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ABC CLASSIFICATION WITH UNCERTAIN DATA. A FUZZY MODEL VS. A PROBABILISTIC MODEL 

Authors: J. Puente;  D. de la Fuente;  P. Priore; R. Pino
DOI: 10.1080/08839510290030309
Publication Frequency: 10 issues per year
Published in: journal Applied Artificial Intelligence, Volume 16, Issue 6 July 2002 , pages 443 - 456
Number of References: 11
Formats available: PDF (English)
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Abstract

This study presents an alternative way of classifying the different productive items of a company. A fuzzy model for the magnitudes involved (demand and cost) is described. This model contrasts with the classic Pareto classification (ABC), which ranks productive items according to their importance in terms of frequency and costs. Whereas rankings obtained using the classical method are based on information about costs and demand over a period in the past, this new method allows new fuzzy information about the future to be included, thus allowing stricter control of the fuzzy ''A-items'' that result from this new classification. Rankings comparing a probabilistic model and its fuzzy counterpart are also provided in this study.
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