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Construction project monitoring by means of RAM-based composite indicators

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Journal of the Operational Research Society

Abstract

This paper aims to assess the performance of a sample of completed building projects in Oregon by employing the range-adjusted measure, a slack-based data envelopment analysis (DEA) model. In the first stage of analysis, project efficiency ratings (ie composite indicators) are derived using selected single performance indicators in a no-output model; whereas in the second stage, censored Tobit regression is employed to model the efficiency ratings. The results indicate that only four out of the 50 sample projects are efficient within the DEA context. Moreover, there is not much evidence for systematic effects of project size on DEA efficiency rating.

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References

  • Atkinson R (1999). Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management 17 (6): 337–342.

    Article  Google Scholar 

  • Baccarini D (1999). The logical framework method for defining project success. Project Management Journal 30 (4): 25–32.

    Google Scholar 

  • Banker RD, Datar SM and Kemerer CF (1991). A model to evaluate variables impacting the productivity of software maintenance projects. Management Science 37 (1): 1–18.

    Article  Google Scholar 

  • Belassi W and Tukel OI (1996). A new framework for determining critical success/failure factors in projects. International Journal of Project Management 14 (3): 141–151.

    Article  Google Scholar 

  • Belout A and Gauvreau C (2004). Factors influencing project success: The impact of human resource management. International Journal of Project Management 22 (1): 1–11.

    Article  Google Scholar 

  • Chan APC, Wong FKW and Lam PTI (2006). Assessing quality relationships in public housing: An empirical study. International Journal of Quality & Reliability Management 23 (8): 909–927.

    Article  Google Scholar 

  • Charnes A, Cooper WW and Rhodes E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6): 429–444.

    Article  Google Scholar 

  • Cooke-Davies T (2002). The ‘real’ success factors on projects. International Journal of Project Management 20 (3): 185–190.

    Article  Google Scholar 

  • Cook WD and Green RH (2000). Project prioritization: A resource-constrained data envelopment analysis approach. Socio-economic Planning Sciences 34 (2): 85–99.

    Article  Google Scholar 

  • Cooper WW, Park KS and Pastor JT (1999). RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis 11 (1): 5–42.

    Article  Google Scholar 

  • Cristóbal JRS (2009). Time, cost, and quality in a road building project. Journal of Construction Engineering and Management 135 (11): 1271–1274.

    Article  Google Scholar 

  • de Wit A (1988). Measurement of project success. International Journal of Project Management 6 (3): 164–170.

    Article  Google Scholar 

  • Dinesh Kumar U, Saranga H, Ramírez-Márquez JE and Nowicki D (2007). Six sigma project selection using data envelopment analysis. The TQM Magazine 19 (5): 419–441.

    Article  Google Scholar 

  • Dweiri FT and Kablan MM (2006). Using fuzzy decision making for the evaluation of the project management internal efficiency. Decision Support Systems 42 (2): 712–726.

    Article  Google Scholar 

  • Eilat H, Golany B and Shtub A (2006). Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology. European Journal of Operational Research 172 (3): 1018–1039.

    Article  Google Scholar 

  • Farris JA, Groesbeck RL, Van Aken EM and Letens G (2006). Evaluating the relative performance of engineering design projects: A case study using data envelopment analysis. IEEE Transactions on Engineering Management 53 (3): 471–482.

    Article  Google Scholar 

  • Freeman M and Beale P (1992). Measuring project success. Project Management Journal 23 (1): 8–17.

    Google Scholar 

  • Gabriel SA, Kumar S, Ordónez J and Nasserian A (2006). A multiobjective optimization model for project selection with probabilistic considerations. Socio-economic Planning Sciences 40 (4): 297–313.

    Article  Google Scholar 

  • Gobeli DH and Larson EW (1987). Relative effectiveness of different project structures. Project Management Journal 18 (2): 81–85.

    Google Scholar 

  • Horta IM, Camanho AS and Moreira da Costa J (2012). Performance assessment of construction companies: A study of factors promoting financial soundness and innovation in the industry. International Journal of Production Economics 137 (1): 84–93.

