Data mining Aided Proficient Approach for Optimal Inventory Control in Supply Chain Management

Optimal inventory control is one of the significant tasks in supply chain management. The optimal inventory control methodologies intend to reduce the supply chain (SC) 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 Data Mining (DM) concepts as well as Genetic Algorithm (GA) for optimal inventory control. The proposed approach consists of two major functions, mining association rules for inventory and selecting SC cost-impact rules. Initially, the association rules are mined from EMA-based inventory data, which is determined from the original historical data. Apriori, a classic data mining algorithm is utilized for mining association rules from EMA-based inventory data. Later, with the aid of genetic algorithm, SC cost-impact rules are selected for every SC member. The obtained SC cost-impact rules will possibly signify the future state of inventory in any SC member. Moreover, the level of holding or reducing the inventory can be determined from the SC cost-impact rules. Thus, the SC cost-impact rules that are derived using the proposed approach greatly facilitate optimal inventory control and hence make the supply chain management more effective.