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A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification

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Abstract

ABC analysis is an efficient and easy-to-use methodology to classify inventory based on a single or multi-criteria basis that may consist of thousands of items. The first study by Dickie (Fact Manag Maint 109(7):92–94, 1951), based on a single criterion, is considered to be limited now. New studies focus on Multi-Criteria-Inventory Classification (MCIC) since such an extension of the criteria fits the realities of modern business decisions. The proposed approach in this study uses three-phased MCIC incorporating analytical hierarchy process (AHP), Fuzzy C-Means (FCM) algorithm, and a newly proposed Revised-Veto (Rveto) phase to meet the ABC classification principles and increase its applicability and flexibility. This new approach is called AHP–FCM–Rveto and proposed in this study for the first time. A numerical example taken from the literature is used to compare AHP–FCM–Rveto with other methods, and the results also show that the proposed methodology performs better. In the real-life example, the main advantage of compliance with the Pareto principle of the proposed method is shown.

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Authors and Affiliations

  1. Department of Industrial Engineering, Istanbul Arel University, Istanbul, Turkey

    Fatih Yiğit

  2. Department of Industrial Engineering, Istanbul University-Cerrahpaşa, Istanbul, Turkey

    Şakir Esnaf

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  1. Fatih Yiğit
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  2. Şakir Esnaf
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Fatih Yigit and Sakir Esnaf. The first draft of the manuscript was written by Fatih Yigit, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Fatih Yiğit.

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Yiğit, F., Esnaf, Ş. A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification. J Intell Manuf 32, 1517–1528 (2021). https://doi.org/10.1007/s10845-020-01633-7

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  • DOI: https://doi.org/10.1007/s10845-020-01633-7

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