An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification
Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to bui...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/18/2/44 |
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| author | Alaa Asim Qaffas Mohamed-Aymen Ben HajKacem Chiheb-Eddine Ben Ncir Olfa Nasraoui |
| author_facet | Alaa Asim Qaffas Mohamed-Aymen Ben HajKacem Chiheb-Eddine Ben Ncir Olfa Nasraoui |
| author_sort | Alaa Asim Qaffas |
| collection | DOAJ |
| description | Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes. |
| first_indexed | 2024-03-11T02:15:07Z |
| format | Article |
| id | doaj.art-1a120c4b1bcd43a5b50f771643e51e51 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-11T02:15:07Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-1a120c4b1bcd43a5b50f771643e51e512023-11-18T11:12:15ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762023-04-0118284886610.3390/jtaer18020044An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item ClassificationAlaa Asim Qaffas0Mohamed-Aymen Ben HajKacem1Chiheb-Eddine Ben Ncir2Olfa Nasraoui3MIS Department, College of Business, University of Jeddah, Jeddah 23218, Saudi ArabiaLARODEC Lab, ISG Tunis, University of Tunis, Le Bardo 2000, TunisiaMIS Department, College of Business, University of Jeddah, Jeddah 23218, Saudi ArabiaKnowledge Discovery & Web Mining Lab, University of Louisville, Louisville, KY 40292, USAMulti-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes.https://www.mdpi.com/0718-1876/18/2/44eXplainable Artificial Intelligence (XAI)explainable clusteringshapely additive explanations (SHAP)ABC modelmulti-criteria ABC classificationinventory management |
| spellingShingle | Alaa Asim Qaffas Mohamed-Aymen Ben HajKacem Chiheb-Eddine Ben Ncir Olfa Nasraoui An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification Journal of Theoretical and Applied Electronic Commerce Research eXplainable Artificial Intelligence (XAI) explainable clustering shapely additive explanations (SHAP) ABC model multi-criteria ABC classification inventory management |
| title | An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification |
| title_full | An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification |
| title_fullStr | An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification |
| title_full_unstemmed | An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification |
| title_short | An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification |
| title_sort | explainable artificial intelligence approach for multi criteria abc item classification |
| topic | eXplainable Artificial Intelligence (XAI) explainable clustering shapely additive explanations (SHAP) ABC model multi-criteria ABC classification inventory management |
| url | https://www.mdpi.com/0718-1876/18/2/44 |
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