Ordering policy using multi-level association rule mining

Reshu Agarwal, Sarla Pareek, Biswajit Sarkar, Mandeep Mittal

Research output: Contribution to journalArticle

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

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Association rules
Managers
Concretes
Experiments
Ordering policy
Association rule mining
Order quantity

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems

Cite this

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Ordering policy using multi-level association rule mining. / Agarwal, Reshu; Pareek, Sarla; Sarkar, Biswajit; Mittal, Mandeep.

In: International Journal of Information Systems and Supply Chain Management, Vol. 11, No. 4, 01.10.2018, p. 84-101.

Research output: Contribution to journalArticle

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