MS project : risk-factored EVM forecasting based on actual data
MS project: πρόβλεψη EVM με ενσωμάτωση παραγόντων κινδύνου, βασιμένη σε πραγματικά δεδομένα
View/
Keywords
Earned Value Management (EVM) ; Risk-factored forecasting ; Contractor performance ; Project risk management ; Estimate at Completion (EAC)Abstract
Earned Value Management (EVM) is a project management method. It is used widely for monitoring cost and schedule performance in projects. However, there are have been determined some limitations. In projects there are a lot of people who get involved, such as the contractors. One fundamental limitation of the EVM analysis is that it treats all contractors the same way. It collects performance data at the project level without distinguishing between individual contractors, their reliability, or their track record. As a result, the underperformance of one contractor can be masked by the good performance of others. So, the forecasts can be misleadingly optimistic. Particularly in the early stages of a project when there is still time to act.
This thesis proposes an improvement of the standard EVM framework called Adjusted Risk-Factored EVM. The model we developed, introduces two key innovations. First, it monitors the performance of each contractor group separately, using weighted average indices to calculate group-level risk factors that reflect how each group performs relative to the overall project. Second, it accounts for the reliability of performance data at different stages of the project lifecycle through an adjusted percent complete factor, which reduces the influence of early-stage measurements that are inherently less stable.
These two elements are combined into two new adjusted indices, the Adjusted Cost Performance Index (ACPI) and the Adjusted Schedule Performance Index (ASPI). These indexes are used to produce more realistic cost and schedule forecasts: the Adjusted EAC and the Adjusted TEAC.
The model is validated through three case studies. Each of them represents a common contractor performance pattern: consistent underperformance, recovery from a poor start, and collapse after strong early results. In all three cases, the adjusted model produces earlier and more conservative warnings than standard EVM. This allows project managers to have more time to intervene before things get out of control.
The findings suggest that incorporating contractor-level risk assessment and data maturity into EVM forecasting leads to more accurate and actionable predictions throughout the project lifecycle.
Postgraduate Studies Programme
Βιομηχανική Διοίκηση και ΤεχνολογίαDepartment
Σχολή Ναυτιλίας και Βιομηχανίας. Tμήμα Βιομηχανικής Διοίκησης και TεχνολογίαςNumber of pages
51Language
EnglishCollections
Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα