Metrics That Matter: Core Predictive and Diagnostic Metrics for Improved Project Controls and Analytics
This article has a reply.
VIEW THE REPLYPublication: Journal of Construction Engineering and Management
Volume 144, Issue 11
Abstract
Project progress and performance assessment is critically important to the successful delivery of capital facility projects. However, there is no standardized approach for the selection and use of project control metrics, making it difficult to analyze project progress and performance for transforming data into meaningful insights. This research identified core predictive and diagnostic metrics that may provide actionable insights into a project’s actual progress, performance, and forecast at completion. The methodology used for identifying these metrics included a literature review, surveys, expert evaluation utilizing the Delphi method, and statistical validation. The researchers analyzed 44 surveys and collected multiple rounds of responses from 16 subject matter experts to validate the findings. Results indicated there are 20 core metrics, seven validation metrics, seven innovative metrics, and 14 other significant metrics, which can be used for multiple project types, sizes, and contracting strategies. Statistical analyses of the survey data were used to further validate the core metrics and demonstrated that use of more core metrics corresponded with project cost performance and using more diagnostic metrics in projects led to better schedule performance.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
This study is a part of a research effort that has been supported by the Construction Industry Institute (CII) Research Team 322 (RT-322), focused on improving project progress measurement, performance assessment, and forecasting through identification of core metrics. The opinions expressed in this paper represent those of the authors and not necessarily those of the CII.
References
Abdi, H., and D. Valentin. 2007. “Multiple correspondence analysis.” In Encyclopedia of measurement and statistics, 651–657. Thousand Oaks, CA: SAGE.
Ackoff, R. 1989. “From data to wisdom.” J. Appl. Syst. Anal. 16: 3–9.
Association for Advanced Cost Engineering International. 2012. Total cost management framework: An integrated approach to portfolio, program, and project management. Revised 1st ed. Morgantown, WV: Association for Advanced Cost Engineering International.
A.T. Kearney. 2012. “ExCap II: Top-level thinking on capital projects.” Accessed June 11, 2017. https://www.atkearney.com.au/latest-article/-/asset_publisher/lON5IOfbQl6C/content/excap-ll-top-level-thinking-on-capital-projects/10192.
Benzecri, J. P. 1979. “Sur le calcul des taux d’inertie dans l’analyse d’un questionnaire.” Cahiers de l’Analyse des Données 4 (3): 377–378.
Benzecri, J. P. 1992. Correspondence analysis handbook. New York: Dekker.
Blanco, J. L., M. Janauskas, and M. J. Ribeirinho. 2016. “Beating the low-productivity trap: How to transform construction operations.” Accessed June 11, 2017. http://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/beating-the-low-productivity-trap-how-to-transform-construction-operations.
Bosch-Rekveldt, M., Y. Jongkind, H. Mooi, H. Bakker, and A. Verbraeck. 2011. “Grasping project complexity in large engineering projects: The TOE (technical, organizational and environmental) framework.” Int. J. Project Manage. 29 (6): 728–739. https://doi.org/10.1016/j.ijproman.2010.07.008.
Changali, S., A. Mohammad, and M. van Nieuwland. 2015. “The construction productivity imperative.” Accessed June 11, 2017. http://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/the-construction-productivity-imperative.
Choi, J., S. D. Anderson, and S. J. Kim. 2006. Forecasting potential risks through leading indicators to project outcome. Austin, TX: Construction Industry Institute.
CIC (Construction Intelligence Center). 2016. Global construction outlook 2020. London: Timetric Report Store.
Construction Industry Institute. 1986. Project control for engineering. Austin, TX: Construction Industry Institute.
Construction Industry Institute. 1987a. Model planning and controlling system for EPC of industrial projects. Austin, TX: Construction Industry Institute.
Construction Industry Institute. 1987b. Project control for construction. Austin, TX: Construction Industry Institute.
Construction Industry Institute. 1990b. Planning construction activity to support the startup process. Austin, TX: Construction Industry Institute.
Construction Industry Institute. 1990a. The impact of changes on construction cost and schedule. Austin, TX: Construction Industry Institute.
Dalkey, N., and O. Helmer. 1963. “An experimental application of the Delphi method to the use of experts.” Manage. Sci. 9 (3): 458–467. https://doi.org/10.1287/mnsc.9.3.458.
Fleming, Q., and J. Koppelman. 2000. Earned value project management. 2nd ed. Newton Square, PA: Project Management Institute.
