Project Duration Forecasting: A Simulation-Based Comparative Assessment of Earned Schedule Method and Earned Duration Management

Authors

  • Mahnaz Mamghaderi Iran University of Science and Technology - Department of Industrial engineering, Islamic Republic Of Iran
  • Homayoun Khamooshi George Washington University - Department of Decision Sciences, School of Business United States
  • Young Hoon Kwak George Washington University - Department of Decision Sciences, School of Business United States

DOI:

https://doi.org/10.19255/JMPM02701

Keywords:

Earned Duration Management (EDM), Earned Schedule Method (ESM), Project duration forecasting, simulation, network topology structure

Abstract

Since the ability to precisely forecast the project’s total duration is of great importance for successful project management, a variety of approaches have been developed to address this issue over the last few decades. Recently, the Earned Duration Management (EDM) method has been proposed for monitoring schedule performance of a project and forecasting its total duration. It has been claimed that EDM method provides more accurate project duration forecasts compared with other EVM based approaches such as Earned Schedule Method (ESM). However, its potential and validity has not yet been adequately tried and tested. Therefore, in this paper, we extensively evaluate and compare the accuracy and reliability of EDM and ESM duration performance indicators to forecast the project’s total duration on a vast variety of simulated projects with various network topology or structures through the Monte Carlo simulation technique. Moreover, the impact of correlation between time and cost profiles as well as the effect of degree of progression toward completion of the project on the forecasting accuracy of the above-mentioned methods are examined. The findings conclusively support Earned Duration Management as a preferred approach compared with Earned Schedule Method regardless of the network topology or completion stages of the project. Furthermore, forecasting accuracy of EDM vs. ESM considering correlated/ uncorrelated profiles of time and cost also yields overall dominance of EDM in the results.

Author Biographies

  • Mahnaz Mamghaderi, Iran University of Science and Technology - Department of Industrial engineering, Islamic Republic Of Iran

    Mahnaz Mamghaderi is a former M.Sc. student (2015 -2018) in the department of Industrial Engineering of the Iran University of Science and Technology (IUST). She received her B.Sc. in Industrial Engineering (optimization) from Urmia University of Technology (UUT), Urmia, Iran in 2013.

    She has several years of experience as a project control and quality assurance expert in medical and automotive industries in Tehran, Iran.

    Her research interests comprise project management, supply chain management, mathematical programming and optimization. She has other papers published and submitted to some other journals as PLOS ONE and Cleaner Production

  • Homayoun Khamooshi, George Washington University - Department of Decision Sciences, School of Business United States

    Homayoun Khamooshi is an Associate Professor in the Department of Decision Sciences at the School of Business of the George Washington University (GWU). He is the chair of internationally known Master of Science in Project Management. Dr. Khamooshi earned his Ph.D. in Management Science (Project Management: Planning and Scheduling) from Lancaster University in 1994 in the United Kingdom, his Master of Engineering in Industrial Engineering and Management from Asian Institute of Technology (AIT) in 1979 in Thailand and a B.Eng. in Mechanical Engineering from Abadan Institute of Technology, Abadan, Iran in 1975.

    Dr. Khamooshi has more than a decade project management experience in Oil, Gas and Petrochemical Industries prior to pursuing his Ph.D. at Lancaster University in the UK.

    His research interests include project management as a dynamic system including Strategic Project Management (SPM), PRAM (Project Risk Analysis and Management), Project Perfromance Management Analytics, Planning and Scheduling, SIPMS (Smart Integrated Project Management Systems) and simulation-based business modeling. He has numerous publications in multiple outlets including Computers and Industrial Engineering, JORS (Journal of Operational Research Society), IEEE (Engineering Management), IJPM (International Journal of Project Management), IJCM (International Journal of Construction Management) and some other journals.

  • Young Hoon Kwak, George Washington University - Department of Decision Sciences, School of Business United States

    Dr. Young Hoon Kwak is a faculty member in the Department of Decision Sciences and serves as a Director of International Center for Project Management at The George Washington University School of Business (GWSB) in Washington, D.C. He also holds a visiting professor position at Department of Business Informatics and Operations Management at Ghent University in Belgium.

