Ordering policy using multi-level association rule mining

Reshu Agarwal, Sarla Pareek, Biswajit Sarkar, Mandeep Mittal

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)84-101
Number of pages18
JournalInternational Journal of Information Systems and Supply Chain Management
Volume11
Issue number4
DOIs
Publication statusPublished - 2018 Oct 1

Bibliographical note

Funding Information:
2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 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 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. International Journal of Information Systems and Supply Chain Management (IJISSCM) 11 4 10 2018-10-01T00:00:00 84 101 18 IJISSCM2018114IJISSCM.2018100105.pdf 10.4018/IJISSCM.2018100105 1935-5726 pdf IJISSCM.2018100105.pdf Association Rule Mining Cross-Selling Imperfect Quality Items Multi-Level Association Rule Mining

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems

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