  Blood transfusion is one of the most crucial and commonly administered
therapeutics worldwide. The need for more accurate and efficient ways to manage
blood demand and supply is an increasing concern. Building a technology-based,
robust blood demand and supply chain that can achieve the goals of reducing
ordering frequency, inventory level, wastage and shortage, while maintaining
the safety of blood usage, is essential in modern healthcare systems. In this
study, we summarize the key challenges in current demand and supply management
for red blood cells (RBCs). We combine ideas from statistical time series
modeling, machine learning, and operations research in developing an ordering
decision strategy for RBCs, through integrating a hybrid demand forecasting
model using clinical predictors and a data-driven multi-period inventory
problem considering inventory and reorder constraints. We have applied the
integrated ordering strategy to the blood inventory management system in
Hamilton, Ontario using a large clinical database from 2008 to 2018. The
proposed hybrid demand forecasting model provides robust and accurate
predictions, and identifies important clinical predictors for short-term RBC
demand forecasting. Compared with the actual historical data, our integrated
ordering strategy reduces the inventory level by 40% and decreases the ordering
frequency by 60%, with low incidence of shortages and wastage due to
expiration. If implemented successfully, our proposed strategy can achieve
significant cost savings for healthcare systems and blood suppliers. The
proposed ordering strategy is generalizable to other blood products or even
other perishable products.
