generated.
Sensitivity Parameter Configuration
Given the user configures sensitivity settings to high, medium, or low, when trend detection runs, then the module applies the selected sensitivity to adjust anomaly thresholds and trend detection criteria accordingly.
Visualization Dashboard Rendering
Given processed trend and anomaly insights are available, when the user accesses the visualization dashboard, then interactive charts for weekly, monthly, and seasonal trends load within three seconds and reflect the configured date ranges and sensitivity settings.
Demand Forecasting Engine
"As a Food Truck Operator, I want to receive item-level demand forecasts per route so that I can determine optimal restock quantities ahead of each shift."
Description

Develop a predictive engine that combines historical sales data, event inputs, and trend insights to generate item-level demand forecasts for each route. The engine outputs daily and per-shift quantity projections with confidence intervals. It supports parameter tuning, model evaluation metrics, and automated retraining to maintain accuracy over time.

Acceptance Criteria
Generating Daily Forecasts for Upcoming Shifts
Given the system has loaded historical sales, event schedules, and trend indicators, when the forecast engine executes, then it outputs item-level demand quantities for each route for the next day and each shift.
Tuning Forecast Parameters for Seasonal Events
When a user adjusts model parameters (e.g., seasonal weight, event impact), then the engine applies these parameters and the forecast results update accordingly within 5 seconds.
Automated Model Retraining After Data Update
Given new sales data or event inputs are ingested, when the scheduled retraining triggers, then the model retrains automatically and generates updated forecast metrics without manual intervention.
Evaluating Forecast Accuracy Metrics
When forecasts are generated, then the system calculates and displays mean absolute percentage error (MAPE) and root mean square error (RMSE) for each route and shift.
Incorporating Confidence Intervals in Forecast Reports
Given forecast outputs, when the report is viewed, then the system shows upper and lower confidence interval bounds for each item-level forecast at a 95% confidence level.
Restock Recommendation Dashboard
"As a Food Truck Operator, I want a dashboard that shows restock recommendations based on forecasts so that I can quickly prepare my inventory for upcoming routes."
Description

Provide a user-facing dashboard presenting forecast outputs and actionable restock recommendations. Features include filtering by date, route, and item category, exporting restock lists, and highlighting items at risk of stockouts. The dashboard offers drill-down views and alerts for significant forecast deviations or unusual demand spikes.

Acceptance Criteria
View Restock Recommendations for Upcoming Route
Given the user has selected a specific upcoming route When they access the Restock Recommendation Dashboard Then the dashboard displays each item’s forecasted demand and recommended restock quantity for that route
Filter Recommendations by Date, Route, and Category
Given the dashboard is loaded When the user applies filters for date range, specific route, and item category Then only items matching the selected criteria are displayed in the recommendations list
Export Restock List
Given the filtered recommendations list is visible When the user clicks the export button Then the system generates and downloads a CSV file containing item names, forecasted demand, and restock quantities
Highlight Stockout Risk Items
Given forecasted demand data is available When the demand for any item exceeds the current stock level Then that item is highlighted in red with a stockout risk indicator on the dashboard
Alert on Forecast Deviation
Given historical forecasts and actual sales data exist When the latest forecast deviates by more than 20% from historical patterns Then the system displays a warning alert specifying the item and deviation percentage
Access Drill-Down Forecast Details
Given an item is listed in the recommendations When the user clicks on the item row Then a detailed view opens showing demand trends, event calendar influences, and underlying forecast data calculations
Offline Forecast Access
"As a Food Truck Operator, I want to access my demand forecasts and restock recommendations when offline so that I can plan even in areas without connectivity."
Description

Enable caching and local storage of forecast data and restock recommendations for use without internet connectivity. The app automatically syncs updated forecasts when the device reconnects. Offline access ensures operators can plan routes and inventory in remote areas or during connectivity issues without losing critical predictive insights.

Acceptance Criteria
Forecast Data Cached on Initial Load
Given the device has internet connectivity and the user accesses the Demand Radar feature for the first time, when the system fetches forecast and restock recommendation data, then it must store the complete dataset in local storage within 10 seconds.
Access Forecast Offline
Given the device is offline, when the user navigates to the Demand Radar view, then the app displays the most recently cached forecast and restock recommendations within 2 seconds.
Automatic Data Sync on Reconnection
Given the device regains internet connectivity after being offline, when the app detects reconnection, then it automatically syncs and updates the local cache with any new forecast and recommendations within 5 minutes and indicates sync success to the user.
Conflict Resolution During Sync
Given local cached forecast data has been modified by the user and newer server data is available, when the sync process runs, then server-side data overrides local changes, and the local cache reflects the latest server data without errors.
Local Storage Capacity Management
Given cached forecast data exceeds the storage limit of 50MB, when new data is fetched, then the system automatically purges the oldest cached entries to ensure the total stored data remains within the 50MB limit, retaining at least the last 30 days of forecasts.

Smart Load Planner

Calculates optimal load distribution across trucks, aligning cargo space with forecasted needs to prevent overloading, minimize waste, and streamline loading operations before departure.

Requirements

Load Capacity Calculation
"As a food truck operator, I want the planner to calculate my truck’s maximum weight and volume capacity so that I can prevent overloading and comply with safety regulations."
Description

Calculate each truck’s maximum permissible weight and volume capacity based on its specifications, internal dimensions, and regulatory limits. By referencing stored truck profile data, the system provides real-time capacity guidelines to ensure safe and compliant loading.

Acceptance Criteria
Standard Truck Capacity Calculation
Given a truck profile with specified maximum weight and volume capacities When the user inputs cargo weight and volume Then the system calculates and displays the remaining weight and volume capacity accurately
Exceeding Maximum Weight Alert
Given a proposed load that pushes the total weight above the truck’s maximum permissible weight When the load is evaluated Then the system triggers an alert stating 'Weight limit exceeded' and prevents the load from being saved
Volume Capacity Check
Given cargo items whose combined volume equals the truck’s maximum volume capacity When the system performs the capacity calculation Then it validates that the load fits within the volume constraints without errors
Missing Profile Data Handling
Given a truck profile with missing weight or dimension information When the user initiates the capacity calculation Then the system displays an error message prompting completion of profile data and halts the calculation
Regulatory Limit Enforcement
Given local regulatory weight limits that are lower than the truck’s manufacturer specifications When the system calculates capacity Then it applies the lower regulatory limit and notifies the user of the override
Forecasted Demand Integration
"As an operations manager, I want load planning to consider forecasted demand so that trucks carry the right products and reduce waste or stockouts."
Description

Integrate historical sales data and demand forecasts into the load planning workflow. Retrieve forecasted item quantities for each route and timeframe, apply buffer stock rules based on demand variance, and adjust planned loads accordingly to minimize stockouts and waste.

Acceptance Criteria
Route Demand Forecast Retrieval
Given a selected route and timeframe, when the user requests demand forecasts, then the system retrieves and displays forecasted item quantities matching historical and forecast data.
Buffer Stock Application
Given forecasted demand and calculated variance, when variance exceeds the defined threshold, then buffer stock rules are applied and additional quantities are added to planned loads.
Load Plan Calculation
Given forecasted quantities and buffer stock adjustments, when generating the load plan, then total planned volume does not exceed truck capacity and cargo is optimally distributed.
Offline Forecast Access
Given loss of connectivity, when the user accesses the load planner, then the most recently synced forecasted demand and buffer stock values are available and editable.
Variance Alert Notification
Given incoming forecast data, when demand variance for any item exceeds 20%, then the system generates an alert notification for user review.
Optimal Load Distribution Algorithm
"As a dispatcher, I want the system to distribute cargo optimally across my fleet so that loading is efficient and no truck is overburdened."
Description

Implement an optimization algorithm to allocate items across multiple trucks, balancing weight distribution and maximizing space utilization. The algorithm will consider item priority, refrigeration needs, and route sequence to generate the most efficient load plan.

Acceptance Criteria
Balanced Weight Distribution
Given a set of items and multiple trucks, when the optimization algorithm executes, then the weight difference between the heaviest and lightest loaded truck does not exceed 10% of the average truck capacity.
Maximized Space Utilization
Given the trucks’ volume capacities and item dimensions, when the algorithm runs, then total utilized space across all trucks is at least 90% of combined available volume.
Refrigeration Constraint Compliance
Given a list of refrigerated items and available refrigeration-equipped trucks, when the optimization executes, then all refrigerated items are assigned exclusively to trucks with refrigeration units and none to non-refrigerated trucks.
Priority-Based Loading Order
Given items tagged with delivery priorities and their route sequence, when the load plan is generated, then higher-priority items are positioned earlier in the loading order in alignment with their delivery stops.
Route Sequence Alignment
Given a predetermined multi-stop delivery route, when the algorithm creates the load plan, then items are arranged within each truck in the order of upcoming drop-off locations to minimize unnecessary handling.
Overload Alert Notifications
"As a warehouse loader, I want to be notified when a planned load exceeds my truck’s capacity so that I can adjust the load before departure."
Description

Generate real-time alerts when a planned load exceeds any truck’s capacity constraints or regulatory limits. Alerts will identify specific items causing the overload and suggest corrective actions, ensuring issues are resolved before departure.

Acceptance Criteria
Single Item Overload Detection
Given a user adds a load item that causes the truck's payload to exceed its maximum capacity, when the user attempts to finalize the load plan, then the system must immediately display an alert specifying the item name, the quantity causing the overload, the truck's maximum capacity, and suggest reducing the item quantity or redistributing it to another truck.
Combined Weight Capacity Exceeded
Given the total weight of all planned items exceeds the truck's weight capacity, when the user views the load summary, then the system must list all items contributing to the excess, show the total overage weight, and provide options to auto-adjust quantities or split the load across other trucks.
Regulatory Axle Weight Limit Breach
Given the distribution of cargo across axles exceeds legal weight limits for any axle, when the load plan is evaluated, then the system must generate an alert indicating the specific axle(s) over limit, the amount of excess weight, and recommend redistributing items or shifting weight within legal limits.
Offline Mode Overload Notification
Given the user is working offline and creates a load plan exceeding truck capacity, when the user saves the plan, then the application must queue an overload alert locally, and upon reconnection, synchronize and display the alert with item details and corrective suggestions.
Load Adjustment Suggestion After Item Change
Given a user modifies item quantities in an existing load plan, when the change leads to an overload, then the system must immediately recalculate capacity, display an updated alert with the new excess details, and offer to adjust other item quantities to resolve the overload.
Visual Load Plan Interface
"As a food truck operator, I want a visual map of how items are loaded so that I can quickly understand and execute the loading process."
Description

Provide an interactive visual interface showing the load plan for each truck, including item breakdown, cargo area mapping, and color-coded load statuses. Planners can review, modify, and export visual load plans for easy execution.

Acceptance Criteria
Viewing Load Plan Overview
Given a planner opens the Visual Load Plan Interface, when the load data is available, then the interface displays each truck's cargo area with color-coded item segments, showing item name, quantity, and load status.
Adjusting Item Placement
Given the planner clicks on an item segment within a truck's cargo area, when the planner drags and drops the segment to a new position, then the system updates the load distribution, recalculates capacity utilization, and refreshes color coding accordingly.
Filtering Items by Category
Given the planner applies a category filter, when the filter is selected, then only items in the chosen category appear on the visual load plan with their load statuses.
Exporting Load Plan
Given the planner selects the export option, when the export is confirmed, then the system generates and downloads a high-resolution PDF of the visual load plan with item breakdowns and color-coded statuses.
Responsive Display on Mobile
Given the planner views the interface on a mobile device, when the orientation changes or screen size varies, then the visual load plan adjusts layout and maintains readability of item segments and status colors without overlap.

Adaptive Route Optimizer

Generates dynamic restock paths in real-time, adjusting for traffic, distance, and stop priorities to cut transit times and ensure timely deliveries to all locations.

Requirements

Real-Time Traffic Data Integration
"As a food truck operator, I want to see traffic-aware route suggestions so that I can avoid delays and ensure timely restocking."
Description

Integrate live traffic data from external providers via APIs to continuously update route estimates, avoid congestion, and adjust delivery schedules. The system polls traffic feeds at configurable intervals, processes speed and incident data, and recalculates ETAs. This feature ensures routes reflect current road conditions, reducing delays and improving overall delivery accuracy and customer satisfaction.

Acceptance Criteria
Sudden Traffic Incident Response
Given the system receives a high-severity traffic incident report on the current route, When the incident is within 5 miles of the next stop, Then the route optimizer must recalculate and present an alternative route bypassing the incident within 10 seconds of receiving the data. Given the incident bypass route is generated, Then the new estimated travel time must not exceed the original route travel time by more than 5 minutes.
Configurable Traffic Data Polling
Given the traffic polling interval is configured to X seconds, When X seconds have elapsed, Then the system must fetch the latest traffic data within 2 seconds of the scheduled poll time. Given the polling interval setting is updated by the user, When the next polling cycle begins, Then the system must apply the new interval without requiring a restart.
ETA Recalculation Accuracy
Given live traffic speed variations exceeding 10% from baseline, When new traffic data is received, Then the ETA must be recalculated and displayed in the UI within 5 seconds. Given recalculated ETAs, Then the updated ETA must have an error margin no greater than 2 minutes compared to actual travel time measured over the next segment.
Cached Traffic Data Fallback
Given loss of internet connectivity, When three consecutive polling attempts fail, Then the route optimizer must load the most recent traffic dataset from cache. Given cached data fallback is in use, Then ETA estimations must not deviate by more than 10% from last known live data estimations.
Real-Time Route Visualization Update
Given a recalculated route due to updated traffic conditions, When the new route is generated, Then the map view must update the route line and display the updated ETA within 3 seconds. Given route updates, Then the displayed turn-by-turn instructions must reflect all changes accurately.
Priority Stop Handling
"As an operator, I want to designate priority stops so that critical locations are restocked first."
Description

Enable marking of stops with priority levels (e.g., critical restock points, VIP clients) so the optimizer services high-priority locations first. The feature integrates into the stop management interface, allows dynamic reprioritization, and influences route sequencing algorithms. It ensures urgent restock needs are met promptly, minimizing stockouts at key locations.

Acceptance Criteria
Marking a Stop as Critical Restock Point
Given a stop is not prioritized and the operator selects 'Critical' priority for that stop; When the operator saves the stop; Then the stop displays a red priority icon in the interface and is assigned 'Critical' priority in the system.
Dynamic Reprioritization of VIP Client Stop
Given an active route with stops scheduled; When the operator changes the priority of a particular stop to 'VIP'; Then upon route optimization, the VIP stop moves to the top of the sequence while other stops maintain order relative to their priorities.
Route Sequencing with Mixed Priority Levels
Given a list of stops with priorities set as 'Critical', 'VIP', and 'Standard'; When the operator triggers route optimization; Then the resulting route sequence orders stops in descending priority and optimizes for shortest travel distance within each priority group.
Offline Mode Priority Stop Handling
Given the app is offline and the operator updates a stop’s priority to 'Critical'; When the operator reconnects to the internet; Then the app synchronizes the priority change to the server and applies it to the route optimization process.
Visual Prioritization in Stop Management Interface
Given a stop has a priority level assigned; When the operator views the stop on both the list and map views; Then the stop is highlighted with its corresponding priority color and label in both views.
Offline Route Caching
"As a mobile operator, I want to access my planned routes even when offline so that I can navigate without interruption."
Description

Cache optimized routes, associated map tiles, and relevant location metadata when connectivity is available, enabling full route planning and turn-by-turn navigation offline. The app stores the latest route snapshot and automatically syncs changes when back online. This ensures continuous operation in areas with poor or no internet connectivity.

Acceptance Criteria
Initial Route Cache Download
Given the user is online and requests a new route, when the route optimizer completes calculation, then the application stores the entire route data, map tiles, and metadata locally within 5 seconds.
Offline Navigation Activation
Given the device has no internet connectivity, when the user selects a previously cached route, then the app provides turn-by-turn navigation solely using the local cache without attempting network calls.
Cache Update on Connectivity Restored
Given the device regains internet access after being offline, when the user reviews cached routes with changed traffic conditions or stop priorities, then the app syncs and updates the local cache within 10 seconds of the review action.
Cache Storage Capacity Enforcement
Given the local cache storage is at or above 80% capacity, when the user attempts to cache a new route, then the system prompts the user to delete the oldest cached route before proceeding to ensure cache usage remains below 80%.
Error Handling for Unavailable Cache
Given the device is offline and the requested route is not in local cache, when the user initiates route planning, then the app displays an appropriate offline error message and suggests nearby cached routes if available.
Dynamic Rerouting
"As a driver, I want my route to adjust automatically if I deviate or add a stop so that I stay on the most efficient path."
Description

Automatically recalculate and deploy updated routes in response to real-time events such as missed stops, added stops, or traffic incidents. The system triggers recalculation within seconds of detecting deviations, merges current traffic and priority data, and updates the navigation path seamlessly to maintain optimal efficiency.

