Supply Chain Optimization
Fine-tuned predictive models to forecast demand accurately, optimizing inventory levels across 400+ warehouses.
The Challenge
An international manufacturing firm struggled with severe bullwhip effects in their supply chain. Inaccurate demand forecasting led to concurrent issues of massive overstock in certain regional warehouses and stockouts in others, resulting in high holding costs and lost sales opportunities.
Our Solution
Our data science team integrated disparate data lakes—combining internal ERP data with external signals like weather patterns, macroeconomic indicators, and local event calendars. We trained advanced deep learning models (LSTMs) to predict highly localized demand for thousands of SKUs up to 12 weeks in advance.
The Results
Inventory holding costs were reduced by 22% network-wide. The accuracy of demand forecasts improved to 94%, practically eliminating regional stockouts for top-tier products and enabling just-in-time manufacturing adjustments.