• صفحه اصلی
  • بهبود پیش‌بینی عملکرد پروژه‌ها با رویکرد یکپارچه ارزش کسب‌شده و تحلیل ریسک فازی

اشتراک گذاری

آدرس مقاله


کد مقاله : IMJ-2212-1815 (R1) بازدید : 211 صفحه: 20 - 40

10.30495/imj.2023.1974732.1815

نوع مقاله: پژوهشی

بهبود پیش‌بینی عملکرد پروژه‌ها با رویکرد یکپارچه ارزش کسب‌شده و تحلیل ریسک فازی

محورهای موضوعی : مدیریت صنعتی

Mojtaba Farrokh 1

1 - Operations Management and Information Technology Department, Faculty of Management, Kharazmi University, Tehran, Iran

تاریخ دریافت : 1401/09/19 تاریخ پذیرش : 1402/01/19 تاریخ انتشار : 1402/03/29

کلید واژه: حالات شکست, شاخص عملکرد آتی, ارزش کسب‌شده, تحلیل ریسک, تئوری فازی,

چکیده مقاله :

با وجود قابلیت‌هایی که تکنیک مدیریت ارزش‌کسب‌شده در ارزیابی عملکرد هزینه و زمان پروژها دارد، قادر به تخمین عملکرد آتی آن‌ها نیست. در واقع مدیریت ارزش‌کسب‌شده عملکرد آینده پروژه را صرفا براساس عملکرد گذشته آن تخمین می‌زند و تغییر شرایط محیطی یا دیگر عوامل موثر بر عملکرد آینده پروژه را در نظر نمی‌گیرد. تکنیک مدیریت ریسک بر خلاف مدیریت ارزش‌کسب‌شده به افق‌های دورتر نظر داشته و با شناسایی حالات شکست به ارزیابی عملکرد پروژه‌ها می‌پردازد. بدین ترتیب با توجه به گذشته‌نگر بودن تکنیک مدیریت ارزش و آینده‌نگر بودن تکنیک مدیریت ریسک در پیش‌بینی عملکرد آینده پروژه، می‌توان با یکپارچه نمودن آن‌ها به هم افزایی قابل توجه‌ای در مدیریت بهتر پروژه‌ها دست یافت. در این مقاله پس از شناسایی و تحلیل شاخص‌های ریسک بکمک تکنیک FMEA، با استفاده از تکنیک مدیریت ریسک شاخص عملکرد آینده پروژه محاسبه و با شاخص‌های کنونی مدیریت ارزش‌کسب‌شده ترکیب و با شاخص‌هایی جدید به پیش‌بینی بهتر عملکرد آینده پروژه‌ها می‌پردازیم. این شاخص‌های جدید، تحت شرایط عدم‌اطمینان و با استفاده از رویکرد فازی تعیین شده است. در پایان، یک مثال کاربردی برای نشان دادن عملکرد رویکرد پیشنهادی ارائه می‌شود. نتایج نشان می‌دهد که در نظر گرفتن شاخص ارزیابی ریسک، عملکرد پیش‌بینی هزینه و زمان تکمیل پروژه را در مدل مدیریت ارزش‌کسب ‌شده بهبود می‌دهد.

چکیده انگلیسی:

This paper proposes an integrated approach of the earned value management and the risk management for predicting of project performance including cost and duration of estimate at completion. In spite of the abilities of earned value management technique in estimating the project future performance merely based on its past, it cannot consider the environmental change or other elements which effect on the future performance of the project. Unlike the earned value management, the risk management technique looks to the farther horizons and deals with the evaluation of the project performance by recognizing the failure modes. In this paper we intend to present the future performance index in combination with the earned value management current indices for better prediction of the projects’ future performance. These indices are determined under real-life and uncertain conditions using the fuzzy approach. In the end, the performance of the proposed method is evaluated by a numerical example. The results show that the consideration of the risk evaluation index improves the prediction of cost and time of projects' completion in the value management model.

