Accounting for Variability: Identifying Critical Activities as a Supplement to the Critical Path
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 5
Abstract
Successful realization of construction activities requires simultaneous integration of various resource input flows, giving rise to considerable sources of flow variability. Such variability might manifest as schedule variations, which can jeopardize the project performance, especially when using deterministic scheduling. Current scheduling techniques fail to efficiently tackle variability and rely on deterministic approaches. Therefore, this study fills the gap by developing a discrete event simulation model, where activity durations are modeled using beta distributions and program evaluation and review technique assumptions. By applying the Spearman correlation coefficient, activities with higher influence on the schedule were identified, highlighting where to reduce variability. An application example was conducted involving a critical path method (CPM) network containing 11 activities. Two types of waste emerging due to variability were identified as waiting time and variation gaps. Out of the 11 activities in the example network, two sets of critical activities were identified. Results revealed that an 80% reduction in variability in these critical activities led to a 51.9% increase in likelihood of completing the project on schedule, 30% decrease in waiting time, and 28.6% decrease in variation gap. An important implication of this research is that near-critical paths could become critical based on the amount of variability contained in the activities lying on each path. Acquiring such information early on during planning provides proactive, eye-opening insights into potential problematic scheduling areas. The study’s contributions include investigating the variability effect on two types of waste in production and providing project planners with a stochastic approach to manage the hidden waste in production systems; the approach examines the effect of reducing variability on the overall project performance characterized by meeting deadlines, avoiding trade idling (reducing waiting time), and exploring potential opportunities for enhancing performance (reducing variation gaps).
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Data Availability Statement
Data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abourizk, S. M., D. W. Halpin, and J. R. Wilson. 1994. “Fitting beta distributions based on sample data.” J. Constr. Eng. Manage. 120 (2): 288–305. https://doi.org/10.1061/(ASCE)0733-9364(1994)120:2(288).
AbouRizk, S. 2010. “Role of simulation in construction engineering and management.” J. Constr. Eng. Manage. 136 (10): 1140–1153. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000220.
Alvanchi, A., S. Lee, and S. Abourizk. 2011. “Modeling framework and architecture of hybrid system dynamics and discrete event simulation for construction.” Comput.-Aided Civ. Infrastruct. Eng. 26 (2): 77–91. https://doi.org/10.1111/j.1467-8667.2010.00650.x.
Alvanchi, A., S. Lee, and S. Abourizk. 2012. “Dynamics of working hours in construction.” J. Constr. Eng. Manage. 138 (1): 66–77. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000384.
Arashpour, M., and M. Arashpour. 2015. “Analysis of workflow variability and its impacts on productivity and performance in construction of multistory buildings.” J. Manage. Eng. 31 (6): 04015006. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000363.
Arashpour, M., R. Wakefield, N. Blismas, and E. W. M. Lee. 2013. “A new approach for modelling variability in residential construction projects.” Aust. J. Constr. Econ. Build. 13 (2): 83–92. https://doi.org/10.5130/AJCEB.v13i2.3120.
Ballard, H. G. 2000. “The last planner system of production control.” Doctoral dissertation, School of Civil Engineering, Univ. of Birmingham.
Ballesteros-Perez, P. 2017. “M-PERT: Manual project-duration estimation technique for teaching scheduling basics.” J. Constr. Eng. Manage. 143 (9): 04017063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001358.
Ballesteros-Perez, P., E. Sanz-Ablanedo, R. Soetanto, M. C. González-Cruz, G. D. Larsen, and A. Cerezo-Narváez. 2020. “Duration and cost variability of construction activities: An empirical study.” J. Constr. Eng. Manage. 146 (1): 04019093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001739.
Brodetskaia, I., R. Sacks, and A. Shapira. 2013. “Stabilizing production flow of interior and finishing works with reentrant flow in building construction.” J. Constr. Eng. Manage. 139 (6): 665–674. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000595.
Che-Hao, C., T. Yeou-Koung, and Y. Jinn-Chuang. 1995. “Evaluation of probability point estimate methods.” Appl. Math. Modell. 19 (2): 95–105. https://doi.org/10.1016/0307-904X(94)00018-2.