    Dr. Kwak is Editor-in-Chief of the Journal of Management in Engineering published by American Society of Civil Engineers (ASCE), specialty editor for the Case Studies section of the Journal of Construction Engineering and Management (ASCE) and Department Editor for Project Management of IEEE Transactions on Engineering Management (IEEE). Dr. Kwak’s primary research interests include project and program management; engineering and technology management; and complex megaproject. Please visit his google scholar (https://scholar.google.com/citations?user=7Dy1PBkAAAAJ&hl=en) for a complete list of publications.

References

Anbari, F. T., (2003) Earned value project management method and extensions, Project Management Journal, 34(4), 12-23.

de Andrade, P. A., Martens, 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.

Ballesteros-Pérez, P., Sanz-Ablanedo, E., Mora-Melia, D., González-Cruz, M. C., Fuentes-Bargues, J. L., & Pellicer, E. (2019). Earned Schedule min-max: Two new EVM metrics for monitoring and controlling projects. Automation in Construction, 103, 279-290.

Batselier, J., & Vanhoucke, M. (2015a). Empirical evaluation of earned value management forecasting accuracy for time and cost. Journal of Construction Engineering and Management, 141(11), 05015010.

Batselier, J., & Vanhoucke, M. (2015b). Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earned value management. International Journal of Project Management, 33(7), 1588-1596.

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.

Colin, J., & Vanhoucke, M. (2015). A comparison of the performance of various project control methods using earned value management systems. Expert Systems with Applications, 42(6), 3159-3175.

Covach, J., Haydon, J. J., & Reither, R. O. (1981). A study to determine indicators and methods to compute estimate at completion (EAC). Virginia: ManTech international corporation, 30.

Demeulemeester, E., Vanhoucke, M., & Herroelen, W. (2003). RanGen: A random network generator for activity-on-the-node networks. Journal of scheduling, 6(1), 17-38.

Elshaer, R. (2013). Impact of sensitivity information on the prediction of project's duration using earned schedule method. International Journal of Project Management, 31(4), 579-588.

Fleming, Q. W., & Koppelman, J. M. (2010). Earned value project management. Project Management Institute.

Hecht, L. (2007). A Case Study of Earned Schedule to do Predictions. The Measurable News, (W), 16.

Henderson, K. (2003). Earned schedule: A breakthrough extension to earned value theory? A retrospective analysis of real project data. The measurable news, 1(2), 13-23.

Henderson, K. (2004). Further developments in earned schedule. The measurable news, 1(1), 15-22.

Cho, J., & Lim, J. (2018). An Analysis of Effect and Limitation when Adapting Earned Schedule Method for Schedule Management and Estimation in Korean Defense Research & Development Projects. Journal of the Korea Institute of Military Science and Technology, 21(3), 396-402.

Khamooshi, H., & Golafshani, H. (2014). EDM: Earned Duration Management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019-1041.

Khamooshi, H., & Abdi, A. (2017). Project duration forecasting using earned duration management with exponential smoothing techniques. Journal of management in engineering, 33(1), 04016032.

Kim, B. C. (2007). Forecasting project progress and early warning of project overruns with probabilistic methods. Texas A&M University.

Kim, B. C., & Reinschmidt, K. F. (2011). Combination of project cost forecasts in earned value management. Journal of Construction Engineering and Management, 137(11), 958-966.

Kim, B. C., & Kwak, Y. H. (2018). Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach. Production Planning & Control, 29(9), 743-760.

Lipke, W. (2009). Project duration forecasting. A Comparison of Earned Value Management Methods to Earned Schedule, The Measurable News, (2), 24-31.

Lipke, W. (2012). Speculations on project duration forecasting. The Measurable News, 3(1), 4-7.

Lipke, W. (2015). Applying statistical forecasting of project duration to earned schedule-longest path. The Measurable News, 2, 31-38.

Lipke, W. (2014). Earned value management and earned schedule performance indexes. Wiley StatsRef: Statistics Reference Online, 1-7.

Lipke, W. (2017). Assessing earned value management and earned schedule forecasting. Coll. o Perform. Manage, 4, 19-27.

