Food Truck Management SaaS

SnackFleet

Never Run Out, Always Roll Forward

SnackFleet equips independent food truck owners with a real-time, cloud-based dashboard to track inventory, manage orders, and automate restocking across multiple trucks. By syncing stock data on any device, it slashes wasted time and ingredients, ensuring every truck stays fully supplied and never misses sales, even on the busiest routes.

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SnackFleet

Product Details

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

Vision & Mission

Vision
Empower every food truck owner to run a thriving, waste-free business through effortless, real-time inventory control anywhere.
Long Term Goal
Empower 10,000 independent food trucks worldwide to eliminate stockouts and reduce ingredient waste by 20% within 5 years, driving $100 million in additional annual revenue for owners.
Impact
Cuts inventory management time by 8 hours per week and reduces ingredient waste by 20% for independent food truck owners, enabling uninterrupted sales of top menu items and boosting weekly revenue by preventing out-of-stock losses across multiple mobile locations.

Problem & Solution

Problem Statement
Independent food truck owners waste hours and lose revenue due to error-prone manual inventory tracking and restocking, while existing restaurant software is too costly, complex, or stationary to support their on-the-move, multi-location operations.
Solution Overview
SnackFleet’s mobile dashboard syncs real-time inventory across all trucks, instantly alerting owners to low stock and enabling one-tap restocking. This eliminates manual tracking headaches and prevents costly ingredient shortages, so food truck operators never miss sales—even when managing multiple busy locations.

Details & Audience

Description
SnackFleet is a cloud dashboard built for independent food truck owners to manage inventory, track orders, and automate restocking from any device. Designed for mobile operators juggling multiple locations, it slashes wasted time and lost sales from manual tracking. Real-time inventory sync across all trucks means owners always know what’s in stock, preventing shortages and ingredient waste.
Target Audience
Independent food truck owners (25-50) juggling busy routes who demand effortless, real-time inventory control.
Inspiration
One afternoon, I watched a local taco truck anxiously cross out crowd favorites from their handwritten menu—out of tortillas by noon, salsa by two. Customers left disappointed, and the owner lost out on hundreds just from running short. That moment made it clear: food trucks need a way to track inventory in real-time, wherever they park. SnackFleet was born to fill that gap.

User Personas

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

S

Swift Sarah

- Age: 32 - Community college culinary certification - Solo food truck operator downtown - Annual revenue around $60k

Background

Starting as a catering assistant, Sarah built her own taco truck to master peak-hour demands. She optimized nightly prep routines to minimize morning restocking.

Needs & Pain Points

Needs

1. Instant inventory alerts to prevent stockouts 2. Automated restocking for uninterrupted service 3. Fast order-processing to sustain customer flow

Pain Points

1. Unexpected shortages during peak lunch rush 2. Manual stock checks causing service slowdowns 3. Delayed deliveries missing critical sales windows

Psychographics

- Craves high-speed operational efficiency - Thrives under pressure in busy environments - Values real-time data-driven decisions - Seeks minimal downtime and delays

Channels

1. Slack (instant updates) 2. SMS (urgent notifications) 3. Mobile app (on-the-go) 4. Instagram Stories (quick announcements) 5. Facebook Groups (community discussions)

G

Green Greg

- Age: 45 - Bachelor’s in environmental science - Suburban neighborhood circuit - Annual revenue around $75k

Background

An ex-environmental consultant, Greg launched a green catering service before debuting his zero-waste food truck. His farm co-op volunteering shaped his eco-first approach.

Needs & Pain Points

Needs

1. Automated waste tracking and reporting features 2. Supplier integrations for certified local produce 3. Real-time waste-to-sales ratio insights

Pain Points

1. Excess stock leading to needless waste 2. Difficulty verifying sustainability credentials 3. Manual waste logs eating into operations

Psychographics

- Champions sustainable and ethical sourcing - Obsessed with minimizing food waste - Values transparent, traceable supply chains - Motivated by environmental stewardship

Channels

1. LinkedIn (professional networking) 2. Email newsletters (detailed updates) 3. Industry forums (sustainability discussions) 4. Instagram (visual storytelling) 5. Local food expos (in-person networking)

N

Night Owl Nina

- Age: 28 - Pastry arts diploma - Urban nightlife districts - Shift: 9pm–3am

Background

Trained in French patisserie, Nina swapped café hours for neon-lit streets. Overnight crowds spurred her to streamline ingredient counts for smooth late shifts.

Needs & Pain Points

Needs

1. Dark-mode dashboard for night visibility 2. Automated low-stock alerts overnight 3. Sync-mobile updates for late shifts

Pain Points

1. Glare-prone screens under streetlights 2. Missing orders during overnight restocks 3. Unpredictable ingredient runs at odd hours

Psychographics

- Thrives in energetic, late-night environments - Values intuitive, low-light dashboard design - Motivated by creative dessert experimentation - Prefers flexible, after-hours operations

Channels

1. Instagram Stories (late-night updates) 2. SMS (urgent alerts) 3. Mobile app (night mode) 4. TikTok (visual menu teasers) 5. Reddit (food truck subreddits)

F

Festive Fiona

- Age: 34 - Business administration degree - Touring festival circuits - Crew size: two support staff

Background

After managing festival logistics, Fiona launched her own themed truck. She mastered foot-traffic patterns and automated supply runs between venues.

Needs & Pain Points

Needs

1. Accurate demand forecasting per festival 2. Bulk order scheduling across events 3. Flexible menu ingredient tracking

Pain Points

1. Over-ordering leads to spoiled bulk stock 2. Underestimating crowds causes stockouts 3. Complex multi-event scheduling errors

Psychographics

- Energized by large, dynamic crowds - Relies on data to anticipate demand - Values bulk-order discounts - Enjoys creative, theme-based menus

Channels

1. Facebook Events (festival schedules) 2. Eventbrite (ticketing insights) 3. Email (vendor communications) 4. WhatsApp (crew coordination) 5. Mobile app (on-site management)

M

Meticulous Mike

- Age: 39 - Culinary arts diploma with safety certification - Farm-to-table district operator - Annual revenue around $120k

Background

A former Michelin kitchen sous-chef, Mike transitioned to a gourmet food truck. He demands granular ingredient tracking for uncompromised quality.

Needs & Pain Points

Needs

1. Batch-level ingredient tracking capabilities 2. Quality control logs per delivery 3. Freshness alerts based on storage time

Pain Points

1. Inconsistent supplier quality batches 2. Lack of detailed freshness timestamps 3. Manual taste-test records time-consuming

Psychographics

- Demands uncompromising ingredient quality - Values granular traceability data - Obsessed with product consistency - Prioritizes customer taste satisfaction

Channels

1. Email (detailed logs) 2. Mobile app (batch scanning) 3. SMS (quality alerts) 4. LinkedIn (professional connections) 5. Local chef forums (peer insights)

Product Features

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

AutoThreshold

Dynamically adjusts low-stock thresholds for each item and truck based on historical sales and real-time usage patterns. This ensures alerts are meaningful and reduces noise, so operators only get notified when stock truly risks running out.

Requirements

Data Ingestion Framework
"As a system administrator, I want a centralized data ingestion framework so that the threshold computation engine has reliable, high-quality data to calculate dynamic low-stock thresholds accurately."
Description

Implement a robust data ingestion framework that collects and stores historical sales data, inventory transactions, and real-time usage metrics for each food item and truck. This framework should support scalable data pipelines, ensure data integrity, and integrate with existing SnackFleet APIs and databases to provide accurate inputs for threshold computation.

Acceptance Criteria
Bulk Historical Data Import
Given a valid CSV file containing historical sales data for Truck A When the ingestion framework processes this file Then all records are stored accurately in the database with matching record count and schema validation passes
Real-Time Usage Metrics Streaming
Given continuous usage metrics generated by Truck B every minute When the ingestion pipeline receives each metric Then metrics are ingested into the real-time store within 5 seconds of arrival with zero packet loss
Data Integrity and Validation
Given incoming inventory transaction records with missing or invalid fields When the ingestion framework validates records Then records with schema violations are rejected, logged to the error log, and do not affect downstream threshold computations
High-Throughput Data Processing
Given simultaneous data streams from 100 trucks at peak operational times When the ingestion pipeline scales horizontally Then end-to-end processing latency remains below 2 seconds and no data is dropped
Integration with SnackFleet APIs
Given an authenticated API request to fetch stored ingestion data via SnackFleet API When the ingestion framework responds Then the API returns HTTP 200 with a valid JSON payload containing the requested data within 1 second
Threshold Computation Engine
"As a food truck operator, I want dynamic threshold calculations that adapt to my sales patterns so that I receive meaningful low-stock alerts tailored to each route and time period."
Description

Develop an adaptive computation engine that applies machine learning algorithms and statistical models to historical and real-time usage data to determine dynamic low-stock thresholds for each item and truck. The engine should continuously retrain models, factor seasonality, event-driven spikes, and route-specific patterns, delivering threshold updates at configurable intervals.

Acceptance Criteria
Initial Historical Data Model Training
Given the engine has access to at least 12 months of historical sales and usage data, When the computation process is initiated, Then the engine generates baseline low-stock thresholds for each item and truck with a confidence level of ≥95%.
Dynamic Adjustment During Peak Event
Given an identified event-driven sales spike (e.g., festival, holiday) in real-time data, When the engine recalculates thresholds, Then the low-stock thresholds increase by at least 20% above baseline within 5 minutes of the spike detection.
Configurable Interval Threshold Update
Given an administrator has set the threshold update interval to hourly, When each hour passes, Then the engine automatically retrains the model with the latest usage data and publishes updated thresholds to the dashboard without manual intervention.
Seasonality Factor Incorporation
Given the current date falls within a known seasonal period, When thresholds are computed, Then the engine adjusts thresholds according to seasonality patterns, resulting in at least a 10% change compared to non-seasonal baseline for affected items.
Model Retraining Convergence Verification
Given the model has undergone 5 consecutive retraining cycles, When evaluating retraining outcomes, Then the variation in threshold values between cycles is ≤2% for stable demand items, indicating convergence.
Real-Time Threshold Adjustment
"As an operator monitoring my truck, I want the system to adjust low-stock thresholds in real time when sudden sales spikes happen so that I always have the most accurate alerts."
Description

Create a real-time processing layer that ingests live inventory changes and sales transactions, recalculates thresholds instantly when significant deviations occur, and maintains system performance under high transaction volumes. This layer should ensure sub-second latency for threshold updates and integrate with the threshold computation engine.

Acceptance Criteria
Real-Time Update on Sudden Sales Surge
Given a sudden increase in sales volume exceeding 50% of the historical five-minute average When live sales transactions are ingested Then thresholds are recalculated within sub-second latency and the updated values are visible on the dashboard within 1 second
Low Traffic Minimal Impact
Given typical sales volumes with no significant deviation When live inventory or sales changes are processed Then the system does not trigger threshold recalculation and maintains current values without unnecessary processing
Bulk Inventory Restock Detection
Given a bulk restock event of more than 100 units for an item When the inventory addition is ingested by the processing layer Then thresholds are recalculated within sub-second latency and no processing time exceeds 100ms per event
Concurrent Multi-Truck Sales
Given simultaneous sales events occurring across 10 different trucks When all transactions are processed in real time Then each truck’s low-stock thresholds are individually updated within sub-second latency without any data loss
Integration Under Peak Load
Given a peak load scenario of 10,000 transactions per minute When the processing layer ingests these transactions Then threshold updates are processed without failures, maintain sub-second latency, and CPU usage remains below 80%
Low-Stock Alerting System
"As a food truck owner, I want to receive low-stock alerts through my preferred channels so that I can restock items before running out, without being overwhelmed by unnecessary notifications."
Description

Design and implement an alerting subsystem that generates notifications when item stock levels approach dynamically computed thresholds. The system should support configurable alert channels (email, SMS, push notifications), allow customizable alert frequency and severity levels, and include debounce logic to minimize noise.

Acceptance Criteria
Threshold Breach Email Notification
Given an item's stock falls below its dynamic threshold, when the threshold is breached, then an email notification containing the item name, current stock level, and threshold value is sent to the configured email address within 1 minute.
Multi-Channel Alert Delivery
Given alert channels SMS and push are enabled, when an item breaches its threshold, then notifications are sent via both SMS and push notification within 2 minutes of detection.
Alert Frequency Limiting
Given frequency is set to one alert per hour for a particular item, when multiple threshold breaches occur within an hour, then only the first breach triggers an alert and subsequent breaches are suppressed until the hour elapses.
Severity Level Based Routing
Given severity level configurations are set to 'high' for critical items and 'low' for non-critical items, when a critical item's stock breaches its threshold, then an immediate SMS alert is sent, and when a non-critical item's stock breaches its threshold, then a push notification is sent.
Debounce Mechanism Handling
Given debounce period is configured as 30 minutes, when an item's stock fluctuates around the threshold multiple times within 30 minutes, then only one alert is sent until the debounce period has passed.
Dynamic Threshold Adjustment Alert
Given historical sales data recalculates thresholds daily at midnight, when the recalculated threshold is lower than the previous threshold and the current stock is already below the new threshold, then an appropriate alert is automatically sent after the recalculation.
Threshold Dashboard Visualization
"As a route manager, I want to see visual representations of my trucks' stock levels against dynamic thresholds so that I can quickly assess inventory health and make informed stocking decisions."
Description

Enhance the SnackFleet dashboard to visualize dynamic low-stock thresholds alongside current inventory levels in an intuitive interface. Include time-series charts, threshold change logs, and filter options by truck, route, and item category, enabling operators to monitor threshold behavior and stock status at a glance.

Acceptance Criteria
Dashboard displays current inventory with dynamic threshold
Given the operator is on the Threshold Dashboard page When they view an item card Then the current inventory level and the dynamically calculated low-stock threshold are displayed side by side with distinct visual indicators
Visualization of threshold adjustments over time
Given the operator selects the time-series chart view When they choose an item Then the chart displays historical inventory levels and threshold changes over the selected timeframe with interactive tooltips
Threshold change audit log access
Given the operator opens the threshold change log panel When a threshold has changed Then the log lists timestamp, previous and updated threshold values, and the change reason in reverse chronological order
Filtering dashboard data by truck, route, and item category
Given the operator applies filters When they select truck(s), route(s), or item category(ies) Then only matching inventory cards, charts, and logs are displayed and the filter tags show active filters
Responsive layout across devices
Given the operator accesses the Threshold Dashboard on desktop or mobile When they resize the browser or change device orientation Then all elements (charts, logs, filters) adjust layout without overlap and remain fully functional
Manual Threshold Override
"As an operator, I want to manually set custom low-stock thresholds for specific items during special events so that I can accommodate unique sales scenarios without waiting for the system to adapt."
Description

Provide functionality for operators to manually override dynamic thresholds on a per-item or per-truck basis. Overrides should be logged, reversible, and include justification fields. The system must respect manual settings unless auto-adjustments exceed safety limits defined by the operator.

Acceptance Criteria
Operator Initiates Manual Override for Single Item
Given the operator is viewing an individual item’s current threshold, When the operator enters a new threshold value and provides a justification, Then the system updates the item’s threshold to the specified value, logs the override with timestamp, user ID, previous threshold, new threshold and justification
Operator Applies Override at Truck Level
Given the operator has selected a specific truck’s settings, When the operator inputs a manual threshold for an item or all items on that truck and confirms with justification, Then the system applies the threshold change to the truck’s items, records each override event with relevant metadata and displays a confirmation message
Operator Reverses an Override
Given an active manual override exists for an item or truck setting, When the operator chooses to revert the override and confirms with a reason, Then the system restores the previous dynamic threshold, marks the override as reversed in the audit log and records the reversal justification
System Prevents Unsafe Manual Overrides
Given the operator attempts to enter a manual threshold outside defined safety limits, When the operator submits the threshold override, Then the system rejects the override, displays an error message indicating the allowable range and logs the rejection attempt with provided justification
Operator Views Override Audit Trail
Given the operator navigates to the override audit section, When the operator filters by item, truck or date range, Then the system displays a list of manual overrides and reversals including timestamp, user, previous and new thresholds, justifications and reversal status

SurgeAlert

Detects sudden spikes in item demand and immediately flags potential stockouts. By spotting unexpected surges, it enables operators to react faster, preventing lost sales during peak service periods.

Requirements

Real-time Demand Monitoring
"As a food truck operator, I want real-time sales demand updates so that I can identify emerging high-demand items and avoid stockouts."
Description

The system continuously tracks sales of all items across connected food trucks, updating demand metrics at one-minute intervals. It calculates current sales velocity and compares it against historical averages to identify unusual spikes, ensuring that emerging high-demand trends are detected promptly. This functionality integrates with the SnackFleet inventory engine, feeding real-time data into SurgeAlert and enabling operators to respond immediately to surges in the dashboard.

Acceptance Criteria
High-Demand Spike Exceeds Threshold
Given the system tracks real-time sales, When the sales velocity for any item in a single minute exceeds 150% of its historical average, Then the system flags that item in SurgeAlert with the correct spike percentage and timestamp.
Dashboard Reflects One-Minute Interval Updates
Given the demand monitoring service is running, When one minute has elapsed since the last update, Then the system retrieves and processes the latest sales data for all items and updates the dashboard within 5 seconds.
Immediate SurgeAlert Notification Delivery
Given a demand spike is detected, When SurgeAlert is generated, Then the dashboard displays the alert notification within 30 seconds of detection.
Accurate Historical Averages Computation
Given historical sales data is available, When calculating sales velocity, Then the system computes the average velocity for the same minute intervals over the previous seven days with an error margin of no more than 2%.
Offline Truck Data Synchronization
Given a food truck temporarily loses connectivity, When the connection is restored, Then all queued sales events are processed and integrated into the demand metrics without data loss within one minute.
Configurable Surge Thresholds
"As an admin, I want to configure surge detection thresholds per item so that alerts match my specific sales patterns."
Description

Operators and administrators can define custom threshold parameters for surge detection, including percentage increase, absolute sales counts, and time window durations. These settings allow SurgeAlert to adapt to varying sales volumes and seasonal patterns, reducing false positives and ensuring alerts remain relevant to each truck’s unique context.

Acceptance Criteria
Percentage-Based Surge Threshold Configuration
Given an administrator is on the SurgeAlert settings page, when they enter 30% for the percentage increase and 15 minutes for the time window and click Save, then the system persists the new percentage-based threshold and displays a success notification.
Absolute Sales Count Threshold Configuration
Given an administrator is on the SurgeAlert settings page, when they enter 50 as the absolute sales count and 10 minutes for the time window and click Save, then the system persists the new absolute sales count threshold and displays a success notification.
Time Window Duration Adjustment
Given an administrator is editing an existing surge threshold, when they change the time window duration to 20 minutes and click Save, then the system updates the threshold rule with the new time window and confirms the update.
Per-Truck Threshold Customization
Given an administrator manages multiple food trucks, when they select Truck A and configure a 25% surge threshold for a 10-minute window and click Save, then the system applies and stores the threshold only for Truck A without affecting other trucks.
Invalid Threshold Input Handling
Given an administrator enters a negative value or non-numeric input in any threshold field and clicks Save, then the system rejects the input, highlights the invalid field, and displays an error message specifying valid input ranges.
Instant Surge Notifications
"As a truck operator, I want to receive immediate alerts when an item’s demand spikes so that I can restock timely."
Description

Upon detecting a demand spike that exceeds configured thresholds, the system generates and dispatches alerts instantly through the SnackFleet dashboard, email, and optional SMS. Notifications include item details, current stock levels, and recommended restocking actions, enabling operators to react swiftly during peak service periods.

Acceptance Criteria
Threshold Breach Detected on Dashboard
Given the demand for an item increases by more than the configured threshold within a 10-minute window, when the system detects the spike, then an alert shall appear on the SnackFleet dashboard within 30 seconds showing item name, current stock level, and recommended restocking action.
Email Alert Dispatched to Operators
Given an instant surge notification is generated, when operators have email notifications enabled, then the system shall send an email to all configured operator email addresses within 1 minute containing item details, current stock levels, and restocking recommendations.
Optional SMS Notification Sent
Given operators have opted in for SMS alerts and a surge threshold is exceeded, when the alert is generated, then the system shall send an SMS to each configured phone number within 1 minute with item name, current stock level, and restocking advice.
Alert Content Accuracy
Given an alert is generated, when the notification is displayed on any channel, then the information must match real-time data for item IDs, current stock levels, and suggested reorder quantities without discrepancies.
Simultaneous Surge Events Handling
Given two or more items exceed surge thresholds at the same time, when the system processes the events, then it shall generate individual alerts for each item on all enabled channels within 30 seconds, ensuring no alert is delayed or lost.
Predictive Restocking Suggestions
"As an operator, I want restocking suggestions based on recent surges so that I only order what I need and avoid overstocking."
Description

SurgeAlert analyzes recent surge events and historical consumption data to calculate optimal restocking quantities and timing. It provides operators with data-driven recommendations to replenish stock before shortages occur, minimizing waste and ensuring consistent availability across multiple trucks.

Acceptance Criteria
Real-time Calculation of Restocking Quantities
Given a surge event occurs and historical consumption data is available, when the operator opens Predictive Restocking Suggestions, then the system displays updated restocking quantities for each item within one minute of surge detection.
Automated Notification for Upcoming Shortages
Given projected stock levels fall below the defined threshold within the next service period, when the suggestion algorithm runs, then the operator receives a notification listing the items and quantities recommended for restocking.
Multi-Truck Consolidated Restocking Plan
Given multiple trucks linked to the same depot, when the operator requests a restocking plan, then the system generates a consolidated suggestion that aggregates orders across trucks and minimizes total delivery shipments.
User Confirmation and Adjustment Workflow
Given suggested restocking quantities are presented, when the operator modifies any quantity or timing, then the system saves the adjustments and updates the final restocking order accordingly.
Historical Data Influence Verification
Given consumption data with recent surge events, when the system calculates a suggestion, then the algorithm applies at least 70% weight to recent surge data and 30% to long-term averages, and logs reflect this weighting breakdown.
Surge Event Dashboard Visualization
"As an operator, I want a visual dashboard that highlights recent surge events so that I can quickly understand demand trends."
Description

The dashboard displays surge events on interactive graphs and timelines, highlighting items experiencing abnormal demand. Users can filter by date range, truck location, and item category to gain insights into demand patterns and the effectiveness of past restocking decisions.

Acceptance Criteria
Date Range Filtering
Given the user selects a start and end date on the dashboard When the date range is applied Then only surge events occurring within that range are displayed on the graphs
Truck Location Filter
Given the user chooses a specific truck location from the filter menu When the filter is applied Then only surge events for that truck appear on the dashboard visualizations
Item Category Selection
Given the user selects one or more item categories When the category filter is activated Then the graphs update to show surge events exclusively for those categories
Interactive Surge Event Tooltip
Given the user hovers over a surge event data point on the graph When the tooltip appears Then it displays item name, timestamp, demand spike percentage, and previous average sales
Timeline Zoom and Pan
Given the dashboard timeline supports zoom and pan controls When the user zooms in or pans along the timeline Then the surge event data dynamically adjusts to the new viewing window without data loss
Restocking Effectiveness Overlay
Given the user toggles the restocking effectiveness overlay When enabled Then the dashboard overlays post-restocking stock levels and flags whether restocking prevented stockouts during surges

QuickRestock

Provides a one-tap reorder option directly from the alert notification, instantly generating and sending purchase orders to preferred suppliers. This minimizes downtime between identifying low stock and replenishing supplies.

Requirements

Low Stock Alert Generation
"As a food truck owner, I want to receive an instant alert when my inventory for any item drops below a critical level so that I can take action before I run out of key ingredients."
Description

Implement a system that continuously monitors inventory levels across all managed food trucks and triggers a real-time alert notification when stock for any item falls below a predefined threshold. The alert should be visible in the dashboard and sent via push notification or email, ensuring timely awareness and minimizing the risk of stockouts.

Acceptance Criteria
Threshold-Based Alert Trigger
Given an item's stock level in any managed food truck falls to or below its predefined threshold When the real-time monitoring service executes Then a Low Stock Alert is generated in the system for that item, including item name, truck ID, current quantity, threshold value, and generation timestamp
Dashboard Alert Visibility
Given a Low Stock Alert has been generated When a user views the SnackFleet dashboard Then the alert appears in the Alerts panel within 5 seconds, displaying item name, truck ID, remaining quantity, and threshold, sorted by most recent first
Push Notification Delivery
Given a Low Stock Alert has been generated for a user with push notifications enabled When the alert is created Then a push notification is sent to the user's registered device within 10 seconds, containing the item name, truck ID, and current stock level
Email Notification Delivery
Given a Low Stock Alert has been generated for a user subscribed to email alerts When the alert is created Then an email is delivered to the user's address within 30 seconds, including item details (name, ID), truck identifier, current stock, and threshold value
Simultaneous Multi-Truck Alerts
Given multiple items in different trucks fall below their thresholds at the same time When the monitoring service detects low stock across trucks Then separate alerts are generated and dispatched for each item-truck combination without delay or omission
Alert Acknowledgment Logging
Given a user acknowledges or dismisses a Low Stock Alert on the dashboard When the user clicks 'Acknowledge' or 'Dismiss' Then the system logs the action with alert ID, user ID, action type, and timestamp for audit purposes
One-Tap Reorder Action
"As a busy food truck owner, I want to reorder supplies with one tap right from the low-stock alert so that I can quickly restock without navigating through multiple screens."
Description

Add a ‘Reorder’ button directly within the low-stock alert notification that allows users to instantly initiate a restocking process with a single tap. This action should automatically compile a purchase order based on current inventory deficits and the user’s default reorder quantities, streamlining the restocking workflow.

Acceptance Criteria
Low-Stock Alert Displays Reorder Button
Given a low-stock alert is displayed on the dashboard notification panel, When the alert appears, Then a clearly labeled ‘Reorder’ button is visible and enabled within the notification.
User Initiates One-Tap Reorder
Given the user sees the low-stock alert with the ‘Reorder’ button, When the user taps the ‘Reorder’ button, Then the system immediately begins generating a purchase order without additional user input.
Purchase Order Compiled with Correct Quantities
Given the one-tap reorder action is triggered, When the system compiles the purchase order, Then it includes items matching current inventory deficits and uses the user’s default reorder quantities for each low-stock item.
Auto-Send Purchase Order to Supplier
Given the purchase order is compiled, When auto-submit is enabled in user settings, Then the system sends the purchase order payload to the user’s preferred supplier endpoint and receives a successful acknowledgement (HTTP 200).
Confirmation Notification Shown to User
Given the purchase order has been successfully sent, When acknowledgement is received, Then the user sees a confirmation notification listing the order ID, supplier name, and estimated delivery date.
Supplier Integration API
"As a food truck owner, I want my system to send purchase orders directly to my preferred suppliers so that I don’t have to manually place orders and can avoid delays."
Description

Develop integrations with preferred supplier systems via secure APIs to enable automatic transmission of generated purchase orders. The integration must support authentication, order validation, and error handling to ensure reliable communication and quick processing of restock requests.

Acceptance Criteria
Successful API Authentication with Supplier
Given valid API credentials for a preferred supplier When SnackFleet initiates an authentication request Then the supplier API responds with a valid access token within 2 seconds and SnackFleet securely stores the token
Automatic Purchase Order Transmission
Given an active low-stock alert and valid supplier authentication When the user taps the one-tap reorder button Then SnackFleet generates a purchase order payload and successfully sends it to the supplier API with HTTP 201 Created within 5 seconds
Order Validation and Confirmation
Given a purchase order is submitted to the supplier API When the API validates the order Then it returns a JSON confirmation containing a unique order ID and expected delivery date and SnackFleet displays this confirmation to the user
Error Handling and Retry Mechanism
Given network failures or supplier API errors (HTTP 5xx) occur when sending a purchase order When SnackFleet detects the error Then it retries up to 3 times with exponential backoff and logs each attempt, and if all retries fail, displays an error notification to the user
Secure Data Transmission
Given any communication with the supplier API When requesting authentication or sending purchase orders Then SnackFleet uses HTTPS with TLS 1.2 or higher and verifies the supplier’s SSL certificate to ensure data is encrypted in transit
Automated Purchase Order Creation
"As an operations manager, I want the system to generate a complete purchase order automatically when I reorder supplies so that I save time on manual document creation and reduce errors."
Description

Design a module that, upon user confirmation, automatically generates a formatted purchase order document including item details, quantities, supplier information, pricing, and delivery instructions. The document should comply with supplier requirements and be stored in the system for record-keeping and auditing purposes.

Acceptance Criteria
Low Stock One-Tap Reorder
Given a low stock alert for an item when the user taps the one-tap reorder button, then a purchase order document is generated automatically and presented for confirmation within 2 seconds.
Purchase Order Document Format Compliance
The generated document must adhere to the supplier’s required template, including header, footer, company logo, and line-item table, and be exported in PDF format.
Supplier Information Accuracy
All supplier fields in the purchase order (name, address, contact, email) must match the supplier profile stored in the system without any discrepancies.
Pricing and Delivery Instructions Inclusion
Each line item in the purchase order must include unit price, quantity, line total, and the document summary must display the overall total price and delivery instructions as specified in supplier settings.
Purchase Order Storage and Audit Trail
Upon confirmation, the purchase order document must be saved in the system’s document repository, tagged with timestamp, user ID, and linked to the originating low-stock alert for auditing.
Order Status Notification
"As a food truck owner, I want to receive updates on the status of my restock orders so that I know when to expect delivery and can plan my operations accordingly."
Description

Implement a feedback mechanism that provides real-time updates on order status, including confirmation of receipt by the supplier, estimated delivery time, and any issues or delays. Notifications should be sent via the dashboard, email, or SMS based on user preferences to keep stakeholders informed.

Acceptance Criteria
Supplier Acknowledgement Receipt
Given a restock order is sent to the supplier, when the supplier confirms receipt, then the system must update the order status to “Confirmed” and send a notification to the user via their selected channel within 2 minutes.
Estimated Delivery Time Update
Given the supplier has provided an estimated delivery time, when the system receives this information, then it must display the updated ETA on the dashboard and send an email notification to the user within 5 minutes.
Delay Alert Notification
Given the supplier reports a delay, when the delay is logged in the system, then an SMS and dashboard notification must be sent to the user immediately, including the reason for delay and revised delivery estimate.
Order Issue Reporting
Given the supplier flags an issue with the order, when the issue is recorded, then the system must change the order status to “Issue Reported,” log the details, and notify the user within 3 minutes.
Multi-Channel Notification Preferences
Given a user has set notification preferences, when an order status update occurs, then the system must send notifications only via the user’s chosen channels (dashboard, email, SMS) and log each notification attempt.

FleetHeatmap

Visualizes current stock levels across all trucks on a single heatmap dashboard, using color gradients to highlight critical shortages at a glance. Operators can spot vulnerabilities quickly and coordinate restocks more efficiently.

