Robust ABC Inventory Classification Using Hybrid TOPSIS-Alternative Factor Extraction Approaches

A. Hadi-Vencheh, P. Wanke (), A. Jamshidi () and Jorge Antunes ()
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A. Hadi-Vencheh: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan 81551-39998, Iran
P. Wanke: COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, Rio de Janeiro 21949-900, Brazil
A. Jamshidi: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan 81551-39998, Iran
Jorge Antunes: COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, Rio de Janeiro 21949-900, Brazil

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 04, 1371-1402

Abstract: In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models.

Keywords: ABC inventory classification; TOPSIS-AFEA; maximal information entropy; GRA; robustness analysis (search for similar items in EconPapers)
JEL-codes: C02 C38 C44 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219622022500729

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