Purpose -The purpose of this paper is to contribute to the diffusion of earned value management (EVM) as a practicable methodology to monitor facility construction and renovation projects in the context of the European industry. Design/methodology/approach -First, a review of the literature reveals how EVM evolved as a tool for facility construction project monitoring together with specific concerns for its application. Then, a review of EVM practice and trends in Europe is provided and finally, applicability and viability of the method is proved through a case demonstration. Findings -EVM practice in the European construction industry is found to be lagging behind other experienced countries and industries, despite EVM having been found to be applicable, adaptable, and predictive of integrated final cost and schedule of facility construction projects. In particular, cost estimate at completion is forecasted by a simple cost performance index (CPI), while for the time estimate at completion, the earned schedule concept is revealed as an accurate predictor.Research limitations/implications -The paper urges the need for research of a European standard as a primary factor for successful diffusion of EVM usage in architecture, engineering and construction projects. Practical implications -This paper helps practitioners to understand the adaptability of EVM practice in the European construction industry and to apply EV tools for effective monitoring of the performance of their projects. Originality/value -Current trends of EVM practice in the European construction context are presented and suggestions for sustaining the diffusion of EVM are given.
An earned schedule-based regression model to improve cost estimate at completion / Narbaev T.; De Marco A.
AbstractTraditional Earned Value Management (EVM) index-based methods for Cost Estimate at Completion (CEAC) of an ongoing project have been known for their limitations inherent with both the assumption that past EVM data is the best available information and early-stage unreliability.In an attempt to overcome such limitations, a new CEAC methodology is proposed based on a modified index-based formula predicting expected cost for the remaining work with the Gompertz growth model via nonlinear regression curve fitting. Moreover, the proposed equation accounts for the schedule progress as a factor of cost performance. To this end, it interpolates into its equation an Earned Schedule-based factor indicating expected duration at completion.The proposed model shows itself to be more accurate and precise in all early, middle, and late stage estimates than those of four compared traditional index-based formulae.The developed methodology is a practical tool for Project Managers to better incorporate the progress status into the task of computing CEAC and is a contribution to extending EVM research to better capture the inherent relation between cost and schedule factors.
To improve the accuracy of early forecasting the final cost at completion of an ongoing construction project, a new regressionbased nonlinear cost estimate at completion (CEAC) methodology is proposed that integrates a growth model with earned schedule (ES) concepts. The methodology provides CEAC computations for project early-stage and middle-stage completion. To this end, this paper establishes three primary objectives, as follows: (1) develop a new formula based on integration of the ES method and four candidate growth models (logistic, Gompertz, Bass, and Weibull), (2) validate the new methodology through its application to nine past projects, and (3) select the equation with the best-performing growth model through testing their statistical validity and comparing the accuracy of their CEAC estimates. Based on statistical validity analysis of the four growth models and comparison of CEAC errors, the CEAC formula based on the Gompertz model is better-fitting and generates more accurate final-cost estimates than those computed by using the other three models and the index-based method. The proposed methodology is a theoretical contribution towards the combination of earned-value metrics with regression-based studies. It also brings practical implications associated with usage of a viable and accurate forecasting technique that considers the schedule impact as a determinant factor of cost behavior.
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