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A Framework for Applying CSFs to ERP Software Selection: An Extension of Fuzzy TOPSIS Approach

A Framework for Applying CSFs to ERP Software Selection: An Extension of Fuzzy TOPSIS Approach

Rekha Gupta, S. Kazim Naqvi
Copyright: © 2017 |Volume: 13 |Issue: 2 |Pages: 22
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522511465|DOI: 10.4018/IJIIT.2017040103
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MLA

Gupta, Rekha, and S. Kazim Naqvi. "A Framework for Applying CSFs to ERP Software Selection: An Extension of Fuzzy TOPSIS Approach." IJIIT vol.13, no.2 2017: pp.41-62. http://doi.org/10.4018/IJIIT.2017040103

APA

Gupta, R. & Naqvi, S. K. (2017). A Framework for Applying CSFs to ERP Software Selection: An Extension of Fuzzy TOPSIS Approach. International Journal of Intelligent Information Technologies (IJIIT), 13(2), 41-62. http://doi.org/10.4018/IJIIT.2017040103

Chicago

Gupta, Rekha, and S. Kazim Naqvi. "A Framework for Applying CSFs to ERP Software Selection: An Extension of Fuzzy TOPSIS Approach," International Journal of Intelligent Information Technologies (IJIIT) 13, no.2: 41-62. http://doi.org/10.4018/IJIIT.2017040103

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Abstract

The literature review studies on ERP system indicate two main areas of thrust: the study of critical success factors for the risk aversion in ERP implementation projects and the ERP system selection studies. However, ERP system selection is a tedious and a time-consuming activity. The critical success factors (CSFs) serve as vital input ingredients to the ERP selection Models. It is however notable that, that none of the CSFs listing propagated by the researchers find a straightforward application in the selection procedure. The paper bridges the gap between the two thrust areas by proposing a framework for applying the prioritized CSFs listed for direct utilization in the selection process. An exhaustive review on the ERP selection techniques reveals the focus on AHP and/or the Fuzzy Logic approaches to ERP selection problem. A new approach to ERP selection problem with the extensions of the Fuzzy-TOPSIS is subsequently introduced and is illustrated by a solved numerical example. Also, the computational simplicity of the extensions of Fuzzy TOPSIS is demonstrated.

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