Hierarchical fuzzy TOPSIS model for selection among logistics information technologies

Author： Kahraman Cengiz Ates NÂ³fer Yasin Ãƒevik Sezi GÂ³lbay Murat Erdogan S. AyÃ¾a

ISSN： 1741-0398

Source： Journal of Enterprise Information Management, Vol.20, Iss.2, 2007-02, pp. : 143-168

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Abstract

Purpose - To develop a multi-attribute decision making model for evaluating and selecting among logistic information technologies. Design/methodology/approach - First a multi-attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented. Findings - Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem. Research limitations/implications - Comparisons with other multi-attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research. Practical implications - This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments. Originality/value - This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi-attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.