1️⃣ Objective
The objective of this capstone is to design and implement a Smart Supply Chain Optimization System that uses Machine Learning (ML) models to generate accurate demand forecasts and provides predictive insights for inventory and logistics planning. The system will integrate historical sales, external economic factors, and seasonal data to minimize stock-outs, reduce holding costs, and optimize last-mile delivery routes.
Key Goals:
✨ Develop a robust data ingestion pipeline for time-series data (sales, inventory, sensor data).
✨ Implement ML models (e.g., Prophet, ARIMA) for accurate 7/30/90-day demand forecasting at the SKU/Location level.
✨ Calculate optimal reorder points and safety stock levels based on forecasted demand and lead times.
✨ Implement a basic route optimization algorithm (e.g., Traveling Salesperson Problem heuristic) for distribution.
✨ Provide a predictive dashboard that visualizes stock-out risk, cost savings, and forecast accuracy.
✨ Establish an automated model retraining and monitoring pipeline for long-term performance.
2️⃣ Problem Statement
Traditional supply chain planning often relies on simple moving averages or manual expert judgment, leading to significant inefficiencies: bullwhip effect, high inventory holding costs from overstocking, and lost sales due to stock-outs. Furthermore, static routing leads to high logistics costs.
This project addresses the critical need for an automated, data-driven decision support system that leverages advanced analytics and ML to transition from reactive management to proactive, predictive supply chain optimization.
3️⃣ Methodology
The project will employ an MLOps-inspired workflow, focusing on model development and integration:
✨ Phase 1 — Data Engineering: Ingest and clean historical sales data (time-series), process external data (holidays, economic indices), and prepare features for ML models.
✨ Phase 2 — Demand Forecasting Model: Train and fine-tune time-series models (e.g., ARIMA, SARIMAX, or Prophet) for multiple SKU/Location combinations. Evaluate using metrics like MAPE and RMSE.
✨ Phase 3 — Inventory Planning Logic: Use the model output to calculate key inventory parameters: Economic Order Quantity (EOQ), Reorder Point, and Safety Stock.
✨ Phase 4 — Predictive Logistics: Implement a route optimization module using a heuristic approach (Greedy or Simulated Annealing) to minimize total travel distance for forecasted orders.
✨ Phase 5 — Dashboard & API Integration: Develop a frontend dashboard to visualize forecasts, risk levels, and optimization recommendations. Create a REST API to serve predictions to external systems.
✨ Phase 6 — MLOps Pipeline: Set up a basic pipeline for automated model retraining, versioning, and performance drift monitoring.
4️⃣ Dataset
Sources:
✨ Historical Sales/Demand: Time-series of daily/weekly unit sales per SKU.
✨ Inventory Master: Current stock levels, safety stock, lead times, and holding costs.
✨ Pricing & Promotions: Historical pricing, markdown events, and promotional campaigns.
✨ External Factors: Holidays, weather data, local events, or relevant economic indicators (e.g., inflation).
Data Fields:
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨ Mean Absolute Percentage Error (MAPE): Overall forecast accuracy (lower is better).
✨ Root Mean Square Error (RMSE): Measure of model prediction magnitude error.
✨ Stock-Out Reduction: Percentage decrease in stock-out events compared to a baseline (e.g., simple average).
✨ Inventory Holding Cost Savings: Calculated cost reduction due to optimized safety stock and EOQ.
✨ Logistics Optimization Score: Percentage reduction in total vehicle travel distance/time compared to a non-optimized baseline route.
✨ Model Drift Rate: Frequency of performance degradation requiring retraining.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Visual representation of the system architecture (data flow from resume upload to RAG-based reasoning):
Claims Intake & Sources
Policy DB, Callcenter, Mobile app, Telematics, and Third-party data.
Ingest & Data Lake/Warehouse
S3, Postgres/Snowflake – images, logs, and structured data.
Feature Store & ETL
Normalized features, embeddings, and image vectors for ML.
ML Models (Fraud/Severity)
Fraud classifier, Anomaly detector, Severity regressor (with SHAP).
Decision & Rule Engine
Risk scoring, triage, suggested reserves, and policy checks.
Claims Management System
Payouts, notes, ticketing, repair shop integration, and file storage.
Investigator Dashboard
Flagged claims, timelines, evidence, SHAP explanations, and case assignment.
Analytics & Monitoring
Performance dashboards, model drift alerts, and business KPIs.
Compliance & Audit Log
Immutable log of all decisions, rule firings, and data changes for regulatory purposes.
Final Outcome: Intelligent & Efficient Claims Processing
Reduced fraud, faster payouts, improved customer satisfaction, and lower operational costs.
Claims Intake & Sources
Policy DB, Callcenter, Mobile app, Telematics, and Third-party data.
Ingest & Data Lake/Warehouse
S3, Postgres/Snowflake – images, logs, and structured data.
Feature Store & ETL
Normalized features, embeddings, and image vectors for ML.
ML Models (Fraud/Severity)
Fraud classifier, Anomaly detector, Severity regressor (with SHAP).
Decision & Rule Engine
Risk scoring, triage, suggested reserves, and policy checks.
Claims Management System
Payouts, notes, ticketing, repair shop integration, and file storage.
Investigator Dashboard
Flagged claims, timelines, evidence, SHAP explanations, and case assignment.
Analytics & Monitoring
Performance dashboards, model drift alerts, and business KPIs.
Compliance & Audit Log
Immutable log of all decisions, rule firings, and data changes for regulatory purposes.
Final Outcome: Intelligent & Efficient Claims Processing
Reduced fraud, faster payouts, improved customer satisfaction, and lower operational costs.
9️⃣ Expected Outcome
✨ A fully operational Prediction API providing accurate demand forecasts and optimal inventory parameters for various SKUs.
✨ A demonstrable reduction in two key supply chain metrics: MAPE (forecasting error) and Logistics Cost (routing efficiency).
✨ A comprehensive MLOps pipeline that demonstrates best practices in model management and continuous integration/deployment.
✨ An interactive Web Dashboard for stakeholders to monitor key metrics, visualize forecasts, and see inventory recommendations in real-time.