Forecasting cost at completion with growth models and Earned Value Management

Reliable forecasting of the final cost at completion is one of the vital components of project monitoring. Accuracy and stability in the forecast of an ongoing project is a critical criterion that ensures the project’s on budget and timely completion. The purpose of this dissertation is to develop a new Cost Estimate at Completion (CEAC) methodology to assist project managers in the task of forecasting the final cost at completion of ongoing projects. This forecasting methodology interpolates intrinsic characteristics of an S-shaped growth model and combines the Earned Schedule (ES) concepts into its equation to provide more accurate and stable cost estimates. Widely used conventional index-based methods for CEAC have inherent limitations such as reliance on past performance only, unreliable forecasts in early stages of a project life, and no count of forecasting statistics. To achieve its purpose the dissertation carried out five tasks. It, first, developed the method’s equation based on the integration of the four candidate S-shaped models and the earned schedule concepts. Second, the models’ equations were tested on past projects to assess their applicability and, then, the accuracy of CEACs was compared with ones found by the Cost Performance Index (CPI)-based formula. The scope of third task included comparing CEACs found by statistically valid and the most accurate Gompertz model (GM)-based equation against ones computed with the CPI-based method at each time point of the projects life. Then, the stability test was performed to determine if the method, with its corresponding performance indices that achieves the earlier stability, provides more accurate CEAC. Finally, the analysis was conducted to determine the existence of a correlation between schedule progress and the CEAC accuracy. Based on the research results it was determined that the GM-based method is the only valid model for cost estimates in all three stages and it provides more accurate estimates than the CPI-based formula does. Further comparative analysis showed that the two (the GM and CPI-based) methods’ performance index that achieved the earlier stability provided more accurate CEACs for that method, and finally, the new methodology takes into account the schedule impact as a factor of the cost performance in forecasting the CEAC. The developed methodology enhances forecasting capabilities of the existing Earned Value Management methods by refining traditional index-based approach through nonlinear regression analysis. The main novelty of the research is that this is a cost-schedule integrated approach which interpolates characteristics of a sigmoidal growth model with the ES technique to calculate a project’s CEAC. Two major contributions are brought to the Project Management. First, the dissertation extends the body of knowledge by introducing the methodology which combined two separate methods in one statistical technique that, so far, have been considered as two separate streams of project management research. Second, this technique advances the project management practice as it is a practical cost-schedule integrated approach that takes into account schedule progress (advance/delay) as a factor of cost behavior in calculation of CEAC.