11Workforce Deployment Analysis

This chapter contains the following:

Overview of Analyze Workforce Deployment

The Analyze Workforce Deployment business process enables line managers and human resource (HR) specialists to view statistical and employment-related information for individual workers and the workforce. HR specialists and line managers can perform actions for individual workers.

The business activities of this process are:

  • Generate Workforce Deployment Intelligence

    In the Workforce Predictions work area, line managers view system-generated predictions of high performance and voluntary termination for their direct and indirect reports. In the Worker Predictions work area, line managers perform what-if analyses.

  • Evaluate Workforce Deployment Performance

    OTBI provides the ability to report on HCM and can deliver such reports using dashboards, Infolets, or email. Alternatively you can embed them into the HCM Cloud transactions.

Generate Workforce Deployment Intelligence

You can use system-generated performance predictions to validate your assessments of employees. Let's try and reduce voluntary terminations by looking at performance and voluntary termination predictions.

Settings That Affect Performance Predictions

Performance predictions are based on data from all employees. The Collect Data and Perform Data Mining for predictions process collects relevant data and generates the predictions. You can manage predictive models using the Manage Predictive Models task in the Setup and Maintenance work area.

Did you know that you can perform data collection either for the enterprise or for a specified manager assignment? The data-mining stage of the process is always performed on the latest available data.

To get accurate results when the volume of relevant transactions in your enterprise (such as hires, terminations, and promotions) is high, we recommend you schedule the process weekly. The process has no default schedule. Remember to schedule the process when there's less activity to avoid any impact on performance.

How Performance Is Predicted

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  1. For all employee work relationships, the process collects the values of a large set of attributes, such as:

    • Time in grade

    • Current job

    • Latest salary increase

    • Performance rating

    • Number of sickness absences in the previous year

    You're interested in the attributes which show a correlation with high performance. In some cases, simple values, such as manager name are required; in others, such as percentage increase in sickness leave, the process calculates the values. Most of these attributes are held at the assignment level; therefore, for work relationships with multiple assignments, multiple values are collected.

    Don't forget that contingent worker and nonworker work relationships are excluded.

    The collected attribute values are passed to Oracle Data Mining, which identifies patterns and relationships in the data and builds a model for predicting employee performance.

  2. Oracle Data Mining makes performance predictions for current employees according to the predictive model. For example, if performance is high in a particular job and grade, current employees with that job and grade are more likely to perform higher than workers in other jobs and grades.

    Each prediction relates to an employee assignment. If an employee reports to a single manager in multiple assignments, the manager sees multiple predictions for that employee.

Performance predictions are available for both teams and individual assignments.

  • Apart from showing the average predicted performance for the team, team predictions shows the percentage of employee assignments for which the factor is the main contributory factor.

  • Individual predictions show the predicted performance for the employee assignment. The values of relevant factors, such as previous performance, and the relative contribution that each factor makes to the prediction, also appear.

People are often the enterprise's greatest asset, and their loss can be expensive for many reasons. System predictions can make you aware of potential issues and their likely causes so that you can address them. For example, if an employee whose performance is predicted to be high is also identified as likely to leave voluntarily, you can consider changes to relevant factors, such as grade or location, to reduce the risk. Voluntary predictions appear on the Workforce Predictions work area.

Settings That Affect Prediction of Voluntary Termination

Predictions of voluntary termination are based on existing data from all work relationships. The process that collects relevant data and generates the predictions is Collect Data and Perform Data Mining for Predictive Analytics, which uses Oracle Data Mining and also predicts performance.

You can perform data collection either for the enterprise or for a specified manager assignment; however, the data-mining stage of the process is always performed on all of the latest available data.

The process has no default schedule. You are recommended to run the process:

  • Weekly if the volume of relevant transactions in your enterprise (such as hires, terminations, and promotions) is high

  • At least monthly if the volume of transactions isn't high

Schedule the process at a time of low system activity to avoid performance impacts.

How Voluntary Termination Is Predicted

Each prediction is a percentage value, which is the predicted probability of voluntary termination. It is calculated as follows.

  1. For all employee work relationships, the process collects the values of a large set of attributes. The attributes include, for example, time in grade, current job, latest salary increase, performance rating, and number of sickness absences in the previous year. The attributes of interest include those most likely to show a correlation with voluntary termination. In some cases, simple values, such as manager name, are required; in others, such as percentage increase in sickness leave, the process calculates the values. Most of these attributes are held at the assignment level; therefore, for work relationships with multiple assignments, multiple values are collected.

    Contingent worker and nonworker work relationships are excluded.

