AI-Powered Supply Chain Optimization
Machine learning forecasting engine and dynamic route optimization system that reduced logistics costs by 28% while improving on-time delivery rates to 98.5% across a pan-India distribution network.
Business Impact
Technologies
Timeline
7 months — Pilot to Full Rollout
The Challenge
A large logistics provider managing 2,400+ trucks across 300+ routes relied on static delivery schedules and manual dispatch decisions. Demand volatility, traffic patterns, and vehicle breakdowns were addressed reactively — resulting in 22% empty-return trips, frequent missed SLAs, and annual fuel costs spiralling 15% year-over-year. Dispatchers had no visibility into real-time fleet status or predictive demand signals.
The Solution
We built an ML-powered supply chain brain. Time-series forecasting models (Prophet + LSTM ensembles) predict demand at the warehouse-SKU level with 94% accuracy. A custom route optimisation engine solves the vehicle routing problem with time windows (VRPTW) using a hybrid genetic algorithm, updating routes dynamically based on real-time traffic, weather, and fleet telemetry. Dispatchers monitor everything through a live geospatial dashboard built with React and Mapbox.