Exploring the shortcomings in formal criteria selection for multicriteria decision making based inventory classification models: a systematic review and future directions

• Published in: International journal of production research 2024-10, Vol.62 (19), p.7279-7299
• Main Authors: Theunissen, Frank Michael, Bezuidenhout, Carel Nicolaas, Alam, Shafiq
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Classification; Criteria; criteria selection; criteria selection process; inventory classification; Inventory management; Multicriteria decision making; Multiple criteria decision making; Qualitative analysis
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2024.2320680

Criteria selection significantly impacts the reliability and utility of multicriteria decision making (MCDM) models. While criteria may vary across industries, a formalised criteria selection process is influential in determining MCDM model outcomes. This article analyses and compares the criteria selection approaches used in 62 articles that apply MCDM-based inventory classification models, contrasting them with methodologies outside the field. Our findings reveal a conspicuous absence of formal criteria selection methods within MCDM-based inventory classification research. The limited application of quantitative and qualitative approaches indicates that this field has not kept pace with methodological advances in criteria selection. To bridge this gap, we advocate for further research aimed at developing a conceptual framework for criteria selection tailored to inventory classification. We also suggest evaluating the impact of formal criteria selection processes on inventory management decisions and exploring the benefits of integrating artificial intelligence into criteria selection for inventory classification studies. Additionally, this article identifies several limitations related to criteria selection for practitioners employing MCDM-based inventory classification models.