The Challenge
- Regulatory guidelines and KPIs meant pharmaceutical companies required strict controls to avoid under-delivery of pharmaceutical products to hospitals.
- Inventory costs and waste on perishables were a top financial priority.
- Thousands of SKUs, inconsistency in supplier delivery time.
- Demand exposed to shocks like pandemics and natural disasters.
There was no national view of demand or stock. Planning was regional and manual, and KPIs were slipping.
The Solution
An ontology-driven Operational Decision System was deployed using Cauldrn's AI Cloud Foundry. The solution forecasted demand using multiple ML models that dynamically recalibrated against history.
- A decision engine, not just a dashboard, recommending order quantities to meet KPIs without under- or over-delivering. The decision engine set safety stock levels by product criticality, and recommended suppliers based on shortest and consistent delivery times.
- Data from seven systems (ERP, data warehouses, and spreadsheets) were unified into one model spanning inventory, procurement, sales, supply, and transport.
- A real-time AI analyst flagging areas of risk and surfacing external information relevant to decision-making.
Regional, manual planning replaced with one connected platform and a shared national view.
The Results
One national decision system, one resilient operating model, no longer dependent on local knowledge.
- Demand forecast accuracy improved and downstream supply chain benefited significantly
- Results against regulator KPIs improved. Inventory costs and perishable waste reduced
- Planners become specialists in products and suppliers, rather than geographies
- Procurement shifted from administration to performance managing supplies and bundling strategic orders at a national level
- Full audit trail on past decisions and the context of the decision was used to improve future decisions
Outcomes at comparable AI-driven pharma programs identified:
- Inventory levels: 20–30% reduction, from AI-driven demand forecasting and dynamic segmentation
- Critical stockouts: up to 80% fewer, from AI forecasting models tuned to product criticality
- Fill rates: 5–8% improvement, from AI-enabled control-tower visibility across the network
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