Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy

Sackey, Solomon; Lee, Dong‐Eun; Kim, Byung Soo

doi:10.1007/s12205-020-0407-5

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Therefore, the margin of error may be increased. To compare the accuracy of the ANN model with the EVM developed by [21], the EVM provides a MAPE value of 12.96%, which is close to the MAPE of the ANN model (12.22%) for this paper. On the other hand, the MAPE of the ANN model performed by Pewdum et al [15] was 6.2% on average.…”

Section: Resultsmentioning

confidence: 58%

“…Urgilés et al [20] examined the adequate EVM and value schedule to forecast the final duration of hydroelectric power generation projects. Sackey et al [21] also developed a new method based on the EVM to forecast the final construction contract duration (FCCD). They used the actual time spent on each activity.…”

Section: Forecast Model Based On Characteristics Of a Contract Or Pro...mentioning

confidence: 99%

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Artificial Neural Network Model to Predict Final Construction Contract Duration

Alsugair,

Al-Gahtani,

Alsanabani

et al. 2023

Applied Sciences

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Forecasting the final construction contract duration at an early stage plays a vital role in the progress of a project. An inaccurate project duration prediction may lead to the project’s benefits being lost. It is essential to precisely predict the duration due to the presence of several different factors. This paper contributed to developing a model to predict final construction contract duration (FCCD) in the early stages based on parameters characterized as few and shared for any contract. (contract cost, contract duration, and sector). This paper developed an Artificial Neural Network (ANN) model based on 135 Saudi construction project data. The development model has three stages. The first stage was standardization and augmentation using Zavadskas and Turskis’ logarithmic and Pasini methods. The second and third stages were the first and second analyses of the ANN models, respectively. The first analysis aimed to promote the used data and integrate them into the second analysis to develop the ANN model. The ANN models were compared with three linear regression (LR) models (LR1, LR2, and LR3) and other models in the literature. The results revealed that the accuracy of the ANN model provides reasonable accuracy with an average mean absolute percentage error (MAPE) of 12.22%, which is lower than the LR3′s MAPE by 27.03%. The accuracy of the ANN model is similar to that of earned value management (EVM) in the previous study. This paper supports research to deal with relatively little data and integrate them into a neural network. The ANN model assists the stakeholder in making appropriate decisions for the project during the pre-tendering phase by predicting the actual contract duration based on the CC, CD, and project sector.

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Section: Resultsmentioning

confidence: 58%

Section: Forecast Model Based On Characteristics Of a Contract Or Pro...mentioning

confidence: 99%

Artificial Neural Network Model to Predict Final Construction Contract Duration

Alsugair,

Al-Gahtani,

Alsanabani

et al. 2023

Applied Sciences

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“…EMV, as previously mentioned, compares what has been generated or gained to what has been spent. EVM combines cost, time, and scope to anticipate project performance in the future [ 28 ]. This technique continuously tracks the project's percentage completion rate, as well as the Planned Value (PV), Earned Value (EV), and Actual Costs (AC) spent to complete the project's work.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated DEMATEL and system dynamic model for project cost prediction

Alsugair,

Al-Gahtani,

Alsanabani

et al. 2024

Heliyon

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“…It is a project management tool that integrates cost, schedule, and scope. EVM provides many benefits to the organizations, some include, cost and duration forecasts, objective accomplishment measurement, deviations in the scope, and early warnings of cost overrun and delays [4]. EVM indices indicate project performance efficiency.…”

Section: Traditional Earned Value Management (Evm)mentioning

confidence: 99%

Review of Earned Value Management Extensions

2020

IJETER

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To successful project completion, the project management team requires to monitor and control the actual work performed and update informationat each instant of the lifecycle. Earned Value Management (EVM) is a familiar method that serves best for this purpose.It integrates cost performance and schedule performance into a single window toforecast both expected cost and time at completion. These forecasts help the project management team to monitor and control any critical deviations. Forecasting activity parameters is essential step in construction project management thus the feedback from Estimates at Completion maximize probability of getting schedule, quality, cost as per project plan by highlighting possible need of corrective actions. Many EVM extensions have been developed in recent years to improve forecasting accuracy. This paper reviews various duration forecasting extensions of EVM.

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Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy

Cited by 13 publications

References 14 publications

Artificial Neural Network Model to Predict Final Construction Contract Duration

Artificial Neural Network Model to Predict Final Construction Contract Duration

An integrated DEMATEL and system dynamic model for project cost prediction

Review of Earned Value Management Extensions

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