Lipke, W. (2003). Schedule is different. The measurable news, 31(4), 31-34.

Henderson, K., & Lipke, W. (2006). Earned schedule: An emerging enhancement to earned value management. Cross Talk, Journal of Defense Software Engineering, 26-30.

Mortaji, S. T. H., Noori, S., Noorossana, R., & Bagherpour, M. (2018). An ex ante control chart for project monitoring using earned duration management observations. Journal of Industrial Engineering International, 14(4), 793-806.

Naeni, L. M., & Salehipour, A. (2011). Evaluating fuzzy earned value indices and estimates by applying alpha cuts. Expert systems with Applications, 38(7), 8193-8198.

Guide, A. (2017). Project management body of knowledge (pmbok® guide). In Project Management Institute.

Rujirayanyong, T. (2009). A comparison of three completion date predicting methods for construction projects. Journal of Research in Engineering and Technology, 6, 305-318.

Liana, Sadykova. (2017). Comparative Evaluation of Project Cost Forecasting Methods Based on Earned Value Management.

San Juan, J. L. G., & Polancos, R. V. (2019, December). An Earned Duration Management Model Integrating Quality Management and Resource Performance Monitoring. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 170-174). IEEE.

Teicholz, P. (1993). Forecasting final cost and budget of construction projects. Journal of computing in Civil Engineering, 7(4), 511-529.

Tzaveas, T., Katsavounis, S., & Kalfakakou, G. (2010, May). Analysis of project performance of a real case study and assessment of earned value and earned schedule techniques for the prediction of project completion date. In Proceedings of IPMA Conference (pp. 752-759).

Urgilés, P., Claver, J., & Sebastián, M. A. (2019). Analysis of the earned value management and earned schedule techniques in complex hydroelectric power production projects: Cost and time forecast. Complexity, 2019.

Tavares, L. V., Ferreira, J. A., & Coelho, J. S. (1999). The risk of delay of a project in terms of the morphology of its network. European Journal of Operational Research, 119(2), 510-537.

Tavares, L. V., Ferreira, J. A., & Coelho, J. S. (2002). A comparative morphologic analysis of benchmark sets of project networks. International Journal of Project Management, 20(6), 475-485.

Vandevoorde, S., & Vanhoucke, M. (2006). A comparison of different project duration forecasting methods using earned value metrics. International journal of project management, 24(4), 289-302.

Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B., & Tavares, L. V. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187(2), 511-524.

Vanhoucke, M. (2009). Measuring time: Improving project performance using earned value management (Vol. 136). Springer Science & Business Media.

Vanhoucke, M. (2012). Measuring the efficiency of project control using fictitious and empirical project data. International journal of project management, 30(2), 252-263.

Vanhoucke, M. (2014). Integrated project management and control. Springer. doi, 10, 978-3.

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(10), 1361-1374.

Vanhoucke, M. and Vandevoorde, S. (2008) ‘Measuring the accuracy of earned value/earned schedule forecasting predicitions’, The Measurable News, 5–9.

Vanhoucke, M., & Vandevoorde, S. (2009). Forecasting a project’s duration under various topological structures. The Measurable News, 1, 26-30.

Votto, R., Lee 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), 04020001.

Wood, D. A. (2018). A critical-path focus for earned duration increases its sensitivity for project-duration monitoring and forecasting in deterministic, fuzzy and stochastic network analysis. Journal of Computational Methods in Sciences and Engineering, 18(2), 359-386.

Yousefi, N., Sobhani, A., Naeni, L. M., & Currie, K. R. (2019). Using statistical control charts to monitor duration-based performance of project. arXiv preprint arXiv:1902.02270.

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Published

2022-05-20

Issue

Vol. 9 No. 2 (2021): The Journal of Modern Project Management

Section

Articles-JMPM

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Copyright (c) 2022 Mahnaz Mamghaderi, Homayoun Khamooshi, Young Hoon Kwak

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Project Duration Forecasting: A Simulation-Based Comparative Assessment of Earned Schedule Method and Earned Duration Management. (2022). The Journal of Modern Project Management, 9(2). https://doi.org/10.19255/JMPM02701

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