AgileEVM - Earned Value Management in Scrum Projects

Author(s):  
T. Sulaiman ◽  
B. Barton ◽  
T. Blackburn
2021 ◽  
pp. 1-14
Author(s):  
Seyed Taha Hossein Mortaji ◽  
Siamak Noori ◽  
Morteza Bagherpour

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.


2014 ◽  
Vol 971-973 ◽  
pp. 2317-2320
Author(s):  
Xiang Jun Yu ◽  
Chao Xie ◽  
Tian Ming Huang

This paper briefly introduces the basic connotation of earned value management, determine, from the target variable management process design, system function design and system implementation four aspects that the management of defense scientific research project management system design and implementation process based on the earned value, and some reasonable countermeasures to promote the use of the system.


Author(s):  
Philip J. Beck ◽  
Dennis Kovacs

The traditional approach of managing project performance is with the use of Earned Value Management. There is a recent trend towards the expansion of traditional Earned Value Management practices to include the concept of Earned Schedule. Whereas Earned Value provides insight as to how the project is trending in relation to the plan by assessing cost and schedule variances, Earned Schedule focuses on the time element of schedule performance throughout the project execution phase. Earned Value, although very effective at providing visibility to cost performance, is not as transparent when it comes to schedule performance over time. Case in point, at completion, irrespective as to how work progressed on the schedule (ahead or behind plan) at completion, the schedule performance index will always be 1.0. Earned Schedule overcomes this drawback, providing useful tools to report on schedule performance, and providing visibility to the project state from which to base informed decisions. To perform the analysis, Earned Schedule analysis incorporates detail from the baseline and forecast schedules as well as the integrated project management cost report (earned versus planned). In addition to looking at Earned Schedule metrics, other key metrics are factored into this approach to assess overall schedule performance. Key metrics derived from the schedule and highlighted in this approach include: • Critical Path Length Index (CPLI) • Baseline Execution Index (BEI) • Total Float Consumption Index (TFCI) • To Complete Schedule Performance Index (TSPI) • Predicted Forecast Finish Date (PFFD) • Schedule Performance Index (time) (SPIt) • Independent Estimate At Complete (time) (IEACt) The intent of these metrics is to identify trends and assist in predicting project outcomes based on past performance. Since this approach is highly dependent on the schedule data, the more compliant a schedule is to industry best practices the better the quality of the results. The metrics are negatively impacted by recent re-baselining as this causes us to lose historical performance detail. Frequent analysis of the schedule execution reporting metrics defined above provides transparency of project performance and brings visibility to early risk triggers in support of a proactive approach to project execution monitoring and control. This paper will present a case study demonstrating how additional transparency through this approach highlighted a potential schedule risk. This increased visibility allowed the project team to reprioritize and implement proactive corrective actions to mitigate any potential impact to the project In Service Date (ISD).


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