A stochastic data envelopment analysis approach for multi-criteria ABC inventory classification

Mohammad Tavassoli, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

One of the common methods for classifying inventory items is ABC classification approach. In many cases, the data might be stochastic. In the current study, using stochastic data envelopment analysis model, we present a new approach to categorize inventory items given stochastic data and nature of criteria. Then, a new stochastic mixed integer programming model is proposed to forecast classes of the new inventory items. The proposed stochastic mixed integer programming model does not impose subjective judgment on the classification of inventory items and can be used for multi-group classification. The developed approach can classify inventory items and forecast the class of new items with both qualitative and quantitative criteria. The applicability of developed stochastic data envelopment analysis and stochastic mixed integer programming models is demonstrated by a case study.

Original languageEnglish
Pages (from-to)415-429
Number of pages15
JournalJournal of Industrial and Production Engineering
Volume39
Issue number6
DOIs
Publication statusPublished - 2022

Keywords

  • Inventory classification
  • stochastic data envelopment analysis
  • stochastic discriminant analysis
  • stochastic mixed integer programming

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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