Data-Driven Platelet Inventory Management Under Uncertainty in the Remaining Shelf-Life of Units

Platelets are perishable (5-7 day shelf-life) blood products required for a variety of clinical treatments. In North America, hospitals typically procure platelet units from a central supplier. As such, the remaining shelf-life of the delivered units could be subject to high uncertainty. Our work focuses on developing new models that leverage the increasingly available data from hospital information systems to prescribe ordering decisions in the presence of this uncertainty. Specifically, we consider a periodic-review, perishable inventory system with zero lead-time and uncertainty in demand and remaining shelf-life of orders, operating under an Oldest-Unit, First-Out (OUFO) allocation policy. We consider a family of base-stock policies and adopt an Empirical Risk Minimization (ERM) approach to estimate the required inventory at the beginning of each period. The required inventory level for each period is assumed to be a linear function of a set of observed features in that period and the coefficients of the linear model are obtained by minimizing an approximate measure of the in-sample empirical cost, comprised of a weighted sum of shortage and expiry costs. Our fixed initial age model assumes a constant remaining shelf-life for all units. Our \emph{robust model} assumes that an adversary selects the remaining shelf-life of units subject to an uncertainty budget determined through an endogenous uncertainty set. We investigate the out-of-sample performance of the proposed models in a case study using data from two Canadian hospitals and in comparison to the hospitals' historical performances as well as other benchmarks. Both models achieve significant improvements over the historical decisions. For instance, the fixed initial age model achieves a 53% and 93% reduction in expiry rate, and a 82% and 99% reduction in shortage rate for the two hospitals, respectively. Further, it either outperforms or performs as well as the other benchmarks. The robust model achieves better out-of-sample generalizability and demonstrates a more "robust" performance under counterfactual remaining age distributions