Rough-set multiple-criteria ABC analysis

Authors:
Ye Chen;Kevin W. Li;Jason Levy;Keith W. Hipel;D. Marc Kilgour
Affiliations:
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Odette School of Business, University of Windsor, Windsor, Ontario, Canada;Department of Applied Disaster and Emergency Studies, Brandon University, Brandon, Manitoba, Canada;Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

Citing 3
Cited 1

Classifying inventory using an artificial neural network approach

Computers and Industrial Engineering
Multiple criteria classification with an application in water resources planning

Computers and Operations Research
A case-based distance model for multiple criteria ABC analysis

Computers and Operations Research

A rough set approach to multiple criteria ABC analysis

Transactions on rough sets VIII

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Abstract

Multiple-criteria ABC (MCABC) analysis is conducted using a dominance-based rough set approach. ABC analysis, a well-known technique for inventory planning and control, divides stock-keeping units (SKUs) into three classes according to annual dollar usage. But MCABC analysis offers more managerial flexibility by including other criteria, such as lead time and criticality, in the classification of SKUs. The objective of this paper is to propose an MCABC method that uses the dominance-based rough set approach to generate linguistic rules that represent a decision-maker’s preferences based on the classification of a test data set. These linguistic rules are then used to classify all SKUs. A case study demonstrates that the procedure is feasible.