Davis, R. 2008. “Teaching note—Teaching project simulation in Excel using PERT-beta distributions.” INFORMS Trans. Educ. 8 (3): 139–148. https://doi.org/10.1287/ited.1080.0013.
Erol, H., I. Dikmen, and M. T. Birgonul. 2017. “Measuring the impact of lean construction practices on project duration and variability: A simulation-based study on residential buildings.” J. Civ. Eng. Manage. 23 (2): 241–251. https://doi.org/10.3846/13923730.2015.1068846.
Fard, M., S. Terouhid, T. Boyle, R. Brown, S. Ishac, J. Orr, M. Takabi, and R. Winter. 2017. Analyzing near-critical paths. Morgantown, WV: Association for the Advancement of Cost Engineering.
Farid, F., and T. L. Koning. 1994. “Simulation verifies queuing program for selecting loader-truck fleets.” J. Constr. Eng. Manage. 120 (2): 386–404. https://doi.org/10.1061/(ASCE)0733-9364(1994)120:2(386).
Forcael, E., M. González, J. Soto, F. Ramis, and C. Rodríguez. 2018. “Simplified scheduling of a building construction process using discrete event simulation.” In Proc., 16th LACCEI Int. Multi-Conf. for Engineering, Education, and Technology, 19–21. Boca Raton, FL: LACCEI. https://doi.org/10.18687/LACCEI2018.1.1.194.
Garcia-Lopez, N. P., and M. Fischer. 2016. “A construction workflow model for analyzing the impact of in-project variability.” In Proc., Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan, 1998–2007. Boca Raton, FL: CRC Prees. https://doi.org/10.1061/9780784479827.199.
Hamzeh, F. 2009. “Improving construction workflow-The role of production planning and control.” Doctoral dissertation, Dept. of Civil and Environmental Engineering, Univ. of California, Berkeley.
Hamzeh, F., G. Ballard, and I. D. Tommelein. 2012. “Rethinking lookahead planning to optimize construction workflow.” Lean Constr. J. 2012: 15–34.
Hendradewa, A. P. 2019. “Schedule risk analysis by different phases of construction project using CPM-PERT and Monte-Carlo simulation.” IOP Conf. Ser. Mater. Sci. Eng. 528 (1): 012035. https://doi.org/10.1088/1757-899X/528/1/012035.
Hopp, W. J., and M. L. Spearman. 2000. Factory physics: Foundations of manufacturing management. 1st ed. Boston: Irwin/McGraw-Hill.
Howell, G. A., G. Ballard, I. D. Tommelein, and L. Koskela. 2004. “Discussion of ‘Reducing variability to improve performance as a lean construction principle’ by H. Randolph Thomas, Michael J. Horman, Ubiraci Espinelli Lemes de Souza, and Ivica Zavřski.” J. Constr. Eng. Manage. 130 (2): 299–300. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(299).
Huber, B., and P. Reiser. 2003. “The marriage of CPM and lean construction.” In Vol. 11 of Proc., Annual Conf. of the Int. Group for Lean Construction. Auckland, New Zealand: Univ. of Auckland.
Ingalls, R. G., and D. J. Morrice. 2004. “PERT scheduling with resource constraints using qualitative simulation graphs.” Project Manage. J. 35 (3): 5–14. https://doi.org/10.1177/875697280403500303.
Karabulut, M. 2017. “Application of Monte Carlo simulation and PERT/CPM techniques in planning of construction projects: A case study.” Period. Eng. Nat. Sci. 5 (3): 408–420. https://doi.org/10.21533/pen.v5i3.152.
Khamooshi, H., and D. F. Cioffi. 2013. “Uncertainty in task duration and cost estimates: Fusion of probabilistic forecasts and deterministic scheduling.” J. Constr. Eng. Manage. 139 (5): 488–497. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000616.
Kirytopoulos, K. A., V. N. Leopoulos, and V. K. Diamantas. 2008. “PERT vs. Monte Carlo Simulation along with the suitable distribution effect.” Int. J. Project Organ. Manage. 1 (1): 24–46. https://doi.org/10.1504/IJPOM.2008.020027.