Requirements

Real-time Data Aggregation
"As a fleet operator, I want live inventory updates from every truck so that I can immediately spot and address stock shortages."
Description

Collect and consolidate live inventory data from all trucks at regular short intervals, ensuring the heatmap reflects current stock levels. Implement efficient data sync mechanisms via our cloud platform to minimize latency and avoid data conflicts, providing operators with up-to-the-second visibility across the fleet.

Acceptance Criteria
Initial Heatmap Load with Live Data
Given a fleet of active trucks exists When the heatmap dashboard is loaded Then inventory levels for each truck must be updated within the last 5 seconds And no data older than the defined sync interval is displayed
Concurrent Inventory Updates
Given two trucks update the same inventory item simultaneously When the data sync mechanism runs Then no duplicate records or data conflicts occur And the system applies last-write-wins resolution within 2 seconds
Offline Truck Reconnection
Given a truck was disconnected for at least one minute When it reconnects to the cloud platform Then all missed inventory updates are backfilled automatically And the heatmap reflects reconciled stock levels immediately
High-Frequency Update Under Load
Given 100 inventory changes occur per second across the fleet When the heatmap refreshes at the configured 5-second interval Then all updates are processed without loss And the dashboard latency remains under 3 seconds
Error Handling for Failed Sync
Given the cloud sync returns an error response When data aggregation fails Then the system automatically retries up to 3 times And logs the error in the audit trail And displays an operator alert in the UI
Dynamic Heatmap Rendering
"As a fleet manager, I want a color-gradient map of truck stock levels so that I can quickly identify which locations need restocking."
Description

Render a color-coded map overlay that visualizes stock levels for each truck location. Use gradient scales to highlight critical, low, and healthy inventory states, ensuring clarity in how shortages propagate geographically. Optimize for desktop and mobile responsiveness.

Acceptance Criteria
Viewing Heatmap on Desktop
Given the user is on a desktop browser, when the FleetHeatmap feature is accessed, then the map overlay displays all truck locations with appropriately color-coded markers and an interactive legend visible without horizontal scrolling.
Viewing Heatmap on Mobile Device
Given the user is on a mobile device, when the FleetHeatmap feature is accessed, then the heatmap and legend are fully responsive, markers remain legible, and zoom/pan controls function correctly.
Identifying Critical Stock Shortages
Given a truck’s stock level falls below the critical threshold, when the heatmap renders, then that truck’s marker is colored in the designated critical color and displays a tooltip showing the exact stock percentage on hover or tap.
Updating Data in Real Time
Given new inventory data arrives, when the data feed updates, then the heatmap refreshes within 5 seconds, smoothly transitions marker colors without a full page reload, and maintains the user’s current map view.
Adaptive Color Gradient Scaling
Given varying stock levels across trucks, when the heatmap scale is applied, then the gradient automatically adjusts its min/max bounds based on the current data range to ensure clear differentiation between low, medium, and healthy stock states.
Interactive Drill-down
"As an operator, I want to click on a truck on the heatmap so that I can see exactly which items are low and by how much."
Description

Enable users to click or tap on individual trucks within the heatmap to view detailed inventory breakdowns. Provide a pop-up or side panel showing item counts, trends, and restock history. Ensure smooth transitions and minimal loading time.

Acceptance Criteria
Truck Selection Drill-down
Given the user is viewing the heatmap When they click on a truck marker Then a side panel appears within 500ms showing the selected truck’s detailed inventory breakdown, including current item counts, 7-day usage trends, and restock history
Mobile Tap Interaction
Given the user is on a mobile device When they tap on a truck icon in the heatmap Then a responsive pop-up displays the truck’s inventory breakdown with scrollable content if needed and fully renders within 1 second
Loading State Feedback
Given the user initiates a drill-down When the system fetches data Then a loading indicator or skeleton UI is shown immediately and is replaced by the data view within 2 seconds of data arrival
Empty Inventory Category Display
Given the selected truck has categories with no stock When the user views the detailed breakdown Then those categories display a ‘No items in this category’ message instead of an empty table
Data Accuracy Verification
Given the user views the detailed inventory breakdown When comparing displayed counts to backend records Then the numbers match the backend data within a 30-second synchronization window
Threshold-based Alerts
"As a supply coordinator, I want automatic alerts when any truck falls below my defined thresholds so that I can proactively schedule restocks."
Description

Implement configurable alert thresholds that, when crossed, change truck markers’ colors or trigger notification banners. Allow operators to set per-item or overall stock thresholds and receive in-app or email alerts highlighting critical shortages.

Acceptance Criteria
Configurable Per-Item Threshold Settings
Given an operator sets a minimum quantity for a specific item and saves, Then the system persists the threshold and applies it to all trucks.
Overall Fleet Threshold Configuration
Given an operator sets an overall stock threshold percentage and saves, Then the system persists the threshold and displays the aggregated fleet alert.
Visual Marker Color Change on Threshold Breach
Given a truck marker displaying stock levels, when any item’s stock falls below its configured threshold, Then the marker color changes to the defined alert color within 5 seconds.
In-App Notification Banner for Critical Shortage
Given a threshold breach for a truck item, when detected by the system, Then an in-app banner appears listing truck ID, item name, and current quantity.
Email Alert Delivery for Threshold Breach
Given email alerts are enabled, when any configured threshold is breached, Then the system sends an email within 2 minutes containing truck ID, item name, threshold value, and current stock level.
Historical Trend Overlay
"As an analyst, I want to see past inventory trends on the map so that I can identify recurring shortages and optimize restocking schedules."
Description

Overlay historical inventory data on the heatmap to visualize stock level changes over customizable time windows. Provide a slider or date picker to animate or compare past vs. current stock distributions, facilitating trend analysis.

Acceptance Criteria
Daily Trend Animation
Given the user selects the past 24-hour time window and activates the animation slider, when the animation plays, then the heatmap updates in one-hour intervals displaying historical stock levels with corresponding timestamps.
Custom Date Range Comparison
Given the user picks a start and end date via the date picker, when the range is applied, then two heatmaps appear side-by-side comparing stock distributions for the selected dates and differences are visually highlighted.
Multi-Truck Historical Snapshot
Given the user selects a specific historical timestamp, when the timestamp is confirmed, then the heatmap reflects stock levels at that moment for all trucks and hovering over a truck displays its exact inventory count.
Peak Hours Stock Fluctuation
Given the user filters for peak service hours (e.g., 12:00–14:00), when the filter is applied, then the heatmap animates showing stock level changes during that period and marks trucks with critical shortages in red.
Restock Request Trigger Analysis
Given the user sets a stock decrease threshold (e.g., 20%), when the historical overlay is compared to current data, then the system highlights trucks exceeding the threshold and generates a restock suggestion list.

OmniNotify

Delivers prioritized low-stock alerts via multiple channels—mobile push, SMS, email, or in-dashboard—to ensure no critical notification is missed. Users can customize their preferred channels for each alert type.

Requirements

Real-Time Low-Stock Monitoring
"As a food truck manager, I want to receive immediate low-stock alerts so that I can restock before I run out and avoid lost sales."
Description

Continuously monitor inventory levels across all food trucks in real time and trigger alerts when stock for any item falls below its defined threshold. Integrate seamlessly with existing inventory tracking systems and IoT sensors or manual stock updates. Ensure that low-stock events are detected immediately to prevent stockouts and lost sales, enabling proactive restocking actions.

Acceptance Criteria
Threshold Breach Detection During Peak Sales
Given an item’s stock level is equal to or falls below its defined threshold during high transaction volume, when the last sale is processed, then a low-stock alert must be generated within 5 seconds containing the item ID, truck ID, and current quantity.
IoT Sensor Data Integration
Given an IoT sensor reports a stock update for an item below its threshold, when the sensor payload is received by the system, then the system must automatically generate a low-stock alert in the dashboard and queue it for notifications.
Manual Stock Update Trigger
Given a user submits a manual stock adjustment that results in an item’s quantity falling below its threshold, when the update is saved, then the system must immediately generate a low-stock alert and log the user who performed the action.
Simultaneous Multi-Truck Alerts
Given two or more trucks run low on the same item at the same time, when inventory data is processed, then the system must generate separate low-stock alerts for each truck within 5 seconds, ensuring no data overlap or loss.
Missing Data Fallback Detection
Given no inventory update (IoT or manual) is received for an item for more than 2 minutes, when the timeout threshold is reached, then the system must generate a data-quality alert indicating a potential sensor failure or missing update.
Alert Threshold Configuration
"As a food truck manager, I want to customize low-stock thresholds for each ingredient so that alerts are tailored to each product's usage rate."
Description

Allow users to set and adjust stock level thresholds for each product on a per-truck basis through an intuitive settings interface. Store these thresholds in the system and use them to determine when low-stock alerts should be triggered. Provide default values and the ability to customize them based on usage patterns, ensuring alerts are relevant and actionable.

Acceptance Criteria
Setting Default Thresholds for a New Truck
Given a user adds a new truck to their account, When they view the Alert Threshold Configuration page, Then default stock level thresholds are pre-populated for each product based on system recommendations.
Customizing Thresholds After Stock Analysis
Given a user reviews historical sales data for a truck, When they adjust the low-stock threshold for a product and save changes, Then the new threshold is applied immediately and displayed in the dashboard.
Resetting Thresholds to System Default
Given a user has customized thresholds, When they select the "Reset to Default" option for a product and confirm, Then the threshold reverts to the original default value.
Validation of Threshold Input Range
Given a user enters a threshold value outside the allowed range (e.g., negative or above maximum), When they attempt to save, Then an inline error message is displayed and the invalid value is not accepted.
Threshold Persistence Across Devices and Sessions
Given a user sets or updates thresholds on one device, When they log in from another device or after a new session, Then the configured thresholds are consistent and reflect the latest saved values.
Channel Preference Setup
"As a food truck manager, I want to choose my preferred notification channels for different alert types so that I receive alerts in the most convenient way."
Description

Enable users to choose their preferred notification channels (mobile push, SMS, email, or in-dashboard) for each type of alert. Provide a user-friendly interface for mapping alert types to channels, saving preferences to the user profile, and allowing updates at any time. Ensure that user selections drive the delivery mechanism so alerts are received via chosen mediums.

Acceptance Criteria
Initial Channel Preference Configuration
Given a logged-in user on the Channel Preferences page When the user views an alert type Then all available channels (mobile push, SMS, email, in-dashboard) are listed with checkboxes and defaulted to off.
Saving and Persisting Preferences
Given the user has selected one or more channels for an alert type When the user clicks Save Preferences Then the selection is stored in the user profile and persists after logout, page refresh, or new session.
Updating Existing Preferences
Given the user has existing channel preferences for an alert type When the user changes the channel selections and saves Then the previous preferences are overwritten and the new selections are reflected immediately.
Validation for No Channel Selection
Given the user deselects all channels for an alert type When the user attempts to save Then the system displays a validation message requiring at least one channel and prevents saving until a channel is selected.
Channel-Driven Alert Delivery
Given an alert of a configured type is triggered When the user has saved channel preferences Then the system sends the alert via each selected channel and logs the delivery status for each.
Multi-Channel Notification Delivery
"As a food truck manager, I want alerts delivered via mobile push, SMS, email, or dashboard so that I can be notified no matter what I'm doing."
Description

Implement a robust notification engine capable of dispatching alerts via mobile push, SMS, email, and in-dashboard slots. Integrate with external messaging services and ensure messages are formatted correctly for each channel. Support concurrent channel dispatch, optional fallbacks, and throttling to guarantee timely delivery and avoid spam or rate-limit issues.

Acceptance Criteria
Critical Low Stock Alert via Email
Given a product’s stock level drops below the critical threshold When the notification engine processes alerts Then an email with the correct template and product details is sent to the user’s configured email address within 60 seconds
Low Stock Push Notification with Fallback
Given the user has enabled push notifications and SMS fallback When a low-stock alert is triggered and the push service returns an error Then the system sends an SMS with the alert content and logs the fallback event
High Priority SMS Alert
Given an item is marked as high priority stock When the stock falls below its threshold Then the system sends an SMS to all user-enabled phone numbers and retries up to 3 times if delivery fails, respecting rate limits
In-Dashboard Notification Display
Given the user is logged into the dashboard with pending alerts When they navigate to the notifications panel Then all unread alerts are displayed in chronological order with a 'Mark as Read' action available
Notification Throttling under High Frequency
Given multiple low-stock triggers occur within a 5-minute window When the notification engine processes them Then only one alert per channel per product is dispatched within the window and subsequent triggers are aggregated
In-Dashboard Notification Center
"As a food truck manager, I want to view all past and current alerts in a dashboard notification center so that I can track and manage my alerts history."
Description

Create a centralized notification center within the dashboard to display all alerts with filters for truck, item, priority, and status. Allow users to mark alerts as read or dismiss them, view alert history, and access details on when and why each alert was triggered. Ensure the center is responsive and searchable for efficient alert management.

Acceptance Criteria
Filtering Alerts by Truck, Item, Priority, and Status
Given the user is on the Notification Center When the user applies filters for truck "Truck A", item "Soda", priority "High", and status "Unread" Then only alerts matching all selected filters are displayed And the total count reflects the number of matching alerts
Marking Alerts as Read or Dismissed
Given the user has one or more alerts displayed When the user selects alerts and clicks "Mark as Read" Then the selected alerts update to status "Read" And visually render with the "Read" style Given the user views an alert When the user clicks the "Dismiss" icon on an alert Then the alert is removed from the active list And is moved to the Dismissed alerts history
Viewing Detailed Alert Information
Given an alert is visible in the list When the user clicks on the alert entry Then a details panel opens showing timestamp, trigger reason, associated truck, item, current stock level, and threshold value
Searching Alerts by Keyword
Given the user enters a keyword in the search input When the user initiates the search Then only alerts containing the keyword in the reason or item name are displayed And the search supports case-insensitive and partial matches
Mobile View Responsiveness
Given the user accesses the Notification Center on a mobile device When the Notification Center loads Then the layout adapts to a single-column list And filter controls collapse into an accessible hamburger menu
Sorting Alerts by Date and Priority
Given the user is viewing the alert list When the user selects "Sort by Date Descending" Then alerts reorder with the newest alerts at the top When the user selects "Sort by Priority Ascending" Then alerts reorder from lowest to highest priority
Delivery Failure Handling
"As a food truck manager, I want the system to retry failed alerts and escalate them if necessary so that I don't miss critical notifications due to delivery issues."
Description

Implement retry logic for notifications that fail to send, including exponential backoff and configurable retry counts. Log delivery failures and provide fallback mechanisms to alternate channels if the primary channel remains unreachable. After repeated failures, escalate the alert through a backup channel or notify system administrators to ensure critical alerts are never lost.

Acceptance Criteria
Primary Channel Retry with Exponential Backoff
Given a notification send attempt fails on the primary channel When the system retries with exponential backoff Then each retry interval doubles and stops after the configured retry count
Fallback to Secondary Channel After Primary Channel Unreachable
Given all retry attempts on the primary channel are exhausted When the primary channel remains unreachable Then the system sends the notification via the configured secondary channel
Notification Failure Logging Verification
Given a notification failure occurs When the system logs the failure Then the log entry includes timestamp, notification ID, channel, error code, and retry attempt count
Escalation to System Administrator After Maximum Retry Attempts
Given the notification fails on both primary and secondary channels When the maximum retry attempts are reached Then the system sends an escalation alert to the system administrator via email and in-dashboard notification
Configurable Retry Count Adjustment Effectiveness
Given the retry count configuration is changed by the user When a notification fails Then the system retries exactly the updated number of times before invoking fallback or escalation mechanisms

AlertPriority

Organizes low-stock notifications into a dynamic priority queue based on severity, travel time to restock locations, and sales velocity. This helps operators tackle the most urgent restocks first, optimizing time and resources.

Requirements

Dynamic Priority Calculation
"As an operator, I want low-stock alerts ranked by urgency based on stock levels, travel time, and sales velocity so that I can restock the most critical items first."
Description

Implement an algorithm that dynamically calculates and ranks low-stock alerts based on three core factors: severity of stock depletion (remaining percentage), estimated travel time to the nearest restocking location, and current sales velocity. This ensures the most urgent restocking needs are surfaced first, optimizing operator focus and resource allocation.

Acceptance Criteria
High Sales Velocity Trigger
Given a truck with an item at 20% stock remaining and current sales velocity above 50 units/hour, When the priority algorithm runs, Then the alert for this item ranks higher than alerts for items with equal stock levels but lower sales velocity.
Long Travel Time Prioritization
Given two items both at 30% stock remaining and equal sales velocity, When the algorithm calculates priorities, Then the item with a longer estimated travel time to the nearest restock point ranks higher.
Critical Depletion Alert
Given an item’s stock falls below 10%, When generating the priority queue, Then this item appears within the top 5% of all alerts regardless of travel time or sales velocity.
Combined Factor Ranking Consistency
Given a set of low-stock items with varying severity, travel times, and sales velocities, When the priority calculation is executed multiple times within five minutes without metric changes, Then the relative ordering of the items remains identical.
Real-time Update Handling
Given live updates to stock levels, sales velocity, or travel time estimates, When any metric changes, Then the priority queue is recalculated and updated within ten seconds to reflect the new data.
Edge Case: Zero Travel Time
Given an item that can be restocked on-site with zero estimated travel time and moderate stock depletion, When calculating priorities, Then this alert ranks above distant restock alerts with higher sales velocity.
Configurable Severity Thresholds
"As an operator, I want to configure threshold levels for low-stock alerts so that notifications reflect my specific inventory usage and prevent unnecessary restocks."
Description

Enable operators to define and customize threshold values for stock levels and sales velocity that determine alert severity. This customization ensures alerts are aligned with each truck’s unique sales patterns and inventory strategy, reducing false positives and improving restock relevance.

Acceptance Criteria
Default Threshold Initialization
Given a new truck profile is created, when the operator opens the threshold settings, then the system must display default low-stock threshold of 10 units and default sales velocity threshold of 5 units/hour.
Custom Stock Level Threshold Update
Given the operator is on the threshold configuration page, when they set a low-stock threshold to a value between 1 and 1000 and save, then the system must persist the new value and apply it to subsequent alert severity calculations.
Invalid Threshold Input Validation
When the operator enters a stock threshold below 1 or above 1000 or a sales velocity threshold below 1 or above 100 units/hour and attempts to save, then the system must display an inline error "Threshold must be within allowed range" and block the save operation.
Bulk Threshold Configuration for Multiple Trucks
Given the operator selects multiple trucks from the dashboard, when they apply a new sales velocity threshold and confirm the action, then the system must update the threshold for all selected trucks and display a confirmation summary of updated trucks.
Threshold Impact on Alert Priority Queue
Given custom thresholds have been configured, when stock levels fall or sales velocity exceeds thresholds, then alerts must be generated with severity levels based on the configured thresholds and listed in the priority queue in correct order of urgency.
Geolocation-Based Routing Integration
"As an operator, I want travel time to restock locations factored into alert priority so that I can plan efficient restocking trips and reduce wasted driving time."
Description

Integrate with a mapping service API to calculate real-time travel times between active food trucks and their designated restock depots. Incorporate these travel time metrics into the alert priority score to optimize restocking routes and minimize downtime.

Acceptance Criteria
Real-Time Travel Time Retrieval
Given a food truck triggers a low-stock alert When the system calls the mapping service API Then the API returns a travel time estimate within 5 seconds and the system stores it successfully
Travel Time Integration into Priority Score
Given retrieved travel times for active trucks When computing alert priorities Then the system incorporates travel time, severity, and sales velocity to calculate a dynamic priority score for each low-stock alert
Prioritized Alert Queue Display
Given multiple low-stock alerts with computed priority scores When an operator views the dashboard Then alerts are ordered in descending priority and displayed along with travel time, stock level, and sales velocity
Mapping API Failure Fallback
Given the mapping service API is unreachable or returns an error When requesting travel time Then the system applies a default maximum travel time value and logs the failure for review
Continuous Travel Time Refresh
Given an active restocking operation When a truck’s location updates every 60 seconds Then the system re-queries travel time and updates the alert priority accordingly within 10 seconds of receiving location data
Priority Alert Dashboard
"As an operator, I want a dashboard that displays low-stock alerts sorted by priority so that I can immediately see and address my most urgent tasks."
Description

Design and build a dedicated dashboard view that displays low-stock alerts sorted by calculated priority in real-time. Include features for manual sorting, filtering by product or location, and visual severity indicators (colors or icons) to help operators quickly identify and act on the highest-priority restocks.

Acceptance Criteria
Real-Time Alert Sorting
Given low-stock alerts are generated, when the dashboard loads or updates, then alerts are sorted in descending order of computed priority, and the top alert reflects the highest combined severity, nearest restock travel time, and highest sales velocity.
Manual Sorting and Filtering
Given the operator has low-stock alerts displayed, when the operator applies a filter by product or location or selects a manual sort option, then the alert list updates instantly to reflect the selected criteria, and a 'Reset to Default' control restores the original priority order.
Visual Severity Indicators
Given each low-stock alert, then the dashboard displays a color-coded icon (red for critical, orange for high, yellow for medium, green for low) next to each item, and a visible legend explains each color’s meaning.
Real-Time Data Synchronization
Given inventory changes occur in the backend, when stock levels cross alert thresholds or reach sufficient levels, then the dashboard automatically refreshes within 5 seconds without manual action, adding new alerts and removing cleared ones.
Alert Detail Drill-Down
Given an alert appears on the dashboard, when the operator clicks the alert row, then a detail panel opens showing product name, current stock level, restock location, travel time, recent sales velocity, and recommended restock quantity.
Notification Escalation and Reminders
"As an operator, I want to receive reminders or escalations for unaddressed critical alerts so that urgent restocking tasks don’t slip through the cracks."
Description

Implement an escalation engine that tracks the time since each critical alert was generated. Send automated reminders or escalate notifications through multiple channels (email, SMS, in-app) if high-priority items remain unaddressed beyond configurable time windows, ensuring no vital restocks are missed.

Acceptance Criteria
Initial Reminder Dispatch
Given a critical low-stock alert is generated for an item and remains unacknowledged, When the configured reminder interval (e.g., 30 minutes) has elapsed, Then the system automatically sends a reminder via the primary notification channel (email) to the assigned operator.
Secondary Channel Escalation
Given a reminder has been sent via the primary channel and remains unacknowledged after an additional configured interval, When the escalation timer expires, Then the system automatically sends the alert through the secondary channel (SMS) to the operator and the backup contact.
In-App Push Notification
Given an alert has not been acknowledged via email or SMS within the total escalation window, When the final escalation threshold is reached, Then the system posts an in-app push notification and displays a persistent banner on the dashboard until acknowledged.
Configurable Time Window Update
Given an operator updates the escalation time window settings in their profile, When the settings are saved, Then subsequent reminders and escalations use the new time intervals for all new critical alerts.
Bulk Unaddressed Alerts Summary
Given there are three or more unacknowledged critical alerts across multiple trucks and no acknowledgments have been received, When the next escalation timer triggers, Then the system sends a consolidated summary notification via email and SMS listing all pending alerts and their durations.

Real-Time Route Optimizer

Automatically recalibrates each truck’s restock path using live sales and inventory data, ensuring the fastest, most cost-effective route to suppliers and reducing travel time by up to 30%.

Requirements

Live Inventory Data Integration
"As a food truck operator, I want real-time inventory data to feed into the route optimizer so that restocking routes align with the truck's actual supply needs."
Description

Integrate real-time inventory data from each food truck into the route optimizer, continuously syncing stock levels with the restocking engine to ensure routes reflect current supply needs. This integration will leverage cloud-based APIs to pull live count data, trigger automatic recalculation when thresholds are met, and maintain data consistency across all devices.

Acceptance Criteria
Real-Time Inventory Sync Initialization
Given a truck’s inventory service connects to the cloud API When the service starts Then the latest stock levels are fetched within 5 seconds and displayed accurately in the dashboard.
Threshold-Based Route Recalculation
Given any item’s stock falls below its predefined threshold When the cloud integration triggers a recalculation Then the route optimizer recalculates and displays the new restock path within 3 seconds.
Data Consistency Across Devices
Given a stock update occurs on one device When another user views the dashboard on a different device Then both devices show identical inventory counts within 2 seconds of the update.
API Failure Handling
Given the cloud API returns an error or times out When the integration request fails Then an error notification is shown to the user and the system retries up to 3 times with exponential backoff.
High-Frequency Update Performance
Given rapid inventory changes of more than 50 updates per minute When the system processes these updates Then none of the updates are lost and the average processing time per update remains below 200ms.
Dynamic Traffic Analysis
"As a food truck manager, I want the optimizer to consider live traffic conditions so that my restocking routes avoid delays and ensure timely resupply."
Description

Incorporate live traffic data into the route optimizer by integrating with traffic APIs to analyze current road conditions, congestion, and transit times. The system will dynamically adjust planned routes, factor in event-based delays, and provide alternative paths to ensure the fastest possible restocking trips.

Acceptance Criteria
Normal Traffic Route Calculation
Given live traffic API returns current speeds for all road segments When the optimizer recalculates the route Then the new route reduces estimated travel time by at least 10% compared to the static historical data baseline
Handling Sudden Congestion Events
Given a road segment's travel time increases by more than 20% during a trip When the optimizer detects this congestion Then it recalculates and provides an alternative route within 30 seconds, ensuring the new estimated travel time does not exceed the original plan by more than 5%
Event-based Delay Integration
Given a scheduled public event is detected within 1km of the planned path When the optimizer receives event delay data Then it adjusts the route to avoid the event area unless the alternate path increases travel time by more than 15%
Multi-stop Route Adjustment
Given a restock path includes three or more supplier stops When traffic data changes for any segment Then the optimizer resequences stops and provides an updated path that minimizes total travel time within 60 seconds
API Failure Graceful Degradation
Given the traffic API is unavailable or returns an error When the optimizer calculates routes Then it falls back to the last known good traffic data and flags the route as 'Estimated with stale data' on the dashboard
Supplier Database Connectivity
"As an operator, I want the optimizer to access supplier details so that routes include valid and open suppliers with necessary inventory."
Description

Connect to a centralized supplier database to retrieve essential details such as supplier locations, operating hours, available inventory, and pricing. This connectivity will allow the optimizer to validate destination availability, schedule visits only to open suppliers, and automatically update supplier data at regular intervals.

Acceptance Criteria
Retrieving Supplier Data on Demand
Given the real-time optimizer user opens the route planning interface When the system requests supplier data Then the system retrieves locations, operating hours, available inventory, and pricing for all suppliers within the service area within 2 seconds and displays them correctly formatted
Excluding Closed Suppliers From Route Calculation
Given the system has current supplier data and it's outside a supplier’s operating hours When optimizing routes Then the system excludes those suppliers from the route calculation
Scheduled Supplier Data Updates
Given the system is running scheduled updates at configured intervals (every 15 minutes) When an update triggers Then the system fetches and replaces local supplier data with the latest records from the centralized database without errors
Handling Invalid Supplier Data
Given the supplier database returns incomplete or malformed records When processing the update Then the system logs validation errors, skips invalid entries, and continues updating valid supplier data
Optimizing Route Based on Supplier Availability
Given live inventory and pricing data from connected suppliers and current truck stock levels When generating restock routes Then the optimizer schedules visits to ensure all required items can be purchased and reduces total travel time by at least 20%
Cost-Efficiency Modeling
"As a business owner, I want the optimizer to weigh cost factors so that I can minimize expenses while maintaining efficient restocking."
Description

Extend the route optimization algorithm to factor in cost metrics, including transportation expenses, current fuel prices, and available supplier discounts. The system will calculate total estimated cost for each potential route and prioritize routes that minimize overall expenses while meeting time and inventory requirements.

Acceptance Criteria
Cost Calculation Accuracy
Given a set of possible routes with defined transportation expenses, current fuel prices, and supplier discounts, when the system computes total estimated costs for each route, then it produces cost figures that match manual calculations within a ±1% margin of error.
Discount Integration Priority
Given multiple suppliers offering varying discounts, when the optimizer evaluates routes, then it prioritizes routes that leverage the highest total discount, provided the discount yields at least a 5% reduction in overall costs.
Real-Time Fuel Price Adjustment
Given live updates to fuel prices, when a price change is received, then the system recalculates cost estimates for all active routes within 30 seconds, reflecting the new fuel price in its cost-efficiency rankings.
Combined Time and Cost Optimization
Given a requirement to balance travel time and expenses, when generating the optimal route, then the system offers a route whose total cost does not exceed the absolute minimum cost by more than 10% if it reduces overall travel time by at least 20%.
Supplier Availability Constraints
Given supplier availability data indicating closures or capacity limits for the next 24 hours, when calculating cost-efficient routes, then the system excludes any routes that rely on unavailable suppliers.
Multi-Truck Coordination
"As a fleet manager, I want coordinated route planning for all trucks so that our entire fleet restocks efficiently without redundant trips."
Description

Enable the optimizer to plan and assign restocking routes for multiple trucks simultaneously, distributing supplier visits across the fleet to avoid overlap and reduce total travel distance. The system will consider each truck’s location, inventory needs, and schedule to deliver a cohesive, fleet-wide routing plan.

Acceptance Criteria
Parallel Truck Route Assignment
Given a fleet of three trucks with overlapping supplier requirements, when the optimizer runs, then each truck is assigned a unique supplier-path schedule, ensuring no two trucks visit the same supplier concurrently and reducing combined travel distance by at least 20% compared to independent routing.
Dynamic Inventory-Imbalance Correction
Given real-time inventory data for four trucks, when one truck’s stock falls below its threshold mid-shift, then the optimizer recalibrates all routes so that the understocked truck receives restock from the nearest supplier without increasing total fleet travel time by more than 10%.
Time-Window Constraint Satisfaction
Given supplier operating hours and each truck’s availability window, when generating the route plan, then all restocking visits occur within both supplier hours and truck availability times with zero violations.
Priority-Based Critical Restock
Given one truck’s critical item level drops below 15% while others remain above 50%, when optimizing routes, then the high-priority truck is scheduled for the earliest restock without delaying any other truck’s next scheduled service stop.
Load Balancing Under Peak Demand
Given forecasted peak sales causing multiple trucks to require restock at the same supplier, when the optimizer processes routes, then supplier visits are distributed across trucks proportional to their remaining capacity while keeping overall route efficiency within 5% of the optimal single-truck path.

DemandHeat Mapping

Transforms live sales patterns into color-coded demand zones on the map, highlighting high-priority restock stops and predicted shortages to guide smarter, data-driven route decisions.

Requirements

Real-time Sales Data Integration
"As a fleet operator, I want real-time sales data aggregated across all trucks so that I can see demand patterns instantly."
Description

Ingest and aggregate live sales transactions from all connected food trucks into the cloud dashboard with sub-minute latency, ensuring the demand heat map reflects the latest purchase data. This integration enhances decision-making by providing up-to-the-second visibility into sales patterns, minimizes data syncing delays across devices, and forms the foundation for accurate demand zone calculations.

