EOQ: A Simulation approach for perishable products

It is well known that the inventory cost represents a non-negligible quota of the total logistics cost and for this reason, nowadays there are several models and approaches available to optimize its management. Minimizing the inventory cost, however, is challenging, because of the huge number of factors involved, some of which are stochastic in nature and difficult to control. Because of this complexity, there are no general approaches suitable to be applied in any context or scenario. The problem is exacerbated when handling perishable products, that cover a huge part of products such as food or pharmaceutical items. Indeed, the problem of obsolescence is particularly relevant for these items and involves additional costs. Even if products are completely different (e.g., high-tech products or fashion items), obsolescence still represents a main problem, as therefore the inventory models that can be utilized are almost the same as perishable products. In this paper, we revisit the classical Economic Order Quantity (EOQ) inventory model and develop a simulation approach to determine the control policies for perishable products. The goal of the model is to reduce the total inventory management cost by determining the best combination of EOQ and order point (OP). To be more precise, using a simulation approach, an optimal trade-off between the inventory cost, order cost, stock out cost, and disposal cost is determined. The model takes into account a uniformly distributed stochastic demand and assumes a fixed replenishment lead time; it also admits backorders when the available stock is not sufficient to meet the demand and adopts a First-In-First-Out policy (FIFO) to satisfy customer orders. Finally, the impact of product shelf-life and procurement lead time on system behavior is evaluated, by defining a proper simulative campaign and performing a sensitivity analysis on the results obtained. This study aims to help the decision makers in predicting how the perishability of the product and the choice of the supplier would impact on the total management cost, improving their understanding of the system behavior and increasing their control over its variables.