Abstract

Introduction: Optimal inventory levels are necessary for a firm to avoid shortage/ excess of an item. The shortage of an item leads to stock out conditions resulting in loss of profit. When items are correlated with each other, the stock out condition of one item may result in the nonpurchase of its associated items also which, in turn, further brings down the profit. In this paper, this loss in profit is used to modify the opportunity cost of an item resulting in its modified EOQ.

Methods: One illustrative example has been discussed which incorporates purchase dependencies in retail multi-item inventory management. The model discussed in this research paper will be motivational for researchers and inventory managers and provides a method for incorporating correlation among items while managing inventory.

Results: The EOQs of items are estimated both by using the traditional method and then by using modified opportunity cost (modeled as loss profit). Results show that in frequent itemset {A, B, D}, EOQs of all three items increased when correlation among them is considered, resulting in an increase in the profit.

Discussion: In inventory management system, for increasing the profit of a firm, EOQs of items need to be calculated in order to avoid shortage or excess of inventory. For explaining the approach, a very small database is taken consisting of only 5 items and 10 transactions, therefore, the increase in profit is minimal however when this approach is applied on a real database consisting of thousands of items and transactions, the increase in profit will be significant.

Conclusion: One of the major focus areas of inventory management is to determine when and how much quantity of items needs to be ordered so that total inventory cost can be minimized and the profit of a firm can be maximized. However, while calculating the true value of an item and the profit it brings to the firm, it is very essential to analyze its effect on the sale of other items. Association rule mining provides a way to correlate items by calculating support and confidence factor.

Keywords: Terms, Data Mining, Inventory Management, Purchase Dependency, Multi-Item Inventory, opportunity cost.

Graphical Abstract