Case Study SupplyIQ – Predictive Demand Forecasting & Inventory AI (Germany)

Project Overview

SupplyIQ is an enterprise AI supply chain forecasting solution built for a major automotive parts distributor headquartered in Munich, Germany. The platform predicts regional parts demand across 14 European logistics hubs to minimize both stockouts and excess inventory holding costs.

Client & Location

SupplyIQ Logistics GmbH
Location: Munich, Germany
Industry: Automotive & Supply Chain

Challenges

• Supply chain disruptions causing erratic lead times and frequent warehouse stockouts.
• Traditional statistical models failed to account for macroeconomic indicators, seasonality, and shipping route delays.
• High warehousing carrying costs for slow-moving inventory.

Solution

We built an ensemble machine learning model combining XGBoost, Prophet, and LSTM neural networks. The system continuously ingests historical sales, ERP order feeds, supplier transit times, and weather patterns to generate automated reordering recommendations.

Technologies Used

• Python (Scikit-Learn, XGBoost, Prophet)
• PostgreSQL & Redis Cache
• Docker & Kubernetes
• Automated REST API integration with SAP ERP

Results & Business Impact

• 94.8% forecast accuracy achieved across top 5,000 fast-moving SKU parts.
• 28% reduction in excess inventory capital locked in European fulfillment centers.
• Stockout incidents decreased by 46% during peak seasonal quarters.