AI assisted demand prediction model for surgical procedure packs
KTP Project with Unisurge International's, Newmarket, England
- Funding Amount: £316,270
- Funder: Innovate UK (formerly Technology Strategy Board)
- Role: Project Lead/Principal Investigator
- Academic Team: Professor Abdellah Salhi (Co-I), Professor Xinan Yang.
- KTP Associate: TBH
- Industry Lead: Mr Zak Wilson and Amir Farboud
- Year: 2025 - 2028
This KTP aims to position Unisurge International as an industry leader by developing an innovative, data-driven predictive model for surgical pack demand—an approach new to both the company and the wider sector. By integrating NHS HES data with Unisurge’s internal records, the project seeks to enhance forecasting accuracy, reduce reliance on costly third-party logistics, and improve delivery efficiency. Advanced techniques such as machine learning, stochastic optimisation, and reinforcement learning will be employed. Collaboration with NHS hospitals will ensure stakeholder-informed development. The project will ultimately reduce costs, environmental impact, and inventory waste, delivering significant operational and patient care benefits.