Int. J. of Data Science   »   2015 Vol.1, No.2

 

 

Title: Ordering policy using temporal association rule mining

 

Authors: Mandeep Mittal; Sarla Pareek; Reshu Agarwal

 

Addresses:
Department of Computer Science Engineering, Amity School of Engineering and Technology, Bijwasan, New Delhi – 110061, India
Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan – 304022, India
Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan – 304022, India

 

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.

 

Keywords: EOQ; economic order quantity; imperfect quality items; temporal association rules; association rule mining; cross-selling effect; inventory management; ordering policy.

 

DOI: 10.1504/IJDS.2015.072419

 

Int. J. of Data Science, 2015 Vol.1, No.2, pp.157 - 171

 

Available online: 13 Oct 2015

 

 

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