Acceptance Criteria
Missed Stop Detection
Given the driver fails to reach a scheduled stop within a 2-minute buffer window, When the system detects the delay, Then it must recalculate the route within 5 seconds and update the navigation display to skip or reschedule the missed stop while maintaining original priority of remaining stops.
Added Stop Mid-Route
Given a new stop is added by the dispatcher during an active route, When the update is received, Then the system recalculates the optimal path including the new stop within 5 seconds and presents the adjusted route without disrupting current navigation.
Traffic Incident Update
Given a traffic incident is reported along the current route, When real-time traffic data indicates congestion exceeding a 5-minute delay, Then the system recalculates a new route within 3 seconds to avoid congestion and displays it to the driver.
High-Priority Stop Rerouting
Given a stop is marked high priority during transit, When the flag is set, Then the system recalculates the route within 5 seconds to place the high-priority stop earlier while minimizing additional travel time.
Offline Reconnection Rerouting
Given the device loses connectivity and later reconnects with updated location data, When the connection is restored, Then the system recalculates the route within 5 seconds based on current position and traffic information and seamlessly resumes navigation.
Fuel Efficiency Optimization
"As a food truck owner, I want the system to minimize fuel consumption so that I reduce operating costs."
Description

Incorporate vehicle-specific fuel consumption profiles and route parameters to favor routes that balance shortest distance and minimal idling in traffic. The optimizer calculates estimated fuel usage for alternate paths, highlights cost-saving routes, and integrates consumption metrics into driver dashboards for transparent operational insights.

Acceptance Criteria
Fuel Consumption Estimation for Alternate Routes
Given a start and end location and vehicle profile, when multiple routes are generated, then the system calculates and displays the estimated fuel consumption for each route within 5% accuracy. Given road conditions and traffic data, when fuel estimates are updated, then new consumption values are reflected in route comparisons in under 2 seconds.
Cost-Saving Route Highlighting
Given calculated fuel consumption for all available routes, when a route offers at least 5% fuel savings compared to the shortest-distance route, then that route is visually highlighted on the map and labeled as “Cost-Saving.” When no route meets the 5% threshold, then the system displays a message: “No significant fuel savings available.”
Driver Dashboard Integration of Fuel Metrics
Given a selected delivery route, when fuel consumption and idling time are calculated, then the driver’s dashboard displays total estimated fuel usage, breakdown by driving vs. idling, and comparative percentages against baseline consumption. When values are updated due to rerouting, then the dashboard refreshes metrics in real time without page reload.
Dynamic Route Recalculation under Traffic Conditions
Given live traffic congestion exceeding a 3-minute idling threshold, when the system detects the delay, then it recalculates alternative routes and updates the driver’s recommended path within 10 seconds. When a new route reduces estimated fuel usage by at least 3%, then the driver receives an in-app alert: “More fuel-efficient route available.”
Route Comparison Filter by Fuel Efficiency
Given multiple route options, when the user applies the “Fuel Efficiency” filter, then only the routes sorted by ascending estimated fuel consumption are displayed. When the filter is cleared, then all original route options reappear in the default prioritization order.

Geo-Triggered Alerts

Sends instant notifications to drivers and managers when trucks enter or exit geofenced restock zones with low inventory, enabling prompt restocking or rerouting decisions on the fly.

Requirements

Geofence Configuration Interface
"As a food truck manager, I want to configure custom geofenced restock zones so that I can receive alerts when a truck enters or exits those areas for timely restocking."
Description

Provide an intuitive interface within the TruckTally mobile app and web dashboard that allows managers to define, edit, and delete geofenced restock zones on a map. The interface must support drawing custom polygonal or circular zones, setting metadata (e.g., zone name, restock location), and assigning zone-specific inventory thresholds. It should validate zone boundaries, prevent overlaps or invalid shapes, and save configurations to the central database. The geofence definitions will integrate with the location-tracking engine to trigger alerts when trucks cross zone boundaries.

Acceptance Criteria
Define New Circular Geofence
Given a manager is on the geofence configuration interface, When they select the circular zone tool and draw a circle on the map with a center point and radius, Then the system should render the circle correctly, allow input of zone name, restock location, and inventory threshold, and enable the Save button.
Create Custom Polygonal Geofence
Given a manager needs a non-circular restock area, When they use the polygon tool to place at least three vertices on the map, Then the system must close the polygon, prompt for metadata fields, and activate the Save function.
Prevent Overlapping Geofences
Given existing geofence zones stored in the database, When a manager draws or modifies a new zone that overlaps any existing zone, Then the system should display an error message indicating overlap and disable the Save button until the overlap is resolved.
Validate Invalid Shapes
Given a manager draws a zone with less than three points for a polygon or zero radius for a circle, When they attempt to save, Then the system should block the action and display a validation error explaining the shape requirement.
Persist Geofence to Database
Given a manager completes metadata entry and draws a valid geofence, When they click Save, Then the geofence definition—including coordinates, name, restock location, and threshold—should be stored in the central database and visible on both mobile and web dashboards.
Real-Time GPS Tracking Engine
"As a driver, I want my truck’s location to be tracked in real time so that the system can detect geofence crossings and generate alerts reliably."
Description

Implement a robust real-time GPS tracking engine on the mobile app that continuously monitors the truck’s location, even in varying signal conditions. The engine must sample location data at configurable intervals, calculate geofence entry and exit events with low latency, and queue events when offline. Once connectivity is restored, the engine should synchronize any queued events with the backend. It must be optimized for battery efficiency and integrate seamlessly with the geofence configuration module.

Acceptance Criteria
Configurable GPS Sampling Interval
Given a sampling interval configured by the user, when the tracking engine runs, then GPS coordinates are collected at the configured interval (±1 second) for 60 consecutive samples.
Geofence Entry/Exit Event Detection
Given a defined geofence boundary, when the truck crosses that boundary, then an entry or exit event is generated and dispatched within 2 seconds of the crossing.
Offline Event Queuing and Synchronization
Given the device is offline, when geofence entry/exit events occur, then they are queued locally and synchronized with the backend within 30 seconds of restoring connectivity.
Battery Efficiency Threshold
Given continuous real-time tracking for 2 hours under default settings, when monitoring battery usage, then the tracking engine consumes no more than 5% battery per hour on average.
Robust Location Tracking in Low Signal
Given the device is in low-signal conditions, when the tracking engine attempts to obtain location, then it returns a location fix within 10 seconds or falls back to the last known valid coordinate if no new fix is acquired.
Inventory Threshold Monitoring
"As a manager, I want the system to monitor inventory levels against predefined thresholds so that I get notified when stock falls below acceptable levels while entering or exiting a restock zone."
Description

Develop a background service that continuously monitors inventory levels on each truck in relation to the thresholds defined for each geofence zone. The service must fetch live inventory data from the local database, compare against zone-specific low-stock thresholds, and flag potential restock needs. It should also handle data integrity checks, alert deduplication, and provide an API endpoint for querying current restock status across all active trucks.

Acceptance Criteria
Inventory threshold comparison on geofence entry
Given a truck enters a geofenced restock zone and the background service fetches its current inventory from the local database, When the service compares each item’s stock level against the zone-specific low-stock thresholds, Then any item below its threshold is flagged for restock with truck ID, item ID, and timestamp recorded.
Handling missing or corrupt inventory data
Given an inventory record is missing or contains malformed fields, When the background service processes the dataset, Then it logs the data integrity error with clear diagnostics and continues processing other records without crashing.
Alert deduplication for repeated low-stock events
Given an item for a specific truck has triggered a low-stock alert within the last 30 minutes, When the same item remains below threshold on subsequent checks, Then the service does not generate a duplicate alert for that truck-item combination.
API returns current restock statuses for all active trucks
Given a GET request to the /restock-status API endpoint, When there are flagged restock needs across multiple trucks, Then the response returns HTTP 200 and a JSON array listing each active truck ID, each item ID requiring restock, its threshold, and current stock level.
Periodic inventory monitoring interval execution
Given the background service is configured to run every 5 minutes, When monitoring inventory levels across up to 100 active trucks, Then the complete threshold comparison and flagging process completes within 60 seconds.
Instant Push Notification System
"As a manager, I want to receive immediate notifications when a truck enters or exits a restock zone with low inventory so that I can coordinate restocking or rerouting swiftly."
Description

Create an event-driven notification system that sends instant push notifications to drivers and managers when geofence events occur under low-inventory conditions. Notifications should include truck ID, zone name, inventory status, and recommended action (restock or reroute). The system must support both mobile push and email channels, allow customizable notification templates, and ensure delivery confirmation. It should log all notifications for audit and allow users to mute or acknowledge alerts.

Acceptance Criteria
Low Inventory Zone Entry Notification
Given a truck’s inventory falls below the low threshold and it enters a designated restock zone When the geofence entry event triggers Then a push notification is sent to the driver’s mobile app within 5 seconds; Notification content includes truck ID, zone name, current inventory level, and recommended restock action; The notification is logged in the audit trail with timestamp; Delivery confirmation is received within 10 seconds.
Low Inventory Zone Exit Notification
Given a truck’s inventory is below threshold and it exits a designated restock zone When the geofence exit event occurs Then push and email notifications are sent to both the driver and manager within 10 seconds; Notifications include truck ID, zone name, inventory status, and recommended reroute action; All notifications are logged; Delivery confirmations are recorded.
Custom Notification Template Usage
Given a user has created and saved a custom notification template When any geofence-triggered alert is sent Then the notification uses the custom template and correctly populates placeholders for truck ID, zone name, inventory status, and recommended action; If any placeholder fails to resolve, the system defaults to the standard template without errors.
Email Fallback on Push Failure
Given a push notification attempt to a mobile device fails after two retry attempts When the system detects the push failure Then an email notification is automatically sent to the user within 30 seconds; The email contains the same content as the push notification; Both the push failure and email delivery status are logged in the audit trail.
Alert Mute and Acknowledge Functionality
Given a user has received an active alert notification When the user mutes that alert type Then no further notifications of that type are sent for the user’s configured mute duration; When the user acknowledges the alert Then the alert status updates to 'Acknowledged' and no duplicate notifications for the same event are sent; All mute and acknowledge actions are recorded in the notification log.
Offline Event Sync and Conflict Resolution
"As a driver, I want alerts generated offline to sync correctly when I regain connectivity so that I don’t miss any critical restock notifications."
Description

Ensure that geofence entry/exit events and inventory alerts generated while the device is offline are reliably stored locally and synchronized with the server once connectivity is restored. The sync mechanism must handle conflicts, duplicate events, and maintain chronological integrity of alerts. It should provide visual feedback in the app about pending sync items and automatically retry failed uploads in the background without user intervention.

Acceptance Criteria
Local Storage of Offline Geofence Events
Given the device is offline and a truck enters or exits a geofenced restock zone, when the event occurs, then the app stores the event locally with accurate timestamp and unique identifier.
Automatic Sync upon Connectivity Restoration
Given pending offline events exist and the device regains connectivity, when within one minute of reconnection, then the app automatically synchronizes all pending events to the server without user intervention.
Conflict Resolution for Duplicate Events
Given two events with identical unique identifiers but differing timestamps, when syncing, then the system retains the event with the earliest timestamp and discards duplicates both locally and on the server.
Chronological Integrity of Alert Timeline
When syncing a batch of offline events, then the events are transmitted and stored on the server in ascending order of their original occurrence timestamps, preserving the correct sequence in the alert timeline.
Visual Feedback of Pending Sync Items
When offline events are pending synchronization, then the app displays a badge indicating the count of pending items; when all items are synced, the badge is removed.
Automatic Retry of Failed Uploads
Given an event upload fails due to a server error, then the app automatically retries up to three times with exponential backoff intervals; after final failure, the event remains queued and the user is notified of ongoing retry attempts.

Stop Sequence Scheduler

Organizes multiple restock stops into the most efficient sequence based on proximity, urgency, and truck capacity, reducing travel distance and downtime between stops.

Requirements

Multi-Criteria Stop Ranking
"As a food truck owner, I want the system to automatically rank my restock stops using distance, urgency, and capacity data so that I can follow the most efficient route without manual planning."
Description

Implement an algorithm that ranks restock stops based on distance, stock urgency, and remaining truck capacity to determine the optimal service order. The system evaluates each stop’s proximity using geospatial data, assesses inventory levels to prioritize critical restocks, and factors in truck storage constraints to avoid mid-route capacity overloads. This functionality integrates with the core scheduling engine and inventory database, ensuring that the generated route maximizes efficiency, minimizes travel time, and prevents stockouts during service.

Acceptance Criteria
Proximity-Based Stop Prioritization
Given a set of restock stops with known geospatial coordinates When the ranking algorithm is executed Then the stops are ordered from nearest to farthest relative to the truck’s current location
Urgent Stock Replenishment Prioritization
Given multiple stops where each stop has an associated stock urgency score When the ranking algorithm processes the list Then stops with stock levels below the critical threshold are positioned ahead of less urgent stops
Capacity Constraint Enforcement
Given the truck’s maximum storage capacity and the volume requirements of each restock stop When scheduling stops Then the cumulative volume of scheduled stops never exceeds the truck’s capacity
Combined Multi-Criteria Ranking Validation
Given weight factors for distance, stock urgency, and remaining capacity When the algorithm calculates a composite score for each stop Then each stop’s score reflects the normalized combination of these factors and the stops are sorted in descending order of composite score
Integration with Inventory Database
Given real-time inventory levels fetched from the database When the ranking algorithm runs Then the algorithm correctly retrieves and uses up-to-date inventory data to compute stock urgency scores
Dynamic Re-Optimization
"As a food truck owner, I want my stop sequence to adapt in real time to traffic and inventory changes so that I can avoid delays and serve customers without interruption."
Description

Enable on-the-fly route adjustments in response to real-time changes such as traffic delays, last-minute stop additions, or inventory level updates. The scheduler continuously monitors external data sources (traffic APIs, manual stop edits, warehouse dispatch notifications) and recalculates the optimal stop sequence to accommodate new constraints. This feature integrates with both the route planner and notification system, allowing the driver to receive instant updates and revised directions to maintain operational efficiency throughout the day.

Acceptance Criteria
Route Adjustment Due to Traffic Delay
Given an active delivery route and detection of a traffic delay exceeding 10 minutes, when the scheduler receives real-time traffic data indicating the delay, then the system recalculates the stop sequence within 5 seconds to minimize total travel time, updates ETAs for all remaining stops, and ensures no stop is omitted.
Addition of a Last-Minute Stop
Given the driver adds a new stop via the mobile app during an active route, when the new stop details are saved, then the scheduler recalculates the optimal sequence within 3 seconds, updates the route map and ETAs, and confirms the addition without exceeding truck capacity constraints.
Inventory Level Update Impact
Given a warehouse dispatch notification of low inventory for a future scheduled stop, when the inventory alert is received, then the scheduler integrates the closest restock stop into the current route sequence within 5 seconds, updates capacity calculations, and notifies the driver of the revised route.
External Traffic API Latency or Failure
Given the traffic API fails to respond within 5 seconds or returns an error, when the system detects the timeout or error, then it uses the most recent valid traffic data to recalculate the route, logs the API failure event, and alerts the driver that traffic data may be outdated.
Driver Receives and Acknowledges Reroute Notification
Given a recalculated route is generated, when the new route is available, then the system sends a push notification to the driver with updated directions, allows the driver to acknowledge or reject the reroute within the app, and logs the acknowledgment timestamp.
Real-Time Geolocation Integration
"As a food truck owner, I want the scheduler to use my real-time GPS position so that the stop sequence always reflects my actual location and travel progress."
Description

Integrate GPS tracking to capture the truck’s current location and calculate precise distances to upcoming stops. The system pulls location data at regular intervals, feeds it into the scheduling engine, and updates estimated arrival times. This integration enhances route accuracy, allows for dynamic adjustments based on actual position, and provides visibility into progress for both drivers and operators through the mobile interface.

Acceptance Criteria
Frequent Location Polling
Given the driver’s app is active, When the GPS module polls location data every 30 seconds, Then the recorded latitude and longitude must be updated and delivered to the scheduling engine within 5 seconds of capture.
Accurate Distance Computation
Given the current location and next stop coordinates are available, When distance is calculated by the engine, Then the computed distance must be accurate within 5 meters of the actual path-based distance.
Dynamic Route Adjustment
Given the truck deviates by more than 100 meters from the planned route, When the scheduling engine receives the new location, Then it must recalculate and display an updated route and ETA within 10 seconds.
Real-Time ETA Update
Given the latest location data is received, When the scheduling engine processes the update, Then the ETA displayed in the driver’s interface must refresh to reflect the change within 5 seconds.
Operator Location Tracking
Given the truck’s location is transmitted, When an operator views the mobile interface dashboard, Then the truck’s real-time position must be displayed on the map and refresh at the same 30-second interval as the location polling frequency.
Capacity-Aware Scheduling
"As a food truck owner, I want the scheduler to consider my truck’s remaining capacity so that I never overfill and can complete all stops without exceeding storage limits."
Description

Incorporate truck capacity constraints into the scheduling algorithm by tracking current inventory load and forecasting storage availability at each stop. Before finalizing the route, the system simulates adding restock quantities per stop, alerts the user if capacity limits are exceeded, and suggests load balancing or additional trips if necessary. This requirement ties the inventory management module directly to route planning to prevent in-route stock overflows and ensure seamless service.

