A decision integration strategy for short-term demand forecasting and ordering for red blood cell components

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