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Robustness of multi-criteria ABC inventory classification under objective, equal, and AI-assisted subjective weighting approaches |
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| รหัสดีโอไอ | |
| Title | Robustness of multi-criteria ABC inventory classification under objective, equal, and AI-assisted subjective weighting approaches |
| Creator | Ekphawee Bunloi |
| Contributor | Warut Pannakkong, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | Inventory classification, Multi-criteria decision-making (MCDM), EDAS, Weighting methods |
| Abstract | This study evaluates the robustness of multi-criteria ABC classification under diverse weighting approaches, including equal weighting, AI-assisted subjective weighting, the Best-Worst Method (BWM), and objective weighting using a hybrid Entropy-CRITIC method. The resulting weights are integrated with the Evaluation Based on Distance from Average Solution (EDAS) method to rank and classify inventory items, utilizing a data-driven classification approach based on Appraisal Score (AS).The study utilizes a dataset of 98 Stock Keeping Units (SKUs) from a garment SME, evaluated against four criteria: order frequency, cost per kilogram, total order amount, and inventory value. The classification outcomes from each weighting approach are subsequently compared through similarity analysis. Results indicate a high degree of similarity across weighting approaches, ranging from 87.76% to 96.94%, suggesting that the classification framework is robust for the studied dataset. This study provides practical guidance for SMEs in selecting appropriate inventory classification strategies based on resource availability and operational requirements. |