Acceptance Criteria
Live Sale Processing Latency Verification
Given a sale transaction occurs on any connected food truck, when the transaction is sent to the cloud dashboard, then the transaction data must be aggregated and visible on the demand heat map within 60 seconds.
High-Throughput Data Handling during Peak Events
Given simultaneous sale transactions from at least 10 food trucks within a 30-second window, when these transactions are ingested, then the system must process and display all transactions without loss and within 90 seconds.
Offline Queue Synchronization after Reconnection
Given a food truck has been offline for up to 15 minutes, when it reconnects to the network, then all queued sales transactions must be synchronized and reflected on the dashboard and demand heat map within 2 minutes of reconnection.
Uniform Dashboard Data Propagation
Given a sale transaction is ingested, when the data is updated in the cloud, then the updated demand heat map must display the change consistently across web and mobile dashboards within 30 seconds.
Error Detection and Retry Mechanism
Given a failure in transaction ingestion due to network error, when the system detects the failure, then it must retry ingestion up to three times and log errors, and ultimately ensure the transaction is displayed on the dashboard within 5 minutes or record a failure for manual intervention.
Dynamic Color-Coded Demand Zones
"As a route planner, I want to see color-coded demand zones so that I can identify high-demand areas and prioritize restocking stops."
Description

Overlay a geographical map with color-coded zones representing demand intensity levels. Zones should update dynamically based on incoming sales data, include a legend for interpreting color scales, and support customization of color thresholds. This visualization simplifies identifying high-priority restock stops and allows users to quickly assess demand hotspots.

Acceptance Criteria
Initial Map Load
Given the user navigates to the DemandHeat map, When the map is fully loaded, Then color-coded zones appear for all regions using default thresholds and the legend is clearly displayed.
Real-Time Data Update
Given live sales data is received, When a sale is recorded, Then the affected zone updates its color within 5 seconds to reflect the new demand level.
Custom Threshold Configuration
Given the user adjusts demand thresholds in settings, When the new thresholds are saved, Then all map zones immediately recalculate and apply colors based on the updated thresholds.
Legend Interpretation
Given the color scale legend is visible, When the user views any zone, Then the zone’s color corresponds exactly to one of the legend entries and is distinguishable by hue and intensity.
Performance Under Load
Given a burst of 10,000 sales events in one minute, When the map processes these events, Then zone color updates occur without UI lag exceeding 2 seconds per update batch.
Predictive Shortage Alert System
"As a food truck owner, I want to receive alerts for predicted inventory shortages so that I can restock before running out of items."
Description

Analyze historical sales velocity and current inventory levels to predict potential stock shortages within specified timeframes. Generate notifications when forecasted shortages exceed configurable thresholds and highlight at-risk zones on the map. This proactive alerting helps prevent stockouts and ensures uninterrupted service in high-demand areas.

Acceptance Criteria
Upcoming Shortage Prediction Notification
Given historical sales velocity and current inventory levels When the projected stock of any menu item is forecasted to fall below the user-configured threshold within the next 24 hours Then the system generates a predictive shortage alert listing the item, predicted stock-out time, and required restock quantity
Configurable Threshold Alert Trigger
Given the user has set a custom shortage threshold for each inventory item When the forecasted shortage for an item exceeds its custom threshold Then the system triggers a notification and highlights the item in the shortage dashboard
At-Risk Zone Highlighting on Map
Given multiple truck locations with inventory data When one or more trucks are forecasted to run low on an item within their upcoming route Then the map view displays color-coded zones indicating high-risk areas and the specific trucks at risk
Real-Time Inventory Analysis under Peak Demand
Given a spike in sales velocity during a high-traffic event When real-time sales data is ingested Then the predictive engine recalculates shortages every 30 minutes and issues new alerts if any item’s forecasted shortage crosses the threshold
Notification Delivery across Devices
Given the user has the SnackFleet dashboard open on desktop and mobile When a predictive shortage alert is generated Then the notification appears simultaneously on both devices, and acknowledgement on one device marks it as read on the other
Interactive Map Filtering Controls
"As a dispatcher, I want to filter the demand heat map by time range and item type so that I can analyze specific sales trends."
Description

Provide user controls for filtering the demand heat map by time range, product categories, individual trucks, and demand intensity thresholds. Filters should apply in real-time, enabling users to drill down into specific data subsets and tailor the visualization to their analytical needs. This enhances the map’s usability for granular demand analysis.

Acceptance Criteria
Time Range Filter Application
Given the user opens the demand heat map and selects a time range filter for a specific period, When the user applies the filter, Then the map displays only demand zones corresponding to sales within that time range, and zones outside the range are hidden.
Product Category Filter Application
Given the user clicks on one or more product category checkboxes in the filter panel, When the filter is applied, Then the map updates to show demand zones for only the selected product categories, with all other products excluded.
Individual Truck Filter Application
Given the user chooses a specific truck or set of trucks from the truck list filter, When the filter is confirmed, Then the heat map refreshes to display demand zones generated by sales from only those trucks.
Demand Intensity Threshold Filter Application
Given the user adjusts the demand intensity slider to a threshold level, When the slider value changes, Then zones with demand intensity below the threshold are removed and only zones at or above the threshold remain highlighted.
Combined Filters Interaction
Given the user has applied multiple filters (time range, product category, individual truck, intensity), When any filter is added, removed, or modified, Then the map dynamically updates to reflect the intersection of all active filters in real time.
Real-Time Update Performance
Given the user applies or modifies any filter on the demand heat map, When the filter changes, Then the map updates and renders all new zones within 1 second, ensuring smooth user interaction.
Route Optimization Recommendations
"As a fleet manager, I want optimized restocking route suggestions so that I can minimize travel time while meeting demand."
Description

Leverage demand heat zones, predicted shortages, current truck locations, and real-time traffic data to generate optimized restocking routes. Recommendations should balance minimizing travel time against maximizing service in high-demand areas and allow users to select between fastest, shortest, or demand-priority routing options.

Acceptance Criteria
Shortest Route Selection
Given a user selects the "shortest" routing option, When the system computes a route, Then the returned route must have the minimum total distance compared to all other available options.
Fastest Route Selection
Given a user selects the "fastest" routing option, When the system computes a route using real-time traffic data, Then the returned route must have the lowest estimated travel time among all options.
Demand-Priority Route Selection
Given a user selects the "demand-priority" routing option, When the system generates a route, Then the route must prioritize stops in high-demand zones and include locations with predicted shortages before other stops.
Real-Time Traffic Update Response
Given that traffic conditions change while a route is active, When new traffic data is received, Then the system must recalculate and update the recommended route within 60 seconds.
Predicted Shortage Integration
Given a predicted shortage is identified for an upcoming stop, When computing route recommendations, Then the system must include that stop before the predicted depletion threshold and alert the user to potential shortages.

FleetRoute Orchestrator

Coordinates restock routes across multiple vehicles in a fleet to avoid overlap, balance supply distribution, and maximize overall operational efficiency for fleet coordinators.

Requirements

Dynamic Route Optimization
"As a fleet coordinator, I want the system to automatically plan the most efficient restock routes for all my trucks so that I can minimize travel time, reduce fuel costs, and ensure timely supply replenishment."
Description

Automatically generates the most efficient restock routes for each truck by analyzing real-time inventory levels, depot locations, historical restock patterns, and geographic distances. The system prioritizes minimizing total travel time and distance, thereby reducing fuel costs, avoiding unnecessary mileage, and ensuring timely restocking. Integration with the existing inventory dashboard allows seamless synchronization of stock data and dynamic route recalculations as inventory thresholds are met.

Acceptance Criteria
Real-time Route Calculation Trigger
Given real-time inventory levels are synced for all trucks When any truck’s inventory falls below the defined restock threshold Then the system generates a complete restock route for that truck within 60 seconds
Depot Proximity Prioritization
Given multiple depots are available When generating a restock route Then the system selects the closest depot based on geographic distance and includes it in the route plan
Route Recalculation Upon Inventory Change
Given a route has been generated and inventory levels change mid-route When a truck’s inventory drops further or increases above threshold Then the system dynamically recalculates and updates the remaining route in under 2 minutes
Historical Pattern Integration
Given the system has access to historical restock data When generating new routes Then the system incorporates past traffic and delivery time patterns to optimize total travel time by at least 10% compared to a distance-only calculation
Fuel Efficiency Constraint Compliance
Given fuel cost data is available for all routes When calculating optimized restock routes Then the system prioritizes routes that minimize overall fuel consumption and ensures fuel cost per route does not exceed predefined budget limits
Overlap Conflict Detection
"As a fleet coordinator, I want the system to detect when multiple trucks are assigned to the same restock location at the same time so that I can avoid redundant visits and optimize fleet utilization."
Description

Identifies and prevents route overlaps where two or more trucks are scheduled to service the same location or depot within overlapping time windows. The system flags conflicts and provides alternative routing suggestions to ensure that restocks are evenly distributed, avoid duplicated visits, and improve overall operational efficiency.

Acceptance Criteria
Scheduling Overlap Detection
Given two or more trucks are assigned to the same location within overlapping time windows, When the routes are saved, Then the system flags a conflict notification within 2 seconds.
UI Conflict Visualization
Given a flagged route conflict exists, When the coordinator views the map interface, Then overlapping route segments are highlighted in red and each conflicting truck’s path is displayed in distinct colors.
Providing Alternative Route Suggestions
Given a conflict is flagged for a scheduled route, When the coordinator requests alternatives, Then the system generates at least three rerouting suggestions sorted by minimal additional travel time and allows the coordinator to apply one with a single click.
Bulk Schedule Processing for Conflicts
Given a CSV file containing up to 500 route assignments is uploaded, When processing completes, Then the system identifies all overlapping assignments and returns a conflict report within 60 seconds.
Live Conflict Alert on Route Modification
Given a dispatcher edits an existing route that affects time windows or locations, When the change is saved, Then the system recalculates conflicts and displays any new alerts within 5 seconds without requiring a page refresh.
Load Balancing Across Fleet
"As a fleet coordinator, I want the system to balance restock loads across all trucks so that each vehicle is utilized effectively and none exceeds its capacity."
Description

Distributes restock quantities among trucks based on individual vehicle capacity, current load, and upcoming route demands. The feature ensures no single truck is overloaded or underutilized by automatically reallocating restock tasks to achieve balanced workloads and maximize fleet throughput.

Acceptance Criteria
Balanced Restock Allocation for Peak Demand
When the fleet coordinator initiates a restocking cycle during peak operational hours, the system calculates and allocates inventory quantities to each truck such that no truck exceeds 90% of its capacity and overall fleet demand is met within a 5% margin of error.
Dynamic Reallocation upon Capacity Constraints
If a truck’s remaining capacity drops below the required restock volume mid-route, the system automatically reallocates surplus items to other trucks with available capacity without user intervention.
Threshold-Based Minimum Stock Enforcement
Every truck must maintain at least a configurable minimum stock level (e.g., 20 units) throughout the route; if any truck is predicted to fall below this threshold, the system adjusts allocations to ensure compliance.
Real-Time Load Adjustment on Route Change
When a route change is detected for any truck, the system recalculates and redistributes pending restock quantities across the fleet within 60 seconds, ensuring no single truck becomes overloaded.
Even Distribution with Demand Prioritization
For products with high anticipated sell-through rates, the system increases allocation to trucks on high-demand routes by up to 30% while still ensuring all other trucks receive at least 80% of their standard allocation.
Real-Time Traffic Integration
"As a fleet coordinator, I want the route planner to use real-time traffic data so that I can avoid delays caused by congestion and ensure timely deliveries."
Description

Incorporates live traffic and road condition data into route planning to adjust restock schedules dynamically. By consuming third-party traffic APIs and combining them with restock priorities, the system recalculates routes to avoid congestion, road closures, and delays, ensuring restocks happen on time.

Acceptance Criteria
Live Traffic Data Fetch
Given a valid API token for the traffic data provider When the system initiates a traffic data request for a route Then the system receives traffic speed, congestion, and road status data and stores it in the route optimization module within 5 seconds
Dynamic Route Recalculation
Given an assigned restock route and incoming traffic data indicating a delay of more than 5 minutes on any segment When the delay is detected Then the system recalculates the route to minimize delay and updates the dispatcher and driver interfaces with the new route within 2 minutes
Road Closure Avoidance
Given an active restock route When real-time data indicates a road closure on the planned path Then the system automatically excludes the closed road from the route, finds an alternative path, and updates ETAs and navigation instructions within 1 minute
Priority Delivery Alignment
Given multiple trucks with varying restock priorities When traffic conditions degrade for lower-priority routes Then the system reallocates high-priority stops to the least congested nearby truck and adjusts all affected routes and ETAs accordingly
Real-Time ETA Updates
Given any change in traffic or route adjustments When the system recalculates ETAs Then the dashboard displays updated ETAs for all trucks within 30 seconds of data change
Manual Route Adjustment Interface
"As a fleet coordinator, I want to manually tweak restock routes on a map so that I can accommodate special instructions or last-minute changes."
Description

Provides an interactive map-based UI that allows fleet coordinators to manually adjust suggested restock routes. Users can drag waypoints, add or remove stops, and override automated plans while immediately viewing the impact on travel time and load distribution.

Acceptance Criteria
Dragging Waypoint Adjusts Route Real-Time
Given a suggested route is displayed; When the coordinator drags a waypoint to a new location; Then the map updates the route path instantly; And the travel time and load distribution metrics refresh within 2 seconds.
Adding New Stop on Map
Given an active route; When the coordinator clicks 'Add Stop' and selects a map location; Then the new stop is inserted into the route sequence; And the system recalculates and displays updated metrics within 2 seconds.
Removing Stop from Route
Given an active route with multiple stops; When the coordinator selects a stop and clicks 'Remove'; Then the stop is removed from the route; And the recalculated route and metrics are displayed within 2 seconds.
Overriding Automated Restock Plan
Given an automated suggested plan is displayed; When the coordinator toggles override mode and manually rearranges stops; Then an override indicator appears; And automated updates are paused until override is disabled.
Real-Time Impact Analysis Display
Given any manual modification to the route; When the change is applied; Then updated travel time and load distribution are displayed in under 2 seconds; And color-coded alerts appear if any truck's load exceeds capacity.
Restock Demand Forecasting
"As a fleet coordinator, I want the system to predict when each truck will run low on stock so that I can plan restocks proactively and avoid last-minute shortages."
Description

Analyzes historical sales data, seasonal trends, and current on-hand inventory to forecast future restock needs for each truck. Forecasts are used to proactively schedule restock routes before inventory levels reach critical thresholds, reducing stockouts and wasted time on emergency restocks.

Acceptance Criteria
Critical Inventory Threshold Forecast
Given the forecast system processes sales history and current inventory, when projected levels for any item in a truck fall below the critical threshold (10% of average daily usage) within the next 48 hours, then the system flags the item and includes it in the next restock schedule.
Daily Sales Trend Analysis
Given the system aggregates the previous seven days of sales data, when generating forecasts for the next day, then it predicts daily restock quantities within a ±10% margin of actual next-day usage.
Seasonal Demand Adjustment
Given known seasonal patterns (holidays, events) tagged in the system, when forecasting demand for the upcoming period, then the forecast adjusts restock quantities by at least 20% compared to baseline for identified peak seasons.
Cross-Truck Demand Balancing
Given multiple trucks operating in overlapping territories, when forecasting individual truck demand, then the system distributes restock quantities across trucks to ensure variance between trucks remains within ±20%.
Proactive Route Scheduling
Given forecasted restock requirements at least 24 hours in advance, when generating restock routes, then the system auto-schedules and dispatches routes covering all flagged trucks without overlap and with minimized total travel distance.

Offline Route Cache

Pre-downloads optimized routes and map data for each restock trip, ensuring seamless navigation and auto-adjustments even in areas with limited or no connectivity.

Requirements

Offline Map Download
"As a food truck operator, I want the application to download and store map data for my scheduled restock routes so that I can navigate seamlessly even in areas with no cellular coverage."
Description

The system must pre-fetch and store high-resolution map tiles for all designated restocking routes and operational areas, enabling uninterrupted map rendering without network connectivity. This includes downloading base maps, points of interest, and relevant overlays (traffic, terrain) within configurable geographic boundaries. The implementation must integrate seamlessly with existing navigation modules, ensuring fast map load times, efficient storage usage, and the ability to update or remove cached regions. The outcome is reliable offline map visualization for drivers on the road, reducing downtime and navigation errors.

Acceptance Criteria
Initial Offline Map Download Initialization
Given the driver accesses the route settings page on a device with network connectivity, When the driver initiates an offline map download for a designated restocking route, Then the system must pre-fetch and store all high-resolution map tiles, points of interest, and relevant overlays for that route without errors.
Download Within Configured Geographic Boundaries
Given a configured geographic boundary for a restocking area, When the offline map download runs, Then only map tiles and overlays within the defined boundary must be downloaded and stored locally, and no data outside the boundary should be fetched.
Manage Cached Regions
Given existing cached map regions, When the user triggers an update or removal action on a specific region, Then the system must replace outdated tiles with the latest versions or delete the selected region, and reflect the changes in local storage accurately.
Offline Map Rendering Performance
Given the device is offline and the user navigates within a cached area, When the user pans, zooms, or requests turn-by-turn directions, Then the map must render the requested views within 2 seconds and display navigation instructions correctly using only cached data.
Storage Optimization for Map Tiles
Given multiple routes are cached and storage usage nears the configurable limit, When the total cached map storage exceeds the threshold (e.g., 500MB), Then the system must alert the user and automatically prune the least recently used map tiles to maintain storage below the limit.
Route Optimization Cache
"As a logistics manager, I want the system to store our recommended optimized routes offline so that drivers can follow the best paths even when they lose network signal."
Description

Implement functionality to calculate and cache optimized turn-by-turn routes based on current inventory levels, restock depot locations, and traffic patterns. The cached route data must include all navigation waypoints, estimated times, and direction metadata. This requirement integrates with the routing engine to ensure the same optimized path is available offline, preserving reorder logic and detour handling. Successful implementation ensures that drivers follow the most efficient path regardless of connectivity.

Acceptance Criteria
Initial Route Pre-Download
Given a driver schedules a restock trip while connected to Wi-Fi, when they initiate pre-download, then the system caches the optimized route with all waypoints, ETAs, and direction metadata before departure.
Offline Navigation in No Connectivity Zone
Given the driver loses network connectivity mid-route, when navigation continues, then the app uses the cached route data to provide turn-by-turn directions without error.
Mid-Trip Inventory Triggered Reroute
Given inventory levels change during a trip, when the driver updates inventory and a new route is needed, then the cached routing engine recalculates and caches the updated route with new waypoints and metadata.
Offline Detour Handling
Given an unplanned obstacle requires a detour in an offline zone, when the driver manually chooses an alternate path, then the system integrates the detour into the cached route and updates subsequent navigation instructions.
Cached Route Data Integrity Check
Given the driver opens the app after a long interval offline, when the cached route is loaded, then all navigation metadata is intact, no waypoints are missing, and the ETA calculations match the last cached values.
Automatic Cache Update
"As a food truck owner, I want the offline route and map cache to update automatically when connectivity resumes so that I always have the latest route information without manual steps."
Description

Enable the application to automatically refresh offline route and map caches whenever the device reconnects to a network. The system should detect connectivity, compare existing caches against updated route plans or map changes, and download only incremental updates to minimize bandwidth usage. This process must be background-friendly, respecting device battery and network constraints, and provide status notifications if updates fail or require user intervention.

Acceptance Criteria
Network Reconnection Triggers Cache Update
Given the device was offline and missed updates, When it reconnects to a network, Then the app automatically checks for route or map changes and begins downloading incremental cache updates within 30 seconds without user intervention.
Selective Incremental Update Download
Given the app identifies changes in route plans or map tiles, When incremental updates are available, Then only the changed map segments and route adjustments are downloaded, reducing data usage by at least 70% compared to a full cache refresh.
Background Cache Update Respecting Battery Constraints
Given the device battery level is above 20% and not in low-power mode, When the app is running in the background, Then the cache update continues uninterrupted, but if battery falls below 20% or low-power mode is active, the update pauses and automatically resumes under acceptable conditions.
Wi-Fi Only Update Option
Given the user has enabled the 'Wi-Fi only' updates setting, When the device is connected via a cellular network, Then no cache updates are initiated until a Wi-Fi connection is detected, and once Wi-Fi is available the update starts automatically.
Update Failure Notification and Retry
Given a cache update attempt fails due to network or server errors, When retries exceed three attempts, Then the user receives a persistent in-app notification indicating the failure with an option to retry manually, and detailed error logs are recorded for diagnostics.
Cache Storage Management
"As a system administrator, I want the application to manage its offline cache storage automatically so that it doesn’t consume excessive device space and continues to function optimally."
Description

Design and implement a storage management component that monitors local disk usage for map and route caches. It should enforce configurable storage quotas, automatically remove the oldest or least-used caches when space limits are approached, and prompt users with warnings when manual intervention is required. The module must provide clear status indicators in the dashboard and allow administrators to adjust storage policies per device.

Acceptance Criteria
Approaching Storage Quota Threshold
Given the local cache storage quota is set to 500MB and current usage reaches or exceeds 450MB (90%), when the next cache download completes, then the system automatically deletes the oldest cache files until usage falls below 80% of the quota and logs each deletion.
Automatic Cache Cleanup Execution
Given local storage usage exceeds the configured quota, when the cleanup job runs, then it identifies and removes the least recently accessed cache directories first, confirms freed space, and ensures total usage remains below the quota.
User Warning Prompt for Manual Intervention
Given local storage usage exceeds 95% of the quota, when automatic cleanup fails to free sufficient space, then the system displays a non-blocking warning notification on the dashboard with clear instructions for manual cleanup.
Administrator Adjusts Storage Policy
Given an administrator accesses the device settings, when they update the cache storage quota or retention policy, then the new settings are saved persistently and a confirmation message is displayed and logged.
Dashboard Status Indicator Update
Given any change in local cache usage, when the dashboard refreshes, then it displays the current usage percentage, quota limit, and next scheduled cleanup time with correct values without requiring a page reload.
Offline Navigation Adjustments
"As a driver, I want the navigation to recalculate routes locally when I deviate offline so that I can still reach my restocking points without network support."
Description

Provide the ability for the navigation engine to perform dynamic rerouting and turn adjustments using only pre-cached map and route data when online guidance is unavailable. This involves detecting deviations from the original path, recalculating alternate paths locally, and updating estimated arrival times. Integration with the user interface must ensure smooth transitions, clear deviation alerts, and continued guidance until connectivity is restored.

Acceptance Criteria
Route Deviation Detection
Given the user’s GPS position deviates more than 20 meters from the pre-cached path and connectivity is lost, When the navigation engine monitors GPS data against the cached route, Then it must detect the deviation within 5 seconds.
Local Reroute Computation
Given a detected deviation from the original pre-cached route, When recalculating an alternate path using only locally cached map data, Then the engine must compute a valid reroute within 3 seconds and ensure it leads to the original destination.
ETA Update Without Connectivity
Given a new alternate route is computed locally, When the rerouted path is finalized, Then the system must update the estimated arrival time within 2 seconds and display it on the dashboard.
Off-Route Alert Presentation
Given the user goes off the original pre-cached route more than 20 meters, When the deviation is detected offline, Then the UI must display an alert within 1 second indicating the deviation and provide options to continue or accept the new route.
Seamless Sync After Reconnection
Given the device regains connectivity after offline navigation adjustments, When the app reconnects to the server, Then it must sync the updated route and ETA data within 5 seconds without user intervention and remove offline mode indicators.

Eco-Drive Routing

Integrates traffic conditions, distance, and vehicle-specific fuel consumption models to recommend the most eco-friendly and cost-efficient restock routes, lowering both travel time and emissions.

Requirements

Real-time Traffic Data Integration
"As a fleet manager, I want real-time traffic data integrated into my routing dashboard so that I can adjust restock routes on the fly to avoid delays."
Description

Integrate live traffic data from multiple providers (e.g., Google Maps, Waze) via APIs, normalize and cache the information, and feed it into the Eco-Drive Routing module. Ensure low-latency updates to reflect current conditions and incidents, allowing route recalculation on-the-fly. Implement error handling and fallback mechanisms to maintain availability when a data source is unavailable.

Acceptance Criteria
Initial Traffic Data Retrieval
Given the Eco-Drive module is initialized and API credentials are valid When a traffic data request is made Then the system retrieves live traffic data from at least two providers within 2 seconds
Data Normalization and Caching
Given raw traffic data is received When the normalization process runs Then all provider data are converted to a unified schema and stored in cache within 1 second of retrieval
Low-Latency Update Handling
Given traffic conditions change When new incident or congestion data arrives Then the system updates the cached data and propagates changes to the routing engine in under 3 seconds
Error Handling and Fallback Activation
Given one traffic data provider returns an error or times out When the primary API call fails Then the system switches to a backup provider and logs the failure without interrupting routing functions
On-the-Fly Route Recalculation
Given a cached traffic update indicates increased delay on the current route When the driver requests a route check Then the system recalculates and displays an optimized eco-friendly route within 5 seconds
Vehicle-specific Fuel Model Configuration
"As an operations coordinator, I want to configure fuel consumption profiles for each truck so that the Eco-Drive Routing engine can provide accurate cost and emission estimates."
Description

Allow users to define and manage fuel consumption models for each food truck based on engine type, load weight, and historical usage data. Store and retrieve vehicle profiles, apply models dynamically during route calculations, and provide an interface for updating parameters as vehicles age or are modified.

Acceptance Criteria
Adding a New Vehicle Fuel Model
Given valid engine type, load weight range, and baseline consumption data, When the user saves the fuel model, Then the model is stored in the vehicle profile and listed in the fuel models table.
Updating Fuel Model Parameters for an Existing Vehicle
Given an existing vehicle fuel model, When the user modifies engine type or consumption rates and saves changes, Then the updated parameters are persisted and visible in the model detail view.
Retrieving Fuel Model Data During Route Calculation
Given a route calculation request, When the system retrieves the vehicle’s profile, Then it applies the stored fuel model and returns estimated fuel consumption in the routing results.
Applying Fuel Model Adjustments Based on Vehicle Load
Given a vehicle with specified load weight, When the user inputs load changes into the fuel model, Then the system recalculates consumption rates and displays updated fuel estimates.
Validating Fuel Model Input Error Handling
Given incomplete or invalid model parameters, When the user attempts to save the fuel model, Then the system blocks submission and shows specific validation errors for each invalid field.
Eco-Routing Algorithm Engine
"As a route planner, I want an algorithm that recommends the most eco-friendly paths so that I can reduce fuel costs and carbon emissions on my supply runs."
Description

Develop the core optimization engine that computes routes minimizing fuel usage and emissions while balancing distance and traffic delays. Leverage multi-criteria optimization techniques (e.g., weighted least-cost path algorithms) to generate recommended routes. Ensure scalability to handle multiple simultaneous requests across a fleet.

Acceptance Criteria
Single Truck Eco-Friendly Route Calculation
Given a valid start and end location and truck profile, When the Eco-Routing engine is invoked, Then the recommended route must minimize estimated fuel consumption based on vehicle-specific fuel consumption models and current traffic data.
Multiple Trucks Concurrent Route Requests
Given multiple simultaneous route requests across the fleet, When the engine receives up to 50 concurrent requests, Then it must process and return each route recommendation within 2 seconds per request without errors.
Real-Time Traffic Data Integration
Given updated traffic information from integrated APIs every 5 minutes, When a reroute is requested, Then the engine must incorporate the latest data and provide a new route within 10 seconds that reflects current traffic conditions.
Weighted Criteria Adjustment Impact
Given user-defined weights for fuel efficiency, emissions reduction, and travel time, When weights are adjusted in the preferences, Then the engine’s recommended route must reflect the new weight distribution, prioritizing criteria according to specified values.
High-Volume Fleet Scalability
Given a fleet generating up to 200 route requests per minute, When the engine operates under this load, Then it must maintain an average response time below 3 seconds and a failure rate under 5%.
Interactive Route Visualization
"As a food truck owner, I want to visualize eco-friendly routes on a map so that I can easily understand the benefits and choose the best path."
Description

Design and implement an interactive map interface that displays recommended eco-routes alongside alternative options. Include overlays for traffic congestion, estimated fuel consumption, carbon emissions, and time savings. Provide pan, zoom, and tooltip features to explore route details and comparisons.

Acceptance Criteria
Eco-Route Display with Metrics Overview
Given the user selects a restock destination and opens the map interface When the interface loads the recommended eco-friendly route Then the map displays the route path with clearly visible overlays for estimated fuel consumption, carbon emissions, and time savings
Alternative Route Comparison
Given multiple route options are available When the user toggles between recommended and alternative routes Then each route’s metrics (distance, fuel, emissions, time) update correctly and are visually distinguishable
Interactive Tooltip Functionality
Given the user hovers over any point on the route When the tooltip appears Then it shows specific data for that segment, including real-time traffic delay, segment distance, and estimated fuel usage
Map Pan and Zoom Responsiveness
Given the user performs a pan or zoom gesture on the map When the map view changes scale or position Then all route overlays and tooltips adjust and render smoothly without misalignment or lag
Real-Time Traffic Overlay Update
Given changes in traffic conditions occur during planning When the real-time traffic overlay updates Then the route’s estimated time and emissions metrics recalculate and refresh within two seconds
Route Optimization Notifications
"As a driver, I want to receive alerts when a better restock route becomes available so that I can adjust my journey and save time and fuel."
Description

Implement a notification system to alert users when significant changes in traffic or route efficiency occur. Send real-time push notifications or emails highlighting new optimal routes, potential delays, or excessive fuel usage projections, enabling quick decision-making.

Acceptance Criteria
Traffic Change Push Notification
Given traffic data indicates current route travel time has increased by more than 15%, when the system recalculates the optimal route, then a push notification is sent to the user within 30 seconds containing the new route details, estimated time savings, and fuel consumption reduction.
Excessive Fuel Usage Email Alert
Given the projected fuel usage of the recommended route exceeds the predefined threshold, when the calculation is complete, then an email alert is dispatched to the user within 1 minute, including the projected fuel usage, route options, and tips to reduce consumption.
Notification Action Triggers Route Update
Given the user taps on the push notification, when the notification is opened, then the dashboard automatically updates to the newly recommended route and opens the device's navigation app with the route loaded.
Notification Retry on Delivery Failure
Given a push notification delivery fails due to network issues, when the first retry occurs 30 seconds after the initial failure, then the system attempts up to three retries; if all retries fail, an email alert is sent to the user and the incident is logged.
User Notification Preference Enforcement
Given the user has selected only email notifications in their preferences, when a route update or alert is triggered, then no push notifications are sent and all alerts are delivered via email within the defined delivery window.

SurgeGuard Alerts

Monitors sudden sales spikes or unexpected delays in real time and pushes instant notifications to operators and drivers, enabling immediate route pivots to prevent stockouts.

