Abstract: A method, system, and/or computer program product predicts trouble during a transition phase of a current project. Past operational data and past trouble data for past healthy projects and past troubled projects before transition phases is collected. A trouble correlation between the past operational data and the past trouble data is then determined. A set of key metrics that describes a current health of a current project before transitioning from a current phase to a future phase is defined, and values of the set of key metrics for the current project are identified. Identified values of the set of key metrics for the current project are compared to the past operational data for past healthy and troubled projects in order to generate a pattern comparison. This pattern comparison, along with the trouble correlation, is used to generate a predicted quality of transition for the current project.

Abstract: A method, system, and/or computer program product predicts trouble during a transition phase of a current project. Past operational data and past trouble data for past healthy projects and past troubled projects before transition phases is collected. A trouble correlation between the past operational data and the past trouble data is then determined. A set of key metrics that describes a current health of a current project before transitioning from a current phase to a future phase is defined, and values of the set of key metrics for the current project are identified. Identified values of the set of key metrics for the current project are compared to the past operational data for past healthy and troubled projects in order to generate a pattern comparison. This pattern comparison, along with the trouble correlation, is used to generate a predicted quality of transition for the current project.

Abstract: A computer implemented method, system, and/or computer program product predicts the profitability of a current project. Historical data from the current project represents profit data points at predetermined intervals of time during the current project. If a linear regression model of the historical data does not adequately describe the historical data, then a polynomial regression model to describe the historical data is generated. If the polynomial model does not adequately describe the historical data, then another project that is similar to the current project is located. This other project has its own set of historical data. If the historical data from the other project fits the polynomial regression model created from the historical data of the current project, then the polynomial regression model is trusted to accurately predict the profitability of the current project, even though the current project's historical data did not adequately fit the polynomial regression model.