Forecasting Project Performance : Testing Earned Value Management and Earned Schedule metrics using real life project data

Abstract

Projects in the construction industry struggle to meet set goals. This is especially true in long term projects where distress is often noticed too late in project completion. The purpose of this study was to find and test metrics that support forecasting project performance. The research questions was: Do met rics identified by current research support project performance forecasting when validated by real life project data? The theoretical framework for this research was based on the Earned Value Management and Earned Schedule approaches. The basic principles and usefulness of Earned Value Management and Earned Schedule approaches were discussed. In addition, metrics that support project progress and performance forecasting were covered. Nine projects were chosen as the sample for this study. The data for this study was gathered from a project database and project documentation library. A quantitative, reductive approach was used in the empirical part to reduce the data into more interpretative form. Reduction was done by calculating the Earned Value Management and Earned Schedule metrics using the gathered data. For Earned Value Management project cost forecasting metrics, the results of this study did support the conclusions of the current literature. Earned Value Management metrics provide a valuable tool for the project manager to evaluate project’s current cost performance as well as estimate project’s total costs at the end of the project. The findings of this study suggest that Earned Schedule metrics as such do not recognize project work that is irregular during a project’s life span. This can lead to too positive outlook for the project schedule. This does not mean that the Earned Schedule metrics are useless; the metrics or the way real life project data is used could be adjusted for this kind of project work in order for the metrics to provide valuable information.