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A non-compensatory classification approach for multi-criteria ABC analysis

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Abstract

ABC analysis is a widespread inventory management technique designed to classify inventory items—based on their weighted scores—into three ordered categories A, B and C, where category A contains the most important items and category C includes the least important ones. This paper proposes a new ABC classification approach which involves a non-compensatory aggregation procedure, based on a simplified ELECTRE III method, to compute the score of each inventory item. A non-compensatory aggregation scheme means that the bad scores of an item on some significant criteria could not be offset by its high performances on the other criteria. This way of proceeding prohibits this kind of items from being classified into good categories and therefore generates a more realistic ABC classification of inventory items. Since the application of the simplified ELECTRE III method requires the knowledge of some parameter values, the continuous variable neighborhood search meta-heuristic will be used for their estimation. The comparative study—conducted on two real datasets—shows that the classification of items produced by our proposed approach has generated the lowest inventory cost value among those produced by all tested classification models.

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Notes

  1. The name VIKOR appeared in 1990 Opricovic (1990) from Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje, which means: Multi-criteria Optimization and Compromise Solution.

  2. In this pseudocode, the computation of the cost function \(f_{TRC}\) of any solution z requires the execution of the three following steps: (1) apply the simplified ELECTRE III method by using z as parameter vector to generate a ranking of inventory items, (2) generate an ABC classification, i.e., ABC(z), by splitting the above item ranking according to a predefined distribution and (3) evaluate the generated ABC classification by using the Total Relevant Cost (TRC) function, i.e., \({f_{TRC}}\left( {\mathrm{{ABC}}\left( z \right) } \right) \).

  3. \(w_j^N = \frac{{{w_j}}}{{\sum \limits _{i = 1}^n {{w_i}} }}\) is the normalized weight of criterion j (\(j=1\ldots n\)).

  4. In our case, only one distribution type will be used.

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  1. Institut Supérieur de Gestion de Tunis, Université de Tunis, Le Bardo, 2000, Tunis, Tunisia

    Mohamed Radhouane Douissa

  2. Institut Supérieur de Gestion de Bizerte, Université de Carthage, Menzel Abderrahmane, 7035, Bizerte, Tunisia

    Khaled Jabeur

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Douissa, M.R., Jabeur, K. A non-compensatory classification approach for multi-criteria ABC analysis. Soft Comput 24, 9525–9556 (2020). https://doi.org/10.1007/s00500-019-04462-w

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