Acceptance Criteria
Overcapacity Detection During Route Creation
Given a set of restock stops with specified quantities, when the user initiates route simulation, then the system calculates cumulative load per stop and displays an alert if projected inventory exceeds truck capacity.
Valid Route Confirmation Within Capacity
Given a proposed sequence of stops where total restock quantities do not exceed capacity, when the user reviews the simulated route, then the system allows confirmation without any overcapacity warnings.
Suggested Additional Trip for Excess Load
Given restock quantities exceeding available capacity by more than 15%, when the capacity simulation runs, then the system suggests scheduling an additional trip and displays the estimated extra distance and time.
Load Balancing Recommendation Across Stops
Given cumulative load at certain stops approaches capacity, when the user requests optimization, then the system provides a balanced distribution of split quantities across stops to stay within capacity limits.
Capacity Alert in Offline Mode
Given no internet connectivity during route planning, when the user adds or edits restock stops, then the system simulates capacity constraints offline and alerts the user if limits are exceeded.
Offline Route Caching
"As a food truck owner, I want my stop sequence and navigation to work offline so that I can stay on schedule even when I lose internet access."
Description

Develop a mechanism to cache scheduled routes and relevant map data locally on the device, enabling full scheduling functionality without internet connectivity. The app downloads and stores geospatial tiles and route instructions when connectivity is available and seamlessly switches to offline mode when disconnected. This ensures that drivers can access their stop sequence and navigation guidance in areas with poor or no signal, maintaining productivity on the road.

Acceptance Criteria
Initial Route Data Download
Given the device is online and a new stop sequence is generated, when the user confirms download, then geospatial tiles and route instructions for the entire route are stored locally within 30 seconds and consume no more than the allocated cache storage.
Offline Route Access
Given the device is offline, when the user opens the scheduled stops view, then the app displays the pre-downloaded route and navigation instructions without errors and makes no network requests.
Automatic Cache Expiration
Given cached route data older than 7 days exists, when the user attempts to access it, then the app warns the user of stale data and prompts to refresh when back online.
Manual Cache Refresh
Given the device is online, when the user initiates a cache refresh, then the app re-downloads the latest route data, replaces the old cache within 20 seconds, and displays a success confirmation.
Insufficient Storage Handling
Given the device’s available storage is below 50 MB, when the user attempts to download route data, then the app notifies the user of low storage, purges older caches automatically, and retries the download successfully.

Fleet Route Insights

Provides a centralized dashboard showing live route progress, stock statuses, and performance metrics, empowering fleet managers to monitor operations and reassign resources instantly.

Requirements

Live Route Tracking Dashboard
"As a fleet manager, I want to view live locations and routes of all trucks so that I can monitor progress and address delays promptly."
Description

The system shall display live GPS-based locations and routes of all trucks on a centralized dashboard, updating every minute to provide fleet managers with current positions, estimated arrival times, and route deviations. It integrates with existing GPS modules, offers map-based views and color-coded status indicators, enabling quick identification of delays or detours. Expected outcome: improved monitoring and decision-making to ensure timely operations.

Acceptance Criteria
Real-Time Location Refresh
Given the fleet manager opens the dashboard, when the GPS module sends a new location update for any truck, then the dashboard updates the truck’s marker on the map within one minute.
Accurate ETA Calculation
Given the truck’s current location and route data, when the dashboard calculates the estimated arrival time, then the displayed ETA must be accurate within +/- two minutes of the actual arrival time.
Route Deviation Detection
Given a truck deviates from its planned route by more than 500 meters, when monitoring occurs, then the dashboard flags the truck with a deviation alert and highlights its route segment in red.
Status Indicator Display
Given each truck’s status as on-schedule, delayed, or off-route, when the dashboard renders the truck marker, then it uses green for on-schedule, yellow for delayed, and red for off-route.
Interactive Map Navigation
Given the dashboard map view, when the user zooms or pans the map, then all truck markers, routes, and status indicators remain visible and responsive without performance degradation.
Real-time Stock Level Integration
"As a fleet manager, I want to see real-time stock levels of each truck on the route dashboard so that I can plan resupplies and avoid stockouts."
Description

Integrate inventory data from each truck with the route dashboard, displaying current stock statuses alongside route positions. Fetch stock updates every five minutes, flag low-stock alerts, and correlate them with location data, enabling managers to anticipate re-stocking needs. Seamless integration with the mobile inventory module ensures data consistency and actionable insights.

Acceptance Criteria
Initial Dashboard Load
Given the fleet manager accesses the route dashboard; When the dashboard loads; Then stock levels for all trucks are fetched within 5 seconds and displayed alongside their current route positions.
Periodic Stock Update
Given the dashboard is open; When 5 minutes have elapsed since the last update; Then the system automatically fetches and refreshes stock data for each truck without user intervention.
Low-Stock Alert Generation
Given a truck’s stock level falls below the predefined threshold; When the next data synchronization occurs; Then the dashboard highlights the truck in red and displays a ‘Low Stock’ alert icon next to its name.
Geo-Correlation of Stock Alerts
Given a low-stock alert is generated; When displayed on the map; Then the alert marker is placed accurately at the truck’s last known GPS location within a 100-meter radius.
Data Consistency Verification
Given a mobile user updates stock levels offline; When the device reconnects; Then the dashboard reflects the updated stock counts and no discrepancies exist between mobile and dashboard data.
Performance Metrics Visualization
"As a fleet manager, I want to view performance metrics in graphical form so that I can assess efficiency and make data-driven improvements."
Description

Provide analytics widgets on the dashboard showing metrics such as on-time arrival rate, average serving time, sales per route, and distance covered. Support customizable date ranges and filters, with graphical representations like line charts, bar graphs, and heat maps. Integrate historical and live data to support performance reviews and operational optimization.

Acceptance Criteria
On-Time Arrival Rate Analysis
Given the manager selects a date range, when viewing the on-time arrival widget, then the dashboard displays the percentage of routes starting and ending within scheduled times, accurate to within 1% and refreshed every 5 minutes.
Average Serving Time Trend Visualization
Given the manager filters by service window, when viewing the average serving time chart, then it shows daily average service times per route as an interactive line chart for the selected period.
Sales Per Route Comparison
Given the manager selects two or more routes and a date range, when viewing the sales comparison graph, then the bar chart displays total sales per route side by side with correct labels and values accurate to the nearest dollar.
Distance Covered Heat Map Display
Given the manager opens the heat map view and applies date filters, when viewing the map, then it color-codes distance covered per route segment according to predefined thresholds with a matching legend.
Historical vs Live Performance Toggle
Given the manager toggles between historical and live data modes, when switching modes, then all dashboard widgets update within 2 seconds to reflect the selected data source without a full page reload.
Automated Resource Reassignment Alerts
"As a fleet manager, I want to receive automated alerts for route delays or stock issues with reassignment suggestions so that I can quickly reallocate resources."
Description

Automatically trigger alerts when trucks deviate significantly from schedules or run low on stock, suggesting alternative trucks or support resources. Send notifications via email and in-app messages, providing actionable recommendations such as the nearest available truck that can assist. Leverage predefined rules and machine learning thresholds to minimize manual monitoring.

Acceptance Criteria
Schedule Deviation Alert Trigger
Given a truck deviates more than 10 minutes or 5 kilometers from its scheduled route When the deviation occurs Then the system must generate an alert within 2 minutes
Low Stock Alert Generation
Given a truck’s inventory for any menu item falls below 15% of its initial load When the threshold is crossed Then an alert must be generated within 1 minute
Alternative Resource Recommendation
Given an active alert for deviation or low stock When recommending support Then the system must list at least one nearby truck within 20 kilometers that has sufficient stock or capacity
Email Notification Dispatch
Given an alert is generated When dispatching notifications Then an email is sent to the fleet manager within 1 minute and includes truck ID, issue type, and recommended support options
In-App Notification Rendering
Given an alert is generated When the user opens the Fleet Route Insights dashboard Then an in-app notification appears within 30 seconds with the alert details and a link to reassign resources
Offline Data Synchronization
"As a fleet manager, I want the dashboard to function offline and sync updates automatically so that I can maintain visibility even without reliable internet."
Description

Support offline mode for areas with poor connectivity, caching updates locally and synchronizing data when network is restored. Ensure the latest available route, stock, and performance data is displayed, preventing data loss and enabling continuous monitoring. Implement conflict resolution mechanisms to handle data discrepancies during synchronization.

Acceptance Criteria
Viewing Cached Route Data Offline
Given the user is offline and opens the fleet route dashboard, when the app loads, then it displays the most recently synced routes with accurate timestamps and no placeholder errors.
Local Order Updates When Offline
Given the user creates or updates an order while offline, when the network is unavailable, then the order is saved locally, visible in the order list, and marked as pending synchronization.
Automatic Data Synchronization Upon Reconnection
Given there are pending local changes after an offline session, when network connectivity is restored, then the app automatically synchronizes all local changes with the server and updates the local cache without user intervention.
Conflict Resolution for Concurrent Updates
Given a data conflict between local updates and server data during synchronization, when the app detects the conflict, then it applies the predefined resolution strategy (e.g., last-write-wins or user prompt) to resolve discrepancies and logs the resolution outcome.
Data Integrity After Sync
Given the synchronization process completes, when the app verifies data integrity, then the local cache and server data for routes, stock levels, and orders are fully consistent with no missing or duplicated records.

WasteSnapshot

Provides a real-time overview of food waste by item, time, and location. Operators can quickly log discarded ingredients with a tap and view instant summaries, enabling swift identification of high-waste areas during each shift.

Requirements

Quick Waste Logging
"As a food truck operator, I want to log discarded ingredients with a single tap so that I can quickly record waste without interrupting service."
Description

Enables operators to log discarded ingredients with a single tap, capturing item type, quantity, time, and location. Integrated into the main interface, this feature minimizes data entry steps, allowing for immediate waste recording even during high-volume periods. The streamlined workflow reduces errors, ensures accurate waste tracking, and enhances inventory insights across shifts.

Acceptance Criteria
Single Tap Logging on Main Interface
Given the operator is on the main interface When they tap the 'Discard' icon for a specific ingredient Then a waste record is created immediately with the selected item type, default quantity, current timestamp, and current location, and a confirmation message is displayed
Accurate Timestamp and Location Capture
Given a waste log entry is created When the entry is saved Then the stored timestamp must match the device’s current time and the location must match the device’s GPS coordinates within a 10-meter radius, and these values are displayed correctly in the entry details
Flexible Quantity Entry in Waste Log
Given the operator has logged a waste entry When they adjust the quantity using the on-screen '+' or '–' controls Then the quantity updates in real time between 1 and 100 units and the updated quantity is saved accurately in the entry
Offline Waste Logging
Given the device has no internet connection When the operator taps the waste logging button Then the entry is stored locally with all required fields, a 'Pending Sync' indicator appears, and no error is shown
Validation of Required Waste Log Fields
Given the operator attempts to save a waste log entry When any required field (item type, quantity, time, location) is missing Then the save action is blocked and the missing field is highlighted with an error message indicating ‘Required field’
Offline Waste Logging
"As a food truck operator working in areas with unreliable connectivity, I want to log waste offline so that I never lose waste data during service."
Description

Supports waste logging functionality without an active internet connection, storing entries locally and syncing them to the central database once connectivity is restored. This ensures continuous data capture in remote locations, prevents data loss, and maintains accurate waste records across offline and online contexts.

Acceptance Criteria
Offline Logging Without Connectivity
Given the user is offline, when they log a waste entry with item, quantity, and location, then the entry is stored locally with a timestamp and a status of 'pending sync'.
Automatic Sync Upon Reconnection
Given the device regains internet connectivity, when the app detects network availability, then all locally stored waste entries sync to the central database within 2 minutes without duplication.
Conflict Resolution During Sync
Given a waste entry has been modified both offline and online, when synchronization occurs, then the system applies the entry with the latest timestamp, flags conflicts, and prompts the user for manual resolution only if automatic merge is not possible.
Bulk Offline Entries Logging
Given the user logs multiple waste entries while offline, when the user views the local log, then all entries appear in chronological order with correct details and a status of 'pending sync'.
Data Integrity Verification Post-Sync
Given offline entries have been synced, when viewing the waste summary report, then all previously offline entries appear with accurate timestamps, quantities, and match the totals stored in the central database.
Waste Summary Dashboard
"As a food truck manager, I want to view a dashboard summarizing waste metrics so that I can identify trends and reduce food waste effectively."
Description

Provides a real-time visual dashboard that summarizes food waste by item, time range, and location. Includes interactive charts and tables to highlight top waste contributors, time-of-day trends, and location-specific insights. The dashboard helps operators quickly identify high-waste areas and make informed inventory decisions.

Acceptance Criteria
Operator Views Real-Time Waste Summary
Given the operator opens the Waste Summary Dashboard When the dashboard loads Then real-time waste data is displayed for the selected date range And the data refreshes automatically every minute
Operator Filters Waste by Item
Given the operator selects an item filter When an item is chosen from the dropdown Then the dashboard charts and tables update to show waste data only for that item
Operator Filters Waste by Time of Day
Given the operator applies a time-of-day filter When a specific shift or time range is selected Then the dashboard highlights waste trends corresponding to that period
Operator Selects Location-Specific Waste View
Given the operator chooses a location from the location selector When the location is selected Then the dashboard updates to display waste data exclusively for that location
Operator Exports Waste Data Report
Given the operator clicks the export button When the export is triggered Then a CSV file of the current dashboard view is downloaded And the file contains item, time range, and location waste summaries
Filterable Waste Reports
"As a food truck operator, I want to filter waste records by date, shift, and item so that I can analyze specific waste patterns and optimize inventory."
Description

Allows users to filter waste data by date range, shift, item category, and location. Users can generate custom reports and export data for further analysis. This feature supports targeted investigations into specific waste events and enhances operational decision-making.

Acceptance Criteria
Filter by Date Range
Given the user is on the Waste Reports screen When they select a specific start and end date Then the report displays only waste events logged within that date range and the total waste summary reflects the selected period
Filter by Shift
Given the user is on the Waste Reports screen When they choose a particular shift (e.g., Morning, Afternoon, Evening) Then the report shows only waste events recorded during that shift and labels the report with the selected shift name
Filter by Item Category
Given the user is on the Waste Reports screen When they select one or more item categories Then the report lists waste events exclusively for the chosen categories and updates category-specific waste totals accordingly
Filter by Location
Given the user is on the Waste Reports screen When they select a location from the location dropdown Then the report displays only waste events from that location and indicates the selected location in the report header
Combined Filters and Export
Given the user has applied multiple filters (date range, shift, category, location) When they tap the Export button Then a CSV file is generated containing only the filtered waste data and includes a header row listing the applied filters
Location-based Waste Tracking
"As a food truck chain owner, I want waste entries tagged by location so that I can compare waste levels across different trucks or venues."
Description

Automatically tags each waste entry with the operator’s current location using GPS or assigned truck IDs. Provides location-based breakdowns of waste data in the dashboard and reports, enabling multi-truck operations to compare performance across different locations.

Acceptance Criteria
Automatic GPS Tagging
Given GPS is enabled and the operator logs a waste entry, when the entry is saved, then the record must include the current latitude and longitude coordinates with an accuracy of ±20 meters.
Fallback Truck ID Assignment
Given GPS signal is unavailable, when the operator selects a truck ID from the dropdown before logging waste, then the entry must be tagged with the selected truck ID and GPS fields must be marked as null.
Dashboard Location Filter
Given multiple waste entries across different locations, when the operator applies a location filter in the WasteSnapshot dashboard, then only entries tagged with the selected location or truck ID must be displayed within 2 seconds.
Location-based Report Export
Given the operator generates a waste report, when a date range and one or more locations are selected, then the exported report must group waste totals by each chosen location and include their identifiers.
Location Data Validation
Given a waste entry is created, when the entry is validated before save, then the location field (either GPS coordinates or truck ID) must be present and conform to the list of known valid locations.
Waste Alert Notifications
"As a food truck operator, I want to receive alerts when waste exceeds set thresholds so that I can take immediate corrective actions."
Description

Sends real-time notifications to operators when waste levels for a specific item exceed predefined thresholds during a shift. Allows users to configure threshold values and notification methods. This proactive alerting helps address waste spikes promptly and reduces overall food waste.

Acceptance Criteria
Threshold Configuration Saved
Given the operator sets a waste threshold for an item and selects notification methods, When the operator saves the settings, Then the system stores the threshold value and notification preferences and displays a confirmation message.
Realtime Waste Threshold Exceeded Alert
Given the waste logged for a configured item exceeds its threshold during a shift, When the excess is recorded, Then the system immediately sends notifications via the configured methods to the operator.
Notification Methods Validation
Given the operator selects push, email, or SMS as notification methods, When a threshold breach occurs, Then the system sends alerts through only the enabled channels and logs each sent notification.
Threshold Adjusted Mid-Shift
Given the operator modifies an existing waste threshold during an active shift, When the new threshold is saved, Then the system applies the updated threshold immediately and uses it for subsequent waste logging.
Offline Mode Alert Synchronization
Given the device is offline when a threshold is exceeded, When connectivity is restored, Then the system sends the delayed alert notifications automatically and updates the notification log.

EcoTrend

Analyzes waste data over days, weeks, and events to reveal patterns and trends. Visual charts highlight peak waste periods, ingredient hotspots, and menu items with consistent excess, empowering vendors to make data-driven menu and portion adjustments.

Requirements

Waste Data Ingestion
"As a food truck operator, I want to record waste disposal events in real time so that I can track precise waste metrics even offline."
Description

System captures waste disposal data from food trucks, storing timestamped entries for each event. It integrates with inventory logs and user input, ensuring accurate tracking of waste metrics across shifts and locations.

