Abstract
Large construction projects involve massive capital investments, lengthy execution period, and enormous management uncertainties. Therefore, the task of selecting a capable contractor for smooth project delivery is challenging. Although previous studies have attempted to improve the methods of bidding evaluation, limitations still exist. First, the evaluation results might be biased due to the heterogeneity in a small group of experts with different professional experience and capabilities. Second, multi-correlation can reduce the validity of the weightings of the indexes and evaluation results. Third, the subjectivity of the weightings can be a crucial hurdle to the selection of a suitable contractor for a specific project. To overcome these shortcomings, this study introduces Partial Least Squares (PLS) path modeling and develops a Sequence-Multi-Criteria System which can aggregate the evaluation results from different professional practitioners without the use of pre-defined weighting schemes, thus providing a reliable reference for bid evaluation. We incorporated this approach into an evaluation procedure stipulated in the procurement documents of the World Bank and specifically aimed at selecting suitable contractors for large construction projects. A case study was conducted via comparisons across various scenarios, and the validity of this approach was subsequently proved.
Similar content being viewed by others
References
Aibinu, A. A. and Al-Lawati, A. M. (2010). “Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding.” Automation in Construction, Vol. 19, No. 6, pp. 714–724. DOI: 10.1016/j.autcon.2010.02.016.
Al-Reshaid, K. and Kartam, N. (2005). “Design–build pre-qualification and tendering approach for public projects.” International Journal of Project Management, Vol. 23, No. 4, pp. 309–320, DOI: 10.1016/j.ijproman.2004.11.004.
Arslan, G., Kivrak, S., Birgonul, M. T., and Dikmen, I. (2008). “Improving sub-contractor selection process in construction projects: Web-based suB-contractor Evaluation System (WEBSES).” Automation in Construction, Vol. 17, No. 4, pp. 480–488, DOI: 10.1016/j.autcon.2007.08.004.
Beil, D. R. and Wein, L. M. (2003). “An inverse-optimization-based auction mechanism to support a multiattribute RFQ process.” Management Science, Vol. 49, No. 11, pp. 1529–1545, DOI: org/ 10.1287/mnsc.49.11.1529.20588.
Burge, P., Devlin, N., Appleby, J., Rohr, C., and Grant, J. (2005). London patient choice project evaluation: A model of patients’ choices of hospital from stated and revealed preference choice data, Rand Corporation/The King’s Fund and City University, London. DOI: http://www.rand.org/pubs/technical_reports/TR230.
Chan, A. P. and Chan, A. P. (2004). “Key performance indicators for measuring construction success.” Benchmarking: an International Journal, Vol. 11, No. 2, pp. 203–221.
Cheng, E. W. and Li, H. (2004). “Contractor selection using the analytic network process.” Construction Management and Economics, Vol. 22, No. 10, pp. 1021–1032, DOI: 10.1080/0144619042000202852.
David, E., Azoulay-Schwartz, R., and Kraus, S. (2006). “Bidding in sealed-bid and English multi-attribute auctions.” Decision Support Systems, Vol. 42, No. 2, pp. 527–556, DOI: 1016/j.dss.2005.02.007.
Demirtas, E. A. and Ustun, O. (2008). “An integrated multiobjective decision making process for supplier selection and order allocation.” Omega, Vol. 36, No. 1, pp. 76–90, DOI: 10.1016/j.omega.2005.11.003.
Fong, P. S. W. and Choi, S. K. Y. (2000). “Final contractor selection using the analytical hierarchy process.” Construction Management & Economics, Vol. 18, No. 5, pp. 547–557, DOI: 10.1080/014461900407356.
Gallien, J. and Wein, L. M. (2005). “A smart market for industrial procurement with capacity constraints.” Management Science, Vol. 51, No. 1, pp. 76–91, DOI: org/10.1287/mnsc.1040.0230.
Guinot, C., Latreille, J., and Tenenhaus, M. (2001). “PLS path modelling and multiple table analysis. Application to the cosmetic habits of women in Ile-de-France.” Chemometrics and Intelligent Laboratory Systems, Vol. 58, No. 2, pp. 247–259, DOI: 10.1016/S0169-7439(01)00163-0.
Hatush, Z. and Skitmore, M. (1998). “Contractor selection using multicriteria utility theory: An additive model.” Building and Environment, Vol. 33, No. 2, pp. 105–115, DOI: 10.1016/S0360-1323(97)00016-4.
