Comparison of the Stability and Accuracy of Deterministic Project Cost Prediction Methods in Earned Value Management

5Citations
Citations of this article
45Readers
Mendeley users who have this article in their library.

Abstract

Completing a project on time and on budget are essential factors for the success of any project. One technique that allows predicting the final cost of a project is earned value management (EVM). In this technique, different mathematical methods for predicting the final project cost have been proposed over the last 30 years. These formulas make use of activities’ actual costs and durations as the project progresses. EVM is a technique widely used by many project management professionals. However, very few studies have compared the stability and accuracy of the multiple existing methods for predicting the final cost of the project (commonly abbreviated as estimated cost at completion, EAC). This study compares the stability and accuracy of 30 deterministic cost prediction methods (EAC) in EVM. For this purpose, a representative database of 4100 simulated projects of various topological structures is used. Our results suggest that the methods with the simplest mathematical configurations achieve better stability and accuracy performance. Knowing which EVM methods are the most stable and accurate for predicting the final cost of the project will help project practitioners choose the most reliable cost prediction techniques when they are managing their own projects in real contexts.

References Powered by Scopus

A comparison of different project duration forecasting methods using earned value metrics

260Citations
N/AReaders
Get full text

RanGen: A random network generator for activity-on-the-node networks

245Citations
N/AReaders
Get full text

An evaluation of the adequacy of project network generators with systematically sampled networks

167Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Estimate-at-completion (EAC) prediction using Archimedes optimization with adaptive fuzzy and neural networks

5Citations
N/AReaders
Get full text

Deploying Value Engineering Strategies for Ameliorating Construction Project Management Performance: A Delphi-SWARA Study Approach

5Citations
N/AReaders
Get full text

Improving the effectiveness of project scheduling by using Earned Value Management and Artificial Neural Network

1Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Barrientos-Orellana, A., Ballesteros-Pérez, P., Mora-Melià, D., Cerezo-Narváez, A., & Gutiérrez-Bahamondes, J. H. (2023). Comparison of the Stability and Accuracy of Deterministic Project Cost Prediction Methods in Earned Value Management. Buildings, 13(5). https://doi.org/10.3390/buildings13051206

Readers' Seniority

Tooltip

Professor / Associate Prof. 5

42%

PhD / Post grad / Masters / Doc 4

33%

Researcher 2

17%

Lecturer / Post doc 1

8%

Readers' Discipline

Tooltip

Engineering 11

79%

Social Sciences 2

14%

Earth and Planetary Sciences 1

7%

Article Metrics

Tooltip
Mentions
Blog Mentions: 1
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free