منابع و مأخذ:


  1. Anbari, F. T. (2003). Earned value project management method and extensions. Project management journal, 34(4), 12-23.
    2.
  2. Aramali, V., Gibson Jr, G. E., El Asmar, M., & Cho, N. (2021). Earned value management system state of practice: Identifying critical subprocesses, challenges, and environment factors of a high-performing EVMS. Journal of Management in Engineering, 37(4), 04021031.
    3.
  3. Art Gowan, J., Mathieu, R. G., & Hey, M. B. (2006). Earned value management in a data warehouse project. Information management & computer security, 14(1), 37-50.‏
    4.
  4. Babar, S., Thaheem, M. J., & Ayub, B. (2017). Estimated cost at completion: Integrating risk into earned value management. Journal of Construction Engineering and Management, 143(3), 04016104.
    5.
  5. Clausing, D., & Frey, D. D. (2005). Improving system reliability by failure‐mode avoidance including four concept design strategies.Systems engineering, 8(3), 245-261.
    6.
  6. Christensen, D. S. (1994). Using performance indices to evaluate the estimate at completion. The Journal of Cost Analysis, 11(1), 17-23.
    7.
  7. Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy sets and Systems, 3(1), 37-48.
    8.
  8. De Marco, A., Rosso, M., & Narbaev, T. (2016). Nonlinear cost estimates at completion adjusted with risk contingency. The Journal of Modern Project Management, 4(2).
    9.
  9. 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.
    10.
  10. Enayati Fatollah, S., Dabbagh, R., & Shahsavar Jalavat, A. (2022). An extended approach using failure modes and effects analysis (FMEA) and weighting method for assessment of risk factors in the petrochemical industry. Environment, Development and Sustainability, 1-26.
    11.
  11. Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102-110.
    12.
  12. 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.‏
    13.
  13. Henderson, K. (2004). Further developments in earned schedule. The measurable news, 1(1), 15-22.
    14.
  14. Jacob, D. (2003). Forecasting project schedule completion with earned value metrics. The Measurable News, 1(1), 7-9.‏
    15.
  15. Kim, E., Wells, W. G., & Duffey, M. R. (2003). A model for effective implementation of Earned Value Management methodology. International Journal of Project Management, 21(5), 375-382.‏
    16.
  16. Kamyabniya, A., Seyedhoseini, S. M., & Yaghoubi, S. (2015). Project time and cost estimate at completion based on non-parametric resampling with interval risk. International Journal of Industrial and Systems Engineering, 21(4), 458-473.‏
    17.
  17. Liu, H. C., Liu, L., Bian, Q. H., Lin, Q. L., Dong, N., & Xu, P. C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 38(4), 4403-4415.
    18.
  18. Lipke, W. (2003). Schedule is different. The Measurable News, 31(4), 31-34.
    19.
  19. Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International journal of project management, 27(4), 400-407.
    20.
  20. 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.‏
    21.
  21. Narbaev, T., & De Marco, A. (2017). Earned Value and Cost Contingency Management: A Framework Model for Risk Adjusted Cost Forecasting. The Journal of Modern Project Management, 4(3).
    22.
  22. Mandal, S., & Maiti, J. (2014). Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach. Expert Systems with Applications, 41(7), 3527-3537.
    23.
  23. Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021). Directed earned value management based on ordered fuzzy numbers. Journal of Intelligent & Fuzzy Systems, 40(5), 10183-10196.
    24.
  24. Moradi, N., Mousavi, S. M., & Vahdani, B. (2017). An earned value model with risk analysis for project management under uncertain conditions. Journal of Intelligent & Fuzzy Systems, 32(1), 97-113.
    25.
  25. Moradi, N., Mousavi, S. M., & Vahdani, B. (2018). An Interval Type-2 Fuzzy Model for Project-earned Value Analysis Under Uncertainty. Journal of Multiple-Valued Logic & Soft Computing, 30(1).
    26.
  26. Muriana, C., & Vizzini, G. (2017). Project risk management: A deterministic quantitative technique for assessment and mitigation. International Journal of Project Management, 35(3), 320-340.
    27.
  27. Pajares, J., & Lopez-Paredes, A. (2011). An extension of the EVM analysis for project monitoring: The Cost Control Index and the Schedule Control Index. International Journal of Project Management, 29(5), 615-621.
    28.
  28. Roghabadi, M. A., & Moselhi, O. (2022). Forecasting project duration using risk-based earned duration management. International Journal of Construction Management, 22(16), 3077-3087.
    29.
  29. Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
    30.
  30. Song, J., Martens, A., & Vanhoucke, M. (2022). Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control. European Journal of Operational Research, 297(2), 451-466.
    31.
  31. Tabriz, A. A., Farrokh, M., Nooshabadi, G. M., & Nia, H. H. (2013). A combined approach of the earned value management and the risk management for estimating final results of projects in fuzzy environment. Business Management and Strategy, 4(1), 32-52.
    32.
  32. 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.
    33.
  33. Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207.
    34.
  34. Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
    35.
  35. Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Systems with Applications, 60, 141-155.
    36.
  36. Yu, F., Chen, X., Cory, C. A., Yang, Z., & Hu, Y. (2021). An active construction dynamic schedule management model: using the fuzzy earned value management and BP neural network. KSCE Journal of Civil Engineering, 25(7), 2335-2349.
    37.
  37. Zarghami, S. A. (2022). Forecasting project duration in the face of disruptive events: A resource-based approach. Journal of Construction Engineering and Management, 148(5), 04022016.
    38.
  38. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
    39.
_||_

