Predictive Supply Chain for Pharma Distribution

AI-driven forecasting predicting vaccine demand with 92% accuracy, reducing waste by incorporating 50+ external data signals.

92%

Forecast Accuracy

30%

Waste reduction

22%

Higher availability

$4.5M

ROI (Year 1)

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Key Result

30% reduction in medicine waste and 22% improvement in regional drug availability.

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The Challenge

A major pharmaceutical distributor was losing $15M annually to expired medication. Their traditional forecasting models failed to account for sudden weather shifts and hyper-local health trends.

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Our Approach

We built a time-series transformer model that ingest historical sales, local weather pattern, social sentiment, and hospital admission data to provide SKU-level demand predictions.

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The Impact

Over-stocking was reduced by 30% while stock-out events dropped by 22%. The system now automates 80% of regional ordering, allowing logistical teams to focus on emergency response.

Project Timeline

Phase 1

External Data Integration

Integrated 50+ real-time data API streams covering weather, social trends, and public health reports.

Phase 2

Transformer Modeling

Developed multi-horizon time-series transformers capable of predicting demand from 1 to 90 days out.

Phase 3

Automated replenishment

Integrated the prediction engine directly with the ERP to trigger automated regional inventory transfers.

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"This isn't just about efficiency; it's about getting life-saving medicine where it needs to be, exactly when it's needed. The accuracy is astounding."

Luc Villeneuve

Supply Chain Director

Nordic Health Logistics