Koskela, L. 2000. An exploration towards a production theory and its application to construction. Espoo, Finland: Technical Research Centre of Finland.
Lee, D. E. 2005. “Probability of project completion using stochastic project scheduling simulation.” J. Constr. Eng. Manage. 131 (3): 310–318. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:3(310).
Lee, D. E., and D. Arditi. 2006. “Automated statistical analysis in stochastic project scheduling simulation.” J. Constr. Eng. Manage. 132 (3): 268–277. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:3(268).
Lee, D. E., C. Y. Yi, T. K. Lim, and D. Arditi. 2010. “Integrated simulation system for construction operation and project scheduling.” J. Comput. Civ. Eng. 24 (6): 557–569. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000061.
Li, H., P. E. D. Love, and D. S. Drew. 2000. “Effects of overtime work and additional resources on project cost and quality.” Eng. Constr. Archit. Manage. 7 (3): 211–220. https://doi.org/10.1108/eb021146.
Lindhard, S. 2014. “Understanding the effect of variation in a production system.” J. Constr. Eng. Manage. 140 (11): 04014051. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000887.
Lindhard, S., and S. Wandahl. 2014. “Scheduling of large, complex, and constrained construction projects–an exploration of LPS application.” Int. J. Project Organ. Manage. 6 (3): 237–253. https://doi.org/10.1504/IJPOM.2014.065258.
Lindhard, S. M., F. Hamzeh, V. A. Gonzalez, S. Wandahl, and L. F. Ussing. 2019. “Impact of activity sequencing on reducing variability.” J. Constr. Eng. Manage. 145 (3): 04019001. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001618.
Lindhard, S. M., H. Neve, B. Terje Kalsaas, D. E. Møller, and S. Wandahl. 2020. “Ranking and comparing key factors causing time-overruns in on-site construction.” Int. J. Constr. Manage. 1–7. https://doi.org/10.1080/15623599.2020.1820659.
Lu, M. 2002. “Enhancing project evaluation and review technique simulation through artificial neural network-based input modeling.” J. Constr. Eng. Manage. 128 (5): 438–445. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(438).
Lu, M., and S. M. AbouRizk. 2000. “Simplified CPM/PERT simulation model.” J. Constr. Eng. Manage. 126 (3): 219–226. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:3(219).
Moitra, S. D. 1990. “Skewness and the beta distribution.” J. Oper. Res. Soc. 41 (10): 953–961. https://doi.org/10.1057/jors.1990.147.
Moradi, S., F. Nasirzadeh, and F. Golkhoo. 2015. “A hybrid SD–DES simulation approach to model construction projects.” Constr. Innovation. 15 (1): 66–83. https://doi.org/10.1108/CI-10-2013-0045.
Nguyen, L. D., D. H. Phan, and L. C. Tang. 2013. “Simulating construction duration for multistory buildings with controlling activities.” J. Constr. Eng. Manage. 139 (8): 951–959. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000677.
Nicholas, J. M., and H. Steyn. 2020. Project management for engineering, business and technology. New York: Routledge.
Noyce, D. A., and A. S. Hanna. 1998. “Planned and unplanned schedule compression: The impact on labour.” Construct. Manage. Econ. 16 (1): 79–90. https://doi.org/10.1080/014461998372619.
Palaniappan, S., K. D. Walsh, A. Sawhney, and H. H. Bashford. 2007. Special purpose simulation template for workflow analysis in construction. In Proc., 2007 Winter Simulation Conf., 2090–2098. New York: IEEE.
Parkinson, C. N., and R. C. Osborn. 1957. Vol. 24 of Parkinson’s law, and other studies in administration. Boston: Houghton Mifflin.
Poshdar, M., V. A. González, G. M. Raftery, and F. Orozco. 2014. “Characterization of process variability in construction.” J. Constr. Eng. Manage. 140 (11): 05014009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000901.
Ragel, L. J. B. 2021. “Limitations of PERT/CPM in construction management planning: Inputs to mathematics in architecture education.” Turkish J. Comput. Math. Educ. 12 (10): 5218–5223.
