AI Supply Chain Optimisation for Platelets to Reduce Costs
- Improve productivity and performance
- Cutting-edge AI solution to optimise a complex, short-life blood product supply chain.
- 54% reduction in expired platelets.
- 100% elimination of costly ad hoc transport.
- Significant reduction in expiries while maintaining a high “In Full” delivery rate.
Background
Platelets are critical blood products with a shelf life of only 7 days. Hospitals must manage diverse blood types and antigens to meet patient needs while minimizing overstocking and wastage. This involves complex coordination of supply, manufacturing, distribution, stock holding, and logistics.
Objectives
- Cut ad hoc transport costs by 50%.
- Reduce expiries by 50%.
Approach
- Data Management: Ensured accuracy from three Wellness Network trusts.
- Forecasting: Predicted demand for 40 blood products across 15 hubs.
- Advanced Modelling: Built a model with 700,000 variables to cut costs; validated with a simulator.
- Data Science Methodology: Used time series analysis and Kortical's AutoML to identify XGBoost.
- Deployment: Streamlined with Kortical Cloud API; created a team dashboard.
Results
- 54% reduction in expired platelets.
- 100% reduction in costly ad hoc transport.
- Maintained high “In Full” delivery rates while reducing expiries.
Case Study Sliding
Releated Case Study
customer business solutiondesign - customer centric solutionweb development based companydesign - customer centric solutionmarketing based devlopmentcustomer business solutiondesign - customer centric solutionweb development based companydesign - customer centric solutionmarketing based devlopmentcustomer business solutiondesign - customer centric solutionweb development based companydesign - customer centric solutionmarketing based devlopment