    Article  Google Scholar 

  • Ling FYY and Liu M (2004). Using neural network to predict performance of design-build projects in Singapore. Building and Environment 39 (10): 1263–1274.

    Article  Google Scholar 

  • Lozano S and Gutierrez E (2008). Data envelopment analysis of the human development index. International Journal of Society Systems Science 1 (2): 132–150.

    Article  Google Scholar 

  • Low SP and Goh KH (1994). Construction quality assurance: Problems of implementation at infancy stage in Singapore. International Journal of Quality & Reliability Management 11 (1): 23–37.

    Google Scholar 

  • McCollum JK and Sherman DJ (1991). The effect of matrix organization size and number of project assignments on performance. IEEE Transactions on Engineering Management 38 (1): 75–78.

    Article  Google Scholar 

  • Munns AK and Bjeirmi BF (1996). The role of project management in achieving project success. International Journal of Project Management 14 (2): 81–87.

    Article  Google Scholar 

  • Murphy A and Ledwith A (2007). Project management tools and techniques in high-technology SMEs. Management Research News 30 (2): 153–166.

    Article  Google Scholar 

  • Oral M, Kettani O and Lang P (1991). A methodology for collective evaluation and selection of industrial R&D projects. Management Science 37 (7): 871–885.

    Article  Google Scholar 

  • Oral M, Kettani O and Cinar U (2001). Project evaluation and selection in a network of collaboration: A consensual disaggregation multi-criterion approach. European Journal of Operational Research 130 (2): 332–346.

    Article  Google Scholar 

  • Rozenes S, Vitner G and Spraggett S (2004). MPCS: Multidimensional project control system. International Journal of Project Management 22 (2): 109–118.

    Article  Google Scholar 

  • Sarker BR, Egbelu PJ, Liao TW and Yu J (2012). Planning and design models for construction industry: A critical survey. Automation in Construction 22: 123–134.

    Article  Google Scholar 

  • Seyedhoseini SM, Noori S and Hatefi MA (2009). An integrated methodology for assessment and selection of the project risk response actions. Risk Analysis 29 (5): 752–763.

    Article  Google Scholar 

  • Shenhar AJ, Levy O and Dvir D (1997). Mapping the dimensions of project success. Project Management Journal 28 (2): 5–13.

    Google Scholar 

  • Soetanto R and Proverbs DG (2002). Modelling the satisfaction of contractors: The impact of client performance. Engineering, Construction and Architectural Management 9 (5/6): 453–465.

    Article  Google Scholar 

  • Swink M, Talluri S and Pandejpong T (2006). Faster, better, cheaper: A study of NPD project efficiency and performance tradeoffs. Journal of Operations Management 24 (5): 542–562.

    Article  Google Scholar 

  • Tsolas I (2011). Modelling profitability and effectiveness of Greek-listed construction firms: An integrated DEA and ratio analysis. Construction Management and Economics 29 (8): 795–807.

    Article  Google Scholar 

  • Verma D and Sinha KK (2002). Toward a theory of project interdependencies in high tech R&D environments. Journal of Operations Management 20 (5): 451–468.

    Article  Google Scholar 

  • Vitner G, Rozenes S and Spraggett S (2006). Using data envelope analysis to compare project efficiency in a multi-project environment. International Journal of Project Management 24 (4): 323–329.

    Article  Google Scholar 

  • Williams Jr GH (2003). An evaluation of construction contracting methods for the public building sector in Oregon, 1986–2002, using data envelopment analysis. PhD Thesis, Portland State University.

  • Yasamis F, Arditi D and Mohammadi J (2002). Assessing contractor quality performance. Construction Management and Economics 20 (3): 211–223.

    Article  Google Scholar 

Download references

Acknowledgements

The author acknowledges the valuable suggestions of two anonymous reviewers.

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  1. National Technical University of Athens, Zografou, Greece

    I E Tsolas

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  1. I E Tsolas
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Correspondence to I E Tsolas.

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Tsolas, I. Construction project monitoring by means of RAM-based composite indicators. J Oper Res Soc 64, 1291–1297 (2013). https://doi.org/10.1057/jors.2012.147

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  • DOI: https://doi.org/10.1057/jors.2012.147

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