Ordering Policy Using Multi-Level Association Rule Mining

Reshu Agarwal, Sarla Pareek, Biswajit Sarkar and Mandeep Mittal
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Reshu Agarwal: G. L. Bajaj Institute of Technology and Management, Greater Noida, India
Sarla Pareek: Banasthali University, Banasthali, India
Biswajit Sarkar: Hanyang University, Ansan, Republic of Korea
Mandeep Mittal: Amity School of Engineering and Technology, New Delhi, India

International Journal of Information Systems and Supply Chain Management (IJISSCM), 2018, vol. 11, issue 4, 84-101

Abstract: In this article, an inventory model for a retailer's ordering policy is studied. Multi-level association rule mining is used to find frequent item-sets at each level by applying different threshold at different levels. During order quantity estimation, category, content, and brand of the items are considered, which leads to the discovery of more specific and concrete knowledge of the required order quantity. At each level, optimum order quantity of frequent items is determined. This assists inventory manager to order optimal quantity of items as per the actual requirement of the item with respect to their category, content and brand. An example is devised to explain the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on the exiting dataset.

Date: 2018
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