Requirements

Real-time Sales Spike Detection
"As an operator, I want the system to detect sudden sales spikes in real time so that I can respond quickly to high-demand situations."
Description

Implement a monitoring engine that continuously analyzes live sales data across all food trucks, identifying sudden increases in transaction volumes within configurable time windows. This requirement ensures that the system can detect anomalies indicative of demand surges, enabling proactive management and preventing potential stockouts during peak periods.

Acceptance Criteria
Lunch Rush Spike Detection
Given streaming sales data for a single food truck When the number of transactions in any rolling 15-minute window increases by 50% compared to the same window on the previous day Then a real-time surge alert is generated and sent to the operator within 30 seconds
Event-Driven Sales Surge
Given streaming sales data across all trucks When a single truck records a 100% increase in transaction count within a 10-minute window and the event tag is detected Then the system triggers an instant surge notification to the driver and operator within 20 seconds
Promo Offer Uplift Spike
Given a promotional campaign is active and sales data streams continuously When total transactions using the promo code exceed the baseline by 40% within 30 minutes Then a promo-specific surge alert is dispatched to the operator and restocking engine within 1 minute
Fleet-Wide Surge Alert
Given aggregated sales data from all trucks When the combined fleet sales in a 10-minute window surpass the rolling average by 75% Then a fleet-wide surge alert is issued to both the operations dashboard and mobile app in under 45 seconds
Sustained Spike Escalation
Given continuous monitoring of sales spikes When three consecutive 10-minute windows each exceed a 50% increase threshold Then the alert level escalates to 'High Priority' and additional notifications are sent to the supply manager within 30 seconds
Instant Operator Notifications
"As an operator, I want to receive instant notifications when anomalies occur so that I can take immediate action."
Description

Develop a notification service that pushes alerts to operators via multiple channels (push notifications, email, or SMS) the moment a sales spike or delivery delay is detected. The service must ensure low latency, reliability, and configurable delivery options to guarantee that operators are immediately informed of critical events.

Acceptance Criteria
Sales Spike Detection Notification
Given a truck’s sales exceed the configured threshold within a rolling 10-minute window When the spike is detected Then the system must send a notification via all selected channels (push, email, SMS) to the operator within 30 seconds
Delivery Delay Alert
Given a scheduled delivery is running more than 5 minutes behind its estimated arrival time When the delay threshold is crossed Then the system must notify the operator via all configured channels within 60 seconds
Channel Configuration Override
Given an operator updates notification channel preferences in the dashboard When the changes are saved Then the system must apply the new settings and use them for subsequent alerts within 5 minutes
Retry on Transmission Failure
Given the first notification attempt fails for any channel When the failure is detected Then the system must retry up to three times at 30-second intervals and log each attempt
Low Latency Under Load
Given the system processes up to 1000 alert events per minute Under peak load conditions Then 95% of notifications must be delivered within 60 seconds and delivery times must be logged for monitoring
Driver Route Optimization Alerts
"As a driver, I want to receive route optimization alerts so that I can pivot to the nearest supply center and prevent stockouts."
Description

Integrate with the dispatch module to send real-time route adjustment alerts to drivers based on detected spikes or delays. The system should calculate alternative paths to the nearest supply hub or reassign deliveries, minimizing downtime and ensuring timely restocking and order fulfillment.

Acceptance Criteria
Spike Detection Route Alert
Given a detected sales spike ≥20% above average for a truck, when the spike is identified, then the system pushes a notification to the driver within 30 seconds with an alternative route to the nearest supply hub and estimated arrival time.
Delay-Based Route Reassignment
Given an estimated arrival delay ≥10 minutes compared to the planned route, when the delay threshold is crossed, then the system recalculates and sends a new optimized route to the driver within 1 minute to minimize further delay.
Supply Hub Reroute Calculation
Given the need for restocking due to a detected spike or delay, when recalculating routes, then the system generates the top three nearest supply hubs sorted by shortest travel time and presents them to both the dispatcher and driver.
Driver Alert Acknowledgement
Given a route adjustment alert is sent, when the driver receives the alert, then the driver must acknowledge or reject the new route within 2 minutes, and the system logs the response and notifies the dispatcher of the outcome.
Multi-Stop Delivery Optimization
Given multiple pending deliveries and low inventory detected mid-route, when inventory drops below threshold, then the system suggests reassigning select deliveries to another truck, optimizes both routes, and sends alerts to both drivers within 2 minutes.
Historical Trend Analysis for Thresholds
"As a product manager, I want historical trend analysis to set dynamic alert thresholds so that spike detection is tailored to each truck's demand patterns."
Description

Implement a data analytics component that processes historical sales and delay records to establish baseline thresholds for spike detection. The component should automatically adjust alert sensitivity for each truck based on past performance, seasonal trends, and location-specific demand patterns.

Acceptance Criteria
Initial Threshold Computation for New Truck
Given a truck has at least 30 days of historical sales and delay records When the analytics component executes Then it computes and stores baseline thresholds for spike detection for that truck
Seasonal Trend Recalibration Pre-Event
Given one month of data preceding a major seasonal event When the component processes historical sales Then sensitivity thresholds adjust by at least 20% compared to non-event periods
Location Demand Pattern Adaptation
Given a truck relocates to a new venue with at least 14 days of demand data When thresholds are recalculated Then new thresholds reflect average daily sales within a 10% variance of the latest week’s actuals
Weekly Threshold Update Job Execution
Given a scheduled weekly batch at 02:00 UTC every Monday When the job runs Then thresholds for all trucks are updated, persisted, and a success log entry is generated
False Positive Rate Control in Alert Generation
Given test data with normal sales fluctuations When thresholds are applied Then no alerts are triggered over 30 days indicating correct sensitivity settings
Customizable Alert Preferences
"As an operator, I want to customize alert preferences so that I only receive relevant notifications at desired channels and times."
Description

Provide a settings interface allowing users to configure alert channels, severity levels, frequency, and time windows per truck or operator. Users should be able to customize thresholds for spikes and delays and select preferred notification methods to reduce noise and improve relevance.

Acceptance Criteria
Sales Spike Threshold Configuration
Given the user opens the alert preferences for a specific truck, when they set the sales spike threshold to 50 orders per hour and save, then the threshold value is persisted and displayed correctly on the preferences screen.
Delay Alert Severity and Channel Selection
Given the user configures delay alerts in the settings, when they select 'High' severity and choose email and SMS channels and click Save, then alerts for delays exceeding the defined threshold are sent via both email and SMS with a high-priority flag.
Alert Frequency Scheduling
Given the user sets the alert frequency to no more than one notification per hour for sales spikes, when consecutive spike events occur within 30 minutes, then only the first alert is delivered and subsequent alerts are suppressed until the next hour window.
Time Window Restriction Setup
Given the user limits alert delivery to between 08:00 and 18:00 for a truck, when a spike or delay event occurs at 19:00, then no alert is sent and the event is logged for review in the dashboard.
Default Preferences for New Trucks
Given an operator adds a new truck to the system, when default alert preferences are applied, then the truck inherits the global default thresholds, channels, severity levels, frequency, and time windows as defined in the operator's settings.

FlavorPulse Insights

Visualizes real-time sales spikes for individual menu items, enabling operators to quickly identify emerging taste trends and capitalize on them before competitors catch on.

Requirements

Real-Time Data Streaming
"As a food truck operator, I want to see sales transactions update in real time so that I can monitor item performance instantly and make data-driven decisions on the fly."
Description

Ingest live sales transactions from each truck and transmit them to FlavorPulse Insights with a maximum end-to-end latency of 2 seconds. The streaming pipeline should leverage a robust, scalable messaging system (e.g., Kafka or WebSockets) to ensure data consistency and reliability even under peak loads. This capability enables operators to see up-to-the-moment sales figures, forming the real-time backbone of trend analysis and visualization.

Acceptance Criteria
Initial Sales Data Ingestion
Given a sales transaction occurs on Truck A, when the transaction is recorded, then the event must be received by FlavorPulse Insights within 2 seconds.
Peak Hour Traffic Handling
Given 500 sales transactions per minute across all trucks, when events are ingested, then no transaction should be delayed more than 2 seconds and system error rate should not exceed 0.1%.
Network Interruption Recovery
Given a temporary network outage lasting up to 30 seconds, when connectivity is restored, then all buffered sales events must be delivered to FlavorPulse Insights in order and within 60 seconds of restoration.
Scalable Throughput Validation
Given the system scales from 5 to 50 concurrent trucks, when streaming throughput increases, then the latency for each event should remain at or below 2 seconds.
Data Consistency Across Trucks
Given multiple trucks report identical menu item sales concurrently, when those events arrive, then the total count for that item in FlavorPulse Insights must exactly match the sum of all individual events within 2 seconds.
Trend Spike Detection Engine
"As a food truck operator, I want the system to automatically identify sudden increases in sales for individual items so that I can capitalize on trending flavors before they peak."
Description

Develop an analytics engine that continuously processes incoming sales data to detect statistically significant spikes in item sales. The engine should use configurable thresholds and moving averages to identify emerging trends, triggering downstream visualization and notifications. It must support dynamic threshold adjustments by the user and handle multivariate inputs such as volume, time of day, and location.

Acceptance Criteria
Real-Time Sales Spike Detection
Given real-time sales input and a configured threshold, when an item's sales within the defined time window exceed the moving average by the threshold, then the engine must flag the spike and record the event within 2 seconds.
Dynamic Threshold Configuration
Given the user accesses threshold settings, when a new threshold value is entered and saved, then the system must apply the updated threshold to all subsequent spike detections and confirm the change in the UI.
Spike Notification Delivered to Dashboard
Given a spike is detected, when the detection event occurs, then the dashboard must display an alert containing item name, timestamp, and percentage increase within 5 seconds.
Trend Visualization Update
Given new spike data, when the user views the trend chart, then the visualization must highlight the spike point and adjust the trend line to reflect the updated moving average.
Multivariate Data Handling
Given sales data including volume, time of day, and location, when detecting spikes, then the engine must evaluate all variables and only trigger when combined metrics exceed their respective thresholds.
Interactive Trend Visualization Dashboard
"As a food truck operator, I want to visually explore sales trends for each menu item so that I can quickly spot when demand for a flavor is rising."
Description

Implement a user interface component within the SnackFleet dashboard that graphically displays real-time and historical sales trends for menu items. The visualization should include timeline charts, peak markers, and color-coded highlights of spikes. Users should be able to hover for tooltips, zoom, and pan through data. The component must be responsive across devices and integrate seamlessly with existing dashboard modules.

Acceptance Criteria
Real-Time Trend Visualization
Given the dashboard is connected to the live sales data stream, when a sale occurs for any menu item, then the timeline chart updates within 2 seconds to reflect the new data point.
Historical Trend Navigation
Given a user selects a date range filter, when the filter is applied, then the chart displays data points exclusively within the selected range and rescales the axes accordingly.
Peak Marker Highlighting
Given sales data is displayed, when the sales for a menu item exceed its historical average by 25% within a defined period, then a distinct peak marker appears at the corresponding timestamp with a red highlight.
Tooltip Data Accuracy
Given a user hovers over a data point, when the hover event is detected, then within 0.5 seconds a tooltip appears displaying the accurate menu item name, timestamp, and sales count.
Zoom and Pan Interaction
Given the chart is visible, when the user performs a pinch-zoom gesture or click-and-drag, then the chart zooms in/out or pans smoothly, preserving data resolution and clear axis labels.
Responsive Layout Adaptation
Given the dashboard is loaded on any device with a viewport width between 320px and 1920px, when the component renders, then the visualization layout adjusts dynamically without overlapping or clipping elements.
Customizable Alerts & Notifications
"As a food truck operator, I want to receive alerts when an item’s sales spike so that I can adjust inventory and marketing efforts immediately."
Description

Enable operators to configure real-time alerts when specific items experience sales spikes beyond defined thresholds. Notifications should be deliverable via email, SMS, and in-app messages. The configuration interface must allow users to set threshold values, notification channels, and quiet hours. The notification service should batch alerts intelligently to avoid spamming during high activity periods.

Acceptance Criteria
High-Volume Spike Alert Configuration
When an item's sales spike by more than the user-defined threshold within a 10-minute window, the system sends a batched notification via the selected channels, with no more than one notification per 30 minutes per item.
Quiet Hours Suppression
If a sales spike occurs during user-configured quiet hours, the system suppresses notifications until quiet hours end and then aggregates all missed alerts into a single batched message.
Channel Preference Enforcement
Notifications are delivered only through channels that the user has enabled for that specific alert type, and disabled channels receive no messages.
Threshold Update Propagation
When a user updates the spike threshold for an item, subsequent spikes respect the new threshold immediately, and no notifications are sent based on the old threshold after the update.
Multi-Truck Alert Distribution
For a user managing multiple trucks, spike alerts for items sold across more than one truck are merged into a single notification summarizing total spikes per item, delivered via each enabled channel.
Data Filtering and Drill-Down
"As a food truck operator, I want to filter sales trends by location and time period so that I can understand which items perform best on specific routes and days."
Description

Provide filtering functionality that allows operators to refine trend analysis by date range, location, menu category, and specific items. The drill-down feature should enable users to click on a spike event to view detailed transaction-level data, including time stamps, quantities, and order sources. Filters should be multi-selectable and persist across sessions for user convenience.

Acceptance Criteria
Date Range Filtering
Given the operator is viewing the FlavorPulse dashboard, When they select a start and end date in the date range filter, Then the sales spike visualization updates to display only data within the specified date range.
Location-Based Filtering
Given the operator has multiple food truck locations available, When they multi-select one or more locations in the location filter, Then the trend analysis refreshes to include sales data only from the selected locations.
Menu Category Filtering
Given the operator is analyzing flavor trends, When they choose one or more menu categories from the category filter, Then the dashboard updates to show spikes only for items within the selected categories.
Item-Specific Drill-Down
Given a spike event appears on the sales chart, When the operator clicks on the spike, Then a drill-down view opens displaying transaction-level details including time stamps, quantities sold, and order sources for that event.
Session Persistence of Filters
Given the operator applies filters (date range, location, category, items) and closes their session, When they log back into the dashboard, Then all previously applied filters remain active and visible.

MenuAlchemy Proposals

Generates AI-driven recommendations for adding, removing, or bundling dishes based on item-specific performance, helping operators optimize their menu for maximum appeal and profitability.

Requirements

Historical Sales Aggregation
"As a food truck operator, I want the system to automatically gather and consolidate my past sales data so that I can trust the accuracy of menu performance insights."
Description

Collect, normalize, and store historical sales data from each truck in real time, including item-level sales, timestamps, and contextual details such as location and event. This pipeline supports accurate performance analysis and recommendation generation by integrating with existing inventory and order management modules, providing the AI engine with clean, up-to-date data and ensuring consistency across the dashboard.

Acceptance Criteria
Real-Time Peak Hour Data Ingestion
Given simultaneous sales from at least 10 trucks during peak hours When the data pipeline receives sales events Then all events are ingested into the central store within 2 seconds per event with 99.9% success rate
Cross-Truck Data Normalization
Given disparate item identifiers across trucks When data is normalized Then each sale record uses standardized item codes matching the master catalog with zero mismatches
Contextual Metadata Capture
Given a sales transaction includes location and event tags When stored Then each record contains timestamp, GPS coordinates, and event ID fields populated and valid
Historical Data Reconciliation
Given the pipeline restarts or recovers from failure When reconnecting to data sources Then all missed sales events from downtime are ingested and no duplicate records exist
Inventory Integration Consistency
Given new sales data is written When inventory module queries sales Then it returns up-to-date aggregated item counts reflecting all transactions in real-time
Dish Performance Analysis
"As a food truck operator, I want to see clear performance metrics for each menu item so that I can understand which dishes drive my revenue and which need adjustment."
Description

Calculate key performance metrics for each menu item—such as sales volume, profit margin, and popularity trends—over configurable time windows. The analyzer identifies underperforming and high-potential dishes, enabling the AI engine to make data-driven recommendations for additions, removals, and bundles. It integrates seamlessly with the data pipeline and supports real-time updates on the operator’s dashboard.

Acceptance Criteria
Sales Volume Calculation for Menu Items
Given the operator selects a menu item and a date range, When the system processes sales records for that item within the date range, Then the dashboard displays the total units sold, matching the sum of individual orders.
Profit Margin Computation Scenario
Given cost and price data are available for each menu item, When the system analyzes transactions over the configured time window, Then the dashboard shows profit margin percentages for each item, calculated as (revenue – cost) / revenue.
Popularity Trend Identification Scenario
Given historical sales data for all menu items, When the operator views the trend report for a selected time period, Then items are ranked by week-over-week sales change, and trending items are highlighted correctly.
Underperforming Dish Detection Scenario
Given performance thresholds are defined, When the analyzer scans menu items over the specified window, Then any item falling below the thresholds for sales volume or profit margin is flagged as underperforming in the report.
Real-time Dashboard Integration Scenario
Given new order data arrives in the pipeline, When the data pipeline updates the analyzer, Then the dashboard reflects updated performance metrics within five seconds of data arrival.
AI Recommendation Engine
"As a food truck operator, I want AI-driven suggestions for menu changes so that I can optimize my offerings for maximum profitability with minimal manual analysis."
Description

Leverage machine learning models to generate recommendations for adding, removing, or bundling dishes based on item performance, seasonal trends, and customer preferences. The engine produces ranked proposals with confidence scores and rationale, integrating with the performance analyzer and delivering context-aware suggestions via the dashboard API to ensure recommendations remain current.

Acceptance Criteria
Initial Recommendations on Dashboard Load
Given the operator logs into the dashboard and selects the MenuAlchemy Proposals tab When data from the performance analyzer is fetched Then the engine generates and displays the top 5 add/remove/bundle proposals with confidence scores and rationales within 5 seconds
Seasonal Trend-Based Menu Suggestions
Given the current date falls within a defined seasonal period When generating recommendations Then the engine incorporates seasonal trend data and ranks at least 3 menu suggestions aligned with seasonal performance with confidence scores above 70%
High-Confidence Bundling Proposals
Given multiple high-performing items with complementary profiles When analyzing performance data Then the engine recommends at least two bundling options with confidence scores above 80% and includes rationale based on cross-item sales correlations
Low-Performing Item Removal Recommendations
Given dishes that have sold below 10 units in the past month When proposing items to remove Then the engine flags these items with a confidence score above 60% and provides rationale related to poor sales performance
Real-Time Update on Performance Data
Given updated sales data is received through the dashboard API When the operator requests fresh recommendations Then the engine recalculates proposals within 3 seconds and updates displayed recommendations without page reload
Recommendations Dashboard Integration
"As a food truck operator, I want an intuitive dashboard where I can review and apply menu suggestions so that I can quickly implement changes without navigating away from my management console."
Description

Design and implement a dashboard component that displays AI-generated menu proposals, including recommended actions, projected impact, and interactive controls to accept, modify, or schedule changes. The component must align with SnackFleet’s UI standards and support filtering, sorting, and exporting recommendations to ensure operators can easily review and apply suggestions within their management console.

Acceptance Criteria
Viewing AI Recommendations on Dashboard
Given the operator navigates to the Recommendations Dashboard, when the AI proposals have been fetched, then each proposal displays the dish name, recommended action, projected impact percentage, and corresponding action buttons (Accept, Modify, Schedule).
Filtering and Sorting Recommendations
Given AI-generated proposals are displayed, when the operator applies a filter by projected impact greater than 15% and sorts by dish popularity descending, then only proposals meeting the filter are shown in sorted order.
Interacting with Recommendation Controls
Given a displayed proposal, when the operator clicks Accept, then the proposal status changes to Approved and updates the pending changes list; when the operator clicks Modify, then a modal opens allowing adjustment of recommended parameters; when the operator clicks Schedule, then a date picker appears and schedules the change on the selected date.
Exporting Proposals to CSV
Given the operator is viewing the Recommendations Dashboard, when the operator clicks the Export button, then a CSV file downloads containing all current proposals with their details (dish name, action, projected impact, date scheduled).
Performance Under Peak Load
Given 500 concurrent users access the Recommendations Dashboard, when proposals are loaded, then the dashboard fully renders within 2 seconds and all interactive controls respond within 500ms.
Continuous Feedback Loop
"As a food truck operator, I want to give feedback on AI suggestions so that the system learns my unique style and improves future recommendations."
Description

Enable operators to provide feedback on recommendations—approve, reject, or modify—and capture subsequent sales outcomes. This feedback is fed back into the AI model to support continuous learning and accuracy improvement. The loop integrates user actions and real-world results, ensuring the recommendation engine evolves based on operator preferences and performance.

Acceptance Criteria
Approval of AI Recommendation
Given a list of AI-generated menu recommendations for a specific food truck, when the operator clicks 'Approve' on a recommendation, then the system records the approval action with operator ID and timestamp and queues the recommendation for performance tracking.
Rejection of AI Recommendation
Given an AI-generated recommendation presented to the operator, when the operator selects 'Reject' and provides an optional rejection reason, then the system logs the rejection with reason, operator ID, and timestamp and excludes the recommendation from similar future suggestions.
Modification of AI Recommendation
Given an AI-generated suggestion, when the operator edits the proposed dish attributes (e.g., price, ingredients, bundle options) and saves the changes, then the system records the modified recommendation, links it to the original recommendation ID, and stores the details for model retraining.
Sales Outcome Feedback Integration
Given approved or modified recommendations, when sales data is collected over the defined evaluation period, then the system associates each recommendation with actual performance metrics (e.g., units sold, revenue) and updates the feedback database.
Model Retraining Trigger
Given new feedback entries from operator actions and sales outcomes, when the feedback database reaches the retraining threshold (e.g., 100 entries or weekly schedule), then the system automatically initiates the AI model retraining process and notifies the operator upon completion.

ForecastFlavor Predictor

Leverages historical and live sales data to predict next-day and next-week demand for each dish, allowing proactive ingredient ordering and staffing to meet peak demand without overstocking.

Requirements

Historical Sales Data Ingestion
"As a data engineer, I want to import and process historical sales data so that the forecasting model has sufficient past information to learn demand patterns."
Description

Implement a robust data pipeline that automatically retrieves, cleans, and stores historical sales records from all food trucks, ensuring data consistency and completeness. This pipeline must handle various data formats, perform data validation, and integrate seamlessly with the central data warehouse to provide reliable inputs for demand forecasting.

Acceptance Criteria
Retrieval of Historical Sales Data
Given the data pipeline is scheduled to run, When it connects to each truck's sales database or storage endpoint, Then it successfully retrieves all sales records for the specified date range without errors.
Data Format Handling
Given retrieved sales files of varying formats (CSV, JSON, XML), When the pipeline processes these files, Then it correctly identifies the format, parses the content, and transforms it into the standardized internal schema.
Data Validation and Cleaning
Given parsed sales records, When the pipeline validates the data, Then it detects and removes duplicate entries, fills or flags missing mandatory fields, and normalizes date and currency fields according to company standards.
Integration with Central Data Warehouse
Given cleaned and standardized sales data, When the pipeline loads the data, Then all records are inserted or updated in the central data warehouse's 'historical_sales' table matching the defined schema, with no data loss or schema mismatches.
Monitoring and Alerting for Pipeline Failures
Given the ingestion pipeline encounters an error during any stage, When the error occurs, Then the system logs the detailed error message and sends an alert notification to the DevOps team within 5 minutes.
Retry Mechanism on Ingestion Failures
Given a transient error occurs during data retrieval or loading, When the pipeline run fails, Then the system automatically retries the operation up to 3 times with exponential backoff before marking the task as failed.
Real-Time Sales Data Connector
"As an operations manager, I want real-time sales data streaming so that demand predictions reflect the latest trends and sudden spikes."
Description

Develop a live integration module that streams current sales transactions and inventory changes from each truck to the forecasting system in real time. The connector should support low-latency updates, error handling, and retry mechanisms to maintain continuous data flow and model accuracy.

Acceptance Criteria
Stream Initialization on Startup
Given the Real-Time Sales Data Connector is launched, when the module initializes, then it must establish a connection to all registered food trucks’ data streams within 2 seconds without errors.
Continuous Low-Latency Data Streaming
Given the connector is running, when sales transactions occur or inventory levels change, then updates must be propagated to the forecasting system with a latency of no more than 500 milliseconds.
Error Detection and Logging
Given the streaming data flow is active, when any data transmission error or malformed payload is detected, then the connector must log the error with a timestamp and truck identifier and continue streaming remaining data.
Automatic Retry Mechanism
Given a failed data push to the forecasting system, when a transmission error occurs, then the connector must retry up to 5 times with exponential backoff and log each retry attempt.
Data Consistency Verification
Given data is received by the forecasting system, when comparing source and received records over any 1-minute window, then there must be a match rate of 100% for transaction IDs and inventory change events.
Predictive Analytics Engine
"As a food truck owner, I want accurate demand forecasts so that I can order ingredients and schedule staff proactively."
Description

Build and deploy a scalable machine learning engine that ingests historical and real-time data to generate next-day and next-week demand forecasts for each menu item. The engine should support multiple algorithms, automated hyperparameter tuning, and provide confidence intervals for each prediction.

Acceptance Criteria
Data Ingestion and Preprocessing
Given connected data sources, when the engine runs, then historical and real-time sales data are ingested, cleaned, and stored in the forecasting database within 5 minutes.
Next-Day Forecast Generation
Given up-to-date data, when generating next-day demand forecasts, then the engine outputs predictions for each menu item within 2 minutes and logs generation time.
Next-Week Forecast Generation
Given the latest datasets, when computing next-week forecasts, then the engine produces daily demand predictions for the upcoming week and completes execution within 5 minutes.
Automated Hyperparameter Tuning
Given model configuration changes or new data, when tuning is triggered, then automated search runs, selects optimal hyperparameters based on validation metrics, and stores results in the model registry.
Confidence Interval Reporting
Given generated forecasts, when exporting results, then each prediction includes a 95% confidence interval and is available in the dashboard and API responses.
Forecast Visualization Dashboard
"As a manager, I want a visual dashboard of demand forecasts so that I can easily interpret predictions and share insights with my team."
Description

Design an interactive dashboard within SnackFleet that displays demand forecasts, confidence ranges, and historical comparisons. Users should be able to view predictions by truck, location, and menu item, with filters for date ranges and the ability to export reports for planning purposes.

Acceptance Criteria
View Forecast by Truck and Menu Item
Given a user selects a specific truck and menu item and applies the selection, then the dashboard displays the next-day and next-week demand forecasts for that combination, including numeric forecast values and units, matching the data from the backend forecast API within a 5% tolerance.
Filter Forecasts by Date Range
Given a user sets a start date and end date in the date range filter and applies it, then the dashboard updates to show only forecasts and historical comparison data within the inclusive date range, and the selected date range remains active until cleared.
Export Forecast Reports
Given a user clicks the 'Export' button and selects CSV format, then the system generates and downloads a CSV file within 5 seconds containing the current dashboard data with columns for date, truck ID, menu item, forecast value, confidence low, and confidence high.
Visualize Forecast Confidence Ranges
When the dashboard renders forecast data, then it displays shaded confidence intervals on the forecast lines representing the low and high confidence values, with the values matching those returned by the backend API.
Compare Historical Data with Forecasts
Given a user toggles on historical data comparison, then the dashboard overlays historical sales data on the forecast chart with distinct styling and legend, aligns data points by date, and displays both forecast and actual values in the tooltip.
Automated Restocking Recommendations
"As a procurement specialist, I want automated restocking suggestions based on demand forecasts so that I can streamline ordering and minimize stockouts."
Description

Integrate forecasting outputs with the restocking system to automatically generate ingredient order suggestions based on predicted demand. Recommendations should factor in current inventory levels, supplier lead times, and minimum order quantities, and allow users to review and adjust orders before submission.

Acceptance Criteria
Initial Recommendation Generation
Given a food truck has recorded sales data for the past 30 days and current inventory levels are synced, When the ForecastFlavor Predictor runs overnight, Then the system automatically generates restocking recommendations for each ingredient including suggested quantities.
Review and Adjustment of Recommendations
Given the system generated restocking suggestions, When the user reviews the recommendations on the dashboard and modifies quantities or removes items, Then the adjusted order updates in real time and displays a summary of changes compared to the original suggestion.
Incorporate Supplier Lead Times and Minimum Order Quantities
Given each ingredient has associated supplier lead time and minimum order quantity configured, When recommendations are calculated, Then suggested order quantities respect the minimum order quantities and adjust for lead times to ensure stock arrival aligns with predicted demand dates.
Low Inventory Threshold Trigger
Given an ingredient's inventory falls below its defined low threshold during a live sales period, When the system recalculates restocking recommendations in real time, Then the order suggestion includes at least the minimum quantity required to surpass the threshold and meet predicted demand.
Successful Submission of Restock Orders
Given the user has reviewed and finalized the restocking recommendations, When the 'Submit Order' action is triggered, Then the system sends the purchase order to the designated supplier via the configured API or email and logs the order ID in the system.

CrossSell Companion

Analyzes trending main dishes to recommend high-conversion add-ons and side items, boosting average order value with data-backed upsell suggestions that resonate with customer preferences.

Requirements

Data Collection Pipeline
"As a food truck owner, I want the system to automatically capture and consolidate my sales and inventory data in real time so that upsell recommendations are based on the latest customer behavior and stock levels."
Description

Implement a robust pipeline to ingest real-time order and inventory data from all connected food trucks, normalize and store it securely in the cloud dashboard for downstream analysis. The pipeline should handle high-frequency data updates, ensure data accuracy, and provide failover mechanisms to prevent data loss during network interruptions.

Acceptance Criteria
High-frequency Data Ingestion
Pipeline ingests at least 1,000 order or inventory events per second with a maximum error rate of 1%
Data Normalization Validation
All incoming events are transformed to match the defined schema with required fields populated and no null values
Secure Storage Confirmation
Ingested data is encrypted at rest in the cloud storage and access logs are generated for each read/write operation
Failover Mechanism Activation
When network connectivity is lost, the pipeline buffers incoming data locally and resumes transmission automatically without any data loss upon reconnection
Dashboard Real-time Update
Normalized data is available and visible on the cloud dashboard within 5 seconds of ingestion
Trending Dish Analysis
"As a marketing manager, I want to see which main dishes are currently trending so that I can tailor upsell suggestions around the most popular items and maximize extra sales."
Description

Develop an analytics module that processes collected order data to identify trending main dishes across all trucks. The module should support customizable time windows (e.g., hourly, daily, weekly) and apply statistical methods to surface the top-performing main dishes that are prime candidates for upsell promotion.

