An integrated multi-criteria approach for systematic and large-scale inventory prioritization
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s12597-026-01283-y
- Dergi Adı: OPSEARCH
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, INSPEC, MathSciNet, zbMATH, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- İstanbul Üniversitesi Adresli: Evet
Özet
The increasing complexity of modern supply chains, coupled with demand uncertainty and volatility, has necessitated the adoption of more advanced approaches for inventory prioritization. Traditional methods, such as ABC analysis, primarily rely on single-criterion evaluations and often overlook critical factors such as lead time, product criticality, order frequency, and cost, leading to limited effectiveness in complex operational environments. To provide a more comprehensive evaluation of inventory items, this study proposes an enhanced inventory prioritization approach based on multi-criteria decision-making (MCDM). Within this approach, the fuzzy Best-Worst Method (FBWM) is employed to determine the relative importance of criteria, offering higher consistency with fewer pairwise comparisons. Decision-makers identify the most and least important criteria, enabling a structured and reliable weighting process. Subsequently, the Evaluation based on Distance from Average Solution (EDAS) method is applied to rank and classify inventory items. Unlike methods that focus solely on ideal solutions, EDAS evaluates alternatives based on their distances from the average solution, providing a more robust and practical assessment. The proposed model generates priority scores for each stock keeping unit (SKU), facilitating the prioritization of inventory control strategies. Findings of the study demonstrate that integrating two MCDM methods offers a comprehensive and effective tool for multi-criteria inventory prioritization, particularly in environments characterized by uncertainty and operational complexity. The integration of comparative and sensitivity analyses significantly enhances methodological robustness and reinforces the empirical validity and practical relevance of the results.