Acceptance Criteria
Timestamp Accuracy Confirmation
Given a waste event is recorded in the app, when the entry is saved, then the system must assign a timestamp that matches the device’s current time within ±5 seconds. Given the system stores timestamps, they must be formatted in UTC ISO 8601 and correctly converted to the user’s local timezone on retrieval.
Integration with Inventory Logs
Given a waste entry is linked to an inventory item, when the data is ingested, then the system must update the inventory log to decrement the corresponding item’s quantity by the recorded waste amount. When querying inventory reports, wasted quantities must be included in total usage calculations to ensure accurate stock levels.
Offline Waste Data Capture
Given the user is offline, when they log a waste event, then the app must store the entry locally with a pending sync status. When connectivity is restored, the system must automatically sync all pending waste entries to the central database without data loss or duplication.
Manual Waste Entry Validation
Given a user manually enters waste data, when the entry is submitted, then the system must validate that the waste quantity is a positive number and the selected item exists in the inventory. If validation fails, the user must see an inline error message indicating the specific issue.
Location Tagging of Waste Entries
Given a waste event occurs at a food truck location, when the entry is recorded, then the system must tag the entry with the device’s GPS coordinates achieving at least 10 meters accuracy. When viewing waste analytics, each entry must include the location data for filtering and mapping purposes.
Trend Visualization Charts
"As an operator, I want to view waste trends in interactive charts so that I can quickly identify when waste peaks occur."
Description

Generates interactive visual charts showing waste trends over selected time periods (daily, weekly, event-based). Chart types include line graphs, bar charts, and heatmaps, enabling quick identification of peak waste times and patterns.

Acceptance Criteria
Daily Waste Trend Overview
Given the user selects the 'Daily' time period filter, When the trend visualization loads, Then a line graph displays total waste for each of the past 7 days with tooltips showing the date and corresponding waste quantity.
Weekly Waste Trend Comparison
Given the user selects the 'Weekly' time period filter, When the trend visualization loads, Then a bar chart displays total waste for each of the past 12 weeks, with each bar labeled with the week range and total waste amount.
Event-Based Waste Hotspot Analysis
Given the user selects a specific event from the event list, When the trend visualization renders, Then a heatmap highlights hours with the highest waste during that event, with cells colored according to waste volume and a legend indicating the scale.
Interactive Chart Type Switching
Given the trend visualization is displayed, When the user switches between line graph, bar chart, and heatmap, Then the chart updates seamlessly within 2 seconds while preserving the selected time period and data points.
Offline Data Rendering
Given the device is offline and cached waste data is available, When the user accesses the trend visualization, Then the charts load with cached data and indicate the last sync timestamp without errors.
Historical Waste Comparison
"As an operator, I want to compare waste data across past events so that I can evaluate the effectiveness of my menu changes."
Description

Provides comparative analysis of waste data across historical periods, highlighting percentage changes and anomalies. The feature supports side-by-side comparisons, aiding operators in evaluating the impact of menu or portion adjustments over time.

Acceptance Criteria
Select Historical Periods for Comparison
Given the operator is on the Historical Waste Comparison screen, when they select two date ranges for comparison, then the system loads and displays waste data for both periods side by side.
Display Percentage Change
Given waste data for both selected periods is available, when the data is rendered, then the system calculates and displays the percentage change for each waste category.
Highlight Anomalies in Waste
Given percentage changes have been calculated, when a waste category’s change exceeds ±20%, then the system highlights that category in red for increases and green for decreases.
Download Comparative Report
Given the side-by-side comparison is displayed, when the operator selects 'Export CSV', then the system generates and downloads a CSV file containing both periods' waste figures, percentage changes, and anomaly indicators.
Offline Access to Recent Comparisons
Given the operator is offline and has previously viewed a comparison within the last 7 days, when they open the Historical Waste Comparison screen, then the system retrieves and displays the last viewed comparison from local cache.
Ingredient Waste Hotspot Detection
"As an operator, I want to see which ingredients contribute most to waste so that I can adjust my inventory and portions accordingly."
Description

Analyzes waste data at the ingredient level, identifying items with consistently high leftover percentages. Highlights hotspots in a dashboard to guide operators in adjusting inventory orders and portion sizes for specific ingredients.

Acceptance Criteria
Daily Waste Hotspot Identification
Given the end of a business day, When the system processes the day’s ingredient waste data, Then it must flag any ingredient with leftover percentage above the configured threshold and list it in the daily waste hotspot report.
Ingredient-Level Waste Threshold Alert
Given a new waste data entry for an ingredient, When its cumulative leftover percentage exceeds 20% over any 7-day period, Then the system must generate an in-app alert labeled "High Waste Ingredient" for the operator.
Dashboard Hotspot Visualization
Given the dashboard view is accessed, When ingredient waste hotspots exist, Then the dashboard must visually highlight those ingredients with a red marker, display their waste percentages, and allow filtering by date range.
Inventory Order Adjustment Recommendation
Given the system has identified a waste hotspot, When presenting hotspot details, Then it must provide a recommended reduction in next order quantity for the ingredient, calculated based on average waste rate, and display the recommendation alongside current order levels.
Portion Size Adjustment Guidance
Given an ingredient shows consistent high waste in at least three separate events, When the operator views the hotspot detail, Then the system must suggest a portion size adjustment percentage and simulate the expected waste reduction impact.
Exportable Waste Reports
"As a food truck owner, I want to export waste reports so that I can share insights with my team and vendors."
Description

Enables generation and export of comprehensive waste reports in PDF and CSV formats, encompassing visual charts and key metrics. Supports scheduled report delivery via email to stakeholders for review and decision-making.

Acceptance Criteria
One-time PDF Report Export
Given the user is on the waste reports page When the user selects PDF format and clicks Export Then the system generates a downloadable PDF containing the selected date range’s waste data, visual charts, and key metrics within 5 seconds
One-time CSV Report Export
Given the user is on the waste reports page When the user selects CSV format and clicks Export Then the system generates and downloads a CSV file with detailed waste data for the chosen period including headers and values
Scheduled Email Delivery
Given the user has configured a report schedule with recipients and frequency When the scheduled time arrives Then the system automatically generates the report in the specified format and emails it to all stakeholders without user intervention
Report Content Verification
Given a generated report (PDF or CSV) When the report is opened Then all charts accurately reflect the waste trends and key metrics match the underlying data in the system for the selected timeframe
Export from Mobile Offline Mode
Given the user is offline in the mobile app and views completed waste analyses When the user triggers an export in PDF or CSV Then the system queues the export and automatically downloads the file once network connectivity is restored

SmartPortion

Utilizes historical sales and waste metrics to recommend optimal portion sizes for each dish. By suggesting adjustments before each service, it reduces over-serving, minimizes leftover scraps, and maximizes ingredient utilization without compromising customer satisfaction.

Requirements

Historical Data Aggregation
"As a food truck operator, I want the system to consolidate all my historical sales and waste data so that portion recommendations are based on a complete and accurate dataset."
Description

Collect and consolidate past sales and waste records from multiple service periods, normalize and store the data in a centralized repository to ensure accurate input for portion recommendation algorithms.

Acceptance Criteria
Daily Data Consolidation
Given multiple sales and waste records in CSV, when the aggregation process runs, then all records are successfully imported into the repository, duplicates are eliminated, and the import count equals the expected total.
Cross-Location Data Normalization
Given raw data from different food truck locations, when normalization executes, then all date/time fields are standardized to UTC, units converted to grams or milliliters, and all fields match the repository schema.
Offline Data Sync Recovery
Given data generated while the mobile app is offline, when the device reconnects, then missing records sync within 5 minutes, no data loss or duplication occurs, and they appear in the centralized repository.
Missing Data Error Handling
Given ingestion of files with missing mandatory fields, when processing runs, then the system logs each invalid record, skips them without stopping the import, and generates an accessible error report.
Data Integrity Verification
Given the aggregated dataset in the repository, when integrity checks run, then checksum validations pass for all records, referential integrity between sales and waste tables is intact, and no orphaned records exist.
Waste Metrics Integration
"As a food truck operator, I want waste metrics integrated into the analysis so that the portion suggestions account for actual leftovers and prep overages."
Description

Integrate waste tracking data, including prep overages and leftover scraps, into the analytics pipeline to enrich the dataset and allow the recommendation engine to factor in actual waste patterns.

Acceptance Criteria
Prep Overages Data Collection
Given a prep overage record is entered in the mobile app When the device syncs with the backend analytics pipeline Then the overage amount, item ID, timestamp, and location code are stored in the waste metrics dataset
Leftover Scraps Data Collection
Given scrap weight is recorded at end of service When the record is submitted Then the scrap quantity, dish ID, date, and truck ID are captured in the analytics database
Data Normalization Pipeline
Given raw waste entries for overages and scraps are present When the normalization job runs Then values are converted to standard units, missing fields are handled, and data is tagged with source type
Integration with Recommendation Engine
Given normalized waste metrics are available in the dataset When the recommendation engine queries ingredient usage Then it factors in average prep overages and scraps per dish into portion size calculations
Data Validation and Quality Check
Given new waste data is ingested When automated validation checks run Then entries with null quantities, invalid IDs, or timestamps outside service windows are flagged and reported for review
Portion Suggestion Engine
"As a food truck operator, I want the system to calculate optimal portion sizes based on my past data so that I can reduce ingredient waste while satisfying customers."
Description

Develop an algorithmic engine that analyzes aggregated historical sales and waste data to generate dynamic portion size recommendations for each dish, optimizing ingredient usage while maintaining customer satisfaction.

Acceptance Criteria
Generating Portion Recommendations Pre-Service
Given aggregated historical sales and waste data for the last 30 days, When the user triggers a portion recommendation request before service, Then the engine returns recommended portion sizes for each dish with a maximum calculation time of 2 seconds and variance no greater than 10% from the waste-minimizing optimal size.
Portion Adjustment for New Menu Item
Given a newly added dish with no historical data, When the engine calculates portion recommendations, Then it assigns a default portion size derived from the closest matching dish category and flags the recommendation confidence as “Low” in the output.
User Interface Displays Portion Suggestions
When portion suggestions are generated, Then the mobile UI lists recommended portion sizes alongside corresponding historical waste and sales metrics, and highlights any high-confidence adjustments within 1 second of engine response.
Updating Recommendations with Real-Time Sales Data
Given live sales transactions during service, When new sales records are synced to the engine, Then portion recommendations refresh automatically and present updated sizes within 5 minutes, ensuring real-time optimization.
Offline Fallback to Last Synced Recommendations
Given loss of internet connectivity during service, When the user requests portion suggestions, Then the app delivers the last successfully synced recommendations from local storage and displays the timestamp of the data used.
Offline Recommendation Cache
"As a food truck operator working in offline conditions, I want to access the latest portion recommendations without internet so that I can prep accurately anywhere."
Description

Cache precomputed portion recommendations and relevant historical data locally on the mobile app, enabling operators to access and retrieve suggestions without network connectivity, with automatic syncing when a connection is restored.

Acceptance Criteria
Initial Data Sync
Given a user logs into the app with network connectivity When the Offline Recommendation Cache feature is accessed for the first time Then the app downloads all precomputed portion recommendations and relevant historical data to local storage and confirms completion to the user within 10 seconds
Offline Recommendation Retrieval
Given the device is offline and has a populated local cache When the operator requests portion recommendations for a menu item Then the app retrieves and displays the last synced recommendations from the cache within 2 seconds
Automatic Cache Sync After Reconnect
Given the device reconnects to the network after an offline period When connectivity is detected Then the app automatically syncs any new or updated portion recommendations from the server to the local cache within 1 minute without user intervention
Local Cache Storage Management
Given the local cache storage exceeds 50MB When new recommendation data is received for caching Then the app purges the oldest cached entries until storage usage falls below 50MB before saving the new data
Corrupted Cache Fallback Handling
Given a cache integrity check fails for recommendation data When the app attempts to load corrupted cache entries Then the app displays a warning, attempts a fresh data fetch from the server when online, and does not crash or hang
Feedback Learning Loop
"As a food truck operator, I want to provide feedback on actual portions served so that the system can learn and improve recommendations over time."
Description

Enable operators to submit actual consumption data and qualitative feedback after each service, feeding this information back into the machine learning models to continuously refine and improve future portion size recommendations.

Acceptance Criteria
Operator Submits Consumption Data
Given an operator completes a service session, When they enter actual ingredient consumption figures for each dish, Then the system records the data, displays a confirmation message, and stores it in the feedback database.
Operator Provides Qualitative Feedback
Given an operator wants to add notes on portion satisfaction or waste causes, When they select feedback categories and submit free-text comments, Then the system validates inputs, attaches them to the respective service record, and confirms submission.
System Integrates Feedback into ML Model
Given new consumption and qualitative feedback have been received, When the nightly update process runs, Then the machine learning engine ingests the feedback, retrains the portion size model, and logs the update without errors.
Operator Reviews Updated Recommendations
Given the portion size model has been retrained, When the operator opens the recommendation dashboard before the next service, Then they see updated portion suggestions, date-stamped and annotated with confidence levels.
Feedback Data Integrity Check
Given feedback entries are submitted, When the system performs a data integrity check, Then it flags any missing or implausible values (e.g., negative quantities) and prompts the operator to correct errors before model ingestion.
Portion Adjustment Alerts
"As a food truck operator, I want to receive timely alerts with updated portion sizes before each service so that I can adjust my prep accordingly."
Description

Provide proactive notifications prior to each service with updated portion recommendations, ensuring operators receive timely alerts to adjust prep quantities before the start of a shift.

Acceptance Criteria
Scheduled Alert Delivery
Given it is 2 hours before the scheduled service start time, when the system computes portion adjustments using historical data, then a push notification with updated portion recommendations is sent to the operator’s device.
Offline Mode Alert Retrieval
Given the device is offline at alert generation time, when the device reconnects within 30 minutes after the alert time, then the system automatically retrieves and displays the missed portion adjustment notification.
Custom Threshold Configuration
Given the operator sets a custom minimum ingredient threshold, when calculated portion recommendations require more ingredients than available under that threshold, then the alert highlights low-stock items needing restock.
Alert Acknowledgment Logging
Given the operator acknowledges the received alert, when the acknowledgment is processed, then the system logs the operator ID and timestamp and marks the alert as resolved in the alert history.
Multiple Service Shift Handling
Given the operator has consecutive service shifts scheduled, when the system generates portion adjustment alerts for each shift, then alerts are ordered by service start time and grouped by shift in the notification center.

WasteAlert

Sends instant notifications when waste volumes for specific ingredients or timeframes exceed configurable thresholds. This early warning lets operators take corrective action—adjusting prep quantities or menu offerings—to prevent recurring excess and control disposal costs.

Requirements

Threshold Configuration
"As a food truck operator, I want to configure waste thresholds for individual ingredients so that I can receive alerts when waste exceeds my specified limits and take corrective action."
Description

The system shall allow operators to define waste volume thresholds for each ingredient and timeframe. It should enable users to set customizable limits (units per day, week, or month) for any tracked ingredient, ensuring flexibility to accommodate varying menu items and usage patterns. The configuration interface must provide default suggested values based on historical data and allow manual overrides. Once thresholds are saved, the system should validate entries to prevent invalid values and persist settings across sessions.

Acceptance Criteria
Defining Custom Threshold for an Ingredient
Given the operator navigates to the Threshold Configuration interface When they select an ingredient and enter a custom threshold value for a specific timeframe Then the system accepts the value, displays it in the configuration list, and marks it as pending save
Applying Default Suggested Threshold Values
Given the operator views the threshold settings for an ingredient When no custom value exists Then the system displays a default suggested threshold based on historical usage data
Validating Threshold Input Values
Given the operator enters a threshold value When the value is below zero, not a number, or exceeds a reasonable upper limit Then the system displays an inline error message and prevents saving until corrected
Persisting Threshold Settings Across Sessions
Given the operator saves threshold settings When they log out and log back in or switch devices Then the previously saved thresholds are loaded and displayed accurately
Configuring Threshold Timeframes (Daily/Weekly/Monthly)
Given the operator selects a timeframe (day, week, month) When they configure threshold values Then the system applies and displays the thresholds correctly for the chosen timeframe
Real-time Waste Tracking
"As a food truck operator, I want the system to track and compare waste data in real time so that I can monitor waste levels throughout a shift."
Description

The application shall continuously collect waste data input by the operator or automatically logged from inventory deductions, calculate waste volumes in real time, and compare against configured thresholds. The tracking mechanism must function both online and offline, storing data locally when offline and synchronizing when connectivity is restored. It should display current waste statistics by ingredient and timeframe on a dashboard.

Acceptance Criteria
Manual Waste Entry Updates
Given the operator enters a waste volume for an ingredient, when the entry is submitted, then the system updates the waste total for that ingredient and reflects the change on the waste dashboard within 1 second.
Automatic Waste Deduction Logging
Given an inventory deduction event occurs for an ingredient, when the deduction is processed, then the waste log increases by the deducted amount and the dashboard displays the updated totals accurately.
Offline Waste Data Capture
Given the app is offline, when the operator records waste entries, then entries are stored locally with correct timestamps and ingredient identifiers without errors.
Data Synchronization Post-Offline
Given local waste entries exist, when connectivity is restored, then the app automatically synchronizes all local entries to the server within 30 seconds and no data is lost.
Threshold Exceedance Alert
Given a waste threshold is configured for an ingredient, when cumulative waste exceeds this threshold in the specified timeframe, then the app sends an instant notification and highlights the exceeded threshold in red on the dashboard.
Dashboard Waste Statistics Accuracy
Given multiple waste entries for an ingredient over different timeframes, when the dashboard loads, then it accurately aggregates total waste per timeframe and correctly calculates percentages relative to thresholds.
Instant Waste Alerts
"As a food truck operator, I want to receive instant alerts when waste crosses my limits so that I can adjust operations to reduce future waste."
Description

When waste volumes exceed configured thresholds, the app shall send immediate notifications to the operator via in-app pop-ups and optional push notifications. Alerts must specify the ingredient, timeframe, current waste figure, and threshold value, offering actionable advice such as adjusting prep quantities. The notification system should support retrying in case of network failures and log all sent alerts for audit purposes.

