A fuzzy DEA–Neural approach to measuring design service performance in PCM projects

In Professional Construction Management (PCM), service performance in the design stage most affects the overall project results. However, currently it is hard to quantify the results and make a proper determination of the quality of the PCM design service because the evaluation mechanism and procedures have not been completely implemented yet. To deal with the problem, this study investigates owners' opinions and thereby establishes the representative performance indicators of PCM design service. An effectiveness analysis method and a neural network method are successfully combined, so as to construct an evaluation approach of PCM service performance in the design stage. This approach can offer a listing of rank and level of design service performance in PCM projects, and further provides a simple appraisal table, so that the owner can effectively measure the performance of PCM design service. Finally, through the case study, this approach is verified to be reasonable and acceptable in practice. The approach can be an effective tool when measuring the PCM design service performance and can help owners to evaluate and choose PCM consultants in the design stage.

[1]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[2]  Y. C. Kog,et al.  Critical Success Factors for Different Project Objectives , 1999 .

[3]  A. Parasuraman,et al.  Communication and Control Processes in the Delivery of Service Quality , 1988 .

[4]  Lukumon O. Oyedele,et al.  Clients’ assessment of architects’ performance in building delivery process: Evidence from Nigeria , 2007 .

[5]  Shiuh-Nan Hwang,et al.  Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan , 2003 .

[6]  John A. Flanagan,et al.  Self-organisation in Kohonen's SOM , 1996, Neural Networks.

[7]  Keith R. Molenaar,et al.  MODEL FOR PUBLIC SECTOR DESIGN-BUILD PROJECT SELECTION , 1998 .

[8]  Min Liu,et al.  Modeling a Contractor’s Markup Estimation , 2005 .

[9]  Pao-Long Chang,et al.  USING DATA ENVELOPMENT ANALYSIS TO MEASURE THE ACHIEVEMENT AND CHANGE OF REGIONAL-DEVELOPMENT IN TAIWAN , 1995 .

[10]  G. D. Holt,et al.  Factors influencing U.K. construction clients' choice of contractor , 1994 .

[11]  David Arditi,et al.  Assessing the corporate service quality performance of design-build contractors using quality function deployment , 2003 .

[12]  We Chiang,et al.  A DEA evaluation of Taipei hotels. , 2004 .

[13]  Clyde B. Tatum.  Evaluating PCM Firm Potential and Performance , 1979 .

[14]  Rodney H. Green,et al.  Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses , 1994 .

[15]  Teuvo Kohonen,et al.  Developments and applications of the self-organizing map and related algorithms , 1996 .

[16]  A. Parasuraman,et al.  Communication and Control Processes in the Delivery of Service Quality , 1988, Journal of Marketing.

[17]  Min-Yuan Cheng,et al.  OBJECT-ORIENTED EVOLUTIONARY FUZZY NEURAL INFERENCE SYSTEM FOR CONSTRUCTION MANAGEMENT , 2003 .

[18]  Xiaolong Xue,et al.  Measuring the Productivity of the Construction Industry in China by Using DEA-Based Malmquist Productivity Indices , 2008 .

[19]  Yaakov Roll,et al.  An application procedure for DEA , 1989 .

[20]  Ching-Hwang Wang,et al.  PCM in Taiwan: A Diagnosis Knowledge-Base in PCM Plan/Design Phase , 2007 .

[21]  Gary David Holt,et al.  Factors influencing UK construction clients' choice of contractors , 1994 .

[22]  A. Parasuraman,et al.  A Conceptual Model of Service Quality and Its Implications for Future Research , 1985 .

[23]  A. Lewin,et al.  Determining organizational effectiveness: another look, and an agenda for research , 1986 .