Ordering policy using temporal association rule mining Online publication date: Tue, 13-Oct-2015
by Mandeep Mittal; Sarla Pareek; Reshu Agarwal
International Journal of Data Science (IJDS), Vol. 1, No. 2, 2015
Abstract: Temporal association rule mining is a variation of association rule mining for finding relationship between items with respect to particular time periods. It is very useful in making business-related decisions, such as catalogue design, cross-marketing, cross-selling, and inventory control. Further, for effective inventory management, economic order quantity (EOQ) of items is determined by considering cross-selling effect among items. In this paper, we propose a new method for EOQ estimation. The method is based on finding the strongest relation between items by using temporal association rule mining. First, opportunity cost of frequent item-sets is determined by the temporal association rule mining, and then this opportunity cost is used to determine the EOQ for imperfect quality items. Furthermore, effect on ordering policy is determined for imperfect quality items and perfect quality items by considering cross-selling effect. A numerical example is illustrated to validate the results.
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