Abstract—In this paper the objective is to determine the optimal allocation of spares for replacement of defective parts on- board of a usage. The optimal inventory control methodologies intend to reduce the supply chain cost by controlling the inventory in an effective manner, such that, the SC members will not be affected by surplus as well as shortage of inventory. In this paper, we propose an efficient approach that effectively utilizes the Genetic Algorithm for optimal inventory control. This paper reports a method based on genetic algorithm to optimize inventory in supply chain management. We focus specifically on determining the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that the total supply chain cost is minimized .So, the overall aim of this paper is to find out the healthy stock level by means of that safety stock is maintained throughout the service period. Keywords—Excess Stock, Genetic algorithm, Inventory, Optimization, Safety stock. I. INTRODUCTION HE design and operation of spare part management systems is very important for automobile sector, Prior relevant system could be grouped in two categories. It is aimed to find optimal demand for a given spare parts management system; that is, how to determine optimal inventory level in order to reduce cost. This paper attempts to solve a comprehensive design problem for a spare part management system. Every automobile sector should proceed systematically and establish an effective Spare parts management system. Inventory encompasses all raw materials, work in process, and finished goods within the supply chain. Changing Inventory policies can dramatically alter the supply chain’s efficiency and responsiveness. Inventory is an important cross functional driver of supply chain performance. An important role that can be satisfied by having the product ready and available when the customer wants it to reduce the customer waiting time in the S.Godwin Barnabas 1. is an Assistant Professor, Mechanical Department in the Velammal College of Engineering and Technology,Madurai,Tamilnadu,India (Phone: +919943338837 ; e-mail: goddwin@rediffmail.com ). I. Ambrose Edward 2. is an Assistant Professor, Mechanical Department in the Velammal College of Engineering and Technology,Madurai,Tamilnadu,India (phone:+919952199565; e-mail:edwardambrose@ymail.com) S.Thandeeswaran 3. is an Assistant Professor, Mechanical Department in the Velammal College of Engineering and Technology,Madurai,Tamilnadu,India (Phone:+919790362087; e-mail: thandeeswaran.s@gmail.com). service sector. Inventory is held throughout the supply chain in the form of raw materials, work in progress, and finished goods. II. LITERATURE REVIEW Supply chain network is a complex network, which consists of multiple manufacturers, multiple suppliers, multiple retailers and multiple customers. The accomplishment of beam-ACO in supply-chain management has been proposed by Caldeira et al.[12]. Beam- ACO has been used to optimize the supplying and logistic agents of a supply chain. A standard ACO algorithm has aided in the optimization of the distributed system. The application of Beam-ACO has enhanced the local and global results of the supply chain. A beneficial industry case applying Genetic Algorithms (GA) has been proposed by Wang et al.[13]. The case has made use of GAs for the optimization of the total cost of a multiple sourcing supply chain system. The system has been exemplified by a multiple sourcing model with stochastic demand. A mathematical model has been implemented to portray the stochastic inventory with the many to many demand and transportation parameters as well as price uncertainty factors. A genetic algorithm which has been approved by Lo [14] deals with the production inventory problem with backlog in the real situations, with time-varied demand and imperfect production due to the defects in production disruption with exponential distribution. Besides optimizing the number of production cycles to generate a (R, Q) inventory policy, an aggregative production plan can also be produced to minimize the total inventory cost on the basis of reproduction interval searching in a given time horizon. P. Radhakrishnanet. al.[18] developed P.Radhakrishnan et. al.[18] developed a new and efficient approach that works on Genetic Algorithms in order to distinctively determine the most probable excess stock level and shortage level required for Inventory optimization in the supply chain such that the total supply chain cost is minimized. Many well-known algorithmic advances in optimization have been made, but it turns out that most have not had the expected impact on the decisions for designing and optimizing supply chain related problems. Some optimization techniques are of little use Spare Parts Inventory Model for Auto Mobile Sector Using Genetic Algorithm S. Godwin Barnabas, I. Ambrose Edward, and S.Thandeeswaran T 3rd International Conference on Trends in Mechanical and Industrial Engineering (ICTMIE'2013) January 8-9, 2013 Kuala Lumpur (Malaysia) 261