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DEASort: Assigning items with data envelopment analysis in ABC classes

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  • Ishizaka, Alessio
  • Lolli, Francesco
  • Balugani, Elia
  • Cavallieri, Rita
  • Gamberini, Rita

Abstract

Multi-criteria inventory classification groups similar items in order to facilitate their management. Data envelopment analysis (DEA) and its many variants have been used extensively for this purpose. However, DEA provides only a ranking and classes are often constructed arbitrarily with percentages. This paper introduces DEASort, a variant of DEA aimed at sorting problems. In order to avoid unrealistic classification, the expertise of decision-makers is incorporated, providing typical examples of items for each class and giving the weights of the criteria with the Analytic Hierarchy Process (AHP). This information bounds the possible weights and is added as a constraint in the model. DEASort is illustrated using a real case study of a company managing warehouses that stock spare parts.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:199:y:2018:i:c:p:7-15
    DOI: 10.1016/j.ijpe.2018.02.007
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    References listed on IDEAS

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    Cited by:

    1. Sheikh-Zadeh, Alireza & Rossetti, Manuel D. & Scott, Marc A., 2021. "Performance-based inventory classification methods for large-Scale multi-echelon replenishment systems," Omega, Elsevier, vol. 101(C).
    2. Zhang, Zeyu & Li, Kevin W. & Guo, Xiaolei & Huang, Jun, 2020. "A probability approach to multiple criteria ABC analysis with misclassification tolerance," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    4. Fan Liu & Ning Ma, 2019. "Multicriteria ABC Inventory Classification Using the Social Choice Theory," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    5. Liang, Decui & Fu, Yuanyuan & Ishizaka, Alessio, 2023. "A consensual group ELECTRE-SORT approach considering the incomparable classes with the application of machine maintenance strategy assignment," Omega, Elsevier, vol. 118(C).
    6. Jindong Qin & Yingying Liang & Luis Martinez & Alessio Ishizaka & Witold Pedrycz, 2023. "ORESTE-SORT: a novel multiple criteria sorting method for sorting port group competitiveness," Annals of Operations Research, Springer, vol. 325(2), pages 875-909, June.
    7. Wang, Liang & Zhang, Zi-Xin & Ishizaka, Alessio & Wang, Ying-Ming & Martínez, Luis, 2023. "TODIMSort: A TODIM based method for sorting problems," Omega, Elsevier, vol. 115(C).
    8. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods—Part I: survey of the literature," 4OR, Springer, vol. 21(1), pages 1-46, March.
    9. Ishizaka, Alessio & Lokman, Banu & Tasiou, Menelaos, 2021. "A Stochastic Multi-criteria divisive hierarchical clustering algorithm," Omega, Elsevier, vol. 103(C).

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