Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts

Abstract There are many vague and uncertain data which affect the quality of demand forecasting and ordering decisions in the supply chain of auto spare parts. This is due to the fact that the demand is not a unique concept for all levels involved in the supply chain. The downstream level of supply chain considers its intended criteria for deciding whether to place an order to the upstream level or not. Therefore, it is very important for the upstream level to discover the main criteria that are considered at the downstream level when issuing orders. In this paper, we intend to study the number of sold cars and their mileages associated to the each of spare parts as most important criteria when retailers are going to send the new orders to the distributor. ABC analysis is done for new criteria including the demand value of spare part in comparison with increase in the total mileages of its relevant cars during the fixed period. Rough set theory helps us induce patterns and rules from uncertain information obtained by ABC analysis over the past periods. We use the extracted rules to forecast the demands of retailers in the future and then place an order based on periodic review approach. Implementation of proposed model in the one of Iranian distributors, shows the significant improvement in the key performance measures such as increasing in service level and reducing the average value and age of inventory.

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