Holt, G. D. (1998). “Which contractor selection methodology?” International Journal of Project Management, Vol. 16, No. 3, pp. 153–164, DOI: 10.1016/S0263-7863(97)00035-5.
Huo, T. F., Liu, B. S., Chen, Y., and Wang, X. Q. (2015). “Research into the Dynamic Evolution Mechanism of the Forming of Chinese Construction Industry Competitiveness in the New Century—On the development strategy after entering GPA.” Operations Research and Management Science[J], Vol. 24, No. 5, pp. 251–258, DOI: 10.12005/orms.2015.0184.
Isik, Z., Arditi, D., Dikmen, I., and Birgonul, M. T. (2009). “Impact of corporate strengths/weaknesses on project management competencies.” International Journal of Project Management, Vol. 27, No. 6, pp. 629–637, DOI: 10.1016/j.ijproman.2008.10.002.
Jakobowicz, E. and Derquenne, C. (2007). “A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy.” Computational Statistics & Data Analysis, Vol. 51, No. 8, pp. 3666–3678, DOI: 10.1016/j.csda.2006.12.004.
Jaselskis, E. J. and Russell, J. S. (1992). “Risk analysis approach to selection of contractor evaluation method.” Journal of construction Engineering and Management, Vol. 118, No. 4, pp. 814–821, DOI: 10.1061/(ASCE)0733-9364(1992)118:4(814).
Jennings, P. and Holt, G. D. (1998). “Prequalification and multicriteria selection: A measure of contractors’ opinions.” Construction Management & Economics, Vol. 16, No. 6, pp. 651–660, DOI: 10.1080/014461998371944.
Kashiwagi, D. and Byfield, R. E. (2002). “Selecting the best contractor to get performance: On time, on budget, meeting quality expectations.” Journal of Facilities Management, Vol. 1, No. 2, pp. 103–116.
Lai, K. K., Liu, S. L., and Wang, S. Y. (2004). “A method used for evaluating bids in the Chinese construction industry.” International Journal of Project Management, Vol. 22, No. 3, pp. 193–201, DOI: 10.1016/S0263-7863(03)00009-7.
Lam, K. C., Ng, S. T., Tiesong, H., Skitmore, M., and Cheung, S. O. (2000). “Decision support system for contractor pre-qualification-artificial neural network model.” Engineering Construction and Architectural Management, Vol. 7, No. 3, pp. 251–266.
Liu, B. S., Huo, T. F., Liang, Y., Sun, Y., and Hu, X. (2015b). Key Factors of Project Features Affecting Project Delivery System Decision-Making—A Case Study with Chinese Data Based on Rough Set Theory, ASCE Journal of Professional Issues in Engineering Education and Practice [J] (accepted in press)
Liu, B. S., Huo, T. F., Liao, P. C., Gong, J., and Xue, B. (2014b). A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling, Group Decision and Negotiation, pp. 1–29, DOI: 10.1007/s10726-014-9418-2.
Liu, B. S., Huo, T. F., Meng, J. N., Gong, J., Shen, Q. P., and Sun, T. (2015a). “Identification of key contractor characteristic factors that affect project success under different project delivery systems.” Journal of Management of Engineering, DOI: 10.1061/(ASCE)ME.1943-5479.0000388.
Liu, B. S., Huo, T. F., Shen, Q. P., Yang, Z. Y., Meng, J. N., and Xue, B. (2014a). “Which owner’s characteristics are key factors affecting project delivery system decision-making?—Empirical analysis based on rough set theory.” Journal of Management of Engineering, DOI: 10.1061/(ASCE) ME.1943-5479.0000298.
Liu, B. S., Huo, T. F., Wang, X. Q, Shen, Q. P., and Chen, Y. (2013). “The decision model of the intuitionistic fuzzy group bid evaluation for urban infrastructure projects considering social costs.” Canadian Journal of Civil Engineering, Vol. 40, No. 3, pp. 263–273, DOI: 10.1139/cjce-2012-0283.
Liu, B. S., Wang, X. Q., and Li, B. (2011). “Analysis of chinese construction industry competitiveness formation mechanism—empirical research of pls-based structural equation model.” Journal of Applied Statistics and Management, Vol. 11, pp. 12–22.
