Earned Value-Based Performance Monitoring of Facility Construction Projects

Purpose – 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 – Firstly, 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 are provided and, finally, applicability and viability of the method is proved through a case demonstration. Findings – The EVM practice in the European construction industry is found to be lagging behind other experienced countries and industries despite EVM is 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 Schedule Performance Index (SPI) 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 effectively monitoring 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

Integrating risk in project cost forecasting

Estimating duration and cost at completion based on Earned Value Management (EVM) data and managing risk contingency accounts in ongoing projects are typically treated by both scholars and practitioners as separate processes of project monitoring. However, project risk is claimed to significantly impact on project schedule and cost performance. As an attempt to combine these two management areas, the paper illustrates a methodology for improved schedule-based cost estimates at completion with the added nonlinear profile of risk contingency cost consumption. In particular, the model builds upon a Gompertz S-curve shaped cost profile equation. The model is applied to a sample of real project datasets. Its estimate accuracy and stability are tested at various early, middle, and late stages of project development. The proposed schedule-cost-risk estimate methodology proves to be a viable and effective tool to compute refined estimates at completion of complex projects involving formal management of contingency escrow accounts. The theoretical contribution is about creating a stronger connection between EVM and risk contingency management theories. Practical implications are inherent with the ability of the methodology to integrate cost contingency (CC) management into cost and schedule monitoring processe

Combination of Growth Model and Earned Schedule to Forecast Project Cost at Completion

To improve the accuracy of early forecasting the final cost at completion of an ongoing construction project, a new regression-based 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, andWeibull), (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 behavio

Combination of a Nonlinear Regression Model and Earned Schedule to Forecast a Project Final Cost

Accurate forecasting of a project's Cost Estimate at Completion (CEAC) based on current performance and progress is one the main issues in project monitoring and control. For decades, Earned Value Management (EVM) has been proved itself as a valuable tool to fulfill this task and cost estimates calculated by its Cost Performance Index (CPI) are widely applicable for projects of any type and size. However, recent studies show that the CPI-based method may be valid only for large projects with long durations. As an alternative to the index-based method, techniques with regression analysis gained a great insight in this direction. The purpose of this work is to propose a new regression-based nonlinear CEAC methodology which integrates Earned Schedule (ES) concept to assume a project progress in calculating CEAC as early as when a project is 20 percent complete. The paper sets three objectives to achieve the research purpose: development of the new equation based on a nonlinear regression modelling and ES method; validation of the new technique through case study application; and, providing a comparison with CPI-based estimates to determine the best performing equation. Testing the prediction accuracy of the proposed and index-based formulae is performed by comparing values of Percentage Error (PE) and Mean Absolute Percentage Error (MAPE). Based on six case studies from construction industry, the comparison reveals that the new methodology generates better estimates (MAPE=2,88 percent) than those calculated by traditional index-based equation (MAPE=9,98 percent

Factors of schedule and cost performance of tunnel construction megaprojects

Aims: This study illustrates the main factors that influence the cost overruns and schedule delays of tunnel construction megaprojects. Objective: An empirical analysis was carried out based on a dataset collected from a number of recent tunnel megaprojects worldwide. Methods: Analyses of variances and regression analyses were conducted to infer statistical significance and understand the relationships that exist between cost overruns, time delays and variables of context, technical, and governance characteristics of the sample projects. Results: The most significant factors are those related to the complexity of infrastructure and the type of contracting system used to deliver the project. In particular, some technical characteristics pertinent to the size of the tunnel reveal to be influencing factors of both schedule delay and cost overrun, while the usage of a traditional contracting mechanism is likely to impact the cost overrun. The type of infrastructure, region, ownership, and funding scheme are not found to be statistically significant determinants of cost and time performance. Conclusion: This analysis reaffirms that the size and complexity are important factors of typical low performance of tunnel construction megaprojects. The results of this study can be used for strategic design and planning by decision-makers, project managers and designers

Combination of a Nonlinear Regression Model and Earned Schedule to Forecast a Project Final Cost

Accurate forecasting of a project’s Cost Estimate at Completion (CEAC) based on current performance and progress is one the main issues in project monitoring and control. For decades, Earned Value Management (EVM) has been proved itself as a valuable tool to fulfill this task and cost estimates calculated by its Cost Performance Index (CPI) are widely applicable for projects of any type and size. However, recent studies show that the CPI-based method may be valid only for large projects with long durations. As an alternative to the index-based method, techniques with regression analysis gained a great insight in this direction. The purpose of this work is to propose a new regression-based nonlinear CEAC methodology which integrates Earned Schedule (ES) concept to assume a project progress in calculating CEAC as early as when a project is 20 percent complete. The paper sets three objectives to achieve the research purpose: development of the new equation based on a nonlinear regression modelling and ES method; validation of the new technique through case study application; and, providing a comparison with CPI-based estimates to determine the best performing equation. Testing the prediction accuracy of the proposed and index-based formulae is performed by comparing values of Percentage Error (PE) and Mean Absolute Percentage Error (MAPE). Based on six case studies from construction industry, the comparison reveals that the new methodology generates better estimates (MAPE=2,88 percent) than those calculated by traditional index-based equation (MAPE=9,98 percent)

Formation and bases of the analysis of the investment portfolio of the enterprise

This article discusses several methods for project design and analysis. After all, they are the key ones in creating a new IT portfolio of the enterprise, according to the standards for the formation and management of projects, which is especially important in investment analysis. Considering these stages, we touch topics from the beginning of an enterprise to its formation, as a working business. After all, every enterprise begins its life with a choice of methods, stopping at one, it chooses a plan and sets tasks. Attraction of investments will be one of the main points in this task. For today, investments are the cause for the consequences of economic processes and various phenomena in the economy. This view will be of interest to specialists in the field of information technology and economic sciences. The idea is substantiated that the analysis of such results gives a good assessment in order to further identify weaknesses, build business processes and solutions from the point of view of forming a new portfolio of the enterprise and tools that allow determining the profitability of the module or the project as a whole in terms of money and technical equivalents. The article helps to reveal the topic and the main problem that is interesting and relevant for today, what method of attracting investors and implementing / shaping the IT portfolio of the project, choose which innovative portfolio management systems should be used and how they differ from traditional ones and how to properly link them with architecture of the enterprise. The key stages of investment analysis will be: increase in profits, accumulation of resources, proper portfolio formation and diversification