Acceptance Criteria
Hourly Trending Dish Identification
Given the analytics module has received order data for the past hour, When the system processes the data with the hourly time window, Then the module lists the top 3 main dishes by order count and percentage change from the previous hour.
Daily Trending Dish Report Generation
Given the system has collected 24 hours of order data, When the daily report is requested, Then the system returns the top 5 main dishes ranked by total orders and displays the daily growth rate compared to the previous day.
Weekly Top Dishes Custom Time Frame
Given a user selects a custom 7-day window, When the module processes the selected time frame, Then it accurately identifies and displays the top 10 main dishes by order volume and highlights any dish with more than 20% week-over-week growth.
Peak-Hour Upsell Candidate Detection
Given peak sales hours are defined as 11:00–14:00, When the module runs analysis for this window, Then it outputs the top 3 dishes with highest order frequency and recommends them for upsell campaigns.
Statistical Significance Filtering
Given the analytics module applies statistical tests, When the trending dishes are calculated, Then only dishes with a p-value < 0.05 for increasing order trend are flagged as trending.
Upsell Recommendation Engine
"As a food truck owner, I want personalized add-on recommendations that are proven to convert so that I can boost my average order value without guessing which sides will sell."
Description

Build an algorithmic engine that leverages trending dish insights, historical upsell conversion rates, and customer preference patterns to generate personalized add-on and side-item suggestions. The engine should rank recommendations by predicted conversion potential and allow for continuous learning as new data arrives.

Acceptance Criteria
Personalized Suggestion for Returning Customer
Given a returning customer with at least three past orders, when they access the menu, then the engine provides at least three personalized add-on recommendations ranked by predicted conversion potential based on their purchase history.
Real-time Trending Dish Upsell Recommendations
Given the latest trending dish metrics are updated, when the engine processes the refreshed data, then the top three side items by predicted conversion potential are delivered within five minutes of the update.
Continuous Model Learning from New Data
Given new order and conversion records arrive hourly, when the engine ingests these updates, then the model auto-recalibrates without manual intervention and demonstrates a minimum 5% improvement in prediction accuracy in the next validation cycle.
Upsell Ranking Accuracy under Load
Given the system handles 1000 recommendation requests per minute, when subject to simulated high load, then at least 95% of responses return correctly ranked upsell suggestions within 500 milliseconds.
Fallback Handling for Sparse Preference Profiles
Given a customer with no prior purchase history, when they request upsell recommendations, then the engine defaults to the top three overall best-converting add-ons and logs the fallback event without errors.
Suggestion Display UI
"As a truck operator, I want to see clear, actionable upsell suggestions in my dashboard so that I can quickly apply them to my menu and communicate offers to my staff."
Description

Design and implement user interface components within the dashboard to present upsell suggestions clearly. This includes a dedicated ‘CrossSell Companion’ panel showing top recommended add-ons for each trending dish, sortable by potential uplift and inventory availability, with quick actions to accept or modify suggestions.

Acceptance Criteria
Viewing Upsell Suggestions for a Trending Dish
Given a trending main dish is selected in the dashboard, the CrossSell Companion panel displays the top five recommended add-on items, each showing name, thumbnail image, and projected uplift percentage.
Sorting Suggestions by Potential Uplift
When the user clicks the “Potential Uplift” column header in the suggestions panel, the list reorders within one second from highest to lowest predicted uplift.
Filtering Suggestions by Inventory Availability
Given current inventory levels, unavailable add-ons (stock below threshold) are either visually marked as out-of-stock or excluded when the user toggles the “Show Available Only” filter.
Accepting a Cross-Sell Suggestion
When the user clicks the “Accept” button on a suggestion, the add-on is immediately added to the current order draft and a confirmation message appears in the UI.
Modifying a Suggested Add-On Before Confirmation
When the user selects “Modify” on a suggestion, a modal opens allowing quantity adjustment between 1 and the maximum available stock, with validation errors for out-of-range quantities and an “Update” button to confirm changes.
Configuration & Customization
"As a truck owner, I want to customize which upsell suggestions I see and set my own thresholds so that recommendations align with my unique menu strategy and inventory constraints."
Description

Provide settings that allow food truck owners to customize upsell parameters, such as minimum conversion rate thresholds, maximum number of suggestions, and specific item exclusions. User preferences should be persisted per truck and override default system recommendations where specified.

Acceptance Criteria
Setting Minimum Conversion Rate Threshold per Truck
Given a truck owner sets a minimum conversion rate threshold to 15% for Truck A When the owner saves settings Then the system stores the threshold for Truck A and applies it to all subsequent upsell recommendations for that truck
Configuring Maximum Number of Upsell Suggestions
Given a truck owner defines a maximum of 3 upsell suggestions When the owner saves the configuration Then each order recommendation displays no more than 3 suggested add-on items
Excluding Specific Items from Recommendations
Given a truck owner excludes ‘Salsa Verde’ and ‘Churros’ from upsell suggestions When recommendations are generated for any order Then ‘Salsa Verde’ and ‘Churros’ are not presented as add-on options
Persistence of Custom Settings Across Sessions
Given a truck owner configures conversion rate and suggestion count for Truck B and logs out When the owner logs back in and selects Truck B’s profile Then the previously saved settings for Truck B are loaded and displayed in the configuration interface
Override Default System Recommendations with User Preferences
Given the system has default upsell parameters and a truck owner customizes thresholds and exclusions for Truck C When recommendations are generated for Truck C Then system uses the truck owner’s custom parameters instead of default values
Performance Analytics Reporting
"As a business analyst, I want detailed reports on upsell performance metrics so that I can measure ROI and refine recommendation strategies over time."
Description

Create reporting features that track and visualize the effectiveness of implemented cross-sell suggestions, including metrics such as suggestion acceptance rate, additional revenue generated, and month-over-month upsell growth to help owners evaluate feature impact.

Acceptance Criteria
Daily Cross-Sell Effectiveness Overview
Given the owner views today’s cross-sell performance dashboard, when the dashboard loads, then it displays total suggestions made, number of accepted suggestions, acceptance rate (as a percentage), and additional revenue generated for the current day, with all data refreshed within 2 seconds.
Revenue Impact Dashboard Access
Given the owner selects the Revenue Impact section in the reporting feature, when the section loads, then it presents a chart comparing upsell revenue against baseline revenue, with accurate figures for at least the past 30 days and tooltips showing exact values for each data point.
Month-over-Month Upsell Growth Trend
Given the owner toggles to the Month-over-Month Growth view, when the view renders, then it shows percentage change in upsell revenue for each month over the last 12 months, highlights months with positive growth in green and negative growth in red, and allows exporting the data as CSV.
Suggestion Acceptance Rate Drill-Down
Given the owner clicks on the acceptance rate metric, when the drill-down opens, then it lists top 10 menu items by suggestion acceptance rate, including item name, times suggested, times accepted, and acceptance percentage, sorted descending.
Custom Date Range Report Generation
Given the owner enters a custom start and end date and clicks Generate Report, when the report completes, then it displays all cross-sell metrics (acceptance rate, additional revenue, suggestion count) for the specified period, and allows downloading the report in PDF and Excel formats.

LocalFlavor Mapper

Maps taste trends by location, time of day, and event type, helping operators tailor menus to specific customer demographics and optimize offerings for each stop on their route.

Requirements

Geo-Tagged Sales Tracking
"As a food truck operator, I want each sale to be automatically tagged with GPS coordinates so that I can identify which locations favor specific menu items."
Description

Captures and tags each order with precise GPS coordinates and time stamp, aggregating location-specific sales data. Data is synced to the cloud in real time, enabling operators to visualize where specific menu items perform best. Integration with SnackFleet’s inventory system ensures data accuracy and minimal manual input.

Acceptance Criteria
Real-Time Geo-Tagged Order Capture
Given a customer completes an order on a food truck device When the order is finalized Then the system records the exact GPS coordinates and timestamp with the order data
Offline Mode Data Queuing
Given the device loses network connectivity When orders are placed during offline periods Then each order is queued locally with GPS coordinates and timestamp and automatically synced when connectivity is restored
Sales Data Synchronization and Aggregation
Given multiple food trucks are operating simultaneously When orders are tagged and synced to the cloud Then the system aggregates location-specific sales data in real time without data loss or duplication
Location-Specific Sales Visualization
Given an operator views the dashboard When selecting a specific menu item Then the heatmap displays the number of orders and revenue by geographical grid cell for that item
Inventory Adjustment Based on Geo-Tagged Sales
Given a geo-tagged sale is recorded When the order data is synced Then the inventory levels for the corresponding truck are automatically decremented and reflected in the dashboard
Time-Segmented Taste Profiling
"As a food truck operator, I want to see which menu items are most popular during different times of the day so that I can schedule inventory restocking and staff shifts more effectively."
Description

Analyzes sales data over defined time intervals (e.g., morning, lunch, dinner) to detect temporal taste trends. Allows users to view popular items by time of day and adjust menu planning and inventory restocking accordingly. Integrates with the LocalFlavor Mapper dashboard to present time-based filters.

Acceptance Criteria
Morning Rush Analysis
Given historical sales data for the morning interval (6:00 AM – 10:00 AM), When the user selects 'Morning' in the time-of-day filter on the LocalFlavor Mapper dashboard, Then the system displays a ranked list of the top 5 best-selling menu items with accurate sales counts and percentage change from the previous day's morning period.
Lunch Peak Popularity
Given real-time and historical sales data between 11:00 AM – 2:00 PM, When the user applies the 'Lunch' time filter, Then the dashboard updates to show the top 5 most popular items, including total units sold, revenue generated, and comparison to the prior week's same time slot.
Dinner Trend Detection
Given consolidated sales data for the dinner interval (5:00 PM – 9:00 PM), When the operator views the 'Dinner' segment, Then the system highlights emerging trends by listing items with the highest growth rate in sales (>10% increase) and provides visual indicators for trending up or down.
Custom Interval Filter Application
Given user-defined time intervals entered (e.g., 2:00 PM – 4:00 PM), When the custom interval filter is applied, Then the system generates a chart and list of top 5 items sold within that period, with accurate sales figures and the ability to export results as CSV.
Automated Restocking Alert
Given time-segmented sales thresholds configured per item, When sales for a specific interval exceed threshold (e.g., over 50 units sold in Lunch), Then the system automatically triggers a restocking alert in the inventory dashboard, specifying item name, quantity sold, and suggested reorder amount.
Event-Based Flavor Analysis
"As a food truck operator, I want to understand which flavors perform best at different local events so that I can customize my menu when attending those events."
Description

Correlates sales patterns with local events (festivals, sports games, concerts) imported via calendar APIs. Highlights flavor preferences unique to each event type, enabling operators to tailor menu offerings for specific occasions. Ensures seamless event data integration and automatic syncing.

Acceptance Criteria
Event Data Integration
Given the operator connects the system to a calendar API, when the sync is initiated, then event details (name, type, location, start/end times) appear in the dashboard within 5 minutes.
Event Sales Correlation
Given event and sales data exist for the same date and location, when analysis is run, then the system displays sales uplift percentages for each flavor during the event compared to baseline period.
Flavor Preference Highlight
Given event-based sales correlations are calculated, when viewing an event’s detail page, then the top three flavors by sales uplift are prominently highlighted.
Automatic Syncing of Event Updates
Given an event is added, updated, or deleted in the connected calendar, when the next scheduled sync occurs, then the system reflects the changes in the event list and recalculates flavor analysis accordingly.
Tailored Menu Recommendation
Given event-specific flavor preferences are identified, when generating menu suggestions for an event, then the system recommends at least four items aligned with the top flavors for that event.
Demographic Preference Segmentation
"As a food truck operator, I want to view taste preferences by demographic segments so that I can develop targeted menu items for different customer groups."
Description

Segments taste trends by demographic groups based on optional customer surveys and third-party data sources. Provides insights into age, gender, and dietary preferences, helping operators refine menu offerings and marketing strategies. Incorporates privacy-compliant data handling procedures.

Acceptance Criteria
Survey-based Age Segmentation
Given the operator uploads customer survey data including age, when the system processes the data, then it segments taste trends by defined age groups (e.g., 18-24, 25-34, etc.) and displays the results in the LocalFlavor Mapper dashboard.
Gender Demographic Analysis
Given integrated third-party demographic data, when the system synchronizes overnight, then it categorizes customer taste preferences by gender and populates separate trend visualizations in the dashboard.
Dietary Preference Integration
Given customers submit dietary preferences via optional surveys, when the system aggregates incoming data, then it correctly tags and filters taste trend results by dietary categories (e.g., vegan, gluten-free) for each location segment.
Privacy-Compliant Data Handling
Given collection of customer demographic data, when storing or processing this data, then the system anonymizes personal identifiers and encrypts all sensitive fields in compliance with GDPR and CCPA requirements.
Real-Time Trend Update
Given real-time demographic data streams, when new segmented data arrives, then the LocalFlavor Mapper dashboard updates all relevant demographic trend graphs within 2 minutes without manual refresh.
Menu Recommendation Engine
"As a food truck operator, I want the system to recommend menu items based on local taste trends so that I can optimize my offerings for maximum customer satisfaction."
Description

Generates AI-driven menu suggestions based on aggregated location, time, and event trend data. Recommends optimal menu item combinations to maximize sales and minimize waste. Allows operators to preview recommendations and automates inventory reorder suggestions.

Acceptance Criteria
Generating Menu Suggestions for a Lunch Event at a Music Festival
Given a selected location tagged as “Music Festival” between 12:00–14:00, When the operator clicks “Generate Recommendations,” Then the engine must return at least three menu item combinations ranked by projected sales lift, preparation time, and waste reduction, including estimated ingredient usage and stock impact.
Previewing AI-Driven Menu Combinations
Given generated recommendations, When the operator opens the preview pane, Then the system displays the top five menu combinations with predicted sale volumes (±10%), ingredient cost, and waste metrics, and allows the operator to manually select or deselect combinations for implementation.
Automated Inventory Reorder Recommendations Post-Selection
Given the operator accepts one or more recommended combinations, When the operator confirms selection, Then the system generates a restocking draft listing items below threshold with suggested order quantities based on projected sales, vendor lead times, and current inventory, ready for review and approval.
Adapting Recommendations Based on Real-Time Sales Data
Given live sales data updates, When sales for a recommended item deviate by more than 15% from projections, Then the system recalculates and reprioritizes menu combinations within five minutes and sends a notification alert to the operator.
Handling Limited Ingredient Availability
Given an ingredient falls below its minimum threshold, When generating or updating recommendations, Then the system excludes any combinations requiring the low-stock ingredient and highlights alternative combinations that use available stock levels.

PricePulse Optimizer

Provides dynamic pricing suggestions for top-selling items, balancing revenue maximization with customer satisfaction by adjusting prices in response to real-time demand fluctuations.

Requirements

Inventory-Aware Pricing
"As a food truck owner, I want price suggestions to consider current stock levels so that I don't overprice limited items and risk unsold inventory."
Description

Integrate real-time inventory data from the cloud dashboard with the PricePulse Optimizer to adjust price suggestions based on available stock levels. By continuously syncing stock counts across multiple trucks, the system can prevent price hikes when limited quantities remain, ensuring customer trust and avoiding stock-outs. The integration leverages existing inventory APIs to fetch data at configurable intervals, calculates stock thresholds, and feeds this information into the dynamic pricing algorithm. Expected outcome: price suggestions reflect both demand and supply constraints, reducing waste and maximizing revenue without compromising satisfaction.

Acceptance Criteria
Low Stock Price Cap
Given the current stock level for an item falls below the predefined low-stock threshold When the PricePulse Optimizer generates a price suggestion Then the suggested price must not exceed the base price and should decrease by at least 5% under average or lower demand conditions.
High Stock Price Increase
Given the stock level for a bestselling item exceeds the high-stock threshold When real-time demand increases by 20% or more Then the suggested price must increase by at least 10%, ensuring supply constraints are reflected.
Sync Interval Compliance
Given the system sync interval is set to 5 minutes When no manual override occurs Then the optimizer must exclusively use inventory data no older than 5 minutes for all price calculations.
Inventory API Error Fallback
Given the inventory API returns an error or times out When fetching stock levels for price computation Then the system must use the last known valid inventory data and log an alert without interrupting price suggestions.
Multi-Truck Aggregate Pricing
Given an item is stocked across multiple trucks When one truck’s inventory depletes to zero Then the optimizer must recalculate price suggestions based on aggregated stock from the remaining trucks and notify the user of the out-of-stock truck.
Demand Forecasting Module
"As a food truck owner, I want the system to forecast demand so that price suggestions anticipate peak periods and low traffic times."
Description

Develop a demand forecasting engine that analyzes historical sales data, time of day, location traffic patterns, and external factors such as weather to predict short-term demand for top-selling items. The module uses statistical models and machine learning algorithms to generate demand curves, which feed into the PricePulse Optimizer. Integration with the data warehouse ensures scalable processing of large datasets. The outcome is improved pricing recommendations that proactively adjust to expected customer flow, maximizing revenue potential while ensuring competitive pricing.

Acceptance Criteria
Scheduled Route Demand Forecast Generation
Given historical sales data for a specific food truck route when the demand forecasting module runs Then it generates item-level predicted unit demand for the next 7 days within 120 seconds and stores the results in the data warehouse
Weather-Adjusted Demand Forecast
Given real-time weather data indicating adverse conditions when the forecasting module executes Then forecasted demand for each top-selling item is adjusted by historical weather impact factors within ±5% accuracy
High-Volume Data Processing
Given 30 days of sales history across 100 trucks (≥1 million records) when performing batch forecasting Then the module completes processing within 300 seconds with no errors and CPU usage stays below 80%
Forecast Accuracy Validation
Given actual sales results for completed time windows when comparing against predicted values Then the mean absolute percentage error (MAPE) is ≤10% for at least 80% of the top-selling items
Integration with PricePulse Optimizer
Given generated demand curves when sending data to the PricePulse Optimizer API Then the optimizer receives correctly formatted JSON (item IDs, predicted volumes, confidence intervals), returns HTTP 200, and pricing suggestions are stored in the system
Configurable Pricing Rules
"As a food truck manager, I want to set pricing rules so that the automated suggestions adhere to my profitability requirements."
Description

Implement a user interface allowing administrators to define pricing rules, including minimum margin, maximum price, time-based adjustments, and item-specific constraints. The pricing rules engine should validate and enforce these constraints when generating dynamic price suggestions, ensuring compliance with business policies and preventing undesirable price swings. Administrators can create, modify, or delete rules, with immediate effect on the PricePulse algorithm. The expected outcome is increased control and customization of pricing strategies, aligning system recommendations with business goals.

Acceptance Criteria
Pricing Rules Interface Access
Given an administrator is logged in and navigates to the Configurable Pricing Rules page, When the page loads, Then the system displays a table of existing pricing rules with columns for rule name, parameters (minimum margin, maximum price, time window, item constraints), status, and action buttons for Create, Edit, and Delete.
Create Valid Pricing Rule
Given the administrator opens the Create Pricing Rule dialog, When they enter a valid minimum margin (e.g., 10%), maximum price (e.g., $5.00), time-based adjustment window (e.g., 08:00–10:00), and specific item constraint (e.g., Item ID 123), And clicks Save, Then the new rule is added to the rules list, persisted in the database, and immediately available for the PricePulse algorithm.
Prevent Invalid Rule Creation
Given the administrator enters invalid rule parameters (such as minimum margin greater than maximum price, negative values, overlapping time windows, or missing required fields), When they attempt to save, Then the system displays inline validation error messages for each invalid field and prevents submission until all errors are resolved.
Modify Existing Pricing Rule
Given an existing pricing rule in the list, When the administrator clicks Edit, changes one or more parameters (e.g., adjusts the maximum price or time window), and clicks Save, Then the updated rule reflects the new parameters in the UI, is saved in the database, and the PricePulse optimizer applies the changes immediately.
Delete Pricing Rule and Verify Effect
Given a pricing rule exists, When the administrator clicks Delete and confirms the action, Then the rule is removed from the list, deleted from the database, and the PricePulse algorithm no longer considers it when generating price suggestions.
Interactive Price Suggestion Dashboard
"As a food truck operator, I want to review and adjust suggested prices directly in the dashboard so that I can balance automated recommendations with my market knowledge."
Description

Create a dashboard interface within SnackFleet where users can view, compare, and accept or override dynamic pricing suggestions in real time. The dashboard displays current sales metrics, suggested price changes, projected revenue impact, and historical performance of past adjustments. Users can filter by truck, item category, or time window, and record manual overrides for audit purposes. Integration with the POS system ensures that accepted suggestions propagate immediately to checkout terminals. The outcome is enhanced transparency and user engagement with pricing controls.

Acceptance Criteria
Filter and View Pricing Suggestions by Truck
Given the user opens the Price Suggestion Dashboard and selects “Truck A” from the truck filter, When the filter is applied, Then only items and suggested price changes for Truck A are displayed within 2 seconds.
Apply Suggested Price Change to POS
Given the user reviews a suggested price change and clicks “Accept” on an item, When the user confirms the acceptance, Then the new price is pushed to the POS system and reflected at checkout within 30 seconds.
Override Suggested Price with Manual Input
Given the user views a suggested price change and clicks “Override”, When the user enters a custom price and saves, Then the manual price is applied to the item, recorded in the audit log, and overrides the suggestion in real time.
Review Projected Revenue Impact
Given the user selects a time window and item category, When the dashboard recalculates projections, Then the projected revenue impact of all accepted and pending price changes is displayed accurately, with values matching backend calculations within a 1% margin of error.
Audit Price Override History
Given the user navigates to the override history view and selects a date range, When the history is fetched, Then all manual overrides are listed with timestamp, user ID, original price, suggested price, and overridden price, sortable and downloadable as a CSV report.
Alert and Notification System
"As a food truck owner, I want to receive alerts if suggested prices exceed my set limits so that I can take timely action to prevent customer dissatisfaction."
Description

Design an alert mechanism to notify users when dynamic pricing suggestions exceed predefined thresholds, when demand forecasts change drastically, or when automated price updates fail. Notifications should be delivered via email, SMS, and in-app messages, with configurable alert levels (info, warning, critical). The system logs all alerts and allows users to acknowledge or dismiss them. This ensures timely attention to pricing anomalies and promotes proactive management of pricing strategies. The expected outcome is reduced risk of pricing errors and improved responsiveness to market changes.

Acceptance Criteria
Threshold Exceeded Notification
Given a dynamic pricing suggestion exceeds a user-defined threshold, When the system detects this, Then it sends notifications via email, SMS, and in-app, And each notification contains the item name, current price, threshold value, and timestamp, And the alert level is set to 'warning' if the threshold exceedance is less than 10% and 'critical' if greater than or equal to 10%.
Demand Forecast Change Alert
Given the demand forecast for a top-selling item changes by more than 20% compared to the previous forecast, When the new forecast is published, Then the system pushes alerts via the user’s selected channels, And the alert message includes the percentage change, item details, forecast period, and a recommended action, And the alert level defaults to 'info'.
Automated Update Failure Alert
Given an automated price update fails due to a system error or network issue, When the failure is detected, Then the system sends notifications via email and in-app within two minutes, And sends an SMS if the user has critical alerts enabled, Including error code, attempted price, item ID, timestamp, and retry count in the notification.
User Acknowledgement and Dismissal
Given an in-app alert is displayed, When the user selects 'Acknowledge' or 'Dismiss', Then the system records the user action with user ID and timestamp, And removes dismissed alerts from the active alert list so they no longer appear on the dashboard.
Alert Logging Verification
Given any alert is generated, When the audit logs are reviewed by an administrator, Then logs include alert type, alert level, delivery channels used, full payload content, user acknowledgments or dismissals with timestamps, And logs are retained for at least 30 days in compliance with retention policy.

ExpirySync

Automatically aggregates ingredients across all trucks based on nearest expiry dates, creating synchronized batch orders that minimize waste and ensure optimal usage before expiration.

Requirements

Expiry Data Aggregation Engine
"As a food truck operator, I want the system to automatically gather and sort all ingredient expiry dates in real time so that I can always know which items need to be used or reordered first."
Description

Develop a core processing engine that continuously collects and consolidates expiration date information for all ingredients across every food truck in real time. This engine will normalize data from various inventory inputs, apply business rules to determine nearest expiries, and output a unified dataset for further processing. It ensures accurate, up-to-date tracking of perishable stock, reducing risk of spoilage and waste.

Acceptance Criteria
Continuous Real-Time Data Ingestion
Given inventory updates from any truck, when data is sent to the engine, then it is ingested and acknowledged within 2 seconds 95% of the time.
Expiry Date Format Normalization
Given expiry dates in diverse formats (e.g., MM/DD/YYYY, DD-MM-YYYY), when processed by the engine, then all dates are normalized to ISO 8601 format without errors.
Nearest Expiry Identification
Given normalized expiry dates for ingredients across trucks, when business rules are applied, then the engine correctly flags the top 10 ingredients approaching expiration in ascending order of days to expiry.
Unified Dataset Output
Given processed and flagged expiry data, when the dataset is exported for batch order creation, then it includes truck IDs, ingredient names, quantities, and days until expiry and matches the master data schema 100%.
Error Handling and Alerting
Given any data ingestion or normalization failure, when an error occurs, then the engine logs the error with a timestamp and triggers an alert notification to the system admin within 1 minute.
Expiry Threshold Alerts
"As a truck manager, I want to receive alerts when ingredients are close to expiring so that I can plan menus or reorder stock before waste occurs."
Description

Implement configurable alert thresholds that notify users when ingredient batches approach their expiration date. Users can define warning periods (e.g., 3 days before expiry) and receive push notifications and dashboard alerts. This feature helps prevent spoilage by prompting timely usage or restocking decisions.

Acceptance Criteria
Configure Alert Threshold for Ingredient Expiry
Given the user navigates to alert settings, when they input a warning period value between 1 and 30 days and click Save, then the system stores the threshold and displays a confirmation message.
Receive Push Notification When Threshold Reached
Given an ingredient batch approaches the user-defined warning period, when the threshold is reached, then the system sends a push notification to all registered devices within 5 minutes.
Display Expiry Alerts on Dashboard
Given there are ingredient batches within their warning period, when the user views the dashboard, then the system highlights these batches in the alerts panel sorted by nearest expiry date.
Aggregate Alerts Across Multiple Trucks
Given the same ingredient batch across multiple trucks reaches its warning period, when thresholds are reached, then the system groups these into a single summary alert listing each truck and batch.
Validate Custom Warning Period Inputs
Given the user enters a warning period outside the allowed range or non-numeric characters, when they attempt to save, then the system prevents submission and displays a validation error message.
Batch Order Scheduler
"As a food truck operator, I want the system to schedule bulk orders for soon-to-expire ingredients so that I minimize waste and maintain supply levels without manual calculation."
Description

Create a scheduling module that automatically generates synchronized batch orders based on aggregated expiry data. It will calculate optimal order quantities to use near-expiry ingredients across trucks, group items into single purchase orders, and schedule order submission dates. This reduces ordering overhead and ensures perishable items are utilized efficiently.

Acceptance Criteria
Scheduling Batch Orders for Imminent Expiry
Given ingredients across trucks have expiry dates within the next 7 days, when the Batch Order Scheduler runs at the scheduled time, then a single batch order per supplier is generated grouping all expiring items with quantities equal to the sum needed to consume existing inventory before expiry.
Grouping Items from Multiple Trucks
Given two or more trucks require the same ingredient nearing expiry, when the system consolidates orders, then the scheduler merges them into one line item per supplier and reflects the combined quantity across all trucks.
Adjusting Order Quantities Based on Consumption Rates
Given historical consumption data is available, when calculating order quantities for expiring ingredients, then the scheduler orders only as much as will be used before expiry plus a one-day usage buffer.
Handling Supplier Stock Shortages
Given a supplier cannot fulfill part of a batch order item, when the scheduler submits the order, then the item is flagged as 'Backorder' and the user receives a notification with alternative supplier options.
Off-Hours Scheduling without Impact
Given the scheduler runs at 2:00 AM daily, when the time triggers, then batch orders are generated and queued without any performance degradation or disruption to active user sessions.
Cross-Truck Synchronization
"As a fleet manager, I want ingredient stock to be shared between trucks based on expiry dates so that I minimize overall waste and reduce unnecessary supplier orders."
Description

Enable cross-truck coordination by linking expiration and order data across multiple vehicles. When one truck has surplus nearing-expiry stock and another needs the same ingredient, the system will recommend transfers before creating purchase orders. This feature optimizes resource allocation, reduces external orders, and cuts logistic costs.

Acceptance Criteria
Transfer Recommendation Trigger
Given Truck A's ingredient count for 'tomato' is ≥ 5 units and expires within 48 hours, And Truck B's same ingredient count is ≤ 3 units, When the synchronization process runs, Then the system generates a suggested transfer order for 'tomato' from Truck A to Truck B.
Inventory Surplus Detection
Given a truck has surplus quantity of an ingredient (stock > reorder threshold) nearing expiry (expiry within 72 hours), When inventory levels update, Then the system flags the surplus item and adds it to the cross-truck suggestion list.
Order Transfer Execution
Given a user approves a recommended transfer, When the user confirms the transfer in the dashboard, Then the system deducts the quantity from the donor truck, adds it to the receiving truck, and logs the transfer event.
Purchase Order Avoidance
Given a recommended transfer fulfills the receiving truck's ingredient shortfall, When the system calculates purchase requirements during restocking, Then no external purchase order is generated for that ingredient.
Expiry Date Prioritization
Given multiple eligible surplus items across trucks, When generating transfer recommendations, Then the system sorts suggestions by nearest expiry date ascending and displays top-priority transfers first.
Expiry Dashboard and Reporting
"As an owner, I want to view reports on expired items and batch orders so that I can assess waste reduction performance and optimize inventory policies."
Description

Design an interactive dashboard displaying key metrics on ingredient expirations, waste prevention, and batch order history. Include filters by truck, ingredient type, and date range. Provide downloadable reports and visual charts for performance analysis and compliance tracking. This empowers users with insights to continuously refine inventory practices.

Acceptance Criteria
Dashboard Load with Expiring Ingredients
Given the user navigates to the Expiry Dashboard When ingredients across all trucks are nearing expiry within the next 7 days Then the expiring ingredients list displays with correct ingredient names, quantities, expiry dates, and associated truck IDs
Waste Prevention Insights Filter
Given the Expiry Dashboard is visible When the user applies filters by truck 'Truck A', ingredient type 'Dairy', and date range '2025-06-01 to 2025-06-30' Then only relevant metrics for Dairy ingredients in Truck A within the specified date range are displayed, and summary counts update accordingly
Batch Order History Download
Given the user selects the 'Batch Order History' table When the user clicks the 'Download CSV' button Then the system generates and downloads a CSV file containing all batch order records within the selected date range, with correct headers and data fields
Visual Chart Rendering
Given the user views the Expiry Dashboard charts When data is loaded Then the bar chart for waste prevention metrics and line chart for expiry trends render within 2 seconds, with accurate data labels and tooltips
Custom Report Generation
Given the user sets custom report parameters (truck(s), ingredient types, date range) When the user clicks 'Generate Report' Then a PDF report is created containing summary metrics, charts, and detailed tables, formatted to A4 size and available for download
Supplier Order Integration
"As an inventory manager, I want batch orders to be automatically sent to our suppliers so that I save time and avoid manual order processing errors."
Description

Integrate with existing supplier APIs and ordering systems to automatically submit generated batch orders. Ensure secure authentication, order confirmation receipt, and error handling. This seamless connection minimizes manual order entry, accelerates restocking, and maintains data consistency between SnackFleet and supplier platforms.