Acceptance Criteria
Real-Time Waste Alert When Threshold Exceeded Online
Given the operator is connected to the internet and waste volume for an ingredient exceeds its configured threshold, When the system detects the excess, Then an in-app pop-up and push notification are immediately displayed with ingredient name, timeframe, current waste, threshold value, and adjustment advice.
Offline Waste Logging and Deferred Notification
Given the operator is offline and waste volume crosses the threshold, When connectivity is restored, Then the app retries sending the missed notification and logs a retry count until successful, with the same alert details as online.
Network Failure and Retry Logic
Given an initial push notification attempt fails due to network error, When the system retries delivery according to the retry policy, Then the notification is successfully sent within three retry attempts and each retry is logged.
Audit Log Entry for Sent Alerts
Given any waste alert is sent, When the notification is delivered successfully or after final retry failure, Then an audit log entry is created containing timestamp, ingredient, timeframe, waste figure, threshold, delivery status, and retry attempts.
Configurable Threshold Adjustment Interaction
Given the operator receives a waste alert, When they tap the alert’s advice link, Then they are navigated to the threshold configuration screen pre-populated with the alerted ingredient’s current threshold value.
Historical Waste Reporting
"As a food truck operator, I want to review historical waste reports so that I can identify patterns and optimize inventory planning."
Description

The feature shall provide a reporting interface where operators can review historical waste data against thresholds. Reports must include charts and tables showing waste trends over selected periods, highlight threshold breaches, and allow exporting data as CSV or PDF. The module should integrate seamlessly with the analytics dashboard and update reports as new waste data arrives.

Acceptance Criteria
Viewing Historical Waste Trends
Given an operator selects a date range on the historical waste report interface, when they apply the selection, then the system displays a line chart showing daily waste volumes for each ingredient within 2 seconds.
Identifying Threshold Breaches
Given historical waste data is displayed, when any ingredient's total waste in the selected period exceeds its configured threshold, then those data points are visually highlighted in red and an alert icon is shown.
Exporting Waste Reports
Given a historical waste report is rendered, when the operator clicks the 'Export' button and chooses CSV or PDF, then the system generates and downloads the file with all displayed data, matching the applied filters and chart visuals.
Real-time Report Updates
Given new waste entries are recorded during a session, when the operator refreshes the historical report view, then the report reloads to include the latest data and updates the charts and tables accordingly.
Integrating with Analytics Dashboard
Given the operator accesses the analytics dashboard, when they navigate to the historical waste reporting module, then the reporting interface loads seamlessly within the dashboard with consistent styling and full functionality.
Offline Data Synchronization
"As a food truck operator working in areas with spotty connectivity, I want the waste alert system to operate offline and sync when online so that I never miss waste tracking or alerts."
Description

The app shall ensure that all waste entries, threshold configurations, and alert logs function when offline, storing changes locally and automatically synchronizing with the backend once the device reconnects to the internet. Conflict resolution policies must be defined to handle simultaneous updates, maintaining data integrity without user intervention.

Acceptance Criteria
Offline Waste Entry Recording
Given the device is offline, When the user records a waste entry with ingredient, quantity, and timestamp, Then the app stores the entry locally marked as pending synchronization.
Offline Threshold Configuration Update
Given the device is offline, When the user updates a waste threshold configuration, Then the app saves the updated threshold locally marked as pending synchronization.
Offline Alert Notification Logging
Given the device is offline and a waste threshold is exceeded, When the app triggers an alert, Then the alert notification and log entry are stored locally marked for later synchronization.
Automatic Synchronization on Reconnection
Given there are pending local changes and the device reconnects to the internet, When the app detects network availability, Then it automatically synchronizes all local waste entries, threshold configurations, and alert logs to the backend without user intervention and clears local pending flags.
Conflict Resolution for Threshold Configurations
Given conflicting threshold updates exist from multiple offline devices, When those updates synchronize, Then the system applies the update with the most recent timestamp and updates all devices to reflect the resolved threshold value.

GreenLeaderboard

Introduces a gamified leaderboard showing waste reduction performance across shifts or team members. By highlighting top performers and progress toward waste goals, it motivates staff to adopt best practices and fosters healthy competition to drive continual improvements.

Requirements

Leaderboard Interface
"As a food truck manager, I want a clear, engaging leaderboard view so that I can quickly see which shifts and staff are excelling at reducing waste and incentivize ongoing improvement."
Description

Design and implement a mobile-optimized UI component within TruckTally that displays the GreenLeaderboard. This interface will present a ranked list of shifts and team members based on waste reduction metrics, including key data points such as waste percentage, rank position, and trend indicators. It will follow brand guidelines, support portrait and landscape orientations, and seamlessly integrate with the main dashboard. The result is an engaging, intuitive view that fosters competition and highlights top performers.

Acceptance Criteria
Display Leaderboard in Portrait Mode
Given the mobile app is in portrait orientation on any supported device, when the user navigates to the GreenLeaderboard section, then the leaderboard displays a ranked list showing rank position, shift/team member name, waste percentage, and trend indicator without requiring horizontal scrolling.
Responsive Layout in Landscape Mode
Given the mobile device is rotated to landscape orientation, when the user views the leaderboard, then the UI adjusts to display additional data columns if available and maintains readability and alignment.
Seamless Dashboard Integration
Given the user accesses the main dashboard, when the user taps the leaderboard widget or navigation link, then the leaderboard component loads inline without a full page reload and reflects current waste metrics.
Real-time Data Refresh and Accuracy
Given new waste reduction data is received from the server, when the leaderboard is visible, then the ranking and waste percentages update within 5 seconds to reflect the latest metrics, and trend indicators adjust accordingly.
Offline Data Caching for Leaderboard
Given the device is offline, when the user opens the leaderboard, then the app displays the last synchronized data with an 'Offline' badge and disables data refresh actions.
Adherence to Brand Styling Guidelines
Given the leaderboard UI is rendered, when inspected, then all fonts, colors, icons, and spacing match the brand design specifications, including primary brand color for top-ranked entries and consistent iconography.
Waste Data Aggregation
"As a system, I want to aggregate waste data from orders and inventory in real time so that the leaderboard can display accurate and up-to-date rankings of waste reduction performance."
Description

Develop a backend service to collect, process, and store waste-related data from individual orders and inventory adjustments. The service will calculate waste reduction metrics per shift and per team member, normalize data across different food trucks, and provide an API for retrieving ranked results. It will ensure accurate, real-time performance tracking and integrate with existing inventory and order modules for seamless data flow.

Acceptance Criteria
Real-Time Waste Data Ingestion
Given a completed order or inventory adjustment event When the event is emitted by the order or inventory module Then the waste data must be ingested and stored in the backend service within 5 seconds without data loss.
Accurate Waste Reduction Metric Calculation
Given stored waste data for a particular shift and team member When the system calculates waste reduction metrics at end-of-shift or on-demand Then metrics must be computed correctly per shift and per team member with a tolerance of ±0.5%.
Cross-Truck Data Normalization
Given waste data collected from multiple trucks using different measurement units When the service processes incoming records Then all waste figures must be converted to a standard unit (grams) and normalized for valid cross-truck comparison.
Leaderboard API Response
Given a request to the leaderboard API endpoint When the request specifies a time range and ranking criteria Then the API must return a JSON payload of the top 10 performers sorted by waste reduction percentage within 2 seconds, including performer name, truck ID, shift ID, and reduction percentage.
Seamless Module Integration
Given existing inventory and order modules emitting waste-related events When those events occur Then the waste aggregation service must automatically receive, process, and acknowledge events with an HTTP 200 response, without requiring manual data transfers.
Real-Time Leaderboard Updates
"As a food truck operator, I want the leaderboard to update in real time so that I and my team can see performance changes instantly and respond accordingly during a shift."
Description

Implement functionality to refresh the GreenLeaderboard in real time as new waste data arrives. This requirement includes establishing WebSocket or push-notification connections between the backend aggregation service and the mobile app, handling data synchronization, and ensuring minimal latency. The live update capability will keep staff informed of performance changes during active shifts, promoting immediate feedback and engagement.

Acceptance Criteria
WebSocket Connection Initialization
Given the user navigates to the GreenLeaderboard screen and the device has internet connectivity When the mobile app establishes a connection Then the WebSocket handshake completes within 2 seconds and the connection status indicator is displayed as 'Connected'
Live Data Synchronization
Given new waste data is processed by the backend aggregation service When the service emits an update over the WebSocket Then the mobile app updates the leaderboard scores within 1 second from emission
Delta Update Efficiency
Given an existing leaderboard data set When incremental waste data arrives Then the app applies only the changed data fields (delta updates) and the payload size is reduced by at least 80% compared to a full refresh
Offline Mode Fallback
Given the device loses internet connectivity during an active shift When the WebSocket connection drops Then the app switches to polling mode (poll every 5 seconds), displays a 'Reconnecting...' message, and automatically re-establishes the WebSocket connection when the network is restored
Latency Alert Logging
Given leaderboard updates are processed continuously When any update takes longer than 3 seconds from server emission to UI display Then the app logs the latency event with a timestamp and displays a warning icon next to the leaderboard
Achievement Badge System
"As a team member, I want to earn and display badges for hitting waste reduction goals so that I feel recognized and motivated to improve continuously."
Description

Create a gamification layer that awards badges and visual rewards to team members when they reach predefined waste reduction milestones. Badges will appear next to user names on the leaderboard and in a dedicated achievements section. The system will define tiered goals (e.g., 10%, 20%, 30% reduction), track progress, and notify users upon badge unlock. This feature enhances motivation by recognizing individual and collective successes.

Acceptance Criteria
10% Waste Reduction Badge Awarded
Given a user has reduced waste by at least 10% compared to their baseline, When the system records the 10% threshold achievement, Then the user is awarded the 10% reduction badge, the badge is displayed next to their name on the leaderboard, and the achievement timestamp is logged.
Badge Appearance in Achievements Section
Given a user has unlocked any badge, When the user navigates to the Achievements section, Then the unlocked badge appears with the correct icon, name, description, and date unlocked.
Notification Upon Badge Unlock
Given a user reaches a predefined waste reduction milestone, When the badge unlock event occurs, Then the system sends an in-app notification and an email to the user containing the badge details within five seconds.
Tier Progress Tracking
Given a user’s waste reduction progress is updated, When the user views their profile or progress dashboard, Then the system displays a progress bar showing the percentage completed toward the next badge tier, color-coded by tier level.
Leaderboard Badge Integration
Given multiple users have earned badges, When the leaderboard is loaded or refreshed, Then each user’s entry displays their earned badges next to their name in the correct order without UI overlap or misalignment.
Offline Leaderboard Sync
"As a food truck operator working in areas with poor connectivity, I want to view and update leaderboard data offline so that my shift performance is always tracked and nothing is lost when I reconnect."
Description

Ensure that the GreenLeaderboard feature works seamlessly in offline mode by caching the latest leaderboard data on the device and queuing new waste entries locally. Upon reconnection, the app will automatically sync cached data with the backend, update rankings, and resolve any data conflicts. This capability guarantees uninterrupted access to leaderboard insights even in areas with intermittent connectivity.

Acceptance Criteria
Cache Latest Leaderboard Data Offline
Given the app is launched without internet connectivity, when the GreenLeaderboard screen is accessed, then the app displays the most recently cached leaderboard data with a visible timestamp of last update.
Queue Waste Entries While Offline
Given the device is offline, when a staff member submits a new waste entry, then the entry is stored locally in a persistent queue until network connectivity is restored.
Automatic Sync Upon Reconnection
Given there are queued local waste entries and cached leaderboard data, when the device regains internet connectivity, then the app automatically uploads all queued entries, fetches updated leaderboard data from the server, and refreshes the display without user intervention.
Resolve Data Conflicts Post-Sync
Given matching waste entries exist locally and on the server, when sync occurs, then the app reconciles duplicates by summing waste amounts per user and shift, ensuring no data loss or duplication.
Display Offline Indicator for Users
Given the app is in offline mode, when the GreenLeaderboard screen is open, then an offline indicator and the timestamp of the last successful sync are clearly displayed in the UI.

CrowdPulse

Displays a real-time heatmap of pedestrian flow around your truck location, directly linked to your inventory dashboard. Operators can visualize where crowds are gathering and adjust stock levels dynamically to match live foot traffic, ensuring you’re always ready for incoming customers.

Requirements

Real-time Heatmap Rendering
"As a food truck operator, I want to see a live heatmap of pedestrian density around my truck so that I can anticipate high traffic areas and adjust inventory accordingly."
Description

Render a live heatmap overlay on the mobile map interface to visualize pedestrian flow intensity around the food truck location in real time. The heatmap should smoothly update as new data arrives, use intuitive color gradients to indicate crowd density, and integrate seamlessly with the inventory dashboard to reflect traffic patterns without performance degradation.

Acceptance Criteria
Initial Heatmap Load After Map Launch
Given the user opens the mobile map interface, when the initial pedestrian flow data is loaded, then the heatmap overlay displays within 2 seconds, centered on the truck location, with the correct default color gradient.
Real-time Incremental Heatmap Updates
Given continuous pedestrian flow data updates every 5 seconds, when new data arrives, then the heatmap updates incrementally without full map reload, maintaining at least 30 FPS.
Color Gradient Consistency for Density Levels
Given defined pedestrian density thresholds, when densities change, then the heatmap color gradient transitions smoothly through the spectrum (blue to red) without abrupt jumps or miscolored zones.
High-frequency Data Stream Performance
Given peak data input of up to 10 updates per second, when high-frequency updates occur, then the heatmap rendering remains responsive, with no UI freeze longer than 200 ms and memory usage under 200 MB.
Inventory Dashboard Synchronization with Heatmap
Given the inventory dashboard is visible, when the heatmap updates crowd density around the truck, then inventory demand forecasts adjust within 1 second to reflect real-time heatmap changes.
Crowd Data Integration
"As a food truck operator, I want the app to automatically pull crowd data from external providers so that I always have up-to-date foot traffic information without manual input."
Description

Integrate pedestrian flow data from CrowdPulse sensors and third-party crowd analytics APIs, normalizing and aggregating incoming data streams in real time. Ensure data reliability by handling missing or inconsistent input, and provide a unified feed for the heatmap engine and inventory adjustment modules.

Acceptance Criteria
Real-Time Data Normalization
Given pedestrian flow data is received in varying units (e.g., per minute, per hour) When the data enters the system Then it is converted to a unified unit format (pedestrians per minute) within 2 seconds
Aggregated Crowd Footfall Feed
Given simultaneous data streams from CrowdPulse sensors and third-party APIs When the system ingests incoming data Then it aggregates and merges the streams into a single feed and updates the heatmap engine at least every 5 seconds
Handling Missing Data Points
Given intermittent sensor outages or third-party API failures When a data point is missing for over 10 seconds Then the system interpolates based on the last known valid data or flags the feed as degraded in the system dashboard
Data Consistency Validation
Given incoming data with overlapping timestamps or unexpected gaps When data is ingested Then the system rejects entries outside expected time windows and logs validation errors for review within 1 minute
Unified Feed Delivery
Given normalized and aggregated pedestrian flow data When requested by the heatmap or inventory modules Then the system returns a unified JSON feed conforming to the agreed schema with response time under 200ms
Offline Data Caching
"As a food truck operator, I want the heatmap data to be available offline so that I can still see crowd patterns even in areas with poor internet."
Description

Implement local caching of recent heatmap and crowd data on the mobile device to enable offline access during connectivity issues. Automatically synchronize cached data with the server when connectivity is restored, ensuring seamless user experience and data consistency.

Acceptance Criteria
Cached Data Accessibility when Offline
Given the app has fetched heatmap and crowd data within the last 30 minutes and the device is offline, when the operator opens CrowdPulse, then the app displays the most recently cached data within 5 seconds.
Automatic Synchronization on Connectivity Restoration
Given the device was offline and cached data exists, when connectivity is restored, then the app automatically synchronizes all cached heatmap and crowd data with the server within 2 minutes and updates the local cache.
Data Consistency Verification after Sync
Given synchronization has completed, when the operator views CrowdPulse, then the displayed data reflects both server-side updates and offline user interactions without duplication or conflict.
Cache Storage Limits and Eviction
Given the cache exceeds 50MB or stores data older than 7 days, when new heatmap data arrives, then the app evicts the oldest cached entries following a first-in-first-out policy to maintain limits.
Error Handling for Cache Operations
Given a cache read or write operation fails, when the error occurs, then the app logs the error, shows a user-friendly notification, and retries the operation up to 3 times.
Dynamic Inventory Adjustment
"As a food truck operator, I want the app to suggest inventory adjustments based on current crowd heatmaps so that I can preemptively restock items before I run out."
Description

Provide a UI component that suggests inventory restocking levels based on live heatmap indicators. Allow operators to review and adjust recommended thresholds dynamically, with changes reflecting immediately in the inventory dashboard and alerting the restock workflow.

