An exploration on adoption of inventory optimization using Metaheuristic techniques

Abstract

A key concern for global manufacturers today is reducing inventory and inventory driven costs across their supply and distribution networks. Therefore inventory management is considered to be an important field in supply chain management. Efficient and effective inventory management carried out throughout the supply chain ultimately enhances customer service. Hence, to ensure minimal cost for the supply chain, the determination of the inventory to be held at various levels in a supply chain is unavoidable. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation is considered. Are involved. In this present research, with focus on these issues of inventory management, Genetic Algorithm and Particle Swarm Optimization based heuristic models have been designed and implemented. The most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined using metaheuristic techniques so as to achieve the minimum total supply chain inventory cost. The inventory optimization models based on metaheuristic techniques were subjected to validation studies in three traditional manufacturing companies situated in India. Though there was little awareness on need for optimizing inventory to minimize supply chain cost, the progressive nature of those companies enabled to conduct the investigations successfully. This kind of success realized during this research indicated that inventory optimization models based on metaheuristic techniques are practically compatible. In order to further assess the reactions of the practitioners, a questionnaire was designed for enabling the competent personnel to express their reactions about inventory optimization models. The results of the analysis of those reactions also confirmed the practical compatibility. newline newline newline