Comparative Analysis of MultiCriteria Inventory Classification Models for ABC Analysis

Hadhami Kaabi
Additional contact information
Hadhami Kaabi: Supply Chain Management Department, College of Business, University of Jeddah, Saudi Arabia2Business Analytics and DEcision Making Lab (BADEM), Tunis Business School, University of Tunis, Tunisia

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 05, 1617-1646

Abstract: ABC analysis is a commonly used inventory classification technique which consists in splitting a large number of inventory items into three categories, A, B and C: category A consists in the most important items, category B consists in the moderately important items and category C consists in the least important ones. Through this classification, inventory items are managed in an efficient way. In this paper, we argue the benefits of cross-fertilization of both Artificial Intelligence (AI) and MultiCriteria Decision Making (MCDM) techniques to carry out the ABC classification of inventory items. For this purpose, we develop some new hybrid inventory classification models based on metaheuristics (AI techniques) to generate the criteria weights and on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method (MCDM technique) to compute the overall weighted score of each item on which the ABC classification is performed. To evaluate the effectiveness of the proposed classification models with respect to some classification models from the literature, a comparative study — based on a service-cost analysis and three real datasets — is conducted. The computational analysis demonstrates that our proposed hybrid models are competitive and produce satisfactory results. The results have also shown, that our proposed models outperform some existing models from the literature.

Keywords: MultiCriteria Inventory Classification; ABC analysis; metaheuristic; TOPSIS; service-cost analysis (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622022500262
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:21:y:2022:i:05:n:s0219622022500262

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622022500262

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().