Predictive Supply Chain Analytics

Problem

Supply chain managers rely on simple historical averages for demand forecasting. This approach fails during market shifts, seasonal anomalies, or disruption events — leading to either excess inventory or stockouts.

Our Approach

We’re researching ML models that combine multiple data signals — sales history, market indicators, weather patterns, social media sentiment — to produce more accurate demand forecasts.

Current Status

Research phase. Evaluating different model architectures (LSTM, Prophet, transformer-based) on historical supply chain datasets. Early results show 23% improvement over baseline forecasting.

Next Steps

• Complete model comparison study
• Build prototype dashboard for real-time forecasting
• Identify pilot industry for validation