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Cost Control Development under Stochastic Performance Control

Published: 20 February 2013
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Abstract

In this research, a model to forecast project’s cost will be presented with due attention to performance time and cost of the project, based on Earned Value Management (EVM) and with regarding real circumstances caused by uncertainties, risk factors and using simulation methods. All the uncertainties will be related to cost of work packages as well as its changes over time by probability distribution functions. Probabilistic distribution functions will be determined based on existing information obtained from previous projects and experts’ opinion. In this model, project’s activities will be classified to subgroups calling control accounts. Each of them has a controlling limit to control project’s performance. Then, using simulation methods, stochastic s-curve for each control account will be determined to clarify project stochastic s-curve from total of these s-curves. When a percentage of the project has been performed, using modern methods of Earned Value Management, the performance of the project will be measured, therefore, it will be possible to adjust probability distribution functions and forecast the future performance of the project using simulation model of Monte Carlo.

Published in International Journal of Economics, Finance and Management Sciences (Volume 1, Issue 1)
DOI 10.11648/j.ijefm.20130101.17
Page(s) 54-60
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Monte Carlo Method, Probabilistic Model, Project Forecasting, Stochastic S-Curve, Project Monitoring

References
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[13] P. Love, X. Wang, C. Sing, and R. Tiong, "Determining the Probability of Project Cost Overruns," Journal of Construction Engineering and Management, ASCE, vol. 139, pp. 321–330, 2013.
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[15] A.S. Hanna, and A.N. Blair, "Computerized approach for forecasting the rate of cost escalation," Proc., Comput. Civ. Build. Tech. Conf., pp. 401–408, 1993.
[16] A. Touran, and R. Lopez, "Modeling Cost Escalation in Large Infrastructure Projects," Journal of Construction Engineering and Management, ASCE, vol. 132, pp. 853–860, 2006.
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Cite This Article
  • APA Style

    Milad Eghtedari Naeini, Milad Eghtedari Naeini. (2013). Cost Control Development under Stochastic Performance Control. International Journal of Economics, Finance and Management Sciences, 1(1), 54-60. https://doi.org/10.11648/j.ijefm.20130101.17

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    ACS Style

    Milad Eghtedari Naeini; Milad Eghtedari Naeini. Cost Control Development under Stochastic Performance Control. Int. J. Econ. Finance Manag. Sci. 2013, 1(1), 54-60. doi: 10.11648/j.ijefm.20130101.17

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    AMA Style

    Milad Eghtedari Naeini, Milad Eghtedari Naeini. Cost Control Development under Stochastic Performance Control. Int J Econ Finance Manag Sci. 2013;1(1):54-60. doi: 10.11648/j.ijefm.20130101.17

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  • @article{10.11648/j.ijefm.20130101.17,
      author = {Milad Eghtedari Naeini and Milad Eghtedari Naeini},
      title = {Cost Control Development under Stochastic Performance Control},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {1},
      number = {1},
      pages = {54-60},
      doi = {10.11648/j.ijefm.20130101.17},
      url = {https://doi.org/10.11648/j.ijefm.20130101.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20130101.17},
      abstract = {In this research, a model to forecast project’s cost will be presented with due attention to performance time and cost of the project, based on Earned Value Management (EVM) and with regarding real circumstances caused by uncertainties, risk factors and using simulation methods. All the uncertainties will be related to cost of work packages as well as its changes over time by probability distribution functions. Probabilistic distribution functions will be determined based on existing information obtained from previous projects and experts’ opinion. In this model, project’s activities will be classified to subgroups calling control accounts. Each of them has a controlling limit to control project’s performance. Then, using simulation methods, stochastic s-curve for each control account will be determined to clarify project stochastic s-curve from total of these s-curves. When a percentage of the project has been performed, using modern methods of Earned Value Management, the performance of the project will be measured, therefore, it will be possible to adjust probability distribution functions and forecast the future performance of the project using simulation model of Monte Carlo.},
     year = {2013}
    }
    

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    AU  - Milad Eghtedari Naeini
    AU  - Milad Eghtedari Naeini
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    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
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    PB  - Science Publishing Group
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    AB  - In this research, a model to forecast project’s cost will be presented with due attention to performance time and cost of the project, based on Earned Value Management (EVM) and with regarding real circumstances caused by uncertainties, risk factors and using simulation methods. All the uncertainties will be related to cost of work packages as well as its changes over time by probability distribution functions. Probabilistic distribution functions will be determined based on existing information obtained from previous projects and experts’ opinion. In this model, project’s activities will be classified to subgroups calling control accounts. Each of them has a controlling limit to control project’s performance. Then, using simulation methods, stochastic s-curve for each control account will be determined to clarify project stochastic s-curve from total of these s-curves. When a percentage of the project has been performed, using modern methods of Earned Value Management, the performance of the project will be measured, therefore, it will be possible to adjust probability distribution functions and forecast the future performance of the project using simulation model of Monte Carlo.
    VL  - 1
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Author Information
  • Civil Engineering Department, Pardis, Iran

  • Civil Engineering Department, Pardis, Iran

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