Grau, D., and E. Back. 2014. Improving the accuracy and timeliness of project outcome predictions. Austin, TX: Construction Industry Institute.
Greenacre, M. J. 1984. Theory and applications of correspondence analysis. London: Academic.
Greenacre, M. J. 1993. Correspondence analysis in practice. London: Academic.
Greenacre, M. J. 2007. Correspondence analysis in practice. 2nd ed. New York: Chapman & Hall.
IBISWorld. 2016. IBISWorld business environment profiles: Value of construction. Los Angeles: IBISWorld Database.
Le Roux, B., and H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks, CA: SAGE.
Linstone, H. A., and M. Turoff, eds. 1975. The Delphi method: Techniques and applications. Reading, MA: Addison-Wesley.
Lipke, W. H. 2003. “Schedule is different.” Measurable News 31 (4): 31–34.
Lipke, W. H. 2009. Earned schedule. Raleigh, NC: Lulu Publishing.
Marr, B. 2016. “How big data and analytics are transforming the construction industry.” Forbes. Accessed March 12, 2017. http://www.forbes.com/sites/bernardmarr/2016/04/19/how-big-data-and-analytics-are-transforming-the-construction-industry/#5d7388915cd0.
McKinsey Global Institute. 2017. “Reinventing construction: A route to higher productivity.” Accessed October 30, 2017. https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/reinventing-construction-through-a-productivity-revolution.
NDIA (National Defense Industry Association). 2014. A guide to managing programs using predictive measures. Arlington, VA: National Defense Industrial Association.
O’Brien, W. J., C. L. Menches, J. Yi, and S. Ali. 2012. Global project controls and management systems. Austin, TX: Construction Industry Institute.
Orgut, R. E. 2017. “Metrics that matter: Improving project controls and analytics in construction industry.” Ph.D. dissertation, Dept. of Civil, Construction, and Environmental Engineering, North Carolina State Univ.
Orgut, R. E., M. Batouli, J. Zhu, A. Mostafavi, and E. J. Jaselskis. 2016. “Metrics that matter: Evaluation of metrics and indicators for project progress measurement, performance assessment, and performance forecasting during construction.” In Proc., Construction Research Congress. Reston, VA: ASCE.
Orgut, R. E., J. Zhu, M. Batouli, A. Mostafavi, and E. J. Jaselskis. 2015. “A review of the current knowledge and practice related to project progress and performance assessment.” In Proc., 5th Int./11th Canadian Society for Civil Engineering Construction Specialty Conf. Montreal: Canadian Society for Civil Engineering.
Project Management Institute. 2013. A guide to the project management body of knowledge (PMBOK guide). 5th ed. Newtown Square, PA: Project Management Institute.
Project Management Institute. 2015. PMI lexicon of project management terms. Version 3. Newtown Square, PA: Project Management Institute.
Running, D. M., J. B. Ligon, and I. Miskioglu. 1999. “Response rate in academic studies: A comparative analysis.” Hum. Relat. 52 (4): 421–438. https://doi.org/10.1023/A:1016905407491.
Russell, J. S., E. J. Jaselskis, S. P. Lawrence, H.-P. Tserng, and M. T. Prestine. 1996. Development of a predictive tool for continuous assessment of project performance. Austin, TX: Construction Industry Institute.
Sourial, N., C. Wolfson, B. Zhu, J. Quail, J. Fletcher, S. Karunananthan, K. Bandeen-Roche, F. Béland, and H. Bergman. 2010. “Correspondence analysis is a useful tool to uncover the relationships among categorical variables.” J. Clin. Epidemiol. 63 (6): 638–646. https://doi.org/10.1016/j.jclinepi.2009.08.008.
Thomas, H. 2000. Principles of construction labor productivity measurement and processing: Final report. University Park, PA: Pennsylvania Transportation Institute.
Vandevoorde, S., and M. Vanhoucke. 2006. “A comparison of different project duration forecasting methods using earned value metrics.” Int. J. Project Manage. 24 (4): 289–302. https://doi.org/10.1016/j.ijproman.2005.10.004.
Vanhoucke, M. 2009. Measuring time: Improving project performance using earned value management. Dordrecht: Springer.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 11 • November 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 21, 2017
Accepted: Mar 16, 2018
Published online: Aug 30, 2018
Published in print: Nov 1, 2018
Discussion open until: Jan 30, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.