SupplyNet

🎯 Inspiration

SupplyNet emerged from analyzing the technical limitations of existing logistics systems. While researching supply chain optimization, we discovered that current solutions suffer from:

Static rule-based systems that can't adapt to changing demand patterns
Batch processing architectures that can't provide real-time optimization
Isolated optimization algorithms that don't consider the full supply chain picture
Limited ML integration — most systems use simple statistical methods instead of deep learning

The Vision: Build a unified AI platform that combines LSTM neural networks, statistical anomaly detection, and operations research optimization in real time.

The Challenge: Create a system where multiple AI services can work together seamlessly, processing streaming data and providing actionable insights within milliseconds.

🚀 What It Does

SupplyNet is a real-time AI orchestration platform that integrates multiple machine learning models and optimization algorithms for end-to-end supply chain optimization.

Technical Architecture

Data Stream → Feature Engineering → Multi-Model AI Pipeline → Real-time Optimization → Actionable Insights

Core AI Services:

LSTM Forecasting Engine: PyTorch-based sequence models with 30-day lookback windows
Statistical Anomaly Detection: Z-score analysis, seasonal decomposition, and trend analysis
ML-Enhanced Inventory Optimization: Dynamic safety stock calculation using demand variability models
OR-Tools VRP Solver: Vehicle routing optimization with capacity, time window, and service constraints

Technical Specifications

Response Time: < 2 seconds for AI predictions
Model Accuracy: 85–95% for 7-day demand forecasts
Data Processing: Real-time streaming with 365-day historical analysis
Scalability: Designed for 1000+ warehouses and 10,000+ SKUs

🛠️ How We Built It

System Architecture: Frontend: React + TypeScript Backend: FastAPI + Python AI Service Layer: PyTorch + Scikit-learn + OR-Tools Data Storage: PostgreSQL + Redis + JSON

Development Methodology:

AI Model Development: LSTM autoencoders and forecasting models using PyTorch
API-First Design: RESTful endpoints with OpenAPI 3.0 specification
Real-time Processing: Async data processing with FastAPI and Uvicorn
Frontend Integration: Reactive UI components with React hooks + TypeScript interfaces
Data Pipeline: Automated feature engineering and model training pipelines

Key Technical Implementations:

LSTM Architecture: 2-layer LSTM (128, 64 neurons) with dropout + early stopping
Feature Engineering: Automated temporal features (day_of_week, month, quarter, is_holiday)
Optimization Constraints: Multi-dimensional constraint handling for VRP with OR-Tools
Real-time Updates: WebSocket integration for live streaming + model updates

🚧 Challenges We Ran Into

AI/ML Challenges:

LSTM Model Convergence: Limited training data → solved with data augmentation, synthetic data generation, transfer learning
Real-time Feature Engineering: Processing streaming data → solved with efficient pipelines using NumPy + Pandas
Model Persistence: Saving/loading trained models → solved with Joblib versioning + automated deployment

System Architecture Challenges

Async Data Processing: Coordinating multiple AI services → solved with FastAPI async endpoints + background tasks
State Management: Complex state across AI services → solved with React Context + custom hooks
API Contract Management: Data consistency → solved with Pydantic validation + TypeScript interfaces

Performance Challenges

Model Inference Speed: Sub-second response → solved with model quantization, caching, optimized preprocessing
Memory Management: Handling large datasets → solved with streaming, chunked processing, memory-efficient algorithms