Acceptance Criteria
Restocking Suggestion Based on Heatmap Data
Given the live heatmap indicates a surge of at least 25% foot traffic in a defined map zone When the operator accesses the restock suggestion panel Then the system provides a proposed restock quantity for each stock item based on historical sales multipliers; And the suggestions adjust automatically within 5 seconds of heatmap update
Operator Adjusts Suggested Restocking Thresholds
Given the system displays restock suggestions When the operator modifies any suggested threshold value and confirms the change Then the system accepts the new threshold, updates the suggestion list accordingly, and logs the manual override action in the audit trail
Inventory Dashboard Reflects Threshold Changes in Real-Time
Given the operator updates restocking thresholds When the adjustment is applied Then the inventory dashboard reflects the updated threshold values and recalculates reorder alerts in real time (within 2 seconds), without requiring a page refresh
Restock Workflow Alert Triggering Upon Threshold Update
Given new restocking thresholds are applied When the thresholds cross predefined overstock or understock levels Then the system generates an alert in the restock workflow module and notifies the operator via in-app notification
Offline Mode Heatmap Data Handling for Restocking Suggestions
Given the device is offline and a cached heatmap is unavailable When the operator opens the restock suggestion panel Then the system uses the last synced heatmap data to generate restock suggestions; And once the device reconnects, it automatically syncs new heatmap data and updates suggestions accordingly
Geolocation & Map Integration
"As a food truck operator, I want the heatmap centered on my current GPS location so that I can track crowd patterns relative to my exact position in real time."
Description

Utilize the device’s GPS to center the heatmap on the truck’s current location, updating the view automatically as the truck moves. Recalculate heatmap boundaries and data fetch regions in real time to ensure accurate spatial representation of pedestrian flow.

Acceptance Criteria
GPS Signal Acquisition on App Launch
Given the user has granted location permissions When the app is launched Then the map centers on the truck's current GPS location within 5 seconds and displays a marker at that position.
Dynamic Map Centering During Movement
Given the truck is moving at speeds up to 60 mph When new GPS coordinates are received Then the map re-centers on the updated location within 2 seconds without user intervention.
Real-time Heatmap Boundary Adjustment
Given the truck moves beyond the current heatmap boundary When the device’s location crosses the boundary Then the heatmap recalculates its boundaries and updates visible regions seamlessly within 1 second.
Data Fetch Region Update After Movement
Given the heatmap is displayed and the truck moves outside the cached data region When new geographical boundaries are defined Then the app fetches and displays heatmap data for the new region within 3 seconds.
Offline GPS Location Handling
Given the device is offline When the app recovers GPS data Then the map updates to the last known location and queues boundary recalculation and data fetching tasks to execute automatically when connectivity resumes.

SurgePredict

Leverages AI-driven analysis of historical footfall and real-time traffic data to forecast upcoming crowd surges hours in advance. Automatically schedules inventory preload tasks to prevent stockouts during peak demand, smoothing operations and maximizing sales opportunities.

Requirements

Real-Time Data Ingestion
"As a food truck operator, I want the system to ingest up-to-date traffic and footfall data so that I can receive accurate surge predictions based on current conditions."
Description

Integrate real-time traffic and footfall data from multiple external API sources, ensuring secure authentication, low-latency retrieval, and data normalization to feed the SurgePredict pipeline. The module must handle data rate limits, perform error handling and retry logic, and seamlessly integrate ingested data into forecasting algorithms to enhance prediction accuracy.

Acceptance Criteria
Authentication with External Data Source
Given valid API credentials are provided, When the ingestion module connects to the external API, Then it successfully authenticates using OAuth2 and retrieves the first data payload within 500ms.
Handling API Rate Limits
Given the external API responds with HTTP 429, When the ingestion module detects a rate limit response, Then it retries the request with exponential backoff up to 3 times and logs each retry event.
Recovery from Network Failures
Given intermittent network failures occur, When the ingestion module fails to connect, Then it retries the connection every 30 seconds up to 5 attempts and raises an alert if all retries fail.
Data Normalization of Ingested Payloads
Given raw data with inconsistent field formats, When the ingestion module processes incoming records, Then it transforms fields to the standard schema, ensures no mandatory fields are null, and converts all timestamps to UTC ISO 8601 format.
Seamless Integration into Forecasting Pipeline
Given normalized data packets are prepared, When ingestion completes, Then the module publishes data to the forecasting queue within 100ms and receives a successful acknowledgment.
Historical Data Analysis Engine
"As a food truck operator, I want the system to analyze historical footfall patterns so that it can identify peak times and inform surge predictions."
Description

Develop a scalable analysis engine to process and model historical footfall and sales data, applying time-series analysis and machine learning techniques to identify patterns, seasonality, and trends. The engine should support data cleaning, feature extraction, model training, and periodic retraining to continuously improve forecast accuracy.

Acceptance Criteria
Data Ingestion of Clean Historical Data
Given a CSV file containing one year of footfall and sales data with standardized column headers, When the analysis engine ingests the file, Then it should complete ingestion within 5 minutes for 1 million rows, And convert all date/time fields to ISO 8601 format, And flag any missing or malformed records for review.
Feature Extraction Accuracy
Given ingested historical data, When the feature extraction module processes the dataset, Then it should generate all required time-based features (day of week, seasonality flags, moving averages) for every record, And achieve 100% feature coverage without null or NaN values, And the computed feature values must match manually validated sample records.
Model Training and Validation
Given a training dataset of cleaned and feature-enhanced historical data, When the machine learning training job executes, Then it must complete within the allocated compute time (under 2 hours), And produce a forecasting model with Mean Absolute Error (MAE) below the target threshold of 5%, And generate a validation report with performance metrics and feature importance rankings.
Periodic Retraining Workflow
Given the deployed forecasting model and a new batch of weekly data, When the scheduled retraining pipeline runs automatically, Then it should retrain the model without errors, And replace the existing model only if validation metrics improve or remain within 1% of previous accuracy, And send a summary notification with new performance metrics to the operations team.
Anomaly Detection During Data Cleaning
Given a raw dataset containing potential outliers or anomalous spikes, When the data cleaning component executes anomaly detection rules, Then it must identify and flag at least 95% of injected synthetic anomalies in test datasets, And log all flagged records with anomaly type and severity score, And exclude confirmed anomalies from the training dataset for modeling.
Surge Forecast Dashboard
"As a food truck operator, I want a clear visual dashboard of predicted surges so that I can plan inventory and staffing effectively."
Description

Design and implement a mobile-responsive dashboard within the TruckTally app that visualizes predicted surge events over the next 24 hours, displaying timelines, expected footfall volumes, confidence intervals, and recommended preparation actions. The dashboard should support interactive graphs, filter options by location and time window, and real-time updates.

Acceptance Criteria
Viewing Surge Forecast Timeline
Given the user opens the Surge Forecast Dashboard on a mobile device, when the dashboard loads, then the timeline for the next 24 hours must display all predicted surge events with time markers and footfall volume labels.
Filtering Surge Data by Location and Time Window
Given the user selects a specific location and adjusts the time window filter, when the filters are applied, then the dashboard must update to show only surge events, volumes, and confidence intervals for that location and time range.
Analyzing Confidence Intervals on Surge Forecast
Given a displayed surge forecast graph, when the user taps or hovers over a surge event marker, then the system must display the upper and lower confidence interval bounds for that prediction.
Reviewing Recommended Preparation Actions
Given a predicted surge event is displayed, when the user views the event details, then the dashboard must list recommended preparation actions with clear descriptions and estimated time requirements.
Receiving Real-Time Surge Updates
Given the dashboard is open for longer than five minutes, when new traffic data arrives, then the dashboard must automatically refresh the displayed surge predictions without a full page reload.
Automated Inventory Preload Scheduler
"As a food truck operator, I want the system to auto-schedule preload tasks so that my inventory is ready for upcoming surges without manual planning."
Description

Create an automated scheduling feature that uses surge forecasts to generate and assign inventory preload tasks. The scheduler must integrate with the existing inventory management system, calculate required stock levels, set task deadlines, and assign tasks to users, ensuring timely preparation for predicted demand peaks.

Acceptance Criteria
Surge Forecast Integration and Task Generation
Given a surge forecast is available for a future time window, When the system processes the forecast, Then the scheduler automatically generates a preload task linked to that forecasted time window.
Inventory Level Calculation and Preload Task Creation
Given current stock levels and predicted surge quantities, When the scheduler runs, Then it calculates the required inventory preload amounts and creates corresponding preload tasks.
Deadline Assignment Based on Demand Timing
Given a preload task for a predicted surge event, When determining task parameters, Then the system assigns a deadline at least four hours prior to the surge start time.
User Assignment and Notification Delivery
Given a newly created preload task, When the task is assigned, Then the system selects an appropriate user based on role and current workload and sends both a push notification and an email alert to the assigned user.
Forecast Update and Task Adjustment
Given an updated surge forecast that changes predicted demand, When the system detects the update, Then it recalculates preload amounts, updates task deadlines if needed, and notifies assigned users of the changes.
Surge Alert Notification System
"As a food truck operator, I want to receive timely alerts about upcoming surges so that I can prepare my stock and staff quickly."
Description

Implement a notification framework that sends push notifications, SMS, or email alerts when a surge is forecasted, including details such as expected start time, estimated customer volume, and suggested actions. Notifications should be configurable by alert type, lead time, and delivery channel to suit operator preferences.

Acceptance Criteria
Configurable Alert Preferences
Users can choose one or more alert types (push, SMS, email); select lead time options (15, 30, 60 minutes); and configure delivery channels in the settings. When saved, preferences must persist across app restarts and be applied to future surge notifications.
Timely Push Notification Delivery
When a surge is forecasted, the system sends a push notification to the user’s device at the configured lead time before the forecast start; delivery success rate exceeds 95%; notifications appear within 5 seconds of trigger.
SMS Fallback Mechanism
If a push notification fails (no delivery confirmation within 10 seconds), the system automatically sends an SMS alert to the user’s phone number within 1 minute; SMS includes surge details and suggested actions.
Email Alert Content Accuracy
All alerts sent via email contain the expected start time, estimated customer volume, and suggested actions formatted correctly; links and formatting render properly across major email clients; no missing or truncated data.
Alert Delivery Lead Time Compliance
For each configured lead time option, notifications (push, SMS, email) are delivered within ±2 minutes of the specified lead time prior to the surge forecast start; system logs record actual delivery timestamps.
Offline Support and Data Sync
"As an operator working offline, I want to access surge forecasts and preload tasks so that I can prepare even without connectivity."
Description

Provide offline caching for key surge forecasts, preload task lists, and recent historical data, allowing operators to access critical information without internet connectivity. Implement reliable data synchronization mechanisms to reconcile offline activity and update forecasts once the device reconnects.

Acceptance Criteria
Offline Access to Surge Forecasts
Given the device is offline and the user opens the SurgePredict screen, the app loads cached surge forecasts for the next three hours within two seconds and displays the data with a timestamp of the last successful sync.
Offline Preload Task List Availability
When the device has no internet connection, the preload inventory task list fetched during the last sync is accessible, and users can mark tasks as completed or edited; these changes are stored locally with accurate timestamps.
Automatic Data Synchronization upon Reconnection
Upon regaining internet connectivity, all locally stored offline actions—completed tasks and viewed forecasts—are automatically sent to the server within ten seconds, and the local cache confirms successful upload for each item.
Conflict Detection and User-Driven Resolution
If a data conflict occurs during synchronization (e.g., a task updated offline differs from the server version), the system prompts the user to select either the local or server version, applies the chosen version consistently, and logs the resolution event.
Offline and Sync Status Indicators
The app displays a persistent offline status indicator when disconnected and, after reconnection, shows a real-time sync progress indicator, updating users on the number of items synced and flagging any failures.

DemandWave Alerts

Sends instant push notifications when foot traffic thresholds are crossed or a surge is imminent, along with tailored recommendations on which popular items to preload. Keep your team informed and responsive to traffic spikes without constantly monitoring data.

Requirements

Threshold Configuration
"As a food truck operator, I want to set custom foot traffic thresholds so that I receive alerts only when traffic levels matter to my inventory planning."
Description

Enables operators to define, adjust, and save custom foot traffic thresholds that trigger DemandWave Alerts. Administrators can configure multiple threshold levels (e.g., low, medium, high) based on historical data and expected customer flow. This ensures alerts are relevant and tailored to each food truck’s unique operating patterns, reducing noise from irrelevant notifications.

Acceptance Criteria
Admin creates a new foot traffic threshold level
Given the operator is on the Threshold Configuration screen When they enter a name and a numeric value for a new threshold level and click Save Then the new threshold appears in the threshold list with the correct name and value
Admin edits an existing threshold level
Given an existing threshold level is listed When the operator modifies the name or value and clicks Update Then the threshold list reflects the updated name and value without creating duplicates
Admin deletes a threshold level
Given multiple threshold levels are configured When the operator selects a threshold and clicks Delete Confirm Then the selected threshold is removed from the list and no longer triggers alerts
Threshold configurations persist across sessions and offline use
Given the operator configures thresholds and closes the app While offline or after restarting the app Then the previously saved threshold levels are displayed and available for alert triggering
Threshold notifications reflect configured levels
Given foot traffic data updates in real time When traffic crosses a configured threshold level Then a push notification is sent within 10 seconds indicating which threshold was crossed
Real-Time Monitoring Engine
"As a food truck operator, I want real-time monitoring of foot traffic so that I can be alerted immediately when a surge is detected."
Description

Implements a background service that continuously collects and processes foot traffic data from multiple sources (GPS, POS transactions, manual inputs). The engine analyzes incoming data in real time, compares it against configured thresholds, and flags potential surges without noticeable delay. This capability ensures the system is always up to date and ready to trigger timely alerts even during peak operating hours.

Acceptance Criteria
Continuous Data Collection
Given the Real-Time Monitoring Engine is running When a new data point is received from GPS or POS Then the engine records the data within 2 seconds and persists it to the data store
Threshold Comparison Processing
Given aggregated foot traffic data is updated When the count exceeds the configured threshold Then the engine flags the surge event and writes an entry to the event log
Late Data Arrival Handling
Given manual traffic inputs collected offline are uploaded When the engine receives the batch Then it integrates the data into current metrics within 5 seconds and updates any existing surge status
High-Load Performance
Given a peak load scenario of 1,000 data events per minute When the engine processes incoming data Then end-to-end processing latency remains below 2 seconds per event with zero dropped records
Surge Alert Trigger
Given a potential traffic surge is detected When the configured threshold is crossed Then the engine generates a surge flag and prepares a push notification payload within 1 second
Push Notification Delivery
"As a food truck operator, I want to receive instant push notifications on my phone so that I can respond quickly to traffic spikes without constantly checking the app."
Description

Delivers instant push notifications to the mobile app when a configured foot traffic threshold is crossed or an imminent surge is predicted. Notifications include context about the level of traffic change and recommended actions. This requirement integrates with both iOS and Android push services to guarantee reliable alert delivery even under varying network conditions.

Acceptance Criteria
Threshold Breach Notification
Given a configured foot traffic threshold When the live traffic count exceeds the threshold Then a push notification is delivered to the user’s iOS and Android devices within 5 seconds containing the threshold value, current count, and recommended preload actions
Surge Prediction Alert
Given an imminent surge predicted within 10 minutes When the prediction model triggers Then a push notification is sent to the mobile app with estimated surge time and tailored item recommendations, allowing the user to snooze for 5 minutes
Network Interruption Handling
Given no network connectivity on the device When a notification is triggered Then the system retries delivery every 30 seconds up to 5 attempts and delivers the notification once connectivity is restored without duplicates
User Interaction Feedback
Given a received notification When the user taps the notification Then the app opens the real-time traffic dashboard within 2 seconds preloaded with context from the notification
Cross-Platform Consistency
Given latest iOS and Android environments When notifications are delivered Then the content, formatting, icons, and actions are identical and function correctly on both platforms
Recommendation Engine
"As a food truck operator, I want personalized recommendations on which items to preload so that I can maximize sales during traffic surges and minimize waste."
Description

Provides tailored preload suggestions for popular menu items based on recent sales trends and real-time traffic data. The engine uses historical performance and current stock levels to recommend which items to prepare in advance, optimizing inventory readiness. This feature empowers teams to meet customer demand efficiently and reduce the risk of stockouts during busy periods.

Acceptance Criteria
High Traffic Surge Preload Suggestion
Given a foot traffic surge threshold is exceeded within 15 minutes When the recommendation engine runs Then it suggests preloading the top 3 selling items from the last 15 minutes
Low Stock Preload Alert
Given an item’s stock level falls below the predefined minimum When the recommendation engine processes current stock Then it generates a preload suggestion for that item
Daily Sales Trend Summary
Given historical sales data for the previous day When the recommendation engine performs its daily analysis at 06:00 AM Then it recommends preloading items that increased in sales volume by at least 20%
Real-Time Menu Popularity Update
Given real-time sales updates during service When the recommendation engine receives sales data every 5 minutes Then it updates the preload suggestions to reflect the current top 5 popular items
Offline Mode Recommendation Validation
Given the app is offline and synchronized stock and sales data is available When the recommendation engine runs locally Then it provides the same preload suggestions as online mode within 5% variance
Alert History and Analytics
"As a food truck operator, I want access to past alert logs and analytics so that I can refine my threshold settings and improve my response strategies."
Description

Stores a log of all triggered alerts, including timestamp, threshold level, actual foot traffic data, and operator response actions. Provides an analytics dashboard where operators can review past alerts, measure the accuracy of surge predictions, and adjust thresholds accordingly. This requirement supports continuous improvement by giving teams insights into traffic patterns and alert performance over time.

