Workforce Modeling Analytics

The Workforce Modeling analytics appear on the Workforce Models and Model pages for the modeled changes as of the model effective date. Use the analytics to view the impact of proposed changes to headcount, salary costs, predictive effectiveness, count of alerts and changes.

Once a model is complete, the analytics are frozen as of the date of the final approval. The Projected Worker Cost and Projected Headcount analytics don't include information for any worker assignments that the model doesn't have the security to see.

The following table describes the Workforce Modeling analytics.

Analytic	Description
Changes	Shows the number of worker assignments with changes. For example, if you move an assignment to a new manager and then to another manager and then make a job change, then all these actions count as one change.
Projected Worker Cost	Displays the change in the total cost for the top manager as a result of modeling. The analytic on the Workforce Models page only displays the cost change due to modeling. The analytic on the Model page displays the cost due to modeling and the change due to modeling. Cost is based on the annualized salary and changes are only included if you have security access to the assignment. For example, you can move a worker to report to you and update their salary. However, if you don't have security access to view the worker's salary, then any change you make to that worker's pay in the workforce model isn't included in the analytic.
Projected Headcount	Displays the change in the headcount for the top manager as a result of modeling based on the workforce measurement value of headcount.
Alerts	Displays the number of outstanding alerts for the model. The two types of alerts are: Validation Error: occurs when the Oracle Fusion assignments are updated on final approval and issues exist Assignment Change: occurs if an assignment has changed in the live application since modeling started and that change has not yet been resolved using the synchronization dialog
Predictive Effectiveness	Displays the factors that were changed during modeling and that made the greatest impact on performance and voluntary termination predictions. Predictive Effectiveness displays the impact of modeling on individual workers, managers, or on the top manager. View the factors that have the largest impact on the change in the prediction as a result of modeling and whether that impact was positive or negative. The analysis represents the impact the attribute had on the change in the prediction and whether the attribute caused the prediction to increase or decrease. Predictive Effectiveness = Predicted performance * (100% - Predicted voluntary termination) / 2