  1. Anbari, F. T. (2003). Earned value project management method and extensions. Project management journal, 34(4), 12-23.
    2.
  2. Aramali, V., Gibson Jr, G. E., El Asmar, M., & Cho, N. (2021). Earned value management system state of practice: Identifying critical subprocesses, challenges, and environment factors of a high-performing EVMS. Journal of Management in Engineering, 37(4), 04021031.
    3.
  3. Art Gowan, J., Mathieu, R. G., & Hey, M. B. (2006). Earned value management in a data warehouse project. Information management & computer security, 14(1), 37-50.‏
    4.
  4. Babar, S., Thaheem, M. J., & Ayub, B. (2017). Estimated cost at completion: Integrating risk into earned value management. Journal of Construction Engineering and Management, 143(3), 04016104.
    5.
  5. Clausing, D., & Frey, D. D. (2005). Improving system reliability by failure‐mode avoidance including four concept design strategies.Systems engineering, 8(3), 245-261.
    6.
  6. Christensen, D. S. (1994). Using performance indices to evaluate the estimate at completion. The Journal of Cost Analysis, 11(1), 17-23.
    7.
  7. Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy sets and Systems, 3(1), 37-48.
    8.
  8. De Marco, A., Rosso, M., & Narbaev, T. (2016). Nonlinear cost estimates at completion adjusted with risk contingency. The Journal of Modern Project Management, 4(2).
    9.
  9. 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.
    10.
  10. Enayati Fatollah, S., Dabbagh, R., & Shahsavar Jalavat, A. (2022). An extended approach using failure modes and effects analysis (FMEA) and weighting method for assessment of risk factors in the petrochemical industry. Environment, Development and Sustainability, 1-26.
    11.
  11. Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102-110.
    12.
  12. 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.‏
    13.
  13. Henderson, K. (2004). Further developments in earned schedule. The measurable news, 1(1), 15-22.
    14.
  14. Jacob, D. (2003). Forecasting project schedule completion with earned value metrics. The Measurable News, 1(1), 7-9.‏
    15.
  15. Kim, E., Wells, W. G., & Duffey, M. R. (2003). A model for effective implementation of Earned Value Management methodology. International Journal of Project Management, 21(5), 375-382.‏
    16.
  16. Kamyabniya, A., Seyedhoseini, S. M., & Yaghoubi, S. (2015). Project time and cost estimate at completion based on non-parametric resampling with interval risk. International Journal of Industrial and Systems Engineering, 21(4), 458-473.‏
    17.
  17. Liu, H. C., Liu, L., Bian, Q. H., Lin, Q. L., Dong, N., & Xu, P. C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 38(4), 4403-4415.
    18.
  18. Lipke, W. (2003). Schedule is different. The Measurable News, 31(4), 31-34.
    19.
  19. Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International journal of project management, 27(4), 400-407.
    20.
  20. 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.‏
    21.
  21. Narbaev, T., & De Marco, A. (2017). Earned Value and Cost Contingency Management: A Framework Model for Risk Adjusted Cost Forecasting. The Journal of Modern Project Management, 4(3).
