A Bayesian Approach to Improve Estimate at Completion in Earned Value Management

2013 ◽  
Vol 44 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Franco Caron ◽  
Fabrizio Ruggeri ◽  
Alessandro Merli
Keyword(s):  
Bayesian Approach ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Improve Estimate

A Bayesian Approach to Project Control

2016 ◽  
pp. 832-844
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Early Stage ◽  
Effective Control ◽  
Reliable Estimate ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Data ◽  
Ongoing Project ◽  
The Bayesian Approach

The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective control of the project. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders raise the need to exploit all the available knowledge sources in order to provide a reliable forecast. Therefore, drawing on a set of case studies, this paper proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by uncertainty and complexity. The Bayesian approach allows to integrate experts' opinions, data records from past projects and data related to the current performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the basic formulas typical of the Earned Value Management (EVM) methodology.

Project Control Using a Bayesian Approach

2018 ◽  
pp. 5679-5689
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Early Stage ◽  
Reliable Estimate ◽  
Value Management ◽  
Project Control ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Complexity ◽  
Project Data ◽  
High Level

The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective project control. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholder increase project complexity and raise the need to exploit all the available knowledge sources in order to improve the forecasting process. Therefore, drawing on a set of case studies, this paper proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by a high level of uncertainty and complexity. The Bayesian approach allows to integrate experts' opinions, data records related to past projects and data related to the performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the traditional formulas typical of the Earned Value Management (EVM) methodology.

A Bayesian Approach to Project Control

2016 ◽  
pp. 125-137
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Early Stage ◽  
Effective Control ◽  
Reliable Estimate ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Data ◽  
Ongoing Project ◽  
The Bayesian Approach

The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective control of the project. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders raise the need to exploit all the available knowledge sources in order to provide a reliable forecast. Therefore, drawing on a set of case studies, this paper proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by uncertainty and complexity. The Bayesian approach allows to integrate experts' opinions, data records from past projects and data related to the current performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the basic formulas typical of the Earned Value Management (EVM) methodology.

Project Control

2021 ◽  
pp. 295-307
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Early Stage ◽  
Reliable Estimate ◽  
Value Management ◽  
Project Control ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Complexity ◽  
Project Data ◽  
High Level

The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective project control. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders increase project complexity and raise the need to exploit all the available knowledge sources in order to improve the forecasting process. Therefore, drawing on a set of case studies, this chapter proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by a high level of uncertainty and complexity. The Bayesian approach allows the authors to integrate experts' opinions, data records related to past projects, and data related to the current performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the traditional formulas typical of the earned value management (EVM) methodology.

Project Control Using a Bayesian Approach

2019 ◽  
pp. 1373-1386
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Early Stage ◽  
Reliable Estimate ◽  
Value Management ◽  
Project Control ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Complexity ◽  
Project Data ◽  
High Level

The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective project control. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders increase project complexity and raise the need to exploit all the available knowledge sources in order to improve the forecasting process. Therefore, drawing on a set of case studies, this chapter proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by a high level of uncertainty and complexity. The Bayesian approach allows the integration of experts' opinions, data records related to past projects, and data related to the performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the traditional formulas typical of the earned value management (EVM) methodology.

Directed earned value management based on ordered fuzzy numbers

2021 ◽  
pp. 1-14
Author(s):  
Seyed Taha Hossein Mortaji ◽  
Siamak Noori ◽  
Morteza Bagherpour
Keyword(s):  
Fuzzy Numbers ◽  
Lower Cost ◽  
Project Managers ◽  
Value Management ◽  
Monitoring And Control ◽  
Earned Value ◽  
Earned Value Management ◽  
Project Monitoring ◽  
And Control

Earned value management is well-known as the most efficient method of project monitoring and control providing relatively reliable information about the project performance. However, this method requires accurate estimates of the progress of project activities, which are always associated with uncertainties that, if ignored or not addressed well, lead to incorrect results. To address this issue, the application of multi-valued logic, in particular fuzzy logic, in earned value management has recently attracted a lot of attention both in practice and research. This paper introduces directed earned value management (DEVM) in which ordered fuzzy numbers are used to express the so-called uncertainties as well as to capture more information about the trend of the project progress. To evaluate the performance of the proposed method, several numerical examples and a case study are presented. The results reveal that compared to the existing methods, DEVM has a lower computational complexity. Also, it doesn’t suffer from the overestimation effect and as a result, it has a higher ability to express project-specific dynamics. In sum, the proposed method allows project managers to make informed decisions that lead to taking preventive and corrective actions promptly and at a lower cost.

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