Development of Fuzzy Inventory Model using EOQ and EPQ

Abstract

One of the most important aspects of a supply chain and production system is inventory management, where companies want to reduce the expenses associated with inventory but also ensure that they have enough on hand. The traditional inventory models like Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ) are based on the assumption that parameters of inventory are known with certainty. The real-world inventory environments, however, are subject to uncertainty in demand, ordering costs, holding costs and production rate which make the deterministic approaches less effective. In this study, integrated fuzzy inventory model is proposed by incorporating fuzzy set theory into the classical EOQ and EPQ models. The parameters of inventory are uncertain in nature and are described by triangular fuzzy numbers and centroid defuzzification method is used to make practical inventory decisions. The mathematical modeling and analysis of fuzzy EOQ and fuzzy EPQ models is performed. A numerical example is given for comparison of the performance of classical and fuzzy inventory models. The results show that fuzzy inventory models give slightly higher order quantities and production quantities as compared to the deterministic inventory models which gives extra safety in case of uncertainty and prevents stockout. Sensitivity analysis also shows that the proposed model is stable and robust in terms of different demand conditions. The results indicate that the fuzzy inventory techniques are more realistic representation of practical inventory systems and they also help to the managerial decision-making process. The model developed in the proposed framework has been added to the field of inventory management literature, since ordering and production decisions have been combined in a single fuzzy environment while keeping the algorithm simple and easy to implement.