Abstract
‘Completion in time’ is a crucial element of project management. Previous studies show that earned value management (EVM), earned schedule method, or earned duration management do not give an accurate project completion time estimation (PCTE) because of theoretical weaknesses. Inaccurate PCTE may provide misleading information, so that the project manager cannot take effective schedule control actions timely, and thus it results in failure of project time management. To improve the above-mentioned problem, this research proposes a quantity-based project duration estimating method (Q-PDEM), which calculates the PCTE using the de facto work quantities and the updated productivity information of activities. The results of two case studies show that the proposed Q-PDEM gives a 7.55% better mean absolute percentage error (MAPE) than the existing methods for predicted project completion duration for Case I, and 24.54% for Case II. It is concluded that the proposed Q-PDEM gives a more accurate estimation of the time for project completion and allows more effective control of the project schedule.
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References
Anbari F (2003) Earned value project management method and extensions. Project Management Journal 34:12–23, DOI: https://doi.org/10.1177/875697280303400403
Asadabadi MR, Zwikael O (2020) Integrating risk into estimations of project activities’ time and cost: A stratified approach. European Journal of Operational Research, DOI: https://doi.org/10.1016/j.ejor.2019.11.018
Barraza GA, Back WE, Mata F (2004) Probabilistic forecasting of project performance using stochastic S curves. Journal of Construction Engineering and Management 130(1):25–32, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(25)
Batselier J, Vanhoucke M (2015) Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earned value. International Journal of Project Management 33(7):1588–1596, DOI: https://doi.org/10.1016/j.ijproman.2015.04.003
Batselier J, Vanhoucke M (2017) Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International Journal of Project Management 35(1):28–43, DOI: https://doi.org/10.1016/j.ijproman.2016.10.003
de Andrade PA, Martensb A, Vanhoucke M (2019) Using real project schedule data to compare earned schedule and earned duration management project time forecasting capabilities. Automation in Construction 99:68–78, DOI: https://doi.org/10.1016/j.autcon.2018.11.030
de Marco A, Briccarello D, Rafele C (2009) Cost and schedule monitoring of industrial building projects: Case study. Journal of Construction Engineering and Management 135(9):853–862, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000055
Elshaer R (2013) Impact of sensitivity information on the prediction of projects duration using earned schedule method. International Journal of Project Management 31:579–588, DOI: https://doi.org/10.1016/j.ijproman.2012.10.006
Fleming QW (1988) Cost/schedule control systems criteria: The management guide to C/SCSC. Probus, Chicago, IL, USA
Hadiana H, Rahimifard A (2019) Multivariate statistical control chart and process capability indices for simultaneous monitoring of project duration and cost. Computers & Industrial Engineering 130:788–797, DOI: https://doi.org/10.1016/j.cie.2019.03.021
Hamzeh AM, Mousavi SA, Gitinavard H (2020) Imprecise earned duration model for time evaluation of construction projects with risk considerations. Automation in Construction 111:102993, DOI: https://doi.org/10.1016/j.autcon.2019.102993
Jacob D (2003) Forecasting project schedule completion with earned value metrics. The Measurable News 1:7–9
Jacob D, Kane M (2004) Forecasting schedule completion using earned value metrics? Revisited. The Measurable News 1:11–17
Khamooshi H, Abdi A (2016) Project duration forecasting using earned duration management with exponential smoothing techniques. Journal of Management in Engineering 33(1):04016032, DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000475
Khamooshi H, Golafshani H (2014) EDM: Earned duration management, a new approach to schedule performance and measurement. International Journal of Project Management 32(6):1019–1041, DOI: https://doi.org/10.1016/j.ijproman.2013.11.002
Kim BC, Kim HJ (2014) Sensitivity of earned value schedule forecasting to S-curve patterns. Journal of Construction Engineering and Management 140(7):04010423, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000856
Lipke W (2003) Schedule is different. The Measurable News 31–34
Martens A, Vanhoucke M (2019) The impact of applying effort to reduce activity variability on the project time and cost performance. European Journal of Operational Research 277(2):442–453, DOI: https://doi.org/10.1016/j.ejor.2019.03.020
Urgilés P, Claver J, Sebastián MA (2019) Analysis of the earned value management and earned schedule techniques in complex hydroelectric power production projects: Cost and time forecast. Complexity 2019:1–11, DOI: https://doi.org/10.1155/2019/3190830
Vanhoucke M (2009) Measuring time - Improving project performance using earned value management. Springer, London, UK
Vanhoucke M, Vandevoorde S (2007) A simulation and evaluation of earned value metrics to forecast the project duration. Journal of the Operational Research Society 58:1361–1374, DOI: https://doi.org/10.1057/palgrave.jors.2602296
Votto R, Ho L, Berssaneti F (2020) Applying and assessing performance of earned duration management control charts for EPC project duration monitoring. Journal of Construction Engineering and Management 146(3), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001765
Wauters M, Vanhoucke M (2015) Study of the stability of earned value management forecasting. Journal of Construction Engineering and Management 141(4):1–10, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000947
Wauters M, Vanhoucke M (2016) A comparative study of artificial intelligence methods for project duration forecasting. Expert Systems with Applications 46:249–261, DOI: https://doi.org/10.1016/j.eswa.2015.10.008
Acknowledgements
This research project was partially funded by the Ministry of Science and Technology, Taiwan, under project No. MOST 108-2221-E-324-005. Sincere appreciations are given to the sponsor by the authors.
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Chang, HK., Yu, WD. & Cheng, TM. A Quantity-Based Method to Predict More Accurate Project Completion Time. KSCE J Civ Eng 24, 2861–2875 (2020). https://doi.org/10.1007/s12205-020-1924-y
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DOI: https://doi.org/10.1007/s12205-020-1924-y