Rausch, C., M. Nahangi, C. Haas, and W. Liang. 2019. “Monte Carlo simulation for tolerance analysis in prefabrication and offsite construction.” Autom. Constr. 103 (Apr): 300–314. https://doi.org/10.1016/j.autcon.2019.03.026.
Schonberger, R. J. 2008. World class manufacturing. New York: Free Press.
Singh, A. 2003. “Accelerated work-schedule design considering efficiency losses for overtime and overmanning.” Eng. Constr. Archit. Manage. 10 (5): 312–321. https://doi.org/10.1108/09699980310502928.
Small, E. P., and M. Baqer. 2016. “Examination of job-site layout approaches and their impact on construction job-site productivity.” Procedia Eng. 164 (Jun): 383–388. https://doi.org/10.1016/j.proeng.2016.11.634.
Spearman, M. L., and W. J. Hopp. 1996. Vol. 439 of Factory physics: Foundations of manufacturing management. Chicago: Irwin.
Thirumalai, C., S. A. Chandhini, and M. Vaishnavi. 2017. “Analysing the concrete compressive strength using Pearson and Spearman. In Vol. 2 of Proc., 2017 Int. Conf. of Electronics, Communication and Aerospace Technology, 215–218. New York: IEEE.
Thomas, H. R., M. J. Horman, U. E. L. De Souza, and I. Zavřski. 2002. “Reducing variability to improve performance as a lean construction principle.” J. Constr. Eng. Manage. 128 (2): 144–154. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:2(144).
Thomas, H. R., M. J. Horman, R. E. Minchin Jr., and D. Chen. 2003. “Improving labor flow reliability for better productivity as lean construction principle.” J. Constr. Eng. Manage. 129 (3): 251–261. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:3(251).
Thomas, H. R., C. T. Mathews, and J. G. Ward. 1986. “Learning curve models of construction productivity.” J. Constr. Eng. Manage. 112 (2): 245–258. https://doi.org/10.1061/(ASCE)0733-9364(1986)112:2(245).
Thomas, H. R., and I. Yiakoumis. 1987. “Factor model of construction productivity.” J. Constr. Eng. Manage. 113 (4): 623–639. https://doi.org/10.1061/(ASCE)0733-9364(1987)113:4(623).
Tommelein, I. D., D. R. Riley, and G. A. Howell. 1999. “Parade game: Impact of work flow variability on trade performance.” J. Constr. Eng. Manage. 125 (5): 304–310. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:5(304).
Trietsch, D., and K. R. Baker. 2012. “PERT 21: Fitting PERT/CPM for use in the 21st century.” Int. J. Project Manage. 30 (4): 490–502. https://doi.org/10.1016/j.ijproman.2011.09.004.
Tsai, C. W., and S. Franceschini. 2005. “Evaluation of probabilistic point estimate methods in uncertainty analysis for environmental engineering applications.” J. Environ. Eng. 131 (3): 387–395. https://doi.org/10.1061/(ASCE)0733-9372(2005)131:3(387).
Tsehayae, A. A., and A. R. Fayek. 2016. “System model for analysing construction labour productivity.” Constr. Innovation. 16 (2): 203–228. https://doi.org/10.1108/CI-07-2015-0040.
Wambeke, B. W., S. M. Hsiang, and M. Liu. 2011. “Causes of variation in construction project task starting times and duration.” J. Constr. Eng. Manage. 137 (9): 663–677. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000342.
Wambeke, B. W., M. Liu, and S. M. Hsiang. 2012. “Using last planner and a risk assessment matrix to reduce variation in mechanical related construction tasks.” J. Constr. Eng. Manage. 138 (4): 491–498. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000444.
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Journal of Construction Engineering and Management
Volume 148 • Issue 5 • May 2022
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© 2022 American Society of Civil Engineers.
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Received: Jul 7, 2021
Accepted: Jan 9, 2022
Published online: Mar 11, 2022
Published in print: May 1, 2022
Discussion open until: Aug 11, 2022
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