Acceptance Criteria
Automated Batch Order Submission
Given a generated batch order for expiring ingredients; When the batch order is finalized; Then SnackFleet must automatically send a POST request to the supplier's order API endpoint with the correct authentication headers and order payload within 2 seconds.
Secure Supplier Authentication
Given valid supplier API credentials stored in SnackFleet; When SnackFleet requests an access token; Then the system must authenticate using OAuth 2.0, encrypt and store the token securely, and ensure the token validity period is at least 1 hour.
Order Confirmation Receipt Processing
Given a successful order submission; When the supplier API returns a confirmation response containing the supplier order ID and estimated delivery date; Then SnackFleet must update the batch order status to 'Confirmed', record the supplier order ID and delivery date, and display a confirmation notification on the dashboard within 1 minute.
API Error Handling and Retry Mechanism
Given a failure response or network timeout from the supplier API; When SnackFleet receives a 5xx error or request timeout; Then SnackFleet must retry the request up to 3 times with exponential backoff, log each attempt, and if all retries fail, change the batch order status to 'Error' and send an alert notification to the operations team.
Data Consistency Verification
Given batch orders submitted and supplier confirmations received; When inventory records are updated; Then SnackFleet's inventory database must reflect pending restock quantities that exactly match the confirmed quantities from the supplier, with zero discrepancies in ingredient counts.

BatchOptimizer

Analyzes current inventory and expiry timelines to generate the most efficient batch combinations, balancing order volumes and delivery schedules for reduced spoilage and improved cost savings.

Requirements

Data Aggregation Integration
"As a Fleet Manager, I want the system to aggregate inventory, sales, and delivery schedule data in real time so that BatchOptimizer has accurate inputs for generating efficient batch combinations."
Description

Integrate with inventory, sales, and route data sources to aggregate current stock levels, order volumes, and delivery schedules in real time, ensuring consistent formatting and availability for BatchOptimizer input.

Acceptance Criteria
Real-Time Inventory Sync
Given the inventory service updates current stock levels, When the aggregation job runs, Then the aggregated data store reflects updated stock within 1 minute using the required JSON schema.
Sales Data Formatting Consistency
Given sales records from multiple POS systems, When data is aggregated, Then all records conform to the standard schema with consistent field names, ISO 8601 date format, and accurate numeric values.
Route Schedule Data Validation
Given delivery schedules from the routing service, When integration executes, Then every schedule entry contains valid timestamps, mapped route IDs, and missing entries trigger a validation alert.
Data Source Failure Handling
Given a data source endpoint returns an error or times out, When aggregation is attempted, Then the process retries up to 3 times, logs any failures, and completes aggregation with available data without crashing.
High-Volume Data Aggregation Performance
Given peak volumes of inventory, sales, and route data, When aggregation is performed, Then the process completes within 30 seconds and utilizes no more than 75% of allocated CPU and memory resources.
Expiry Prediction Algorithm
"As a Food Truck Owner, I want precise expiration predictions for my stock so that BatchOptimizer can minimize spoilage."
Description

Implement a machine learning model to predict expiration timelines for each inventory item based on historical spoilage rates, temperature conditions, and storage durations, delivering precise forecasts to minimize waste.

Acceptance Criteria
Predict expiry upon new item entry
Given historical spoilage rates, temperature conditions, and storage durations are available for a new inventory item When the item is added to the system Then the algorithm must generate an expiry date prediction with an accuracy of ±1 day for at least 95% of test cases
Bulk inventory prediction refresh
Given a batch of 100 existing inventory items with recorded history When the user triggers a bulk expiry prediction refresh Then the system updates all expiry predictions within 2 minutes and maintains an overall prediction accuracy of 90% or higher
Expiry alerts for near-expiry items
Given predicted expiry dates for all items in stock When an item's predicted expiry date is within 48 hours Then the system must generate an alert notification to the dashboard and via email/SMS with at least 99% reliability
Integration with varied temperature logs
Given real-time temperature logs from IoT sensors for refrigerated storage When temperature readings deviate by more than 2°C from the baseline Then the model recalibrates predictions within 5 minutes and reflects updated expiry dates with no data loss
Edge case handling for missing data
Given an inventory item with incomplete historical or temperature data When the algorithm processes the item Then it returns a prediction with a confidence score, and if confidence is below 70%, flags the item for manual review
Batch Combination Engine
"As a Fleet Manager, I want BatchOptimizer to compute optimized batches that consider order sizes, delivery timings, and expiration risks so that I can reduce waste and delivery costs."
Description

Develop the core optimization engine using constraint or linear programming to generate batch combinations that balance order volumes, delivery schedules, and predicted expiration times, supporting adjustable constraints like truck capacity and delivery windows.

Acceptance Criteria
Single Truck Capacity Constraint
Given a set of orders and a truck capacity of 500 units, when the engine generates batches, then no batch exceeds 500 units in total volume. Given multiple orders of varying sizes, when batching, then the engine maximizes total utilized capacity to at least 90%.
Delivery Window Adherence
Given orders assigned delivery windows between 8 AM and 10 AM, when generating batch combinations, then all orders in a batch are scheduled within their specified windows. When an order’s window cannot be met, then the engine flags the order and suggests alternative batch times.
Expiry Date Prioritization
Given inventory items with expiration dates, when creating batches, then items with the soonest expiry are allocated to the earliest delivery batches. When two items share the same expiry, then the engine prioritizes the batch with the highest order volume.
Multi-Truck Load Balancing
Given two trucks with capacities of 500 and 700 units and a set of orders totaling 1,000 units, when generating batches, then the engine distributes orders to utilize at least 85% of each truck’s capacity. When an imbalance occurs, then the engine provides a report of adjusted loads for manual review.
Constraint Adjustment Response
Given a change in truck capacity or delivery window constraints, when the engine reruns batch optimization, then it updates all batch combinations within 10 seconds. When constraints are invalid (e.g., window shorter than delivery time), then the engine returns a descriptive error message.
Real-time Optimization Dashboard
"As a Food Truck Owner, I want to view and adjust batch recommendations on my dashboard so that I can make informed decisions quickly."
Description

Provide an interactive web and mobile dashboard that displays recommended batch groupings, projected spoilage rates, and cost-saving metrics, allowing users to adjust parameters and view instant re-optimization results.

Acceptance Criteria
Dashboard Loading with Live Inventory Data
Given the user is authenticated and inventory data updates every minute, when the dashboard loads, then recommended batch groupings, projected spoilage rates, and cost-saving metrics must display within 3 seconds, reflecting the latest inventory and expiry information.
Batch Re-Optimization After Parameter Adjustment
Given the user adjusts batch size, delivery window, or spoilage threshold, when the user applies changes, then the dashboard must re-calculate and display updated batch groupings and metrics within 2 seconds, preserving previous user inputs.
Spoilage Rate Projection Accuracy Check
Given exported historical inventory and expiry data, when compared to dashboard-projected spoilage rates, then the dashboard’s projections must be within ±5% of actual spoilage for at least 90% of tested datasets.
Cost-Saving Metrics Display and Refresh
Given real-time cost and sales data, when the user views cost-saving metrics section, then it must show current and projected savings updates in real time and refresh automatically at least once per minute without requiring a page reload.
Mobile Responsiveness and Touch Interaction
Given the user accesses the dashboard on an iOS or Android device, when interacting with graphs, sliders, and dropdowns, then all elements must be fully responsive, touch-friendly, and maintain functionality and layout consistency compared to the web version.
Automated Alert System
"As a Fleet Manager, I want to receive alerts when batch recommendations change or items are near expiry so that I can act before spoilage occurs."
Description

Set up notifications via email, SMS, and in-app messaging to inform stakeholders when batch plans change due to new orders or approaching expirations, ensuring timely awareness of critical updates.

Acceptance Criteria
Notification Triggered by New Order Impacting Batch Plan
Given a new customer order modifies an existing batch plan, when the system recalculates the batch, then email, SMS, and in-app notifications are sent to designated stakeholders within 2 minutes.
Alert for Approaching Batch Expiry
Given an ingredient in an active batch is within 48 hours of expiration, when the expiry threshold is reached, then alerts are dispatched via configured channels listing item name, batch ID, and exact expiry date.
Multi-Channel Delivery of Critical Alerts
Given critical alerts are enabled, when a batch plan changes for any reason, then the notification content is delivered in-app, via SMS, and by email with identical messaging and actionable links.
Notification Preferences Management
Given a stakeholder updates their alert channel preferences, when the changes are saved, then subsequent alerts are sent only via the selected channels and the new preferences are confirmed in a summary message.
Timestamped and Audit-Logged Alert Records
Given any alert is generated, when the system delivers the notification, then it logs an entry including timestamp, channel used, recipient identifier, and delivery status in the audit log.
Historical Data Analysis Reporting
"As a Product Owner, I want to review performance reports on BatchOptimizer's impact so that I can assess ROI and make improvements."
Description

Generate periodic reports on optimization performance, including spoilage reduction percentages, cost savings, and fulfillment metrics, to evaluate BatchOptimizer’s effectiveness and guide continuous improvements.

Acceptance Criteria
Monthly Spoilage Reduction Report Generation
Given inventory and expiry data for the previous month is available, when the report is generated, then it calculates total spoilage, compares it to the prior month, and displays the percentage reduction with a chart.
Quarterly Cost Savings Analysis
Given cost and usage records over the last quarter, when the system compiles the quarterly report, then it reports total cost savings achieved by BatchOptimizer and highlights the top three optimized batches.
Weekly Fulfillment Metrics Dashboard Update
Given order fulfillment logs for the past week, when the metrics dashboard is refreshed, then it shows average fulfillment time, percentage of on-time deliveries, and lists any delayed orders.
Custom Date Range Report Export
Given a user selects a custom date range, when the export function is executed, then the system generates and downloads a CSV containing spoilage reduction percentages, cost savings figures, and fulfillment metrics for that range.
Automated Report Email Delivery
Given a scheduled report subscription is configured, when the scheduled time arrives, then the system emails the report in PDF format to all subscribed users with accurate data and attachments.

GreenOrder Automation

Automatically triggers consolidated purchase orders for batches of ingredients nearing expiration, sending optimized restock requests to preferred suppliers with minimal manual intervention.

Requirements

Expiration Threshold Monitoring
"As a food truck operator, I want the system to monitor ingredient expiration dates so that I can automatically restock before items expire and avoid waste."
Description

Implement real-time monitoring of perishable inventory expiration dates across all trucks. The system must track each ingredient’s expiration date, compare it against a configurable threshold (e.g., 72 hours before expiry), and flag items nearing expiration. This data feeds into the GreenOrder Automation pipeline to ensure timely restock actions and minimize waste.

Acceptance Criteria
Flag Items Approaching Expiration Threshold
Given an ingredient with an expiration date within the configured threshold, when the system census runs, then the ingredient is flagged as 'Near Expiry' in the dashboard within 60 seconds.
Configurable Threshold Adjustment
Given an administrator updates the expiration threshold to a new value, when the change is saved, then the system applies the new threshold to all subsequent inventory scans without requiring a restart.
Multi-Truck Real-Time Monitoring
Given perishable items across multiple trucks, when the system performs real-time inventory monitoring, then each truck’s near-expiry items are listed under its unique identifier in the dashboard.
Integration with GreenOrder Pipeline
Given items flagged as near expiry, when the GreenOrder Automation pipeline executes, then a consolidated purchase order including only flagged items is generated and sent to preferred suppliers.
User Notification for Near Expiry Items
Given one or more items are flagged as near expiry, when a user logs into the dashboard, then an in-app notification appears listing the flagged items sorted by ascending expiration date.
Batch Consolidation Engine
"As a food truck operator, I want my near-expiry ingredients from all trucks to be combined into a single order so that I save time and get better pricing."
Description

Develop an algorithm to aggregate near-expiry ingredient requirements across multiple trucks into consolidated purchase orders. The engine should identify common items, sum required quantities, and group orders by supplier to leverage volume discounts and reduce order frequency. This consolidation reduces manual work, lowers costs, and optimizes delivery efficiency.

Acceptance Criteria
Identifying Near-Expiry Ingredients Across Fleet
Given the inventory databases of all trucks contains items with expiration dates, when the consolidation engine runs, then it should select all ingredient entries within 7 days of expiration and flag them for batch consolidation.
Summing Required Quantities for Consolidated Orders
Given flagged near-expiry items from multiple trucks, when the engine aggregates quantities, then it calculates total required quantity per ingredient correctly by summing across all trucks.
Grouping Consolidated Orders by Supplier
Given a consolidated list of ingredient quantities, when the engine matches items to preferred suppliers, then it groups orders by supplier and generates a single purchase order per supplier with appropriate line items.
Applying Volume Discount Thresholds
Given grouped orders per supplier, when the total quantity for an item meets predefined discount thresholds, then the engine applies the corresponding volume discount in the purchase order summary.
Generating Optimized Purchase Orders
Given grouped, discounted line items per supplier, when the consolidation process completes, then the system generates and sends the purchase order in the required supplier format within 2 minutes of engine execution.
Supplier Preference Algorithm
"As a procurement manager, I want the system to choose the best supplier for my orders so that I receive ingredients on time at the best price."
Description

Integrate a supplier optimization module that ranks preferred suppliers based on criteria such as cost, delivery lead time, reliability, and past performance. The algorithm should select the optimal supplier for each consolidated batch, allowing for fallback options if the primary supplier cannot fulfill the order. This ensures cost-effective and reliable restocking.

Acceptance Criteria
Optimal Supplier Selection for Single Batch
Given a consolidated batch of ingredients, when the algorithm evaluates suppliers, then it computes a composite score using cost, delivery lead time, reliability, and past performance weights and selects the supplier with the highest score.
Fallback to Secondary Supplier
Given the primary supplier’s available stock is insufficient, when order placement is attempted, then the algorithm automatically reranks and selects the next supplier with adequate stock and highest score.
Cost vs Lead Time Trade-off
Given two suppliers have cost difference ≤5% and delivery lead time difference ≥2 days, when ranking for selection, then the algorithm prioritizes the supplier with shorter lead time.
Historical Performance Adjustment
Given historical performance metrics over the last six months, when computing reliability scores, then the algorithm increases weighting by 10% for suppliers with on-time delivery rate ≥95%.
Bulk Consolidation Across Multiple Trucks
Given multiple trucks request the same ingredient within 24 hours of expiration, when consolidating orders, then the algorithm groups items into a single batch and selects the optimal supplier based on composite score for the aggregated quantity.
Automated Purchase Order Generation
"As an operations manager, I want purchase orders generated automatically so that I don’t have to manually create and format each order."
Description

Automatically generate purchase orders for consolidated ingredient batches in the required formats (e.g., PDF, CSV, EDI). The system should populate order details, including item descriptions, quantities, preferred supplier information, and delivery instructions. Generated POs must be stored in the system and made available for review or audit.

Acceptance Criteria
Automated Consolidated PO Creation
Given ingredients nearing expiration within 7 days across multiple trucks, when the GreenOrder Automation runs, then a consolidated purchase order is generated in PDF format containing correct item descriptions, quantities, preferred supplier details, and delivery instructions.
Multiple Format PO Generation
Given a requirement to export purchase orders, when the system generates a PO, then the user can download the PO in PDF, CSV, or EDI format with accurate and consistent data.
PO Storage and Retrieval
Given generated purchase orders, when accessed from the dashboard, then all POs are listed chronologically with filter and search capabilities, and users can preview and download each PO.
Audit Trail for Generated POs
Given the need for auditing, when a PO is generated, then the system logs the creation event with timestamp, triggering criteria, and user context, and the logs are accessible via audit interface.
Supplier-Specific Delivery Instructions
Given preferred suppliers have specific delivery instructions, when generating a PO, then the system includes the correct supplier-specific instructions in the PO and flags missing information.
PO Dispatch & Confirmation Tracking
"As a vendor coordinator, I want the system to send orders automatically and track confirmations so that I know my orders are received and can address any issues immediately."
Description

Enable automated dispatch of generated POs to suppliers via email or integrated supplier APIs. The system should track dispatch status, parse acknowledgments or confirmations, and update order statuses in real time. Alerts should be sent for failed deliveries or delays, ensuring transparency and timely follow-up.

Acceptance Criteria
PO Dispatch via Email
Given a generated purchase order When dispatch is triggered Then the system sends an email to the supplier’s configured email address within 1 minute containing the correct PO number, line items, quantities, and delivery details
PO Dispatch via Supplier API
Given a generated purchase order When dispatch is triggered via supplier API Then the system sends a valid HTTP POST request to the supplier’s API endpoint and receives a 2xx response within 30 seconds
Parsing Supplier Confirmations
Given a supplier acknowledgment received via email or API When the acknowledgment is parsed Then the system extracts the supplier’s confirmation number and updates the PO status to “Confirmed” in the dashboard
PO Dispatch Failure Alerts
Given a dispatch attempt fails due to email bounce or API error When the failure is detected Then the system generates and sends an alert notification to the operations team within 5 minutes detailing the PO number and error cause
Real-time Status Update
Given a purchase order in any dispatch state When the supplier status changes (Sent, Confirmed, Delayed, Failed) Then the dashboard reflects the updated status within 2 minutes

ExpiryForecast

Uses historical usage patterns and real-time sales data to predict which ingredients are at risk of expiring unused, enabling proactive adjustments to menu planning or redistribution strategies.

Requirements

Real-time Data Ingestion
"As a food truck operator, I want real-time ingestion of sales and inventory updates so that prediction models have current data for accurate expiry forecasts."
Description

Establish a robust pipeline to ingest sales transactions and inventory updates from all connected food trucks in real time, ensuring the ExpiryForecast feature has up-to-the-minute data. This integration will leverage cloud-based APIs and data streaming services to synchronize data across all devices and maintain data consistency, enabling accurate prediction of ingredient expiration risks.

Acceptance Criteria
Pipeline Connectivity Verification
Given the data ingestion pipeline is configured When a sales transaction occurs on any truck Then the data is received by the central API within 2 seconds And acknowledgement is sent back to the truck device
Data Latency Under Peak Load
Given simultaneous transactions from 50 trucks during peak hours When data is ingested Then 95% of records are processed end-to-end within 3 seconds
Error Handling and Retry Mechanisms
Given a network interruption occurs during data transmission When the pipeline detects the failure Then the system retries transmission up to 3 times with exponential backoff And logs the failure event if retries are exhausted
Data Consistency Across Devices
Given an inventory update is made on Truck A When the update is synced Then the updated inventory level is reflected on all other connected devices within 5 seconds
Secure Data Transmission
Given data is transmitted over the network When data travels between the truck and cloud Then all data is encrypted using TLS 1.2 or higher
Historical Usage Pattern Analysis
"As an inventory manager, I want analysis of historical usage patterns so that forecasts account for seasonal and truck-specific demand."
Description

Develop an analytics module to process and analyze historical sales and consumption data, identifying trends and patterns in ingredient usage over time. This analysis will feed the predictive models, improving their accuracy by considering seasonal variations, truck-specific demand, and menu item popularity.

Acceptance Criteria
Seasonal Demand Variation Identification
Given 12 months of historical ingredient sales data When the analytics module processes the data Then for each ingredient it identifies at least one seasonal usage trend with a confidence score of 80% or higher
Truck-Specific Usage Trend Extraction
Given 6 months of consumption records for each truck When the module analyzes the truck-specific data Then it outputs usage trend lines for every ingredient per truck with an R-squared value of at least 0.7
Menu Item Popularity Correlation
Given three months of menu item sales counts When the module correlates ingredient usage against menu popularity Then it produces correlation coefficients for each ingredient-menu pair with a p-value of 0.05 or less
Data Aggregation Accuracy Check
Given raw sales and consumption input datasets When the module aggregates the data Then the total aggregated values match the sum of the input source data within a 1% variance
Real-Time Historical Data Refresh
Given new daily sales entries are ingested When the analytics module refreshes the historical dataset Then the updated usage data appears accurately in reports within five minutes of ingestion
Expiry Risk Scoring Engine
"As a supply planner, I want a risk score for each ingredient so that I can prioritize actions on high-risk items."
Description

Implement a predictive scoring engine that combines real-time inventory levels, historical usage patterns, and shelf-life data to calculate a risk score for each perishable ingredient, indicating the probability of expiry before usage. The engine should update scores dynamically and provide a clear risk metric for decision-making.

Acceptance Criteria
Dynamic Risk Score Update on Inventory Change
Given current inventory and usage data for a perishable item When a sale or consumption event is recorded Then the engine recalculates and updates the item’s expiry risk score within 5 seconds
Threshold-based Alert Generation for High-Risk Items
Given an ingredient’s risk score is greater than or equal to 0.8 When the risk score is calculated or updated Then the system generates a “High Expiry Risk” alert visible on the dashboard and sent via configured notifications
Daily Batch Processing of Expiry Risk Scores
Given end-of-day sales and inventory data When the nightly batch job runs at 02:00 local time Then all perishable items’ risk scores are recalculated, stored in the database, and logged with success or failure status
Accurate Risk Scoring with Shelf-life Integration
Given each ingredient has defined shelf-life metadata When computing the expiry risk score Then the algorithm factors remaining shelf-life proportionally, producing a probability value between 0 and 1 within a ±5% test case margin
User-friendly Risk Metric Display on Dashboard
Given computed risk scores for perishable items When the dashboard loads Then each item displays its risk score with a color indicator (green for <0.3, yellow for 0.3–0.7, red for >0.7) and a tooltip explaining score interpretation
Proactive Alert System
"As a food truck operator, I want proactive alerts when ingredients are near expiry so that I can take timely measures to reduce waste."
Description

Create a notification system that sends timely alerts to food truck operators when ingredients reach predefined risk thresholds, allowing for quick action such as adjusting menus or transferring stock. Alerts should be configurable by risk level and delivery channel, including mobile push notifications and emails.

Acceptance Criteria
Threshold Risk Level Alert Configuration
Given an operator sets a risk threshold for an ingredient, when saving settings, then the system persists the threshold and applies it to real-time data calculations.
Bulk Email Notification Dispatch
Given multiple ingredients across trucks exceed risk thresholds, when the alert process runs, then the system sends an email to all configured addresses within 5 minutes containing ingredient details and risk levels.
Mobile Push Notification Delivery
Given an operator’s device is registered for push notifications, when an ingredient crosses its risk threshold, then the system delivers a push notification to the device within 1 minute containing actionable details.
Risk Threshold Adjustment Persistence
Given an operator updates a risk threshold on one device, when they log into the dashboard on another device, then the updated threshold is displayed correctly.
Menu Adjustment Prompt
Given an ingredient is at high risk of expiring, when the alert is triggered, then the dashboard displays a prompt suggesting menu items to adjust or redistribute excess stock.
Menu & Redistribution Recommendations
"As an operations manager, I want menu adjustment and redistribution recommendations so that I can optimize ingredient use and minimize spoilage."
Description

Generate actionable recommendations for menu adjustments and ingredient redistribution across trucks based on predicted expiry risks, optimizing utilization and minimizing food waste. The module will suggest alternative menu items, transfer routes, and quantities to rebalance inventory before ingredients expire.

Acceptance Criteria
Low Usage Ingredient Warning
Given ingredient X in truck A is forecasted to expire in ≤3 days and predicted usage < threshold When generating redistribution recommendations Then the system includes a suggestion to transfer at least Y units of X to a truck with forecasted demand above threshold
High Risk Expiry Ingredient Rebalance
Given ingredient X has expiration risk ≥80% within 5 days and surplus quantity > forecasted demand When recommendations are requested Then the module suggests at least two alternative menu items utilizing X and displays expected waste reduction metrics
Redistribution Route Optimization
Given multiple trucks require transfers of ingredient X When computing transfer routes Then the system calculates and displays the optimal transfer route minimizing total distance and fuel cost, with arrival time accuracy within ±15 minutes
Menu Adjustment Suggestion Generation
Given predicted expiry risk of ingredient X ≥80% When generating menu adjustment recommendations Then the system proposes at least two alternative menu items that use X, including impact on inventory levels and projected sales per item
Automated Notification Delivery
Given new recommendations are finalized When distributing recommendations Then the system sends notifications via email and in-dashboard alerts to relevant truck owners and confirms delivery status for each notification

WasteWatch Dashboard

Provides a visual overview of projected versus actual food waste across the fleet, highlighting successful batch orders and areas for improvement to help operators track progress toward waste reduction goals.

Requirements

Real-Time Waste Metrics Visualization
"As a food truck operator, I want real-time visual charts of projected versus actual waste so that I can immediately identify and address waste inefficiencies across my fleet."
Description

Implement interactive, real-time charts and graphs displaying projected versus actual food waste across all food trucks, with filtering and drill-down capabilities by date range, truck, and menu item. This visualization will allow operators to quickly identify waste anomalies, monitor performance against waste reduction goals, and make data-driven decisions to optimize inventory management.

Acceptance Criteria
Dashboard Overview Loads Real-Time Waste Data
Given the operator is on the WasteWatch Dashboard and the real-time data stream is active, when the page loads, then all waste charts display metrics updated within the last minute without errors.
Filtering Waste Metrics by Truck and Date Range
Given the operator selects a specific food truck and a date range in the filter panel, when the filter is applied, then the displayed charts update to show only waste data for the selected truck and dates, and no data outside the range is visible.
Drilling Down into Menu Item Waste Details
Given the operator clicks on a data point in the waste graph, when the drill-down action is triggered, then a detailed breakdown view appears showing waste metrics per menu item for the selected point.
Comparing Projected vs Actual Waste
Given the waste charts are displayed, when the operator toggles the projected vs actual comparison view, then both projected and actual waste lines appear simultaneously with clear labels and accurate data values.
Performance Under High Data Volume
Given the fleet has over 1,000 waste events in the selected period, when the operator interacts with filters or drill-down, then the dashboard responds and updates charts within 2 seconds.
Trend Analysis and Forecasting
"As a fleet manager, I want to view projected waste forecasts based on past trends so that I can plan orders more precisely and minimize future food waste."
Description

Develop predictive analytics models that analyze historical waste data to forecast future waste trends. Integrate these forecasts into the dashboard with visual overlays and confidence intervals to help operators anticipate waste levels, adjust batch orders, and proactively reduce spoilage.

Acceptance Criteria
Weekly Waste Volume Forecast Display
Given an operator selects a specific truck and upcoming week on the dashboard, when the system runs the predictive model on historical waste data, then it displays the forecasted waste volume for that week with an accuracy of ±10%.
Decision-Making Confidence Interval Overlay
Given a displayed waste forecast for a truck, when the forecast loads, then the dashboard overlays upper and lower 95% confidence interval bands around the forecast line.
Batch Order Adjustment Recommendations
Given a forecasted increase in waste beyond predefined thresholds, when the operator views the forecast results, then the system suggests batch order quantity adjustments that can reduce projected waste by at least 15%.
Historical vs. Forecast Trend Comparison
Given the operator enables comparison mode, when historical waste data and forecast data are displayed, then the dashboard overlays both trend lines and highlights any weekly deviations exceeding 20%.
Anomaly Detection Alerting
Given the predictive model processes new data, when it detects waste values beyond two standard deviations from the forecast, then the system flags the truck in the dashboard and sends an alert notification to the operator.
Waste Threshold Alerts
"As an operator, I want to receive alerts when waste surpasses my defined thresholds so that I can take immediate corrective actions to prevent further losses."
Description

Create a configurable alerts system that notifies operators via in-app notifications or email when actual waste exceeds predefined thresholds for specific trucks or menu items. Include settings for threshold levels, notification frequency, and delivery channels to ensure timely interventions.

Acceptance Criteria
Threshold Exceeded for Single Item Waste
Given the actual waste for a menu item exceeds its configured threshold When the system processes waste data Then an alert is sent via the user’s selected delivery channel within 5 minutes
Threshold Configuration and Modification
Given an operator sets or updates a waste threshold for a specific truck or menu item When the operator saves the new threshold Then the system validates the input, persists the updated threshold, and reflects the change in the alerts settings
Notification Frequency Enforcement
Given waste exceeds the threshold multiple times in a single day When the alert frequency is configured to a specific interval Then the system sends subsequent alerts only after the specified interval has elapsed
Delivery Channel Selection
Given an operator selects multiple delivery channels (in-app notification and email) When a waste threshold is breached Then the alert is delivered simultaneously to all selected channels
No Alerts Below Threshold
Given the actual waste remains below or equal to the configured threshold When waste data is evaluated Then no alerts are generated or sent
Batch Order Success Tracker
"As a food truck owner, I want to track the success rate of batch orders compared to actual sales and waste so that I can optimize my order quantities and reduce leftovers."
Description

Build a dashboard module that correlates batch order quantities with actual usage and waste outcomes. Display success rates, overage percentages, and recommendations for optimal batch sizes, helping operators refine ordering processes and reduce excess inventory.

Acceptance Criteria
Review Batch Order Success Metrics After Daily Cycle
Given the operator has completed daily sales and entered actual usage and waste data When they open the Batch Order Success Tracker dashboard Then they see planned batch quantity, actual usage, waste amount, calculated success rate, overage percentage, and a comparative visual chart for each batch
Assess Optimal Batch Size Recommendations
Given at least seven days of historical batch order and waste data When the operator requests batch size recommendations Then the system provides a ranked list of optimal batch quantities with projected waste percentage and confidence intervals
Filter Waste and Success Data by Truck and Date
Given multiple trucks and date entries exist in the system When the operator selects a specific truck and date range Then the dashboard displays only the metrics (batch size, usage, waste, success rate, overage) for that truck and period
Track Waste Reduction Progress Over Time
Given the operator has set a waste reduction goal When viewing the Progress Over Time chart Then the dashboard shows a trend line of success rates and waste percentages against the goal with color-coded indicators for on-track, at-risk, or off-track status
Update Batch Order Metrics on Real-Time Data Sync
Given real-time inventory and usage data synchronization is enabled When new usage or waste entries are recorded Then the Batch Order Success Tracker metrics update automatically within five seconds and reflect the latest values
Detailed Waste Reports Export
"As an operator, I want to export detailed waste reports so that I can share insights with my team and stakeholders and support strategic decision-making."
Description

Enable export functionality for detailed waste data, allowing operators to download comprehensive reports in CSV and PDF formats. Reports should include metrics such as waste by truck, item-level breakdowns, trend summaries, and alert histories for further analysis and sharing with stakeholders.

Acceptance Criteria
CSV Format Export for Date Range
When the operator requests a CSV export for a selected date range, the system generates a downloadable file named WasteReport_<YYYYMMDD>_<HHMMSS>.csv within 5 seconds, containing columns: Truck ID, Date, Item Name, Wasted Quantity, and Waste Reason.
PDF Format Export for Date Range
When the operator requests a PDF export for a selected date range, the system generates a downloadable file named WasteReport_<YYYYMMDD>_<HHMMSS>.pdf within 10 seconds, formatted for A4 portrait and including a table with waste by truck and item-level breakdown.
Filtered Export by Truck Selection
Given the operator applies a truck filter before exporting, the generated report includes only data for the selected trucks and displays a header listing the applied filters.
Inclusion of Trend Summaries in Report
When the operator includes trend summaries in the export, the report contains a chart section displaying waste volume trends over the selected period along with a table of monthly waste totals.
Inclusion of Alert Histories in Report
Given the operator opts to include alert histories, the exported report contains a dedicated section listing all waste alerts generated during the report period, with alert date, truck ID, and description.

