How we helped a multi-national retailer implement an AI-native demand forecasting system, reducing stockouts by 32% and cutting working capital requirements.
32%
Reduction in Stockouts
$18M
Working Capital Freed
8x
ROI in Year 1
The client operated over 500 retail locations across Southeast Asia with a highly complex supply chain. Their legacy forecasting models were rule-based and relied on historical averages, leading to severe stockouts during peak seasons and massive overstock during off-peak periods. They lacked the ability to ingest real-time signals like weather, local events, or social media trends into their purchasing decisions.
Deployed an end-to-end MLOps pipeline on Databricks to clean and unify 5 years of historical transaction data.
Built a gradient boosting model (XGBoost) to predict SKU-level demand across 500+ stores, factoring in 40+ external variables (weather, holidays, competitor pricing).
Implemented an Agentic AI orchestration layer that automatically generated purchase orders and alerted procurement managers only for high-risk anomalies.
Established a robust Model Governance framework to monitor data drift and retrain models autonomously.
The transition from reactive to predictive inventory management transformed the client's working capital dynamics. Within the first 6 months of deployment across their flagship stores, the system achieved a massive reduction in out-of-stock events while simultaneously lowering overall inventory holding costs.
