1,720,981 research outputs found
Forecasting cost at completion with growth models and Earned Value Management
No full textReliable 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 CEA
An earned schedule-based regression model to improve cost estimate at completion
Full text linkTraditional 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 factor
Earned value and cost contingency management: A framework model for risk adjusted cost forecasting
Full text linkThis paper proposes a novel framework model that considers different behaviors of cost contingency (CC) consumption in forecasting risk adjusted fi nal cost during the project execution. The model integrates the dynamics of how project managers can spend their contingencies into three S-shaped cost growth profi les to compute risk adjusted cost estimates at completion (CEAC). The three cost curves are modeled by the Gompertz growth model using nonlinear regression. Respectively, the framework embeds three different CC consumption rates to represent three main categories of aggressive, neutral or passive managerial attitudes in responding to project risk. The usage and viability of the model is demonstrated via a earned value management (EVM) dataset. The paper contributes to the body of knowledge by bridging the gap between the theories of EVM and CC management and provides project managers with a model to estimate the range of possible cost estimates at completion depending on the managerial policies that can be activated driven by different risk attitude
Earned Value-Based Performance Monitoring of Facility Construction Projects
Full text linkPurpose – 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 Estimates at Completion with Cost Contingency Management
No full textForecasting the final cost based on Earned Value Management (EVM) data and managing cost contingency consumption in ongoing projects are typically considered by scholars and practitioners as two distinct duties of the project team. However, the managerial approach to cost contingency management may significantly impact on final cost performance. To this end, this paper proposes a theoretical model that considers different behaviors of cost contingency (CC) consumption to help forecast risk adjusted cost estimates at completion (CEAC). Three possible S-shaped growth profiles are proposed to represent three main categories of managerial attitudes in responding to project risk, namely: aggressive, neutral or passive CC consumption rates. Then, these curves are integrated into schedule-based CEAC prediction models, using nonlinear regression. An earned value management (EVM) dataset is used to show applicability and viability of the methodology. The paper is a contribution to bridging the gap between EVM and CC management. It provides project managers with a model to estimate the range of possible CEACs based on different risk attitudes
Combination of Growth Model and Earned Schedule to Forecast Project Cost at Completion
Full text linkTo 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
No full textAccurate 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)
Waste management, green initiatives, and financial distress in heavily regulated environmental contexts: evidence from the United Kingdom
No full textPurpose: This paper aims to empirically examine the effects of waste management (WM) practices on financial distress (FD) in a heavily regulated environmental context and investigates the moderating role of green initiatives (GINVs) on the WM−FD relationship. Design/methodology/approach: This study uses a sample of 1,667 firm years of UK-based companies from 2002 to 2021 and applies a panel regression analysis controlling for industry- and year-fixed effects. Data on WM, GINVs and governance are sourced from LSEG (formerly known as Refinitiv Asset4 ESG), whereas financial data are collected from WorldScope. The study also adopts alternative measures for FD and WM practices and uses a two-stage least squares analysis and the Heckman selection model as robustness checks. Findings: The findings reveal that FD levels decrease significantly when waste generation declines and waste recycling increases, suggesting that firms with better WM practices have lower FD levels. The results further show the moderating effect of GINVs on the link between waste generation and FD and suggest that increased GINVs are effective in reducing FD by mitigating waste levels. However, waste recycling and GINVs are found to have a substitutive effect on FD. The findings remain robust to the use of alternative measures and endogeneity issues. Originality/value: This work is among the first to investigate the WM-FD nexus and highlights the importance of effective WM practices in improving the financial sustainability of UK firms. The study also extends prior research by testing the moderating impact of GINVs and suggests that firms need to carefully balance their GINVs with waste recycling efforts to achieve optimal financial sustainability in a heavily regulated environmental context, such as the UK.</p
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