Ng, S. T. and Skitmore, R. M. (2001). “Contractor selection criteria: a cost-benefit analysis.” Engineering Management, IEEE Transactions on, Vol. 48, No. 1, pp. 96–106, DOI: 10.1109/17.913169.
Padhi, S. S. and Mohapatra, P. K. (2010). “Centralized bid evaluation for awarding of construction projects–A case of India government.” International Journal of Project Management, Vol. 28, No. 3, pp. 275–284, DOI: 10.1016/j.ijproman.2009.06.001.
Palaneeswaran, E. and Kumaraswamy, M. (2001). “Recent advances and proposed improvements in contractor prequalification methodologies.” Building and Environment, Vol. 36, No. 1, pp. 73–87, DOI: 10.1016/S0360-1323(99)00069-4.
Patil, B. S. (2006). Civil engineering contracts and estimates, University press, New Delhi.
Shyur, H. J. and Shih, H. S. (2006). “A hybrid MCDM model for strategic vendor selection.” Mathematical and Computer Modelling, Vol. 44, No. 7, pp. 749–761, DOI: 10.1016/j.mcm.2005.04.018.
Stein, A., Hawking, P., and Wyld, D. C. (2003). “The 20% solution?: A case study on the efficacy of reverse auctions.” Management Research News, Vol. 26, No. 5, pp. 1–20.
Tenenhaus, A. and Tenenhaus, M. (2011). “Regularized generalized canonical correlation analysis.” Psychometrika, Vol. 76, No. 2, pp. 257–284, DOI: 10.1007/s11336-011-9206-8.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., and Lauro, C. (2005). “PLS path modeling.” Computational Statistics & Data Analysis, Vol. 48, No. 1, pp. 159–205, DOI: 10.1016/j.csda.2004.03.005.
Topcu, Y. I. (2004). “A decision model proposal for construction contractor selection in Turkey.” Building and Environment, Vol. 39, No. 4, pp. 469–481, DOI: 10.1016/j.buildenv.2003.09.009.
Tran, V. H. Q. (2002). Practical frontier in construction pre-qualification using data envelopment analysis, Master thesis, University of Toronto, Toronto, Canada.
Wang, H. W. and Fu, L. H. (2004). “The application research of partial least square path modeling on establishing synthesis evaluation Index.” Systems Engineering-theory & Practice, Vol. 10, No. 10, pp. 80–86.
Wang, J., Xu, Y., and Li, Z. (2009). “Research on project selection system of pre-evaluation of engineering design project bidding.” International Journal of Project Management, Vol. 27, No. 6, pp. 584–599, DOI: 10.1016/j.ijproman.2008.10.003.
Wang, X. and Triantaphyllou, E. (2008). “Ranking irregularities when evaluating alternatives by using some ELECTRE methods.” Omega, Vol. 36, No. 1, pp. 45–63.
Wang, Z. F. and Zhang, Y. (2010). The Project Bid Evaluation Model Based on Entropy Method, Science and Technology Management Research, pp. 47–48.
Watt, D. J., Kayis, B., and Willey, K. (2010). “The relative importance of tender evaluation and contractor selection criteria.” International Journal of Project Management, Vol. 28, No. 1, pp. 51–60, DOI: 10.1016/j.ijproman.2009.04.003.
Wold, H. (1985). “Partial least squares, Kotz S Johnson N L, Encyclopedia of Statistical Sciences.” New York: John Wiley & Sons, Vol. 6, pp. 581–591.
Wong, C. H., Holt, G. D., and Harris, P. (2001). “Multi-criteria selection or lowest price? Investigation of UK construction clients’ tender evaluation preferences.” Engineering Construction and Architectural Management, Vol. 8, No. 4, pp. 257–271, DOI: 10.1046/j.1365-232x.2001.00205.x.
World Bank (1992). Guidelines, Procurement under IBRD Loans and IDA Credits.
Xiao, H. and Proverbs, D. (2003). “Factors influencing contractor performance: An international investigation.” Engineering, Construction and Architectural Management, Vol. 10, No. 5, pp. 322–332.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, B., Huo, T., Liao, Pc. et al. A special Partial Least Squares (PLS) path decision modeling for bid evaluation of large construction projects. KSCE J Civ Eng 21, 579–592 (2017). https://doi.org/10.1007/s12205-016-0702-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-016-0702-3