    22.
  22. Mandal, S., & Maiti, J. (2014). Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach. Expert Systems with Applications, 41(7), 3527-3537.
    23.
  23. Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021). Directed earned value management based on ordered fuzzy numbers. Journal of Intelligent & Fuzzy Systems, 40(5), 10183-10196.
    24.
  24. Moradi, N., Mousavi, S. M., & Vahdani, B. (2017). An earned value model with risk analysis for project management under uncertain conditions. Journal of Intelligent & Fuzzy Systems, 32(1), 97-113.
    25.
  25. Moradi, N., Mousavi, S. M., & Vahdani, B. (2018). An Interval Type-2 Fuzzy Model for Project-earned Value Analysis Under Uncertainty. Journal of Multiple-Valued Logic & Soft Computing, 30(1).
    26.
  26. Muriana, C., & Vizzini, G. (2017). Project risk management: A deterministic quantitative technique for assessment and mitigation. International Journal of Project Management, 35(3), 320-340.
    27.
  27. Pajares, J., & Lopez-Paredes, A. (2011). An extension of the EVM analysis for project monitoring: The Cost Control Index and the Schedule Control Index. International Journal of Project Management, 29(5), 615-621.
    28.
  28. Roghabadi, M. A., & Moselhi, O. (2022). Forecasting project duration using risk-based earned duration management. International Journal of Construction Management, 22(16), 3077-3087.
    29.
  29. Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
    30.
  30. Song, J., Martens, A., & Vanhoucke, M. (2022). Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control. European Journal of Operational Research, 297(2), 451-466.
    31.
  31. Tabriz, A. A., Farrokh, M., Nooshabadi, G. M., & Nia, H. H. (2013). A combined approach of the earned value management and the risk management for estimating final results of projects in fuzzy environment. Business Management and Strategy, 4(1), 32-52.
    32.
  32. 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.
    33.
  33. Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207.
    34.
  34. Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
    35.
  35. Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Systems with Applications, 60, 141-155.
    36.
  36. Yu, F., Chen, X., Cory, C. A., Yang, Z., & Hu, Y. (2021). An active construction dynamic schedule management model: using the fuzzy earned value management and BP neural network. KSCE Journal of Civil Engineering, 25(7), 2335-2349.
    37.
  37. Zarghami, S. A. (2022). Forecasting project duration in the face of disruptive events: A resource-based approach. Journal of Construction Engineering and Management, 148(5), 04022016.
    38.
  38. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
    39.

سکوی نشر دانش

سند یا سکوی نشر دانش ،سامانه ای جهت مدیریت حوزه علمی و پژوهشی نشریات دانشگاه آزاد می باشد

پیوندهای سایت

مراکز مرتبط

پشتیبانی

صفحات رسمی

حقوق این وب‌سایت متعلق به سامانه مدیریت نشریات دانشگاه آزاد اسلامی است.
حق نشر © 1403-1400

صفحه اصلی| عضویت/ ورود| درباره سند| تماس با ما|
[English] [العربية] [en] [ar]