EcoInsights Reports

Delivers detailed periodic reports on waste reduction metrics, cost savings, and order efficiency, empowering operators and supply planners with actionable insights for sustainable operations.

Requirements

Scheduled Report Generation
"As a food truck operator, I want to schedule weekly EcoInsights reports so that I can regularly review waste reduction and efficiency metrics without manual intervention."
Description

Allow operators to schedule automated generation of EcoInsights reports at customizable intervals (daily, weekly, monthly), with filtering options by truck, date range, and selected metrics.

Acceptance Criteria
Daily Report Scheduling
Given the operator is on the EcoInsights report scheduling interface When they select 'Daily' as the interval And choose all trucks And pick the default metrics set And click 'Save' Then the system creates a daily scheduled report And confirms the schedule creation to the operator
Weekly Report with Custom Date Range
Given the operator is on the report scheduling page When they select 'Weekly' as the interval And specify a custom start and end date And choose specific waste reduction and cost metrics And click 'Save' Then the system schedules a weekly report covering the specified date range And displays the schedule details back to the operator
Monthly Report Filtering by Truck
Given the operator is configuring a new scheduled report When they select 'Monthly' as the interval And filter the report to include only Truck A and Truck B And select order efficiency metrics And click 'Save' Then the system sets up a monthly report for the selected trucks And sends a confirmation email with the truck filter details
Metric Selection in Scheduled Report
Given the operator is scheduling a report When they customize the metric list to include only waste reduction and reorder points And leave out cost savings metrics And save the schedule Then the next generated report includes only the selected metrics And excludes any unselected metrics
Editing and Cancelling Scheduled Reports
Given the operator views their list of scheduled reports When they choose to edit an existing report schedule And update the interval from weekly to daily And save changes Then the schedule updates accordingly And sends an update confirmation Given the operator cancels a schedule When they confirm deletion Then the system removes the schedule And no further reports are generated
Real-time Data Aggregation
"As a supply planner, I want real-time aggregation of stock and waste data so that EcoInsights reports reflect current operations and enable timely decision-making."
Description

Automatically aggregate inventory, order, and waste data from all connected trucks in real time to feed into EcoInsights reports, ensuring up-to-date metrics and trends for analysis.

Acceptance Criteria
Real-time Sync Initiation
Given a truck reports an inventory update, when the update is sent, then the dashboard reflects the change within 10 seconds.
Order Data Accuracy
Given a completed order on any truck, when the order is fulfilled, then the aggregated order count and revenue metrics update correctly within 15 seconds.
Waste Data Aggregation
Given end-of-shift waste logs from a truck, when logs are submitted, then the system aggregates and displays total waste for that shift in under 30 seconds with no missing entries.
Data Backfill After Outage
Given a temporary connectivity loss for a truck, when the connection is restored, then all missed inventory, order, and waste records are backfilled accurately without duplicates.
High-Load Scalability
Given 50 trucks reporting simultaneously, when data streams arrive, then aggregation latency remains under 10 seconds and system resource usage stays within thresholds.
Interactive Metrics Dashboard
"As an operator, I want an interactive dashboard in my reports so that I can explore waste reduction trends and identify areas for improvement."
Description

Embed an interactive dashboard within EcoInsights reports allowing operators to drill down into specific waste categories, cost drivers, and truck-level performance through graphical visualizations.

Acceptance Criteria
Waste Category Drill-Down
Given the dashboard displays overall waste metrics, when the operator clicks on a specific waste category, then a detailed breakdown by sub-category with charts and percentages is displayed within two seconds.
Cost Driver Breakdown
Given a selected reporting period, when the operator selects a cost driver in the dashboard legend, then the dashboard updates to show cost trends, contributing factors, and comparative visuals for that driver.
Truck-Level Performance Filter
Given multiple trucks registered in the system, when the operator applies a truck filter, then only the selected truck’s performance metrics and waste data are shown.
Timeframe Adjustment Interaction
Given the default view shows the last month’s data, when the operator adjusts the timeframe to a custom range, then all visualizations update to reflect data from the selected dates.
Export Interactive Chart Data
Given an interactive chart is visible, when the operator clicks the export icon, then the underlying data for that chart is downloaded as a CSV file within ten seconds.
Custom KPI Configuration
"As a food truck owner, I want to configure custom KPIs in my reports so that the insights align with my unique operational objectives."
Description

Provide capability for users to define, modify, and assign weights to custom KPIs (e.g., waste percentage, order fulfillment time, cost per unit) to tailor EcoInsights reports to their specific business goals.

Acceptance Criteria
New KPI Definition
Given the user is on the Custom KPI Configuration page and clicks 'Add KPI', when they enter a unique KPI name, select the metric, set the weight to a value between 0.0 and 1.0, and click 'Save', then the KPI is added to the list with correct details and persists after page refresh.
KPI Weight Modification
Given an existing KPI appears in the configuration list, when the user clicks 'Edit', adjusts the weight slider to a new valid value, and clicks 'Save', then the updated weight is reflected immediately in the list and used in subsequent report calculations.
Report Template KPI Assignment
Given multiple custom KPIs are available, when the user opens a report template, selects desired KPIs via checkboxes, orders them, and clicks 'Apply', then the template displays only the selected KPIs in the specified order and includes them in the generated EcoInsights report.
KPI Deletion
Given a KPI is listed in the configuration page, when the user clicks the 'Delete' icon next to that KPI and confirms deletion, then the KPI is removed from the list, cannot be assigned to reports, and any report template referencing it prompts the user to update assignments.
KPI Weight Sum Validation
Given the user configures multiple KPIs, when they adjust individual weights, the system displays real-time validation ensuring total weight equals 1.0 and prevents saving until the constraint is met.
Export and Share Reports
"As a supply planner, I want to export and share monthly EcoInsights reports so that I can present findings to team members and stakeholders."
Description

Enable export of EcoInsights reports in PDF and CSV formats and allow sharing via email or direct link, facilitating stakeholder communication and record keeping.

Acceptance Criteria
Export Report as PDF
Given the user is viewing an EcoInsights report When the user selects 'Export' and chooses 'PDF' Then the system generates a PDF file that faithfully represents the report’s data and layout, and the file name follows the pattern 'EcoInsights_Report_<YYYY-MM-DD>.pdf'
Export Report as CSV
Given the user is viewing an EcoInsights report When the user selects 'Export' and chooses 'CSV' Then the system generates a CSV file with column headers matching the report fields, all rows of data correctly formatted, and the file name follows the pattern 'EcoInsights_Report_<YYYY-MM-DD>.csv'
Share Report via Email
Given the user is on the report page When the user clicks 'Share via Email', enters at least one valid email address, selects format (PDF or CSV), and confirms Then the system sends an email to each recipient with the chosen format attached, displays a success notification, and records the email dispatch in the activity log
Generate Shareable Direct Link
Given the user is on the report sharing options When the user selects 'Generate Direct Link' and confirms Then the system creates a unique, secure URL that provides access to download the report in the chosen format without additional login, displays the URL in the UI, and ensures the link remains active for the configured duration
Bulk Export Multiple Reports
Given the user has selected multiple EcoInsights reports (by date range or batch selection) When the user clicks 'Bulk Export' and chooses formats (PDF, CSV, or both) Then the system generates a single ZIP archive containing each report in the specified format(s), names the archive 'EcoInsights_BulkExport_<YYYY-MM-DD>.zip', and initiates download or provides a download link

NightVision UI

Transforms the dashboard into a high-contrast, dark-themed interface optimized for low-light conditions. Reduces eye strain during late-night monitoring while maintaining clear visibility of critical inventory data.

Requirements

High-Contrast Mode Toggle
"As a food truck manager monitoring inventory at night, I want to quickly switch to a high-contrast, dark-themed dashboard so that I can read data clearly without straining my eyes in low-light conditions."
Description

Provides a toggle switch that instantly applies a high-contrast, dark-themed interface optimized for low-light environments. When activated, all UI elements invert or adjust their colors to ensure maximum readability of text, icons, and data visualizations without increasing glare. This toggle seamlessly integrates with existing theme settings and persists the user's preference across sessions and devices.

Acceptance Criteria
Toggle Appearance Verification
Given the user is logged in and viewing the dashboard When the user toggles High-Contrast Mode on Then the interface must switch within 1 second to a dark-themed UI with all text, icons, and UI elements adjusted for high contrast without visual artifacts
Preference Persistence Across Sessions
Given the user has activated High-Contrast Mode and logged out When the user logs back in on the same device Then High-Contrast Mode must remain enabled and the UI must render in high-contrast dark theme
Cross-Device Preference Synchronization
Given the user has activated High-Contrast Mode on one device When the user accesses SnackFleet from a different registered device within 5 minutes Then High-Contrast Mode must be enabled automatically and the UI must render in high-contrast dark theme
Data Visualization Compatibility
Given the dashboard displays charts or graphs When High-Contrast Mode is active Then all data visualizations must adjust colors to maintain a minimum contrast ratio of 4.5:1 and remain legible
Accessibility Compliance Verification
Given High-Contrast Mode is enabled When auditing UI elements Then all text and icons must meet WCAG 2.1 AA contrast standards (4.5:1 for normal text, 3:1 for large text) and no element may fall below these ratios
Adaptive Brightness Control
"As a truck operator working overnight, I want the dashboard brightness to adjust automatically based on ambient light so that I don’t have to manually tweak settings and can focus on my operations."
Description

Implements an adaptive brightness feature that automatically adjusts the interface luminance based on ambient light readings or the device's system settings. This control ensures that the dashboard maintains an optimal brightness level after sunset or in dark locations, reducing manual adjustments and preserving battery life on mobile devices.

Acceptance Criteria
Automatic Dimming at Sunset
Given the local time is after sunset or the ambient light sensor reads below 20 lux, When the user opens the dashboard, Then the interface brightness automatically adjusts to a level between 20% and 30%.
Brightening Under Direct Sunlight
Given the ambient light sensor reads above 10,000 lux, When the dashboard is active, Then the interface brightness automatically increases to at least 80%, ensuring all text and controls remain legible.
Manual Brightness Adjustment Retention
Given a user manually sets a custom brightness level, When the ambient light conditions change, Then the system retains the user’s manual setting until the user manually adjusts again.
Low Battery Mode Brightness Reduction
Given the device battery level falls below 20%, When adaptive brightness control is active, Then the interface brightness automatically reduces by 20% from its current level to conserve battery.
Graceful Degradation When Sensor Unavailable
Given the ambient light sensor fails or is unresponsive for more than 5 seconds, When the dashboard is in use, Then the system falls back to the device’s system brightness settings and displays a non-intrusive notification to the user.
Customizable Color Palette
"As a food truck owner, I want to customize the accent colors in the dark theme so that the dashboard aligns with my brand identity and reduces visual fatigue."
Description

Allows users to select from a set of predefined color palettes or define custom accent colors for UI components such as buttons, headers, and chart lines. This customization helps users tailor the NightVision UI to their individual comfort and brand colors while maintaining contrast standards for readability.

Acceptance Criteria
Selecting a Predefined Color Palette
Given the user opens the settings and navigates to the color palette menu When the user selects a predefined palette Then the UI updates instantly to reflect the selected palette on buttons, headers, and chart lines
Defining a Custom Accent Color via Color Picker
Given the user clicks the custom accent color option When the user chooses a valid color via the color picker Then the accent color of specified UI components updates accordingly and is previewed in real time
Saving and Applying Customizations Across Sessions
Given the user customizes the color palette and logs out When the user logs back in Then the previously selected palette persists and is applied across all dashboard pages
Ensuring Contrast Compliance
Given the user selects any predefined palette or custom color When the UI renders Then all text and UI components maintain a minimum contrast ratio of 4.5:1
Resetting to Default Color Scheme
Given the user accesses color settings When the user clicks “Reset to Default” Then the UI reverts to the original NightVision UI colors and all custom settings are cleared
Critical Data Emphasis
"As a food truck manager, I want critical inventory alerts to be visually emphasized in the dark UI so that I can instantly identify issues and take corrective action without hunting for information."
Description

Enhances visibility of critical inventory metrics by adding visual cues such as bold outlines, glow effects, or contrasting backgrounds to key elements like low stock alerts, current order counts, and restock reminders. These emphasis styles ensure that essential information stands out immediately in a dark-themed interface.

Acceptance Criteria
Low Stock Alert Highlighting
Given a product’s stock level falls below the predefined threshold, When the dashboard displays the low stock alert in NightVision UI, Then the alert is outlined with a bold neon border and the background color contrasts by at least WCAG AA ratio of 4.5:1.
Current Order Count Emphasis
Given there are active orders, When viewing the Current Order Count widget in NightVision UI, Then the order count value is displayed with a glow effect and a bold font weight, ensuring it stands out against the dark background.
Restock Reminder Visibility
Given a scheduled restock date is within the next 24 hours, When the restock reminder appears on the dashboard, Then the reminder card’s background color contrasts with the primary dark theme at a minimum 4.5:1 ratio, with a bold outline to draw immediate attention.
Critical Metric Glow in Dark Mode
Given any critical metric widget (low stock, current orders, restocks) is rendered, When NightVision UI is activated, Then each widget’s critical metrics use consistent glow effects (5px blur radius) and high-contrast color codes as per design guidelines.
Dashboard Summary Contrast
Given the dashboard summary section is loaded, When the user views this section in NightVision UI, Then each summary tile’s critical data is emphasized using a contrasting background panel with at least 4.5:1 contrast and bold labels.
Low-Light Notification Styling
"As a night shift vendor, I want notifications styled for the dark theme so that alerts are clearly visible without jarring brightness or unreadable text."
Description

Redesigns notification pop-ups, modals, and toast messages to conform to the dark theme with suitable text contrast, background opacity, and animation timing. This ensures that alerts and confirmations are easily readable and non-distracting in low-light conditions.

Acceptance Criteria
Dark Mode Notification Visibility
Given the dashboard is in NightVision UI mode When any notification pop-up appears Then the notification text must achieve a minimum contrast ratio of 4.5:1 against its background color
Toast Message Readability
Given a toast message is triggered in low-light conditions When it is displayed Then the font size must be at least 14px, text color #FFFFFF, background color #333333, and text must be centered
Modal Dialog Contrast
Given a confirmation modal is opened in dark theme When rendering buttons Then the primary button background must be #0066CC with #FFFFFF text and a contrast ratio of at least 4.5:1; the secondary button must be #555555 with #FFFFFF text
Notification Background Opacity
Given an alert pop-up appears in NightVision UI When displayed Then its background opacity must be 90% (rgba equivalent) and any overlay behind the notification must be rgba(0,0,0,0.5)
Animation Smoothness Timing
Given any notification animation (fade-in or fade-out) When triggered Then the animation duration must be between 200ms and 300ms using an ease-in-out timing function

RedShift Filter

Applies a warm red color filter after sunset to minimize blue light exposure. Automatically adjusts based on ambient light sensors or user-defined hours, promoting better sleep hygiene for overnight operators.

Requirements

Automatic Time-based Activation
"As a food truck operator, I want the red filter to activate automatically after sunset so that I can avoid manual adjustments and reduce eye strain during late-night shifts."
Description

The system must automatically enable a warm red color filter after sunset hours or between user-defined start and end times. This feature ensures that the display reduces blue light exposure during night operations, improving sleep hygiene and reducing eye strain. The activation schedule integrates with the device's clock and respects local sunset times or manual scheduling settings. Upon reaching the activation time, the filter smoothly transitions to the red hue over a configurable duration. When the end time is reached or sunrise occurs, it reverts to the normal display.

Acceptance Criteria
Automatic Filter Activation at Sunset
Given the device’s local time is sunset time calculated by location, When the clock reaches sunset, Then the redshift filter activates automatically without user intervention.
Manual Schedule Activation
Given the user has set custom start and end times for redshift in settings, When the current local time matches the user-defined start time, Then the redshift filter activates automatically.
Smooth Transition Duration
Given the filter is set to activate or deactivate, When activation or deactivation is triggered, Then the display transitions smoothly between normal and red hues over the configurable duration specified in settings.
Automatic Filter Deactivation at Sunrise
Given the device’s local time is sunrise time calculated by location or matches the user-defined end time, When the clock reaches sunrise or the end time, Then the redshift filter deactivates, reverting to the normal display.
Ambient Light Sensor Trigger
Given the ambient light sensor detects illumination below the defined threshold outside of scheduled hours, When low ambient light is detected after sunset or before sunrise, Then the redshift filter activates automatically.
Invalid Time Range Handling
Given the user sets an end time that is earlier than the start time, When the schedule is saved, Then the system displays an error message and prevents saving until a valid time range is entered.
Ambient Light Sensor Integration
"As a night shift operator, I want the red filter intensity to adapt to ambient light so that I can see the dashboard clearly and comfortably regardless of surrounding light."
Description

Integrate the device's ambient light sensor to dynamically adjust the red filter intensity based on current lighting conditions. In darker environments, the filter becomes more pronounced, while in brighter conditions, it subtly adjusts to maintain optimal visibility. This real-time sensor integration enhances usability by ensuring consistently comfortable viewing without compromising readability of the dashboard.

Acceptance Criteria
Dusk Transition Adjustment
Given the device's ambient light sensor detects light levels crossing the threshold for sunset, When transitioning from day to dusk, Then the red filter intensity increases incrementally by at least 10% every minute until reaching the predefined nighttime level.
Bright Daylight Override
Given ambient light levels exceed the maximum visibility threshold, When in direct sunlight or bright environments, Then the red filter reduces to no more than 15% intensity to ensure readability.
Indoor Low-Light Mode
Given the sensor detects indoor low-light conditions (below 100 lux), When the user accesses the dashboard, Then the red filter automatically applies at minimum 70% intensity.
Manual User-Defined Schedule
Given a user-defined active period (e.g., 20:00–06:00), When the current time falls within that range, Then the red filter overrides sensor data and applies the user-specified intensity.
Sensor Failure Fallback
Given the ambient light sensor fails or returns invalid data, When the system detects no valid readings for more than 30 seconds, Then the filter reverts to a default 50% intensity and displays a warning message.
Manual Scheduling Override
"As a food truck manager, I want to manually toggle the red filter at any time so that I can adjust the display to my preference during unexpected late shifts."
Description

Provide users with the ability to manually enable or disable the red filter outside of scheduled hours via the application settings or quick-access toggle. This override respects the user’s immediate needs, allowing temporary suspension or activation without altering the predefined schedule. Visual feedback should confirm the override state and the next automatic activation time.

Acceptance Criteria
Enabling red filter outside scheduled hours via settings
Given the current time is outside the predefined red filter schedule, when the user navigates to application settings and enables the red filter override toggle, then the red filter must activate immediately and the UI must display an active override indicator.
Disabling red filter outside scheduled hours using quick-access toggle
Given the red filter is currently active outside scheduled hours, when the user taps the quick-access toggle to disable the override, then the red filter must deactivate immediately and the UI must update to show the override is off.
Visual confirmation of override state and next activation time
Given the override is active or inactive, when the user views the filter status screen, then the UI must clearly display the current override state (On/Off) and the next automatic activation or deactivation time based on the predefined schedule.
Override does not alter the predefined schedule
Given the user has activated or deactivated the red filter via override, when the override period ends or the next scheduled time is reached, then the system must revert to the original schedule without modifications and apply the red filter according to the predefined times.
Persisting override state across app restarts
Given the user has set an override state, when the application is closed and reopened, then the red filter override state must persist until the user manually changes it or the next scheduled transition occurs.
Intensity Adjustment Slider
"As an operator, I want to fine-tune the red filter intensity so that I can find my preferred balance between visibility and eye comfort."
Description

Implement a slider control in the settings menu that allows users to adjust the warmth level of the red filter from minimal to maximum intensity. The slider provides real-time preview of the filter’s effect on the dashboard. User selections are saved per profile and applied automatically according to the activation schedule.

Acceptance Criteria
Real-time Warmth Adjustment Preview
Given the user moves the intensity slider in the settings menu When the slider value changes Then the dashboard preview updates the red filter warmth level within 500ms
Saving User Warmth Preference
When the user confirms a new slider position Then the selected warmth level is saved to the user’s profile and persists after logout and login
Automatic Warmth Application on Activation
Given the activation schedule is reached or ambient light conditions trigger the filter When the filter turns on Then it uses the stored warmth level automatically
Manual Override During Active Filter
When the filter is active and the user adjusts the slider Then the filter intensity updates in real time without requiring deactivation
Ambient Light Sensor-Driven Activation
Given ambient light sensor readings fall below threshold When sensor triggers activation Then the red filter applies with the saved warmth level
Slider Boundary Limits
When the user moves the slider to minimum or maximum Then the filter warmth corresponds exactly to the defined minimal or maximal intensity levels
Profile Persistence and Sync
"As a mobile operator switching between tablet and phone, I want my red filter settings to sync so that I have a consistent display experience on all devices."
Description

Ensure that each user's red filter settings, including schedule, intensity, and overrides, persist across sessions and sync across all devices connected to the same account. When a user updates their settings on one device, changes propagate in real time to other devices, guaranteeing a consistent experience regardless of which device they're using.

Acceptance Criteria
Persist Settings Across Sessions
Given a user has configured red filter schedule, intensity, and overrides, When the user logs out and logs back in on the same device, Then the exact settings are loaded without requiring reconfiguration.
Synchronize Settings Across Devices
Given a user updates red filter intensity on Device A, When the update completes on the server, Then Device B reflects the new intensity within 5 seconds without manual refresh.
Real-Time Update Propagation
Given multiple devices are online and connected, When a user toggles the red filter override on one device, Then all other connected devices apply the override state within 3 seconds and persist until changed again.
Conflict Resolution for Concurrent Edits
Given a user modifies the filter schedule on Device A and Device B at the same time, When the server detects conflicting changes, Then the most recent timestamped update overrides the earlier one and all devices display the resolved settings.
Offline Mode Synchronization
Given a user makes changes to red filter settings while offline, When the device reconnects to the internet, Then the local changes are synced to the server and propagated to all other devices without data loss.

SilentWave Alerts

Delivers ultra-quiet, vibration-only notifications for after-hours restock reminders. Uses customizable vibration patterns to differentiate alert types without disturbing nearby residents or event attendees.

Requirements

Vibration Pattern Library
"As a food truck owner, I want unique vibration patterns for each type of restock alert so that I can instantly recognize the notification’s purpose without looking at my device."
Description

Implement a library of predefined, distinctive vibration patterns corresponding to different restock alert types (e.g., low stock, critical stock, replenishment confirmation). Each pattern must be optimized for clarity and comfort, tested across popular iOS and Android devices, and seamlessly integrated with the device’s native vibration API to ensure reliable silent notifications.

Acceptance Criteria
Low Stock Alert Vibration
Given an item’s stock falls below the predefined low stock threshold, when the system triggers the alert, then the device vibrates using the low stock pattern consisting of three short pulses of 200ms each with 100ms gaps.
Critical Stock Alert Vibration
Given an item’s stock reaches the critical stock threshold, when the critical alert is initiated, then the device vibrates using the critical stock pattern composed of one long 500ms pulse followed by two short 200ms pulses with 150ms intervals.
Replenishment Confirmation Vibration
Given a restock has been successfully confirmed by the user, when the confirmation event occurs, then the device vibrates using the replenishment confirmation pattern of five rapid 150ms pulses with 50ms gaps.
Cross-Platform Pattern Consistency
When tested on supported iOS and Android devices, each predefined vibration pattern must play with pulse durations and intervals within ±10% of their design specification in 95% of test runs across five different device models.
Native API Integration Reliability
When vibration alerts are triggered in both foreground and background app states, then 100 consecutive vibration calls must execute without errors or missed patterns, confirming reliable integration with the device’s native vibration API.
Custom Vibration Schedules
"As a food truck operator, I want to set specific schedules for my vibration alerts so that I only receive notifications during designated restocking times and avoid bothering nearby residents."
Description

Enable users to configure time windows and frequency rules for when vibration-only alerts are delivered (e.g., after business hours or during designated quiet periods). The system should respect user-defined schedules, automatically toggling alerts on or off based on calendar settings, location, or time of day to avoid unintended disturbances.

Acceptance Criteria
Time-Based Silent Alert Scheduling
Given a user has configured a silent vibration alert schedule from 10:00 PM to 6:00 AM, when a restock event triggers at 11:00 PM, then the system delivers only a vibration alert and no sound notification.
Frequency Rule Enforcement
Given a user sets a maximum of one vibration alert every 15 minutes during a silent period, when multiple restock events occur within 15 minutes, then only the first event generates a vibration and subsequent events within that window are suppressed.
Calendar Quiet Period Integration
Given a user marks an event as quiet on their calendar from 8:00 PM to midnight, when that time window begins, then the system automatically toggles to vibration-only alerts without manual intervention.
Location-Based Alert Suppression
Given a user defines a geofence around a residential area for quiet hours, when the device enters that geofence during the scheduled quiet period, then vibration alerts are enabled and sound alerts are disabled.
User Interface Schedule Management
Given a user accesses the vibration schedule settings, when they add, edit, or delete a time window or frequency rule, then the system reflects the changes immediately and persists them across restarts.
Alert Intensity Control
"As a user, I want to customize the strength of my vibration alerts so that I can feel them reliably without being overpowered by excessive vibration."
Description

Provide adjustable vibration intensity settings (e.g., low, medium, high) to accommodate personal preferences and varying device capabilities. The feature should include a slider or preset options within the SnackFleet dashboard, preview functionality for each intensity level, and adaptive defaults based on device model.

Acceptance Criteria
Adjusting Vibration Intensity via Slider
Given a user accesses Alert Intensity Control settings in the SnackFleet dashboard When the user moves the intensity slider to Low, Medium, or High Then the connected device’s vibration intensity immediately adjusts to the selected level
Selecting Preset Vibration Intensity
Given a user prefers quick selection When the user selects a preset option (Low, Medium, or High) in the intensity settings Then the corresponding vibration intensity is applied to the device without additional input
Previewing Vibration Patterns
Given a user has selected an intensity level When the user clicks the 'Preview' button Then the device vibrates for at least two seconds at the chosen intensity to demonstrate the pattern
Adaptive Default Intensity Based on Device Model
Given a supported device is registered in SnackFleet When the system completes device detection Then the default vibration intensity is set to the recommended level for that specific device model
Validation of Vibration Settings Persistence
Given a user sets a custom vibration intensity When the user navigates away and later returns to the intensity settings page Then the previously selected intensity level is retained and correctly displayed
Silent Mode Override Protection
"As a truck owner, I want my restock reminders to vibrate even if my phone is on silent so that I never miss important alerts, but I still want the option to disable that behavior if I choose."
Description

Ensure vibration-only alerts bypass device silent and do-not-disturb modes with user consent, guaranteeing critical restock notifications are never suppressed. Include an opt-in workflow to grant necessary permissions and clear explanations of why overrides are required, while offering an easy way to revoke those permissions.

Acceptance Criteria
User Opt-in for Override Permissions
Given the user opens the opt-in workflow for vibration override, When the user is presented with a permission request explaining why bypassing silent and DND modes is necessary, Then the user can grant the OS-level permission, And the app displays a confirmation message indicating successful permission granting.
Override Activation after Silent Mode Change
Given the user has granted override permissions, When the device is set to silent or Do-Not-Disturb mode and a restock alert is triggered, Then the device vibrates using the configured vibration pattern regardless of the current sound mode.
Customization of Vibration Patterns
Given the user accesses the SilentWave Alerts settings, When the user selects or creates a custom vibration pattern for restock reminders, Then the selected pattern is saved and applied to all subsequent vibration-only alerts.
Revocation of Override Permissions
Given the user navigates to the app’s permissions management screen, When the user revokes the bypass permission for silent and DND modes, Then the app disables vibration-only alerts override and future alerts follow the device’s current sound settings.
Consent Re-prompt after App Update
Given the app has been updated to a version introducing the override workflow, When the user launches the updated app without having previously granted permissions, Then the app prompts the user again with a clear explanation and blocks silent-mode alerts until permission is granted.
Cross-Device Alert Sync
"As a fleet manager, I want my vibration alert preferences and logs to stay consistent on my phone and tablet so that I have a unified view and don’t get repeated notifications."
Description

Sync vibration alert settings and histories across all devices linked to a SnackFleet account in real time. Changes made on one device should propagate instantly to others, and alerts triggered on any device should mark as received across the entire device ecosystem to prevent duplicate notifications.

Acceptance Criteria
Vibration Pattern Update Propagation
Given a user updates the vibration pattern on Device A, when the change is saved, then Devices B and C receive and apply the new pattern within 5 seconds
Cross-Device Alert Receipt Deduplication
Given an alert is triggered on Device A, when the alert fires, then Devices B and C mark the alert as received and do not vibrate within 3 seconds
Offline Device Settings Sync
Given Device D was offline and the user changed alert settings on Devices A and B, when Device D reconnects, then it retrieves and applies all missed settings updates without conflict
Concurrent Settings Conflict Resolution
Given a user updates the vibration intensity on Device A at the same time as on Device B, when both changes are processed, then the system applies the update with the latest timestamp across all devices
Unified Alert History Display
Given a user views the alert history on any device, when the history screen loads, then the list of alerts and their received timestamps match exactly across Devices A, B, C, and D

AfterDark Scheduler

Allows users to define custom ‘quiet hours’ during which all non-critical notifications are silenced or delayed. Ensures restock reminders occur only at designated times, maintaining operational awareness without unnecessary intrusion.

Requirements

Quiet Hours Configuration Interface
"As a food truck operator, I want to set my daily quiet hours easily so that I only receive restock reminders during my working hours and avoid unnecessary alerts at night."
Description

Provides an intuitive user interface within the SnackFleet dashboard for defining custom quiet hours by selecting start and end times, specifying days of the week, and naming schedules. This feature ensures users can tailor notification silence periods to their operational rhythms, seamlessly integrating with the existing alert system to suppress or delay non-critical notifications during designated periods, thereby reducing unnecessary disruptions and respecting user-defined rest windows.

