Improve the accuracy of project construction forecasts by integrating managed value management with quantitative predictive methods in time series. Case study: Construction projects at AzarAB Company Arak
Subject Areas : Industrial ManagementMehrdad Khazenchin 1 , Amir abbas Shojaie 2
1 - Islamic Azad University- South Tehran Branch
2 - I.E. faculty member of Islamic Azad University- Tehran South Branch
Keywords: actual costs, planned costs, Regression, Forecast, Earn Value management,
Abstract :
In this research, two projects were considered during the construction phase of Azar AB Company to predict construction costs and actual costs of these projects were calculated after data collection, then six regression models were presented in four time periods for each project and then The average model error for each project was calculated in four periods to determine the final and acceptable model for each project. The results showed that models 2 and 3 percent have lower and acceptable errors than other models. The existence of effective modules in models as well as in terms of the nature of projects, we conclude that Model No. 3 predicts the future costs of projects with a reasonable degree of certainty. In sum, we will select Model No. 3 as the top model to predict the future costs of the projects in question. However, we recall that for each project, depending on its nature, another particular equation may provide more accurate predictions. But in general it can be said that regression models are used to predict the costs of different periods The project is applicable to its nature.
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14- Muth, J. (1960). Optimal properties of exponentially weighted forecasts. J. Am. Stat. Assoc. 55, 299–306.
15- Rujirayanyong, T. (2009). A comparison of three completion date predicting methods for construction projects. J. Res. Eng. Technol. 6, 305–318.
16- Soltanpanah, H., Farooqi, H. & Abdi, Rohallah (2012). A Method for Calculating Value Gained in Risk in Fuzzy Conditions. Journal of Industrial Management Studies, Vol. 10, No. 26, 156 -139.
17- Vanhoucke, M. (2010). Measuring Time Improving Project Performance Using Earned Value Management. Volume 136 of International Series in Operations Research and Management Science. Springer.
_||_1- Abolhasani, M. (2010). Applyingthe Scheduling Method in Value Management. 6th International Project Management Conference.
2- Ahmadi, A., Eskandari, A.J., Martagiei, M., Hosseini, S.T. & Nozari, H. (2013). Improving Prediction of Project Execution Time Based on Value Information Management System. Journal of Managing Tomorrow, Twelfth Year, No. 37, 15-1.
3- Batselier ,Jordy, Vanhoucke, Mario (2017). Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International Journal of Project Management 35 , 28–4.
4- Brown, R. (1956). Exponential Smoothing for Predicting Demand. Technical Report, Arthur D. Little Inc., Cambridge, Massachusetts.
5- Brown, R. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill, New York, New York.
6- Brown, R. (1963). Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice-Hall, Englewood Cliffs, New Jersey.
7- Cioffi, D.F. (2006). A scientific notation and an improved formalism for earned value calculation. International Journal of Project Management 24 (2) ,pp.136-144.
8- Golabchi, M., Sepeti, M.H.& Tosi, H. (2008). Determining the Project Performance Coefficient in Managing Rated Value by Using Risk Management to Estimate the Final Results of Contractors' Work. Journal of Technical School, Vol. 41, No. Sixth, 796-787.
9- July. Mortaji, S., Noorossana, R. & Bagherpour, M. (2014). Project completion time and cost prediction using change point analysis. J. Manag. Eng. 31, 04014086.
10- Larqba, E., Salahshoor, J. & Adib, A. (2013). The Impact of Value Management Technique in Increasing the Capacity of Managing Development Projects. 7th National Congress on Civil Engineering, Shahid Nikbakht Engineering Faculty, 8-1.
11- Lipke W. & Vaughn J. (2000). Statistical process control meets Earned Value. Cross Talk The Journal Of defense software engineering, Jun:16-20.
12- Momeni, M. & Qiyomi, A. (2011). Statistical analysis with SPSS. Tehran, Ketabno Publishing.
13- Moslemi, L. & Geroyan, N. (2009). Prediction of Time and Cost of Completion of a Project with Value Management Technique. Kayson Quarterly, New Volume, No. 31, 6-1.
14- Muth, J. (1960). Optimal properties of exponentially weighted forecasts. J. Am. Stat. Assoc. 55, 299–306.
15- Rujirayanyong, T. (2009). A comparison of three completion date predicting methods for construction projects. J. Res. Eng. Technol. 6, 305–318.
16- Soltanpanah, H., Farooqi, H. & Abdi, Rohallah (2012). A Method for Calculating Value Gained in Risk in Fuzzy Conditions. Journal of Industrial Management Studies, Vol. 10, No. 26, 156 -139.
17- Vanhoucke, M. (2010). Measuring Time Improving Project Performance Using Earned Value Management. Volume 136 of International Series in Operations Research and Management Science. Springer.
