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Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains

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Optimization and Inventory Management

Part of the book series: Asset Analytics ((ASAN))

Abstract

Timely identification of recently rising patterns is required in business process. Data mining methods are most appropriate for the characterization, valuable examples extraction, and predications which are essential for business support and decision-making. Some research studies have also expanded the use of this idea in inventory management. However, not very many research analyzes have considered the utilization of the data mining approach for supply chain inventory management. In this chapter, two unique cases for supply chain inventory management dependent on cross-selling effect are presented. First, the cross-selling effect in different clusters is characterized as a basis for deciding the significance of items. Second, the cross-selling in different time periods is considered as a criterion for ranking inventory items. An example is devised to approve the outcomes. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

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Author information

Authors and Affiliations

  1. Amity Institute of Information Technology, Amity University, Noida, India

    Reshu Agarwal

Authors
  1. Reshu Agarwal
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Correspondence to Reshu Agarwal .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Gujarat University, Ahmedabad, India

    Nita H. Shah

  2. Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India

    Mandeep Mittal

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Agarwal, R. (2020). Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains. In: Shah, N., Mittal, M. (eds) Optimization and Inventory Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-9698-4_25

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