Acceptance Criteria
Alert Entry Logging
Given an alert is triggered, when the system records it, then the database entry must include a valid ISO 8601 timestamp, recorded threshold level, actual foot traffic count, and operator response action. Given multiple alerts occur in rapid succession, when logged, then each alert entry must be unique and ordered by timestamp without data loss or duplication.
Alert History Retrieval
Given an operator requests the alert history, when specifying a date range, then the system returns all matching alert entries sorted chronologically. Given no alerts exist in the specified range, when retrieval is performed, then the system displays a clear “No records found” message.
Analytics Dashboard Summary
Given the operator navigates to the analytics dashboard, when loaded, then the dashboard displays total alerts count, average surge prediction accuracy, and most frequent threshold breaches for the selected period. Given data volume is large, when rendering charts, then the system must display charts within 2 seconds without UI blocking.
Surge Prediction Accuracy Metrics
Given historical alerts and actual foot traffic data, when computing accuracy, then the system calculates and displays prediction error rate (in percent) for each threshold level. Given new alert data is added, when metrics are recalculated, then the dashboard updates accuracy figures in real time or less than 5 seconds.
Threshold Adjustment Feedback Loop
Given the operator adjusts a traffic threshold, when saving the new threshold, then the system logs the change action with user ID and timestamp in the history. Given thresholds have been adjusted multiple times, when entering the dashboard, then the system provides a recommendation to revert or fine-tune thresholds based on past accuracy below 80%.

QuickLoad Scheduler

Automates the preloading process by scheduling recommended restocks once predefined footfall triggers are met. Quantities adjust in-app based on projected demand, saving manual effort and guaranteeing optimal inventory levels before the rush hits.

Requirements

Demand Forecast Integration
"As a food truck owner, I want the system to predict upcoming inventory needs based on past sales so that I can preload stock accurately and avoid stockouts."
Description

Integrate a demand forecasting engine that analyzes historical sales data, footfall triggers, and menu items to predict upcoming inventory needs. The system adjusts recommended restock quantities in the QuickLoad Scheduler automatically based on these forecasts, ensuring accurate preload suggestions. This requirement enhances proactive inventory management, minimizes stockouts, and optimizes resource allocation by aligning restocking actions with predicted demand patterns.

Acceptance Criteria
Daily Restock Forecast
Given the system has 30 days of historical sales and footfall data When the QuickLoad Scheduler runs overnight Then it generates restock quantities for all menu items for the next business day based on forecast within ±10% accuracy.
Footfall Surge Prediction
Given a 20% increase in average footfall over the past hour When footfall triggers are met Then the system recalculates restock quantities and notifies the operator within 2 minutes.
Offline Forecast Sync
Given the device is offline When the operator revisits the QuickLoad Scheduler Then the system syncs the latest forecast adjustments within 1 minute of reconnection without data loss.
Menu Item Demand Variation
Given new seasonal menu items with less than 7 days of sales history When forecasting runs Then the system applies a fallback algorithm using similar item data and flags forecast confidence below 70%.
Historical Data Insufficiency Handling
Given fewer than 5 days of historical sales data for a menu item When generating restock suggestions Then the system defaults to manual input and prompts the operator to confirm quantities.
Trigger Definition Interface
"As an operator, I want to define custom footfall triggers in the scheduler so that restock recommendations align with my specific service patterns."
Description

Implement a user-friendly interface within the QuickLoad Scheduler that allows operators to define, customize, and manage footfall-based triggers that initiate restocking recommendations. The interface should support conditional expressions, such as time windows or peak hours, and provide real-time feedback on trigger thresholds. This empowers users to tailor restock triggers to their unique operational patterns, improving control and flexibility.

Acceptance Criteria
Create New Footfall Trigger
Given the operator is on the Trigger Definition Interface and no triggers exist When the operator enters a valid footfall threshold, selects a restock action, and saves Then the system persists the new trigger, displays it in the trigger list, and confirms success
Customize Trigger with Time Window
Given the operator has an existing trigger When the operator edits the trigger to add a time window (e.g., 11:00–14:00) and saves Then the system updates the trigger, enforces the time constraint, and shows the updated schedule
Real-Time Threshold Feedback
Given the operator adjusts the footfall threshold slider or input field When the operator changes the value Then the interface instantly displays projected restock quantities and potential schedule conflicts based on the new threshold
Prevent Invalid Expression Submission
Given the operator enters an invalid conditional expression or leaves required fields blank When the operator attempts to save Then the system blocks submission, highlights errors, and presents contextual error messages
Delete Existing Trigger
Given the operator views the list of defined triggers When the operator selects a trigger and confirms deletion Then the system removes the trigger from the list and confirms deletion
Automatic Restock Execution
"As a manager, I want the system to automatically execute restock orders when triggers are met so that I save time and reduce manual errors."
Description

Develop a fully automated restocking workflow that, upon reaching predefined footfall triggers, executes preload orders with specified suppliers or internal stock movements. The system must handle order creation, quantity adjustments, and confirmation, integrating seamlessly with existing inventory and procurement modules. This automation reduces manual intervention, speeds up restocking processes, and ensures optimal inventory levels before peak operations.

Acceptance Criteria
Footfall Trigger Reached, Supplier Order Created
Given the footfall counter reaches the predefined trigger threshold When the QuickLoad Scheduler runs the restock evaluation Then the system automatically generates a purchase order with the default supplier using the predefined restock quantities
Quantity Adjustment Based on Projected Demand
Given the footfall trigger is met and projected demand is calculated When the restock order is assembled Then the order quantities are adjusted in-app to match the projected demand volume
Internal Stock Movement Execution
Given external suppliers are unavailable or disabled When the restock workflow executes Then the system creates and records an internal stock transfer from the central warehouse to the truck inventory
Order Confirmation Notification Sent
Given a restock order (supplier or internal) is created When the order processing completes or supplier acknowledges Then the system logs the confirmation status and sends a notification to the operator within the app
Inventory and Procurement Module Integration Validated
Given a restock order has been executed When reviewing system records Then inventory levels in the truck and procurement module entries are updated and reconciled accurately
Offline Sync Optimization
"As a user operating offline, I want the scheduler to sync locally stored restock schedules with the server once online so that my data stays consistent and I never miss restocks."
Description

Ensure the QuickLoad Scheduler operates reliably in offline mode by enabling local caching of triggers, forecast data, and restock schedules. Upon reconnecting to the network, the system should efficiently synchronize data with the central server, resolving conflicts and updating records without data loss. This requirement guarantees continuous functionality during connectivity drops, preventing missed restocks and maintaining accurate inventory states.

Acceptance Criteria
Local Caching of Restock Triggers
Given the app is offline, when a footfall trigger is met, then the trigger and restock recommendation are stored in local cache and marked as pending sync.
Forecast Data Availability Offline
Given the user accesses the scheduler without network, when loading forecast data, then the app retrieves the last synced forecast within the last 24 hours from local storage.
Data Synchronization on Reconnection
Given the device regains network connectivity, when the app detects connection, then it automatically syncs cached triggers, forecast data, and restock schedules with the server within 2 minutes.
Conflict Resolution During Sync
Given conflicting updates for the same restock schedule offline and on the server, when syncing, then the app applies the latest timestamp wins rule and logs any overrides for audit.
Data Integrity Post-Sync
Given sync completes successfully, when the user views the restock schedule, then all offline changes are reflected accurately and no duplicates or omissions exist.
Restock Notification Alerts
"As an operator, I want to receive notifications about scheduled and executed restocks so that I stay informed and can address any issues immediately."
Description

Create a notification subsystem that alerts operators via push notifications and in-app messages when restocking actions are scheduled, executed, or encounter errors. Notifications should include key details like item lists, quantities, trigger events, and error descriptions. This feature keeps users informed of inventory actions, allowing prompt responses to exceptions and reinforcing trust in the QuickLoad Scheduler's operations.

Acceptance Criteria
Scheduled Restock Notification Initiation
Given a restock is scheduled by QuickLoad Scheduler, when the scheduling trigger is met, then the system sends both a push notification and an in-app message titled “Restock Scheduled” listing the items and quantities to be restocked.
Restock Execution Confirmation Delivered
Given a scheduled restock action completes successfully, when execution finishes, then the system sends a push notification and an in-app message titled “Restock Completed” confirming the items and quantities restocked.
Restock Error Notification Generated
Given a restock execution encounters an error, when the failure is detected, then the system sends a push notification and an in-app message titled “Restock Error” including the item list, quantities affected, error description, and suggested next steps.
Notification Detail Accuracy
Given any restock-related notification (scheduled, completed, or error), when the notification is generated, then it must include the item names, quantities, trigger event type, timestamp, and error details if applicable.
Offline Notification Queueing and Delivery
Given the user’s device is offline when a restock notification is triggered, when connectivity is restored, then all queued notifications are delivered via push and appear in-app in chronological order without duplication.

HotItem Queue

Generates a prioritized list of likely top-selling items based on live foot traffic patterns and sales velocity. Focus your restocking efforts on high-demand products first, reducing waste and ensuring you never miss a sale during busy periods.

Requirements

Foot Traffic Data Capture
"As a food truck operator, I want the app to capture foot traffic patterns so that the HotItem queue can predict which products will be in highest demand."
Description

Implement functionality to collect real-time foot traffic data around the food truck using device sensors (e.g., camera analytics, Bluetooth beacons, or GPS heatmaps). This data feed should integrate seamlessly with TruckTally’s data processing pipeline, ensuring continuous, accurate input for demand forecasting even under varying environmental conditions. The module must filter noise, aggregate counts over configurable intervals, and store results in local cache for offline access.

Acceptance Criteria
Real-Time Sensor Data Collection
Given the app is operational and device sensors are active, when a pedestrian passes within sensor range, then the system records the detection event with a timestamp and sensor type.
Noise Filtering for Sensor Data
Given multiple sensor inputs including potential false positives, when data is processed, then the system filters out events with confidence below the predefined threshold and excludes duplicate detections within 5 seconds.
Foot Traffic Data Aggregation Over Configurable Interval
Given raw detection events are collected, when the configurable interval elapses, then the system aggregates counts per sensor type and interval and summarizes the data for storage.
Local Cache Storage for Offline Access
Given intermittent network connectivity, when new aggregated data is available, then the system writes the data to the local cache and retains the last 24 hours of data for offline access.
Seamless Integration with Data Pipeline
Given network availability, when data sync is triggered, then the system pushes aggregated foot traffic data to the central processing pipeline within 30 seconds and receives an acknowledgment from the server.
Sales Velocity Computation
"As a food truck operator, I want the system to calculate real-time sales velocity per item so that I can understand which items are selling fastest."
Description

Develop a real-time sales velocity engine that calculates the rate of sales for each menu item over rolling time windows (e.g., last 15, 30, and 60 minutes). This engine should factor in historical baseline data, current shift performance, and time-of-day trends. Results must update dynamically and be accessible to the prioritization algorithm, with the ability to adjust window sizes as per user settings.

Acceptance Criteria
Real-time Sales Velocity Calculation During Peak Hours
Given the app is tracking sales for each menu item over the last 15, 30, and 60 minutes during a peak period When a sale is recorded Then the sales velocity engine updates the rate for each time window within 5 seconds and logs the update timestamp
Dynamic Window Size Adjustment by User
Given the user modifies the rolling time windows from the default values When the new window sizes are saved in settings Then the engine recalculates the sales velocity for all items using the updated window sizes and reflects the changes in the next data refresh
Incorporation of Historical Baseline Data
Given the engine has access to historical sales baselines for each item When computing current sales velocity Then the calculation adjusts the raw rate by comparing against baseline, and flags deviations greater than 20%
Time-of-Day Trend Analysis
Given the current time falls within predefined time-of-day segments When the engine calculates sales velocity Then it applies time-of-day multipliers based on historical trends and outputs both raw and adjusted velocities
Prioritization Algorithm Integration
Given the sales velocity data is available from the engine When the prioritization algorithm runs Then it consumes the latest velocity metrics for each item and generates a ranked hot item queue, updating within 10 seconds of the latest velocity output
Demand Prioritization Algorithm
"As an inventory manager, I want the algorithm to generate a prioritized list of items based on combined demand factors so that I can focus restocking efforts on top sellers."
Description

Create an algorithm that combines foot traffic metrics and sales velocity scores to compute a demand priority index for each product. The algorithm must normalize inputs, apply configurable weightings, and sort items into a ranked queue. It should be modular to accommodate future factors (e.g., weather or event schedules) and provide an API for retrieving the top N hot items on demand.

Acceptance Criteria
Real-Time Hot Items Retrieval Under Stable Connectivity
Given the system is connected to reliable internet access, When an API request is made for the top 10 hot items, Then the response returns a sorted list of 10 products ranked by demand priority index and the API call completes within 200ms.
Offline Mode Demand Prioritization Handling
Given the app is offline, When the user requests the hot item queue, Then the most recent cached demand priority index is served, and the queue order matches the last known online calculation.
Configurable Weight Adjustments Effect Verification
Given the weighting parameters for foot traffic and sales velocity are updated in the configuration, When the algorithm runs, Then the demand priority index calculation reflects the new weightings and the item ranking changes accordingly.
Demand Index Input Normalization Accuracy
Given raw foot traffic and sales velocity values of varying scales, When the normalization function processes inputs, Then all input values are scaled to a 0–1 range before applying weightings, and the normalized outputs sum of squared deviations remains within acceptable tolerance.
Modularity for Future Data Factor Integration
Given a new data factor (e.g., weather score) is added to the system, When the algorithm is invoked, Then the new factor is accepted via the modular interface, included in the demand index computation, and results in an updated ranked queue without core algorithm changes.
HotItem Queue UI Integration
"As a food truck operator, I want to view the HotItem queue in the app interface so that I can quickly identify which items to restock first."
Description

Design and implement a user interface component within the TruckTally mobile app that displays the HotItem queue. The UI must show item names, priority scores, countdown timers for recalc, and color-coded indicators. It should be responsive to both portrait and landscape orientations, support touch interactions (e.g., tap for detailed demand insights), and adhere to TruckTally’s design guidelines for consistency.

Acceptance Criteria
HotItem Queue Display in Portrait Mode
Given the user opens the TruckTally app in portrait orientation When the HotItem Queue UI component loads Then item names, priority scores, countdown timers, and color-coded indicators must be fully visible within the screen width without horizontal scrolling And the layout must adhere to TruckTally’s design guidelines for spacing, typography, and color palette
HotItem Queue Display in Landscape Mode
Given the user rotates the device to landscape orientation When the HotItem Queue UI component re-renders Then the list adapts to the wider layout, maintaining readability and alignment of item names, priority scores, timers, and indicators And no UI elements should overlap or be truncated
Interactive Demand Insight on Tap
Given the user taps on a HotItem entry in the queue When the tap gesture is recognized Then a detailed demand insights panel must appear showing sales velocity graphs, foot traffic trends, and recommended restock quantities And the panel must include a close button that returns to the queue view
Real-Time Priority Score Update
Given live sales and foot traffic data change When new data is received by the app Then the priority scores in the HotItem Queue must automatically recalculate and update within 60 seconds And the countdown timer for the next recalculation must decrement in real time
Color-Coded Indicator Compliance
Given performance thresholds of >80%, 50–80%, and <50% When priority scores are calculated Then each HotItem entry must display a green indicator for >80%, yellow for 50–80%, and red for <50% And a legend explaining the color codes must be visible at the top of the queue
Offline Mode Support for Demand Data
"As a mobile operator, I want the HotItem queue to work even when I’m offline so that I can make restocking decisions in areas with poor connectivity."
Description

Ensure that the HotItem Queue feature functions reliably without internet connectivity by caching the latest foot traffic and sales velocity data locally. Implement background sync to reconcile cached data with the server once connectivity is restored, resolving conflicts and updating the queue accordingly. The offline mode must degrade gracefully, providing best-available insights until full data sync is possible.

Acceptance Criteria
Offline Data Caching Initialization
Given the user launches the app with internet connectivity, When the HotItem Queue feature initializes, Then the latest foot traffic and sales velocity data is downloaded and stored in local cache; And the cache timestamp is recorded.
Queue Access Without Connectivity
Given the app is offline, When the user accesses the HotItem Queue, Then the queue is generated using the latest locally cached data within 5 seconds; And a visual indicator shows data may be outdated.
Background Sync on Connectivity Restore
Given the app has returned online, When connectivity is detected, Then the app automatically syncs local cached foot traffic and sales data with the server in the background; And updates the HotItem Queue accordingly without user action.
Conflict Resolution During Sync
Given conflicting data between local cache and server data, When a sync occurs, Then server data for foot traffic and sales velocity takes precedence; And local changes are merged without data loss.
Graceful Degradation with Stale Data Indicators
Given the cached data is older than 24 hours, When the user views the HotItem Queue offline, Then a warning badge appears next to the queue title; And the app suggests reconnecting to refresh data.
Configurable Demand Thresholds and Weights
"As a power user, I want to adjust the weight and threshold settings for demand metrics so that the HotItem queue reflects my specific sales patterns and preferences."
Description

Provide settings for users to customize demand analysis parameters, including time window sizes, weightings for foot traffic vs. sales velocity, and threshold levels for high-demand alerts. The configuration interface must validate inputs, offer presets for common scenarios, and store preferences per truck location or shift profile. These settings should immediately impact queue calculations without requiring app restarts.

