A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Mohammadi, Farahbod, Sadi, Mohammadali Kazerooni, Nateghi, Fatemeh, Abdullah, Arham, & Skitmore, Martin (2014) A hybrid quality function deployment and cybernetic analytic network process model for project manager selection. Journal of Civil Engineering and Management, 20(6), pp. 795-809.

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Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

Impact and interest:

4 citations in Scopus
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4 citations in Web of Science®

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ID Code: 79802
Item Type: Journal Article
Refereed: Yes
Keywords: Cybernetic Analytic Network Process (CANP), Quality Function Deployment (QFD), QFD-ANP, Project Manager Selection
DOI: 10.3846/13923730.2014.945952
ISSN: 1822-3605
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Vilnius Gediminas Technical University (VGTU) Press
Copyright Statement: The Version of Record of this manuscript has been published and is available in Journal of Civil Engineering and Management, Volume 20, Issue 6, 2014, http://www.tandfonline.com/10.3846/13923730.2014.945952
Deposited On: 07 Jan 2015 02:53
Last Modified: 04 Jan 2016 11:00

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