Data & AICloud & DevOps
Predictive Supply Chain Intelligence Platform for a Global Logistics Enterprise
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The Challenge
A global logistics operator managing 14 distribution centres struggled with reactive disruption management and manual spreadsheet-based demand forecasting.
Supply chain disruptions were identified only after they had already impacted delivery SLAs, and inventory inefficiency was costing millions annually.
Our Approach
- Built a real-time data lakehouse on AWS ingesting POS data, supplier feeds, weather APIs, and geopolitical risk signals
- Trained an ensemble forecasting model combining gradient boosting and LSTM for 30/60/90-day demand prediction
- Developed a disruption early-warning system using anomaly detection on logistics network graphs
- Deployed an LLM-powered analyst copilot allowing supply chain managers to query predictions in natural language
Results
28% reduction in inventory holding costs across all 14 warehouses
19% improvement in on-time delivery rate in the first quarter post-deployment
11 days earlier disruption detection on average, enabling proactive mitigation
Related Services
This project drew on our work in Data & AI Engineering, Cloud & DevOps.
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