Acceptance Criteria
User Creates a New Quiet Hours Schedule
Given the user is on the Quiet Hours Configuration interface When they select a valid start time, end time, days of the week, and provide a unique schedule name Then the schedule is saved successfully and displayed in the schedule list with correct details
User Edits an Existing Schedule
Given an existing schedule in the list When the user opens the schedule, modifies the times or days, and clicks Save Then the updated schedule reflects the new times or days without creating duplicates
User Deletes a Quiet Hours Schedule
Given one or more schedules present in the list When the user clicks the Delete action for a schedule and confirms the deletion Then the schedule is removed from the list and no longer appears in the UI
Notifications Are Suppressed During Quiet Hours
Given the current time falls within a defined quiet hours period When a non-critical notification is triggered Then the notification is not delivered until the quiet hours period ends
Notifications Resume After Quiet Hours End
Given a delayed non-critical notification during quiet hours When the quiet hours period ends Then all suppressed notifications are delivered to the user immediately
Notification Type Categorization
"As a truck manager, I want notifications to be categorized into critical and non-critical so that urgent alerts still come through during quiet hours while minor reminders are silenced."
Description

Implements a tagging system that classifies notifications into critical and non-critical categories based on predefined criteria such as stock level thresholds, order urgencies, and system alerts. This categorization allows the AfterDark Scheduler to selectively silence or delay only non-critical notifications while ensuring critical alerts bypass quiet hours, maintaining essential operational awareness without overwhelming users with low-priority messages.

Acceptance Criteria
Critical Notification Bypass
Given a critical notification and current time within user-defined quiet hours, when the notification is generated, then the system delivers the alert immediately to the user’s dashboard and all connected devices.
Non-Critical Notification Silence
Given a non-critical notification and current time within quiet hours, when the notification is generated, then the system queues the notification and suppresses delivery until quiet hours end, and displays a pending notification indicator.
Dynamic Threshold Tagging
Given a stock level update event, when the stock level crosses predefined critical or non-critical thresholds, then the system tags the notification correctly as ‘critical’ or ‘non-critical’ according to the configured threshold values.
Manual Tag Override
Given a user manually changes the category of a notification type in settings, when the user saves the override, then subsequent notifications of that type are delivered or queued based on the new category and quiet hour rules.
Notification Routing Validation
Given multiple devices are logged into the account, when a critical notification is triggered, then it is immediately delivered to all devices, and when a non-critical notification is triggered during quiet hours, then it is delivered to all devices after quiet hours conclude.
Critical Notification Override
"As a user, I want urgent alerts to bypass quiet hours so that I can respond immediately to critical stock shortages or system issues."
Description

Ensures that critical notifications, including emergency restock alerts and system failure warnings, can bypass defined quiet hours and be delivered immediately via designated channels (push, email, or SMS). This requirement guarantees that vital operational alerts reach users without delay, preventing potential stockouts or downtime even during user-specified quiet periods.

Acceptance Criteria
Emergency Low Inventory Alert
Given a truck’s inventory level for a critical ingredient falls below threshold during quiet hours, When the system detects the low inventory, Then an immediate notification must be delivered via push, email, or SMS bypassing quiet hours settings.
System Failure Warning
Given a backend service outage is detected during quiet hours, When the monitoring system raises a failure warning, Then the alert must be sent instantly through all designated channels regardless of quiet hours.
High-Demand Product Surge
Given sales of a product exceed a defined surge threshold during quiet hours, When the surge is detected, Then the system must immediately notify the user to restock via push, email, or SMS bypassing quiet hours.
Truck Offline Notification
Given a food truck goes offline or loses connectivity during quiet hours, When the disconnection is detected for more than two minutes, Then an urgent offline notification must be sent immediately via the user’s preferred channel.
Unexpected Route Delay
Given GPS tracking indicates a route delay exceeding the scheduled time during quiet hours, When the delay is confirmed, Then the system must override quiet hours to send a critical delay notification via push, email, or SMS.
Timezone and Schedule Persistence
"As a mobile operator, I want my quiet hours to respect my local timezone and sync across devices so that my schedule stays consistent even when I travel or switch devices."
Description

Stores quiet hours schedules in the user’s local timezone and preserves them across sessions, devices, and app updates. This feature synchronizes schedules consistently across web and mobile dashboards, automatically adjusting for daylight saving changes and ensuring that quiet hours remain accurate and effective regardless of location or device.

Acceptance Criteria
Initial Schedule Creation
Given a user selects their local timezone and defines quiet hours from 22:00 to 06:00, when they save the schedule, then the schedule is stored in the database with correct UTC offsets and displayed in the user’s local time on the dashboard.
Cross-Device Schedule Sync
Given a user sets quiet hours on the web dashboard, when they log into the mobile app, then the same quiet-hour schedule appears in the mobile interface adjusted for the device’s local timezone.
Daylight Saving Time Adjustment
Given a user’s quiet-hour schedule spans a daylight saving time change, when the DST transition occurs, then the schedule automatically shifts to maintain the same local start and end times.
App Update Persistence
Given a user has an existing quiet-hour schedule, when the application is updated to a new version, then the preexisting schedule remains unchanged and operational post-update without requiring reconfiguration.
Offline Schedule Access
Given a user is offline and opens the mobile or web app, when they view the quiet-hour settings, then the last synchronized schedule is displayed accurately and enforced if the device can suppress notifications locally.
Schedule Conflict Validation
"As a user, I want the system to warn me about conflicting quiet hours settings so that I can correct any overlaps and ensure my notification schedule is accurate."
Description

Incorporates real-time validation logic that detects and prevents conflicting quiet hours entries, such as overlapping time ranges or invalid durations. The system prompts users with clear error messages and suggestions for resolving conflicts, ensuring schedules are logically consistent and avoiding unintended silencing gaps or overlaps.

Acceptance Criteria
Overlapping Time Range Entry
Given an existing quiet hours entry from 17:00 to 23:00 and a new entry from 22:00 to 06:00, When the user attempts to save the new entry, Then the system displays an error indicating the overlap and suggests adjusting the new start time to 23:00 or later.
Sequential Time Range Entry Without Gaps
Given an existing quiet hours entry from 22:00 to 00:00 and the user defines a new entry from 00:00 to 06:00, When the user saves the schedule, Then the system accepts the new entry without error and displays both entries in chronological order.
Invalid Duration Entry
Given the user sets a quiet hours entry with identical start and end times (e.g., 22:00 to 22:00), When the user saves the entry, Then the system rejects it with an error stating “Duration must be greater than zero” and highlights the end time field.
Multiple Non-Overlapping Entries
Given multiple existing quiet hours entries and a new entry that does not overlap any (e.g., existing 08:00–12:00 and 13:00–17:00, new 18:00–22:00), When the user saves the new entry, Then the system saves it successfully and lists all entries without conflicts.
Boundary Crossing Midnight Conflict
Given an existing quiet hours entry from 01:00 to 04:00 and a new entry from 23:00 to 02:00, When the user attempts to save the new entry, Then the system detects the overlap across midnight, displays an appropriate conflict error, and suggests adjusting the end time to 01:00 or earlier.

WhisperRestock

Automatically generates and queues restock orders in silent mode. Upon the next login or during scheduled check-ins, users receive a concise summary of pending orders, streamlining after-hours inventory management.

Requirements

Instant Restock Generation
"As a food truck owner, I want the system to automatically generate restock orders based on my current inventory so that I don’t have to manually track stock levels and I avoid running out of ingredients."
Description

Automatically generate restock orders based on real-time inventory levels across all trucks, running silently in the background. By integrating with vendor ordering APIs, the system queues orders without user intervention, reducing stockouts and minimizing manual inventory checks. Expected outcomes include efficient replenishment workflows, time savings for owners, and reduced ingredient waste.

Acceptance Criteria
Nightly Restock Order Generation
Given that one or more items across any truck fall below their predefined threshold during off-hours, When the silent restock process runs at the scheduled time, Then restock orders for all low-stock items are automatically queued through the integrated vendor API without user intervention.
On-Demand Inventory Trigger
Given a user logs into the dashboard and current stock levels for any truck are at or below threshold, When the system runs the instant restock generation in the background, Then pending restock orders are created and ready for viewing in the user’s pending orders list.
Vendor API Failure Handling
Given the vendor API rejects or times out during order creation, When the system attempts to queue a restock order, Then it retries up to three times with exponential backoff, logs each failure, and flags the order for manual review if all retries fail.
Multiple Truck Sync Verification
Given multiple trucks reporting real-time stock data with varying low-stock situations, When the restock generation runs, Then unique restock orders are generated per truck without duplication, and each order correctly reflects that truck’s inventory needs.
Scheduled Check-In Summary
Given restock orders have been queued silently overnight, When the user next logs in or the scheduled check-in occurs, Then the dashboard displays a concise summary listing all pending restock orders by truck, including item names, quantities, and vendor details.
Scheduled Inventory Check-In Summaries
"As a food truck owner, I want to receive concise summaries of pending restock orders during my scheduled check-ins so that I can stay informed about my inventory needs without having to navigate detailed reports."
Description

Provide concise summaries of pending restock orders during scheduled check-ins at user-defined intervals (e.g., daily, weekly). Summaries include order details, item counts, and cost estimates, delivered via dashboard notifications upon login or email alerts. This ensures owners stay informed of upcoming orders without manual report generation.

Acceptance Criteria
Daily Login Dashboard Notification
Given a user has a daily check-in scheduled at 08:00, when the user logs in after 08:00, then the dashboard displays a concise summary of all pending restock orders including each item’s name, quantity required, and total cost estimate.
Weekly Email Alert Delivery
Given a user has a weekly check-in scheduled on Monday at 09:00, when the scheduled time is reached, then the system sends an email to the user’s registered address containing a summary of pending restock orders with item counts and cost estimates.
Custom Interval Dashboard Notification
Given a user configures a custom check-in interval (e.g., every 3 days), when that interval elapses, then upon next login a notification badge appears and a summary listing pending orders with details is available in the notifications panel.
Summary Content Accuracy Verification
Given there are pending restock orders in the system, when the summary is generated, then the summary lists each item name, required quantity, unit cost, and calculates the correct total cost matching backend data.
Silent Mode Summary Queuing
Given WhisperRestock silent mode is enabled, when a scheduled check-in occurs outside of user activity, then the system queues the summary and does not display it until the user’s next login, at which point a notification prompts review.
Silent Mode Operations Toggle
"As a food truck owner, I want to toggle silent mode on or off so that I can control when I receive restock notifications based on my current workflow and availability."
Description

Allow users to enable or disable silent mode globally or per truck, determining whether restock orders are generated silently. When enabled, orders queue without real-time alerts; when disabled, users receive immediate notifications for each order. This flexibility adapts notification behaviors to different operational scenarios.

Acceptance Criteria
Enable Silent Mode Globally
Given the user is on the Silent Mode settings page and selects 'Enable' for global silent mode When the user confirms the change Then all new restock orders are generated silently without triggering real-time notifications
Disable Silent Mode for Specific Truck
Given the user is on a truck's settings page and toggles off silent mode for that truck When a restock threshold is reached Then an immediate notification is sent to the user for that specific truck's restock order
Persist Silent Mode Setting Across Sessions
Given the user has set silent mode globally and logs out When the user logs back in Then the global silent mode setting remains enabled
Immediate Notifications When Silent Mode is Disabled
Given silent mode is disabled globally or per truck When a restock order is generated Then an immediate push notification appears summarizing the restock order details
Silent Mode UI Feedback
Given the user toggles silent mode on or off When the change is applied Then the UI provides a confirmation message and updates the toggle's visual state to reflect the current mode
Configurable Threshold Management
"As a food truck owner, I want to configure restock thresholds for each item so that the system generates orders that align with my specific usage and avoid overstocking or shortages."
Description

Enable users to set custom restock thresholds for each inventory item and truck, defining minimum stock levels that trigger automatic order generation. The intuitive settings panel allows adjustments of vendor preferences and order quantities, ensuring restock actions align with actual usage patterns and prevent overstocking.

Acceptance Criteria
Initial Threshold Setup for New Inventory Item
Given a food truck owner adds a new inventory item and accesses the restock threshold settings panel, when the owner enters a custom minimum stock level, selects a vendor preference, and specifies an order quantity, then the system must save and display the threshold and vendor preferences correctly for that specific truck and item.
Updating Existing Restock Thresholds
Given an existing restock threshold configured for an item on a truck, when the user modifies the minimum stock level or order quantity and confirms the changes, then the updated threshold values must persist across sessions and reflect in the inventory dashboard immediately.
Applying Vendor Preferences to Threshold Settings
Given multiple vendor options are available for an item, when the user sets a preferred vendor in the threshold settings panel, then the system must prioritize that vendor for all automated restock orders and show the selected vendor in the summary view.
Bulk Threshold Adjustments Across Multiple Trucks
Given the owner manages thresholds for the same item across multiple trucks, when the user selects several trucks in the settings panel and applies a single threshold change, then the new threshold must apply uniformly to all selected trucks and display confirmation for each.
Threshold Triggered Automatic Order Generation
Given real-time stock levels are monitored, when an item’s stock falls below its configured threshold during a scheduled check-in, then an automatic restock order must be generated, queued in WhisperRestock, and appear in the pending orders summary on the next user login.
After-Hours Summary Notifications
"As a food truck owner, I want to receive a concise summary of restock orders generated after hours when I next log in so that I can quickly review and confirm orders before placing them."
Description

Deliver a concise, aggregated notification of all pending restock orders generated during off-hours when the user next logs in. Notifications highlight critical low-stock items and provide direct navigation to order details, streamlining post-closure inventory management and nightly restock workflows.

Acceptance Criteria
Post-Login Pending Orders Summary
Given pending restock orders exist for one or more trucks When the user logs in after the daily off-hours period Then the system displays a single notification summarizing the total number of pending orders generated during off-hours
Critical Low-Stock Highlight
Given some pending restock orders include items below the critical threshold When the aggregated post-login summary is displayed Then items below the critical threshold are visually highlighted in red with their exact shortfall counts
Direct Navigation to Order Details
Given the aggregated notification is visible on the dashboard When the user taps any order summary entry within the notification Then the system navigates directly to the corresponding restock order details page for that truck
No Pending Orders Handling
Given no restock orders were generated during off-hours When the user logs in after off-hours Then no summary notification is shown and the user is taken directly to the main dashboard
Scheduled Check-In Notification
Given the system runs scheduled off-hours inventory checks and generates restock orders When the user logs in between scheduled checks Then the summary notification includes orders from both scheduled and manual restock generations

Ambient Glow Indicator

Integrates with device sensors to gently pulse a subtle, dim glow on-screen edges when critical stock levels are reached. Provides a minimal, non-intrusive visual cue for urgent restock needs in complete darkness.

Requirements

Glow Activation Threshold
"As a food truck owner, I want the glow indicator to pulse when any item reaches a critical stock level so that I can restock before running out during service."
Description

Enable the system to automatically detect when a food item’s stock level falls at or below a predefined critical threshold and trigger the Ambient Glow Indicator. This requirement ensures that the glow is activated precisely when an item requires restocking, minimizing false alerts and guaranteeing timely notifications. It involves defining configurable threshold values per item, real-time monitoring of inventory changes, and immediate initiation of the glow pulse once the threshold is met.

Acceptance Criteria
Default Threshold Activation
Given an item’s stock level reaches or falls below its predefined threshold, When the system detects this change, Then the Ambient Glow Indicator activates within 1 second.
Custom Threshold Configuration
Given a food truck owner sets a new critical threshold value for an item, When the configuration is saved, Then the system applies the new threshold immediately and stores it persistently.
Real-Time Inventory Update Response
Given an item’s stock decreases due to an order, When the inventory update is processed, Then the system re-evaluates the threshold and triggers the glow if the level is at or below threshold.
Multi-Item Critical Alert
Given multiple items fall below their thresholds simultaneously, When the system processes these events, Then the glow indicator pulses separately for each item in order of stock urgency.
Ambient Light Condition Handling
Given the device’s ambient light sensor reports darkness, When an item’s stock is critical, Then the glow activates at 20% maximum brightness to remain non-intrusive.
Transient Stock Fluctuation Filtering
Given rapid inventory changes due to bulk adjustments, When stock levels fluctuate around the threshold, Then the glow only triggers if the level remains at or below threshold for at least 5 seconds.
Ambient Light Adaptation
"As an operator working in varying light conditions, I want the glow brightness to adapt automatically to ambient light so that alerts are always visible but never distracting."
Description

Integrate with device ambient light sensors to adjust the glow’s brightness dynamically based on surrounding light conditions. The requirement ensures the glow remains subtle and non-intrusive in dark environments while still visible under bright lighting. It covers reading sensor data, calculating optimal brightness levels in real time, and updating glow intensity accordingly without noticeable latency.

Acceptance Criteria
Low-Light Environment Detection
Given ambient light reading below 10 lux, when the device enters a dark environment, then the glow brightness must adjust to no more than 10% of maximum intensity; and brightness must stabilize within ±2% of target within 100ms.
Bright-Light Environment Detection
Given ambient light reading above 1000 lux, when the device is used outdoors in bright light, then the glow brightness must adjust to at least 50% of maximum intensity; and brightness must remain within ±3% of target while maintaining visibility.
Rapid Ambient Light Changes
Given a sudden change in ambient light greater than 200 lux within 500ms, when the sensor updates, then the glow brightness must transition smoothly to the new target level within 200ms without perceptible flicker.
Startup Calibration
Given the app is launched, when ambient light sensor data becomes available, then the initial glow brightness must match the ambient light-adapted target within 1 second and without visible jump in intensity.
Sensor Error Handling
Given the ambient light sensor returns invalid or no data for more than 1 second, when the error state is detected, then the glow brightness must default to 30% of maximum intensity and log an error event.
Custom Glow Profiles
"As a truck operator, I want to customize the glow’s appearance and behavior so that it aligns with my workflow and visibility preferences."
Description

Provide users with the ability to customize the glow’s color, pulse rate, and intensity through the dashboard settings. This requirement enhances user experience by accommodating individual preferences and accessibility needs. It entails building a settings interface, storing user-defined profiles in the cloud, and applying the selected profile instantly during indicator activation.

Acceptance Criteria
Creating a New Glow Profile
Given a logged-in user on the glow settings page When the user enters a profile name, selects a color, pulse rate, and intensity and clicks Save Then a new glow profile is created, appears in the profile list, and is stored in the cloud.
Editing an Existing Glow Profile
Given an existing glow profile in the user’s list When the user modifies color, pulse rate, or intensity and clicks Update Then the profile’s parameters are updated in the cloud and reflected in the dashboard immediately.
Applying Glow Profile in Real-time Indicator Activation
Given stock reaches the critical threshold on any device When the ambient glow indicator activates Then the system retrieves the user’s selected profile and renders the glow with the correct color, pulse rate, and intensity.
Storing and Retrieving Profiles Across Devices
Given a user creates or edits a glow profile on one device When the user logs in on any other device Then all saved profiles are synced from the cloud and available in the dashboard settings.
Accessibility Compliance for Color Blind Users
Given a user with color vision deficiencies When selecting or previewing colors in the glow profile settings Then the system provides a high-contrast palette and accessible labeling ensuring all options meet WCAG AAA guidelines.
Cross-Platform Compatibility
"As a fleet manager using multiple devices, I want the glow indicator to work the same way on web and mobile so that I have a uniform experience anywhere."
Description

Ensure the Ambient Glow Indicator functions consistently across all supported platforms—web dashboards, iOS, and Android apps. This requirement covers implementing uniform APIs, responsive UI components, and platform-specific sensor integrations. It guarantees that users receive identical visual cues and configuration options regardless of device.

Acceptance Criteria
Web Dashboard Glow Activation Under Low Stock
Given the web dashboard is open in dark mode and stock for an item falls below the critical threshold of 10 units, when the page refreshes or stock data syncs, then the screen edges pulse with a dim ambient glow at 1Hz until the stock level is replenished above the threshold.
iOS App Glow Trigger in Dark Environment
Given the iOS app is running in a dark environment with screen brightness below 20%, when any tracked item's stock drops below the configured critical level, then the device screen edges emit a subtle pulsing glow at 1Hz and brightness ≤5% until restocked.
Android Sensor Integration for Ambient Glow
Given the Android app has granted sensor permissions and is in use at night, when the ambient light sensor reads ≤50 lux and a critical stock threshold is crossed, then the app edge glow activates with a pulsing rate of 1Hz and dim intensity, deactivating only after stock restoration.
Cross-Platform Uniform Glow Intensity
Given the Ambient Glow Indicator is enabled on web, iOS, and Android, when a critical threshold is reached on any platform, then the glow intensity level and pulse frequency must match within ±5% across all platforms.
User Configuration Sync Across Devices
Given a user adjusts glow settings (threshold, pulse rate, intensity) on one device, when the user logs into SnackFleet on another supported platform, then the customized settings are applied and visible immediately without additional configuration.
Performance and Battery Optimization
"As a mobile app user, I want the glow feature to run without draining my device’s battery or slowing down the app so that I can rely on alerts throughout my shift."
Description

Optimize the glow indicator’s implementation to minimize CPU usage, memory footprint, and battery consumption on mobile devices. This requirement involves efficient sensor polling strategies, hardware-accelerated animations, and intelligent start-stop controls when the dashboard is inactive. It aims to deliver alerts without degrading overall app performance or device longevity.

Acceptance Criteria
Idle State CPU Efficiency
Given the dashboard has been inactive for 60 seconds, when the glow indicator continues running, then the CPU usage attributed to the glow feature must not exceed 5% on average per minute.
Peak Usage Sensor Polling Optimization
Given the dashboard is actively used on a mobile device for 30 minutes with frequent stock updates, when the glow indicator polls sensors, then the polling frequency must not exceed once every 2 seconds and memory usage must remain under 50MB.
Background Battery Consumption
Given the application transitions to background mode for 15 minutes, when the glow indicator remains registered, then the battery drain attributable to the glow indicator must not exceed 1% of total battery capacity per hour.
Hardware-Accelerated Animation
Given the device supports hardware acceleration, when rendering the glow animation continuously for 5 minutes, then the animation must maintain at least 30 frames per second and CPU usage must not exceed 3%.
Intelligent Start-Stop Activation
Given the user switches away from the dashboard, when the dashboard becomes inactive, then the glow indicator must pause sensor polling and animations within 5 seconds and resume within 2 seconds when reactivated.

Product Ideas

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

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Idea

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Idea

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Analyzes daily item-specific sales spikes and recommends menu tweaks to boost top sellers.

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Groups ingredient orders by expiry date across trucks to cut food waste by 20%.

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NightShift Beacon

Switches to low-light mode and sends silent after-hours restock reminders.

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Press Coverage

Imagined press coverage for this groundbreaking product concept.

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SnackFleet Introduces AI-Driven Real-Time Route Optimizer to Slash Restock Travel Times

Imagined Press Article

CITY, STATE – July 1, 2025 – SnackFleet, the leading cloud-based inventory and order management platform for food trucks, today unveiled its new Real-Time Route Optimizer, an AI-powered feature designed to minimize restock travel times by up to 30 percent. By analyzing live sales, inventory levels and traffic conditions across multiple vehicles, the Real-Time Route Optimizer recalibrates each truck’s supply run on the fly—ensuring operators never miss a beat during peak service hours. “Food truck operators lose precious sales when they’re on the road without critical ingredients,” said Jamie Lee, CEO of SnackFleet. “Our Real-Time Route Optimizer transforms chaotic restock logistics into a smooth, automated process. Operators can focus on serving customers, not fighting traffic.” How It Works Real-Time Route Optimizer continuously pulls live data from SnackFleet’s centralized dashboard, combining sales velocity, low-stock alerts and geolocation to generate the most efficient trip path to suppliers. When sudden spikes in demand emerge, the system instantly flags trucks at risk of running out and pushes updated directions to drivers’ mobile devices. The feature integrates seamlessly with SnackFleet’s OmniNotify alerts and SurgeGuard real-time notifications to guarantee the right information reaches the right person at the right time. Proven Benefits In early beta tests with a national food truck consortium, the Real-Time Route Optimizer reduced average restock drive times by 28 percent and cut fuel consumption by 15 percent. Fleet Coordinator Maria Torres of Tasty Trails reported, “We save nearly an hour of driving each day across our four trucks. That time goes straight back into serving more customers.” User Experience and Accessibility Operators access the Real-Time Route Optimizer through SnackFleet’s intuitive web and mobile dashboards. A color-coded map displays critical stops, with color gradients highlighting urgency. One-tap reroutes allow drivers to adjust mid-run—eliminating manual recalculations and minimizing downtime. Case Study: Swift Sarah’s Downtown Taco Truck Swift Sarah, a solo operator in Chicago’s Loop district, relies on SnackFleet to manage her lunch rush. “During peak lunch hours, every minute counts,” said Sarah. “The Real-Time Route Optimizer quickly recalculates my route when I get a surge of orders. I never worry about running out of tortillas or toppings mid-shift.” Industry Impact and Future Roadmap The launch of Real-Time Route Optimizer marks SnackFleet’s continued commitment to leveraging AI for operational efficiency. Later this year, SnackFleet will introduce cross-fleet orchestration, allowing coordinated supply runs across entire networks to further drive down costs and carbon emissions. About SnackFleet SnackFleet equips independent food truck entrepreneurs and fleet coordinators with a cloud-based dashboard to track inventory, manage orders and automate restocking. With features like AutoThreshold, QuickRestock and the new Real-Time Route Optimizer, SnackFleet empowers operators to maximize uptime, reduce waste and boost revenue. For More Information Media Contact: Morgan Patel, Director of Communications SnackFleet Inc. phone: (555) 123-4567 email: press@snackfleet.com website: www.snackfleet.com

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SnackFleet Launches NightVision UI and WhisperRestock to Empower After-Hours Operators

Imagined Press Article

CITY, STATE – July 2, 2025 – SnackFleet today announced the availability of NightVision UI and WhisperRestock, two complementary features tailored to the unique challenges faced by food truck operators working late-night and event-based hours. NightVision UI transforms the dashboard into a high-contrast, dark-themed interface, while WhisperRestock automatically queues restock orders in silent mode for discreet, non-intrusive after-hours management. “As the food truck industry diversifies into late-night dessert services and festival concessions, operators need tools that prioritize both visibility and discretion,” said Ingrid Chen, Head of Product at SnackFleet. “NightVision UI reduces eye strain under dim lighting, and WhisperRestock ensures restock orders are ready without disturbing event attendees or nearby residents.” NightVision UI: Low-Light Mastery Developed in collaboration with operators like Night Owl Nina of Moonlight Creamery, NightVision UI applies a warm red shimmer to the interface after sunset, minimizing blue light exposure. Its high-contrast elements and customizable red-shift filters ensure critical inventory data remains clear, even under streetlamp glow or event lighting. Users can toggle between ‘Midnight’ and ‘Dusk’ modes or set automatic schedules via ambient light sensors. WhisperRestock: Silent After-Hours Intelligence WhisperRestock leverages SnackFleet’s AfterDark Scheduler to generate and queue restock orders without triggering audible alerts. Operators set ‘quiet hours’ to delay non-critical notifications; WhisperRestock compiles a concise summary for the next login or scheduled check-in. SilentWave vibration patterns distinguish urgent alerts—preventing stockouts while maintaining a low profile at night. Operator Success Stories Night Owl Nina, who serves gourmet desserts outside late-night venues, shared her experience: “My phone’s bright screen used to wake up festival-goers. With NightVision UI, I can monitor my inventory under dim streetlights, and WhisperRestock lines up my orders so I can focus on making churros instead of checking stock.” Technical Highlights NightVision UI seamlessly integrates with SnackFleet’s existing dashboard—no app update required. The red-shift filter dynamically adjusts based on time and user preference. WhisperRestock interfaces with the platform’s QuickRestock engine, automatically converting low-stock alerts into scheduled orders for preferred suppliers, ensuring a frictionless workflow. Looking Ahead Building on customer feedback, SnackFleet plans to introduce Ambient Glow Indicators and SilentWave haptic patterns for even subtler cues in the months ahead. These enhancements will further refine after-hours operations, making SnackFleet the go-to solution for nocturnal food truck ventures. About SnackFleet SnackFleet provides a real-time, cloud-based dashboard that empowers food truck owners and fleet coordinators to track inventory, manage orders and automate restocking across multiple vehicles. From Solo Truck Operators to Event Pop-Up Specialists, SnackFleet’s suite of features drives efficiency, reduces waste and boosts profitability. For Media Inquiries Ellis Rodriguez, Public Relations Manager SnackFleet Inc. phone: (555) 987-6543 email: media@snackfleet.com website: www.snackfleet.com

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SnackFleet Unveils Eco-Drive Routing and GreenOrder Automation for Sustainable Fleet Management

Imagined Press Article

CITY, STATE – July 3, 2025 – SnackFleet today announced two groundbreaking sustainability features—Eco-Drive Routing and GreenOrder Automation—designed to help food truck operators reduce fuel consumption, lower emissions and minimize food waste. These new tools align with global efforts to promote eco-friendly logistics and address growing environmental concerns in on-the-go foodservice. “Sustainability isn’t an afterthought—it’s the future of mobile foodservice,” said Derek Morales, VP of Engineering at SnackFleet. “Our Eco-Drive Routing and GreenOrder Automation features enable operators to lower their carbon footprint while maintaining peak operational efficiency.” Eco-Drive Routing: Smart, Efficient Journeys Eco-Drive Routing combines live traffic data, distance metrics and vehicle-specific fuel consumption models to calculate the most eco-efficient restock paths. Operators can prioritize ‘Green Routes’ that reduce idling time, optimize stop sequences and leverage low-emission zones. Early adopters have seen up to a 20 percent drop in fuel usage per week, translating into significant cost savings and environmental benefits. GreenOrder Automation: Waste-Reducing Purchase Power GreenOrder Automation leverages SnackFleet’s ExpirySync and ExpiryForecast engines to trigger consolidated purchase orders for ingredients nearing their best-by dates. By batching orders strategically across the fleet, the system prevents overstocking, reduces spoilage and maximizes ingredient utilization. Supply Chain Planner Lisa Wang noted, “We cut ingredient waste by 18 percent in our first month using GreenOrder Automation—plus, our suppliers appreciated the predictable, consolidated orders.” Feature Integration and User Experience Eco-Drive Routing and GreenOrder Automation are both accessible through SnackFleet’s main dashboard. A new ‘EcoScore’ indicator helps operators track weekly sustainability metrics, including estimated CO₂ reduction, fuel saved and waste avoided. Detailed EcoInsights Reports provide periodic breakdowns, enabling Data Insights Analysts to refine supply chain strategies and support corporate responsibility goals. Industry Response Festive Fiona, who manages multiple themed trucks at major music festivals, praised SnackFleet’s new tools: “Eco-Drive Routing helped us cut venue-to-venue travel emissions, and GreenOrder Automation meant fewer leftover perishables at the end of each event. It’s a win for our bottom line and the planet.” Commitment to Continuous Improvement SnackFleet will continue expanding its sustainability suite with features like Carbon Offset Integration and Solar Charge Planner later this year. The company remains dedicated to helping operators balance growth and environmental stewardship. About SnackFleet SnackFleet equips independent food truck owners and fleet coordinators with a real-time, cloud-based dashboard to track inventory, manage orders and automate restocking across multiple vehicles. From waste reduction to route optimization, SnackFleet’s innovative features drive profitability and sustainability. Press Contact Riley Chen, Sustainability Communications Lead SnackFleet Inc. phone: (555) 321-7890 email: green@snackfleet.com website: www.snackfleet.com

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