Robust ABC Inventory Classification Using Hybrid TOPSIS-Alternative Factor Extraction Approaches

In this paper, we propose a robust ABC classification for inventories using a hybrid technique for order of preference by similarity to ideal solution-alternative factor extraction approach (TOPSIS-AFEA) as the cornerstone method to calculate and rank importance scores for each item in stock. This is done to mitigate multicollinearity that may exist among different inventory criteria, which artificially inflates total data variance. Besides, and differently from previous research, information reliability techniques such as information entropy and gray relational analysis (GRA) are used as an auxiliary tool to differentiate alternative ABC methods proposed in the literature in terms of the principle of maximal entropy. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. Results suggest that the proposed robust TOPSIS-AFEA provides an adequate representation of score ranks that may be computed on different datasets by using existing alternative ABC inventory classification models.

Automated summary

This paper proposes a robust ABC inventory classification method using a hybrid technique to calculate and rank importance scores for each inventory item. The method mitigates multicollinearity among different inventory criteria by applying the TOPSIS-AFEA approach. Additionally, information reliability techniques such as information entropy and gray relational analysis are used to differentiate alternative ABC methods based on the principle of maximal entropy. The results demonstrate that the proposed method provides an adequate representation of score ranks and is applicable to different datasets.