A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis

Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

Abstract:

Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: Cluster analysis, construction management, earned value, schedule.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316444

References:

[1] M. Sambasivan and Y. Soon, “Causes and effects of delays in Malaysian construction industry.” International Journal of Project Management, Volumen 25, pp. 517-526, 2007.
[2] M. Alkhathami, “Examination of the correlation of critical success and delay factors in construction projects in the kingdom of Saudi Arabia”, Pittsburgh: Doctor of Philosophy Thesis: University of Pittsburgh, 2004.
[3] M. Mohamad, “The factors and effect of delay in government construction project: Case study in juantan.” Malaysia Pahang: Bachelor´s Degree thesis, 2010.
[4] S. Assaf and S. Al-Hejji, “Causes of delay in large construction projects.” International journal of project managment, 4(24), pp. 349-357, 2006.
[5] M. Mulenga, A. Clinton. and T. Wellington, “Effects of construction projects schedule overruns: A Case of the Gauteng Province, South Africa”, Procedia Manufacturing , pp. 1-6, 2015.
[6] W. Chan and M. Kumaraswamy, “A comparative study of causes of time overruns.” International Journal of Project Management, 1(15), pp. 55-63, 1997.
[7] A. De Marco, T. Narbaev. “Earned value-based.” Journal of Facilities Management, 11(1), pp. 69-80, 2013.
[8] G. Aristondo, “Cooperativismo, ayuda y autogestión.” Revista electrónica geográfica y ciencias sociales, 2(146), pp. 41-98, 2003.
[9] E.C, “Strengthening project internal, How to enhance the role of. s.l.” Office for Official Publications of the European Communities, 2007.
[10] PMBOK, “Project Management Body of Knowledge” (PMBOK®). 3rd Edition ed. s.l, Project Management Institute, 2004.
[11] S. Brlecic, M. Dimitric and M. Dalsaso, “Effective Project Management Tools for Modern.” Pomorski zbornik, Volumen 51, pp. 131-145, 2016.
[12] E. Norman, S. Brotherton and R. Fried, “Work Breakdown Structures: The. New Jersey”, John Wiley & Sons. 2008.
[13] W. Lipke, “Achieving normality for cost.” The Measurable News, Volumen (Fall/Winter), pp. 6-11, 2003.
[14] W. Abba and F Niel, F., “Integrating technical performance measurement.” The Measurable News, Volumen 4, pp. 6-8, 2010.
[15] F. Anbari, “Earned value project management method and extensions.” International Journal of Project Management, 34(4), p. 12–23, 2003.
[16] V. Blanco, “Earned value management: a predictive analysis tool.” Navy Supply corps newsletter, 66(2), pp. 24-27, 2003.
[17] R. Bruke, “Project Management Planning and Control Techniques” 4th Edition ed. s.l.:s.n, 2003.
[18] D. Cioffi, “Designing project management: A scientific notation and an improved formalism for earned value calculations“, International Journal of Project Management, Volumen 24, pp. 136-144, 2006.
[19] D. Jacob, “Forecasting project schedule completion with earned value.” The Measurable News, pp. 7-9, 2006.
[20] E. Kim, W. Wells and M. Duffey,” A model for effective implementation of Earned Value Management methodology.” International Journal of Project Management, 21(5), pp. 375-382, 2003.
[21] W. Lipke. “A study of the normality of earned value indicators. The Measurable News, pp. 1-16, 2002.
[22] R. McKim,T. Hagazy, and M. Attalla, “Project performance control in in reconstruction projects”, Journal of Construction Engineering and Management, 126(2), pp. 137-141, 2000.
[23] R. Aliverdi, L. Moslemi, and S. Amir, S., “Monitoring project duration and cost in a construction project by applying.” International Journal of Project Management, Volumen 31, p. 411–423, 2013.
[24] W. Lipke, O. Zwikael, K. Henderson, and F. Anbari, “Prediction of project outcome, the application of statistical methods to earned value management and earned schedule performance indexes.” International Journal of Project Management, Volumen 27, pp. 400-407, 2009.
[25] Q. Fleming, and J. Koppleman, “Earned Value Project Management”. 4th Edition ed. Pensilvania: Project Managment Institute, 2010.
[26] M. Vanhoucke, “Mesuring time-improving project perfomance using earned value managment.” Reserch and managment science, Volumen 136, 2010.
[27] M. Vanhoucke, “Integrated project managment and control: first comes the theory, then the practice.” Managment for proffesionals, Volumen 23, 2014.
[28] M. Liberatone, B. Pollack-Johnson, and C. Smith, “Project Managment in construction: software use and research directions.” Journal of construcion engineering and managment, 127(2), pp. 101-107, 2001.
[29] L. Forbes, and S. Ahmed, ”Modern construction: lean project delivery and integrated practices.” CRC Press, 2010.
[30] N. Rudeli, A. Santilli, I. Puente, and E. Viles, “A statistical model for schedule prediction: Validation in a housing cooperative construction database.” Journal of Construction Engineering and Management, 2017.
[31] A. Senoucia, A. Ismailb, and N. Eldina, “Time Delay and Cost Overrun in Qatari Public Construction Projects.” Procedia Engineering, Volumen 164, p. 368 – 375, 2016.
[32] S. Vandevoorde and M. Vanhoucke, M., “A comparison of different project duration forecasting methods.” International Journal of Project Management, Volumen 24, p. 289–302, 2006.
[33] S. Guo, J. Chen and C. Chiu, “Fuzzy duration forecast model for wind turbine construction project.” Automation in Construction, 2016.
[34] A. Araszkiewicz, “Application of Critical Chain Management in Construction Projects Schedules in a Multi-Project Environment: a Case Study.” Procedia Engineering, Volumen 182, p. 33 – 41, 2017.
[35] C. Sabaté, C. “De la Preservación del Patrimonio a la Ordenación del Paisaje. Intervenciones en Paisajes Culturales en Latinoamérica.” Gerencia Española de Cooperación Internacional para el Desarrollo, Volumen 502-11-004-2, pp. 11-23, 2012.
[36] M. Callahan, D. Quackenbush, and J. Rowings, “Construction Project Scheduling.” New York, NY: McGraw-Hill, Incorporated, 1992.
[37] A. Hernandez, and J. Loayza, “Planeamiento integral, control, construcción y análisis técnico ejecutado en un centro comercial en la ciudad de Arequipa”, Perú: Pontificia Universidad Católica de Perú, 2013.
[38] J. Hair, R. Anderson, R. Tatham and W. Black., “Análisis multivariate 5ta edición”, 799 pag, 2004.
[39] A. Scott, and M. Knott, “A cluster analysis method for grouping means in the analysis of variance.” Biometrics, 507-512, 1974.
[40] W. Dillon, and M. Goldstein, “Multivariate analysis: Methods and applications”, 1984.
[41] A. Santilli, N. Rudeli, E. Viles, M. Tanco, D. Jurburg, “Comportamiento tipo del desarrollo de obras cooperativas de viviendas Uruguayas.”, Memoria Investigaciones en Ingeniería, 15 (2017) ISSN 2301-1092.