Acceptance Criteria
Configuring Time Window Sizes
Given the user opens the demand configuration settings, when they enter minimum and maximum time window sizes within 1–240 minutes and click 'Save', then the system validates the inputs, persists them per location, and displays a confirmation message.
Customizing Weightings for Foot Traffic vs Sales Velocity
Given the user adjusts the foot traffic and sales velocity sliders, when the total allocation equals 100% and the user saves, then the system stores the weightings and uses them in subsequent queue calculations.
Setting High-Demand Threshold Levels
Given the user specifies a high-demand threshold percentage between 0% and 100%, when they save the setting, then the threshold is validated, stored per shift profile, and used to trigger alerts.
Applying Preset Demand Analysis Configurations
Given the user selects a preset configuration (e.g., 'Morning Rush', 'Evening Slow'), when applied, then the system loads the preset parameters into the configuration interface and confirms successful application.
Real-Time Configuration Updates Without Restart
Given the user updates any demand analysis setting and saves changes, when viewing the hot item queue immediately afterward, then the new settings are reflected in the queue calculations without requiring an app restart.

TrendMapper

Combines local event calendars, weather forecasts, and historical sales trends to produce geo-specific demand maps. Use these insights to predict customer preferences at different locations and tailor your menu mix and inventory accordingly.

Requirements

Event Data Integration
"As a food truck operator, I want to access a consolidated list of local events so that I can plan my schedule and inventory to meet potential demand spikes."
Description

Integrate local event calendars from multiple public and private sources, standardize event metadata, and update the system daily to provide timely insights into upcoming gatherings and festivals in target geographies.

Acceptance Criteria
Daily Event Data Synchronization
Given the daily scheduled job runs at midnight UTC When the sync executes Then the system retrieves event lists from all configured sources and imports at least 95% of new events without errors
Event Metadata Standardization
When events are imported from different sources Then event metadata fields (name, date, location, category) conform to the unified schema and pass schema validation checks
Multiple Source Conflict Resolution
Given two sources provide conflicting data for the same event Then the system applies the predefined source priority rules and retains the event data from the higher-priority source
Error Handling and Alerting
When an import error occurs for any source Then the system logs the error with full context and sends an email notification to the admin within 15 minutes
Historical Data Backfill
Given a new event source is added When the backfill job runs Then the system imports up to one year of historical events, completes within 2 hours, and avoids data duplication
Weather Forecast Integration
"As a food truck operator, I want to view upcoming weather conditions so that I can adjust my menu offerings and staffing levels accordingly."
Description

Connect to a reliable weather forecast API to retrieve location-specific weather data including temperature, precipitation, and alerts, and update forecasts at least twice daily for the next seven days.

Acceptance Criteria
Scheduled Twice-Daily Forecast Refresh
Given the mobile app has network connectivity at 08:00 and 20:00 local time, When the scheduled job runs, Then the system sends a request to the weather API and successfully retrieves temperature, precipitation probability, and weather alerts for the next seven days.
Location-Driven Forecast Retrieval
Given a user selects a new service location on the map, When the location’s coordinates are updated, Then the app triggers an immediate API call and displays the corresponding seven-day weather forecast.
Graceful API Failure Handling
Given the weather API request fails due to timeout or non-200 response, When the client receives an error, Then the app logs the failure, retries up to two times, and displays the last cached forecast with a notification to the user about stale data.
Offline Access to Last Known Forecast
Given the device is offline, When a user views the weather section, Then the app retrieves and displays the most recent cached seven-day forecast without errors.
Data Consistency Verification
Given new weather data is fetched, When stored in the local database, Then the data fields for temperature, precipitation, and alerts match the API payload schema and contain no null or out-of-range values.
Historical Sales Trend Analysis
"As a food truck operator, I want to review how past sales were influenced by events and weather so that I can predict future sales more accurately."
Description

Develop an analytics engine that correlates past sales data with event and weather inputs, identifying patterns and seasonality to inform future demand predictions per location and time period.

Acceptance Criteria
Event-Driven Sales Correlation Scenario
Given the engine has ingested at least six months of past sales and local event schedule data, When the analyst requests correlation analysis for a specific location, Then the engine calculates and returns correlation coefficients for each event category with a confidence interval of 95%.
Weather Impact Sales Analysis Scenario
Given the engine has ingested historical sales and weather forecast data for the same time periods, When the analyst triggers weather impact analysis, Then the engine produces a report showing the percentage change in sales for each weather condition type with at least 90% data coverage.
Seasonality Pattern Identification Scenario
Given the engine has access to two years of historical sales data, When the analyst requests seasonal trend analysis for menu items, Then the engine identifies and highlights peak and off-peak periods with a minimum variance threshold of 15%.
Location-Specific Demand Prediction Scenario
Given the engine has processed historical sales, event, and weather data for a specific location, When a user asks for demand prediction for a future date/time, Then the engine provides a demand forecast with an accuracy rate of at least 80% based on historical validation.
Performance and Scalability Validation Scenario
Given a dataset comprising at least 100,000 historical transactions, When the engine runs a full trend analysis, Then it completes processing within 5 minutes and utilizes no more than 75% of allocated compute resources.
Geo-Specific Demand Mapping
"As a food truck operator, I want to see demand heat maps for different areas so that I can choose the best parking spots and plan my menu mix."
Description

Generate interactive maps that overlay predicted demand intensity for menu items based on combined event schedules, weather forecasts, and historical trends, allowing users to zoom and filter by timeframe and location.

Acceptance Criteria
Interactive Demand Map Display
Given a user selects a location and timeframe in TrendMapper, when the system processes event schedules, weather data, and historical trends, then an interactive map displays predicted demand intensity overlays for each menu item within two seconds of selection.
Map Zoom Functionality
Given the demand map is displayed, when the user zooms in or out, then the demand overlays adjust resolution and clustering dynamically without data distortion.
Menu Item Demand Filtering
Given the interactive map is active, when the user filters by a specific menu item, then the map only shows predicted demand intensity for that item, and all other overlays are hidden.
Offline Demand Map Access
Given the user is offline, when they open the TrendMapper app and navigate to the demand map, then the app loads the last-synced demand map data with a visible timestamp and notifies the user that the data is offline.
Region Tooltip Details
Given the user taps or hovers over a map region, when the interaction occurs, then a tooltip displays the predicted demand intensity value along with contributing factors (events, weather, historical) for that region.
Inventory Adjustment Recommendations
"As a food truck operator, I want tailored inventory recommendations so that I can stock the right amount of ingredients and reduce food waste."
Description

Provide automated inventory and menu recommendations grounded in predicted demand, suggesting quantities of ingredients and popular items for specific locations and events to minimize waste and maximize sales.

Acceptance Criteria
Pre-Event Inventory Planning
Given a selected event location and date with available weather forecasts and historical sales data, When the user requests inventory recommendations, Then the system generates a list of ingredient quantities and menu item allocations that align with predicted demand within a 10% accuracy margin.
Real-Time Weather Change Adjustment
Given active weather updates indicating a sudden temperature drop or rain forecast, When the system recalculates recommendations less than 15 minutes after receiving the update, Then it adjusts suggested ingredient quantities and menu mix to reflect decreased or shifted customer demand.
Low Internet Connectivity
Given intermittent or no internet connection on the mobile device, When the user triggers the inventory recommendation feature, Then the system provides offline-generated recommendations based on cached data and synchronizes adjustments once connectivity is restored.
Multi-Day Festival Stock Replenishment
Given a multi-day event with variable attendance per day, When the system processes day-over-day sales trends, Then it delivers updated inventory suggestions each morning that account for remaining stock and projected demand for the remainder of the festival.
End-of-Day Waste Minimization
Given actual sales and leftover inventory data at day’s end, When the user reviews the system’s waste reduction report, Then the system highlights items with excess stock and recommends adjusted order quantities for the next similar event to minimize waste.

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Press Coverage

Imagined press coverage for this groundbreaking product concept.

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TruckTally Launches SurgePredict to Forecast Foot Traffic and Maximize Sales

Imagined Press Article

Metropolis, July 16, 2025 – Today, TruckTally, the leading mobile inventory and order management solution for independent food truck operators, announced the launch of SurgePredict, an AI-driven forecasting feature designed to anticipate foot traffic surges hours in advance. SurgePredict leverages historical sales patterns, real-time pedestrian flow data, weather forecasts, and local event calendars to deliver precise crowd projections. By proactively scheduling inventory preload tasks, vendors can be fully prepared for demand spikes, minimizing stockouts, reducing waste, and maximizing sales opportunities during their busiest shifts. SurgePredict integrates seamlessly into TruckTally’s existing mobile and offline-ready app. Operators simply enable the feature in their dashboard and set preferred lead times for alerts. Once activated, SurgePredict continuously analyzes multiple data streams and issues push notifications when a surge is imminent. These alerts include tailored recommendations on which menu items to preload, supported by live sales velocity metrics. The system can even trigger QuickLoad Scheduler to automatically generate a restock plan, ensuring optimal stock levels without manual intervention. “Food truck operators face unique challenges when predicting demand on the go, especially during festivals, street fairs, and pop-up events,” said Jane Gilbert, Co-Founder and CEO of TruckTally. “SurgePredict equips vendors with actionable intelligence by harnessing AI and real-time data, so they can focus on serving customers rather than scrambling to restock when lines are already out the door.” In beta testing, more than 50 independent food truck owners reported a 20 percent reduction in stockouts and a 15 percent increase in peak-hour sales after adopting SurgePredict. Night Owl Nora, a late-night festival vendor in Austin, Texas, credits the feature with transforming her servicing strategy. “I used to guess what items would sell best under neon lights, but SurgePredict showed me exactly when and what to load,” said Nora. “It’s like having a personal operations analyst riding in my truck.” SurgePredict also benefits fleet managers and pop-up organizers overseeing multiple units. Fleet Manager Francisco Alvarez, who supervises a five-truck network in Southern California, praised the centralized dashboard view. “I can see ahead of time where a surge is building and reassign stock from under-performing trucks before customers even arrive,” he explained. “It’s a game-changer for optimizing our routes and ensuring consistent service across all locations.” Key Features of SurgePredict: • AI-driven crowd forecasting: Combines machine learning with external data sources for high-accuracy predictions. • Customizable lead times: Vendors choose how far in advance they receive surge alerts. • Menu-specific recommendations: Highlights top-selling items based on historical trends and current conditions. • Integration with QuickLoad Scheduler: Automates restocking tasks according to forecasted demand. • Offline compatibility: Caches forecasts and recommendations for areas with limited connectivity. SurgePredict is available today to all TruckTally subscribers at no additional cost. Existing users can enable the feature in their app settings and access a one-click tutorial to get started. For operators new to TruckTally, a free 14-day trial includes full access to SurgePredict and all core inventory management tools. About TruckTally TruckTally empowers independent food truck owners, fleet managers, and event coordinators with a mobile, offline-ready platform for real-time inventory and order management. From AI-driven demand forecasting to one-tap payments, TruckTally’s comprehensive suite of features helps operators reduce manual work, prevent stockouts, and boost sales even in the busiest environments. Founded in 2021, TruckTally has become the trusted solution for thousands of mobile food businesses worldwide. Media Contact: John Ramirez Head of Communications, TruckTally media@trucktally.com (555) 123-4567

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TruckTally Rolls Out EcoTrend Suite to Slash Waste and Drive Sustainability for Food Trucks

Imagined Press Article

Metropolis, July 16, 2025 – TruckTally today unveiled its EcoTrend suite, a comprehensive set of sustainability tools designed to help food truck operators measure, manage, and minimize waste across every shift. With EcoTrend, vendors gain real-time visibility into food waste volumes, ingredient hotspots, and portion variances, empowering teams to make data-driven adjustments that reduce disposal costs and environmental impact without compromising customer satisfaction. The EcoTrend suite includes three core modules: • WasteSnapshot: Provides on-the-spot logging of discarded ingredients, capturing waste data by item, time, and location via an intuitive mobile interface. • EcoTrend Analytics: Aggregates waste logs over days, weeks, and events to reveal patterns and trends, illustrated through interactive charts that highlight peak waste periods and menu items with consistent overages. • SmartPortion Recommendations: Leverages historical sales and waste metrics to suggest optimal serving sizes for each dish before service, minimizing over-serving and leftover scraps while preserving quality and customer experience. “Our mission is to deliver powerful insights that align profitability with sustainability,” said Amanda Lee, Chief Product Officer at TruckTally. “EcoTrend goes beyond waste tracking by actively guiding operators on how to right-size portions and adjust prep quantities. This reduces costs for small businesses while contributing to a circular economy and lowering carbon footprints.” During pilot programs with Sustainable Sam, an eco-focused food truck owner in Portland, Oregon, EcoTrend helped achieve a 25 percent reduction in overall waste within the first month. “I could immediately see which ingredients were consistently being tossed, then tweak my portions and prep lists accordingly,” said Sam. “The SmartPortion tool saved me hundreds of dollars and aligned perfectly with my zero-waste goals.” Complementing the core modules, EcoTrend includes WasteAlert notifications that alert operators when waste volumes for specific ingredients or timeframes exceed configurable thresholds. This early warning enables staff to reevaluate prep strategies mid-service, preventing ongoing excess and unnecessary disposal costs. Additionally, the GreenLeaderboard gamification feature motivates teams by showcasing waste reduction performance across shifts, fostering friendly competition and continuous improvement. EcoTrend’s offline-ready design ensures that waste logging and analytics remain accessible even in areas with unreliable connectivity. Logged data syncs automatically once a connection is restored, guaranteeing uninterrupted tracking. Integration with TruckTally’s existing inventory dashboard means that waste insights can inform reorder thresholds and supplier orders, closing the loop between consumption, disposal, and replenishment. EcoTrend is now available as an add-on for all TruckTally subscribers. New customers can start with a 30-day free trial of the full suite, including WasteSnapshot, EcoTrend Analytics, SmartPortion, WasteAlert, and GreenLeaderboard. For businesses seeking deeper customization, TruckTally offers professional services to tailor waste reporting and sustainability benchmarks to unique operational needs. About TruckTally TruckTally empowers mobile food businesses with real-time inventory, order, payment, and sustainability management. From AI-driven demand forecasting to waste reduction analytics, TruckTally’s all-in-one app streamlines operations, enhances customer service, and supports eco-friendly practices. Trusted by thousands of vendors worldwide, TruckTally continues to innovate for a smarter, greener future. Media Contact: John Ramirez Head of Communications, TruckTally media@trucktally.com (555) 123-4567

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TruckTally Debuts SpeedTap and OfflineBuffer for Instant, Reliable Contactless Payments

Imagined Press Article

Metropolis, July 16, 2025 – TruckTally, the industry-leading mobile inventory and order management platform, today announced the availability of SpeedTap and OfflineBuffer, two new payment innovations that deliver lightning-fast, resilient contactless transactions for food truck vendors. These features ensure every sale is captured, approved, and recorded instantly—even in challenging connectivity conditions—so operators can focus on customer service and growth instead of payment glitches. SpeedTap streamlines checkout with one-tap contactless payments from the home screen. By launching the NFC reader in milliseconds, SpeedTap cuts average transaction times to under two seconds, significantly reducing queues during peak hours. With the rising popularity of mobile wallets and NFC-enabled cards, SpeedTap provides vendors a competitive edge by speeding up service and enhancing customer experience. Complementing SpeedTap, OfflineBuffer addresses the perennial challenge of spotty internet connectivity at remote events and busy city corners. OfflineBuffer securely caches tap-to-pay transactions locally when the network drops and automatically processes them once connectivity is restored. This guarantees that no sale is lost, no charge is declined, and reconciliation is seamless at shift’s end. “Food truck operators cannot afford to miss a sale or frustrate customers with slow or failed payments,” said Michael Chen, Chief Technology Officer at TruckTally. “With SpeedTap and OfflineBuffer, we’re raising the bar for contactless transactions in mobile hospitality. Vendors can now rely on a fast, dependable payment flow that keeps both staff and customers happy, regardless of location or network availability.” In a recent field trial involving 100 solo vendors across major metropolitan areas, operators using SpeedTap reported a 30 percent reduction in average customer wait time and a 12 percent uptick in average tips per transaction, thanks to integrated TallyTip suggestions immediately after payment. OfflineBuffer further ensured zero lost sales, even when trucks participated in off-grid festivals where cellular coverage dipped below 2G. Key Benefits of SpeedTap and OfflineBuffer: • Instant transactions: One-tap NFC checkouts complete in under two seconds, accelerating throughput. • Guaranteed approvals: OfflineBuffer caches and auto-processes transactions to prevent declines. • Enhanced gratuities: TallyTip integration prompts tip options within the payment flow, boosting average tip rates. • Digital receipts: ReceiptVault delivers instant SMS, email, or QR code receipts at confirmation for paperless record-keeping. • Wallet compatibility: WalletSync automatically detects and supports Apple Pay, Google Pay, Samsung Pay, and other popular digital wallets. SpeedTap and OfflineBuffer are available now to all TruckTally customers as part of the platform’s advanced payment package. Existing subscribers can enable the new features through their app settings and access a guided setup tutorial. New users signing up before August 31, 2025, receive a complimentary upgrade to the advanced payment package for their first three months. About TruckTally TruckTally offers a comprehensive, mobile-first solution for inventory, order, payment, and sustainability management tailored to independent food trucks, fleets, and event operators. Through offline-ready design, AI-driven forecasting, contactless payments, and robust analytics, TruckTally empowers vendors to streamline operations, enhance customer experiences, and drive profitability. Media Contact: John Ramirez Head of Communications, TruckTally media@trucktally.com (555) 123-4567

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