Our project followed a structured Analyze – Optimize – Implement framework:
1. Data-Driven Demand Forecasting
- Analyzed spare parts consumption data to identify patterns
- Developed a predictive model using Palantir Foundry for demand forecasting
- Applied machine learning algorithms to differentiate between fast- and slow-moving parts
- Improved accuracy of spare parts demand prediction
2. Supply Chain Process Optimization
- Optimized reorder points and safety stock levels, reducing unnecessary inventory
- Established automated alerts for critical stock levels, preventing service disruptions
3. Implementation & Change Management
- Integrated dashboards to provide real-time supply chain visibility
- Conducted training sessions for supply chain managers on the new forecasting system
- Defined a long-term governance model to ensure continuous improvement
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