1️⃣ Objective
The objective of this capstone project is to develop a complete Full-Stack Food Delivery System. This platform will facilitate the entire process from order placement by a Customer to real-time order fulfillment managed by a Restaurant (Vendor) and tracked by a Delivery Agent. A core feature will be a dynamic Real-Time Order Tracking Module, providing visibility into the order lifecycle from confirmation to delivery completion.
Key Goals:
✨ Develop three distinct user portals: Customer, Restaurant, and Delivery Agent.
✨ Implement a robust menu management system for restaurants to add, update, and manage inventory.
✨ Establish an order processing workflow including payment integration and status updates (Placed, Confirmed, Preparing, Out for Delivery, Delivered).
✨ Integrate real-time tracking using WebSockets or periodic API polling for order status and delivery location.
✨ Incorporate a simple location-based service for calculating estimated delivery time (ETA) and matching orders to nearby agents.
2️⃣ Problem Statement
Traditional food ordering systems lack the synchronization needed between all parties, resulting in poor customer experience due to uncertainty about order status, unexpected delays, and manual communication overhead. Restaurants face inefficiency in managing incoming orders from disparate sources and coordinating with drivers.
This project solves the problem of information fragmentation by creating a single, integrated platform. The system ensures that customers have full transparency into their order’s progress through real-time tracking, while providing restaurants and delivery agents with efficient, dedicated dashboards to manage their tasks, optimizing the entire delivery logistics chain.
3️⃣ Methodology
The development will utilize a layered, multi-tier architecture focusing on the following phases:
✨ Phase 1 — Core Backend & Data Model: Design the database schema (Users, Restaurants, Menus, Orders). Implement core APIs for user authentication, product catalog, and basic CRUD operations.
✨ Phase 2 — Multi-User Interfaces: Develop the Customer UI (menu browsing, checkout), the Restaurant Dashboard (order acceptance/rejection, status updates), and the Delivery Agent App (pickup, delivery, status).
✨ Phase 3 — Order Workflow & Payment: Implement the state machine for order status transitions. Integrate a mock or sandbox payment gateway (e.g., Stripe, PayPal).
✨ Phase 4 — Real-Time Tracking: Use WebSockets (e.g., Socket.IO) or a dedicated service like Firebase for instant, real-time communication of order status updates to all relevant portals.
✨ Phase 5 — Geolocation & Logistics: Implement basic distance calculation (e.g., Haversine formula) to show ETA and match orders to nearest available delivery agents. [Image of the Haversine formula]
✨ Phase 6 — Testing and Deployment: Rigorous end-to-end testing across all three user roles and deployment to a cloud platform (e.g., AWS, Heroku).
4️⃣ Dataset
Core Entities:
✨ Users: Authentication, Role (Customer, Restaurant, Delivery Agent), Location (Address/Coordinates).
✨ Restaurants: Business Details, Menu Items (with price/inventory), Operating Hours.
✨ Menu Items: Name, Description, Price, Category, Restaurant ID.
✨ Orders: Customer ID, Restaurant ID, Total Amount, Order Status, Tracking Info.
Patient Records Table (Sample):
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨ Order Processing Time: Time taken from order placement to restaurant confirmation (Target: < 30 seconds).
✨ Tracking Latency: Delay between order status change and customer notification (Target: < 1 second).
✨ ETA Accuracy: Mean absolute error between predicted ETA and actual delivery time (Target: < 5 minutes).
✨ System Availability: Uptime of all three user portals and the core API services.
✨ Concurrent Orders: Ability of the system to manage a realistic load of simultaneous orders without failure.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Customer App (Frontend)
Browse menus, place orders, view real-time location (Mobile/Web).
Restaurant/Driver Apps
Accept/decline orders, update status, provide delivery GPS data.
External APIs
Payment gateways (Stripe/PayPal), Mapping services (Google Maps/OSM).
API Gateway / Proxy
Handles high-volume HTTP requests and WebSocket connections for real-time data.
Order Processing Service
Manages lifecycle: creation, validation, status updates, and transaction logging.
Geospatial & Dispatch Service
Assigns drivers, calculates estimated arrival time (ETA), and manages routing logic.
Database (PostgreSQL)
Stores persistent data: Menus, User profiles, Historical orders, Restaurant details.
Real-time Broker (Kafka/Redis)
Streams location updates and order state changes across all client apps.
Push Notification Service
Informs customers/restaurants of critical state changes (e.g., “Order is accepted,” “Driver is near”).
Final Outcome: Fast Order Fulfillment & Accurate Real-time Tracking
Efficiently connects customers, restaurants, and drivers for a seamless delivery experience.
User Interface (Mobile/Web)
Manual entry, expense review/editing, budget creation.
Image Upload & Storage
Receipt/invoice images (JPEG, PNG, PDF) uploaded to object storage.
Financial Data Connectors
Optional direct integration with bank/credit card APIs (e.g., Plaid).
API Gateway & Authentication
Routes requests and protects sensitive financial data.
OCR Processing Service
Extracts key data like Total, Date, and Vendor from images.
Data Normalization & Categorization
Cleans text and auto-assigns expense categories using ML.
Transaction Database
Stores structured expense records and user budget limits.
Analytics & Reporting Engine
Generates spending trends and custom tax-ready reports.
Alerting & Notification Service
Triggers alerts for budget overruns or unusual transactions.
Final Outcome: Accurate Financial Visibility
Provides real-time spending insights with automated data input.
9️⃣ Expected Outcome
✨ A fully functional, multi-user system demonstrating proficiency in full-stack development and real-time data handling.
✨ A demonstrable, low-latency order tracking mechanism providing high transparency to the customer.
✨ A modular codebase allowing easy future integration of features like personalized recommendations or chat support.
✨ A significant improvement in the efficiency and visibility of the food delivery process across all user roles.