Demand Forecasting & Auto-Replenishment System (TFT)

Overview

The project involved developing an AI-driven demand forecasting system for a large network of distributed retail locations (scalable to thousands). The solution enabled fully automated inventory replenishment by predicting product demand for fast-moving SKUs across all categories (food, beverages, non-food). The system addressed inefficiencies in manual ordering processes, preventing both stockouts and overstock. Technical details: Forecast horizon: aligned with operational constraints (e.g., delivery cycles) Error metric: MAPE (baseline: simple average-based forecasting) Clustering: HDBSCAN on behavioral patterns Inference mode: batch (triggered by operational events)

Achievements

Forecast accuracy improved ~3x compared to baseline, significantly improving supply chain efficiency and enabling automated ordering without human intervention.

Responsibilities

• Designed and implemented a Temporal Fusion Transformer (TFT) for multi-horizon time series forecasting
• Built feature pipelines incorporating historical sales, seasonality patterns, external signals, and location grouping
• Applied clustering techniques to group locations by behavioral patterns, enabling model generalization
• Implemented cold-start handling via data-driven grouping for onboarding new locations without historical data
• Configured forecasting aligned with operational constraints (e.g., delivery cycles)
• Set up automated pipelines for recurring forecast generation

Technologies Used