Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control

Author(s):  
Jie Song ◽  
Annelies Martens ◽  
Mario Vanhoucke
2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Mario Vanhoucke

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented.


2021 ◽  
Vol 288 (3) ◽  
pp. 736-752 ◽  
Author(s):  
Jie Song ◽  
Annelies Martens ◽  
Mario Vanhoucke

2020 ◽  
Vol 10 (1) ◽  
pp. 35-41
Author(s):  
Jayet Moon

AbstractThe intent of this article is to explore a mathematically sound method to graphically monitor schedule performance index (SPI) such that it enables the project manager to take objective data based decisions regarding the progress of the project schedule. The article aims to leverage the theory and application of control charts, specifically the U chart and Laney U chart and test its applicability to earned value management by trending schedule performance index on a time series chart. Off the shelf software, MinitabTM was used to generate the control charts based on earned value and planned value. While this paper proves that the Laney U chart, with correct interpretation, acts as an effective trigger-based tool for schedule risk management, it also generates further avenues for research into similar use of control charts for cost performance and other quality indices.


Author(s):  
Franco Caron

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.


2016 ◽  
Vol 165 ◽  
pp. 1812-1817 ◽  
Author(s):  
Anastasiia Mishakova ◽  
Anna Vakhrushkina ◽  
Vera Murgul ◽  
Tatiana Sazonova

Author(s):  
Franco Caron

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.


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