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New AHP-based approaches for multi-criteria inventory classification

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  • Lolli, F.
  • Ishizaka, A.
  • Gamberini, R.

Abstract

Multi-Criteria Inventory Classification (MCIC) groups inventory items with respect to several criteria, in order to facilitate their management. This paper introduces a new hybrid method based on AHP and the K-means algorithm. On benchmarking data, it provides a clearly higher clustering validity index than previous sorting methods. However, as with previous methods, it is a full compensatory method. This means that an item scoring badly on one or more key criteria may still be placed in the best class because these bad scores are compensated. In order to prevent these hidden bad scores, a new variant method is introduced: AHP-K-Veto. The sorting is performed on each single criterion, where a veto system prevents an item evaluated as high/bad on at least one criterion to be top/bottom ranked in the global aggregation. This veto system is an assurance against hidden problems but slightly worsens the clustering validity index.

Suggested Citation

  • Lolli, F. & Ishizaka, A. & Gamberini, R., 2014. "New AHP-based approaches for multi-criteria inventory classification," International Journal of Production Economics, Elsevier, vol. 156(C), pages 62-74.
  • Handle: RePEc:eee:proeco:v:156:y:2014:i:c:p:62-74
    DOI: 10.1016/j.ijpe.2014.05.015
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    4. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    5. Alessio Ishizaka & Maynard Gordon, 2017. "MACBETHSort: a multiple criteria decision aid procedure for sorting strategic products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 53-61, January.
    6. Siamak Kheybari & S. Ali Naji & Fariba Mahdi Rezaie & Reza Salehpour, 2019. "ABC classification according to Pareto’s principle: a hybrid methodology," OPSEARCH, Springer;Operational Research Society of India, vol. 56(2), pages 539-562, June.
    7. Ishizaka, Alessio & Lolli, Francesco & Balugani, Elia & Cavallieri, Rita & Gamberini, Rita, 2018. "DEASort: Assigning items with data envelopment analysis in ABC classes," International Journal of Production Economics, Elsevier, vol. 199(C), pages 7-15.
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    13. Hu, Qiwei & Chakhar, Salem & Siraj, Sajid & Labib, Ashraf, 2017. "Spare parts classification in industrial manufacturing using the dominance-based rough set approach," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1136-1163.
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    18. Yang, Liu & Li, Haitao & Campbell, James F. & Sweeney, Donald C., 2017. "Integrated multi-period dynamic inventory classification and control," International Journal of Production Economics, Elsevier, vol. 189(C), pages 86-96.
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    21. Hadhami Kaabi & Khaled Jabeur & Talel Ladhari, 2018. "A Genetic Algorithm-Based Classification Approach for Multicriteria ABC Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1805-1837, November.
    22. Hung-Lung Lin & Yu-Yu Ma, 2021. "A New Method of Storage Management Based on ABC Classification: A Case Study in Chinese Supermarkets’ Distribution Center," SAGE Open, , vol. 11(2), pages 21582440211, June.

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