Interpretable Multi-criteria ABC analysis based on semi-supervised clustering and Explainable Artificial Intelligence

Multi-criteria ABC classification is an effective technique that allows rapid and automatic organization of a growing number of inventory items into classes having different managerial levels. These built classes help decision-makers efficiently control the inventory and optimize the whole supply ch...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Qaffas, A.A., Ben Hajkacem, M-A., Ben Ncir, C-E., Nasraoui, O.
Format: Article
Language:English
Subjects:
ABC classification
Artificial intelligence
Buildings
Classification
Classification algorithms
Clustering
constrained clustering
Costs
Decision making
Explainable artificial intelligence
explainable clustering
eXplainble Artificial Intelligence (XAI)
inventory classification
Mathematical models
Multiple criterion
semi-supervised clustering
Shapley Additive exPlanations (SHAP)
Supply chains
Task analysis
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Summary:Multi-criteria ABC classification is an effective technique that allows rapid and automatic organization of a growing number of inventory items into classes having different managerial levels. These built classes help decision-makers efficiently control the inventory and optimize the whole supply chain. However, existing ABC classification methods work as black-box processes that produce ABC classes without providing any explanations behind the assignment of the items. Given the multi-criteria nature of the ABC classification problem, managers cannot easily analyze and interpret the items' managerial classes. Another problem of existing methods is their inability to follow the Pareto principle which states that items must be Pareto distributed over the ABC classes. To solve these problems, we propose a two-phase semi-supervised explainable approach based on semi-supervised clustering and explainable artificial intelligence. The first phase integrates an intelligent initialization and a constrained clustering process that guides the classification process to lead to Pareto distributed items while the second phase aims to build detailed micro and macro explanations of the ABC managerial classes at the item and the class levels. Application of the proposed approach for the automatic classification of chemical products of a distribution company has shown the effectiveness of the proposed approach in providing accurate, transparent and well-explained ABC classes.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3272403