An economic ordering quantity model for perishable products under stochastic demand and lead time: a case study of blood supply chain

Accurate estimation of the Economic Order Quantity (EOQ) is essential for reducing inventories and minimizing holding costs in supply chains that manage perishable products. Incorporating parameter uncertainty into EOQ estimation enhances inventory management effectiveness. Under stochastic conditions, including demand and lead time variability, four plausible inventory scenarios were identified for perishable-product storage systems. Mathematical formulations were developed to quantify expected holding, shortage, wastage, and opportunity-loss costs. By minimizing total inventory-related costs, the optimal EOQ, reorder point, and scenario probabilities were determined under both deterministic and probabilistic settings. The results indicate that with deterministic parameters, the model operates under a single scenario, whereas probabilistic parameters allow multiple scenarios to occur. Furthermore, under probabilistic conditions, the EOQ and reorder point are up to 25% lower than those obtained with deterministic parameters, while total costs decrease by up to 13%. When products are of critical importance to the organization, inventory levels are set so that the system predominantly operates under cycle 1 (no shortage and no perishability) and cycle 2 (perishability before reorder). In contrast, when product criticality is lower, inventory levels are adjusted to allow the system to more frequently experience cycle 3 (shortage without perishability) and cycle 4 (shortage with perishability).