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Partial average cross‐weight evaluation for ABC inventory classification
Author(s) -
Karagiannis Giannis
Publication year - 2021
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12594
Subject(s) - measure (data warehouse) , mathematics , entropy (arrow of time) , computer science , statistics , mathematical optimization , data mining , physics , quantum mechanics
In this paper, we propose an alternative overall measure, inspired by the notion of average cross‐efficiency, which summarizes achievements across different descending ordering schemes regarding the relative importance of the considered indicators in the Ng model. The proposed overall measure is equal to the arithmetic average of the maximum partial averages across all possible descending ordering schemes. It can also be obtained using the average (across ordering schemes) of the estimated multipliers. We apply the proposed measure to the ABC inventory classification problem, and we compare our results with those by four information theory based methods that may be used for the same purpose, namely the Shannon entropy, distance‐based, weighted least‐square dissimilarity, and maximizing deviation methods.