  2. The attribute values are passed to ODM, which identifies patterns and relationships in the data and builds a predictive model that captures the differences between employees who have terminated voluntarily and all other employees.

  3. ODM makes predictions of voluntary termination for current employees according to the predictive model. For example, if voluntary termination is high in a particular job and department, current employees with that job in that department may have a greater risk of voluntary termination than workers in other jobs or departments.

    Each prediction relates to an employee assignment. For employees with multiple assignments, multiple voluntary-termination predictions are made (one for each assignment). If an employee reports to a single manager in multiple assignments, the manager sees multiple predictions for that employee.

These predictions enable you to identify employees at highest risk of voluntary termination. The absolute risk of voluntary termination for the high-risk group may still be low in percentage terms, but relative to that for other groups of employees, the risk is high.

Voluntary-termination predictions are available for both teams and individual assignments:

  • Team predictions show the average risk for the team. They also show, for each factor, such as current salary or grade, the percentage of employee assignments for which the factor is the main risk factor.

  • Individual predictions show the predicted risk for the employee assignment. The values of relevant factors, such as current salary, and the relative contribution that each factor makes to the prediction, also appear.

Predictive Attributes

Voluntary-termination and performance predictions are based on specific attributes from a worker's personal, employment, absence, compensation, and talent management information, most of which are held at the assignment level. This topic identifies the relevant attributes by their factor names, as they appear in predictive analytics, and explains how each attribute value is calculated or derived.

Person Attributes

Person attributes are described in the following table.

Factor Name Description

Worker is an employee

Worker has a current employee work relationship

Home city

City from the worker's current home address

Home country

Country from the worker's current home address

Time until work permit or visa expiration

Number of weeks until the worker's next visa or work-permit expiration

Has a second passport

Worker has a second passport

Worker is a rehire

Worker was previously employed by the enterprise

Tobacco user

Worker uses tobacco

Time until contract expiration

Number of months until expiration of the worker's contract

Willing to relocate domestically

Worker is willing to move to a different location in the same country

Willing to relocate internationally

Worker is willing to move to a different country

Employment Attributes

Employment attributes are described in the following table.

Factor Name Description

Current legal employer

Legal employer from the assignment

Current enterprise

Current enterprise

Worker category

Worker category from the assignment

Length of service

Worker's enterprise service in years

Time since last probation ended

Number of months since completion of the worker's latest probation period

Current assignment status

Status of the assignment

Legislation

Legislation of the legal employer

Current or most recent manager

Line manager of the assignment

Time with current manager

Number of months the worker has been reporting to the current manager

Average time with each manager

Average number of months the worker has reported to each manager in all employee assignments in the enterprise

Number of manager changes in the last 5 years

Number of manager changes in all of the worker's employee assignments in the last 5 years

Normal start time

Work start time from the assignment

Normal end time

Work end time from the assignment

Normal working hours

Expected number of hours worked each day

FTE

Sum of the FTE values from the worker's current employee assignments

Current grade

Grade from the assignment

Time in current grade

Number of months between the last grade change or the start of the assignment and the current date

Average time in each grade

Average number of months between grade changes for all of the worker's past and current employee assignments in the enterprise

Number of different grades

Number of different grades for this worker in all past and current employee assignments in the enterprise

Number of grade changes in the last 2 years

Number of different grades for this worker in all employee assignments in the last 2 years

Current department

Department from the assignment

Time in current department

Number of months between the last department change or the start of the assignment and the current date

Number of different departments

Number of different departments for this worker in all past and current employee assignments in the enterprise

Average time in each department

Average number of months between department changes for all of the worker's past and current employee assignments in the enterprise

Current job

Job from the assignment

Time in current job

Number of months between the last job change or the start of the assignment and the current date

Average time in each job

Average number of months between job changes for all of the worker's past and current employee assignments in the enterprise

Current position

Position from the assignment

Time in current position

Number of months between the last position change or the start of the assignment and the current date

Average time in each position

Average number of months between position changes for all of the worker's past and current employee assignments in the enterprise

Current location

Location from the assignment

Absence Attributes

Absence attributes are described in the following table.

Factor Name Description

Amount of leave taken in the previous year

Number of days' leave taken in the previous year

Total enterprise leave

Number of days' leave taken since the start of the worker's enterprise service

Time since last leave

Number of months between the latest leave and the current date

Amount of sickness in the current year

Number of sickness days taken in the current year

Amount of sickness in the previous year

Number of sickness days taken in the previous year

Increase in sickness over previous year

Percentage change in the number of sickness days for the year to date compared with the previous year

Time since last sickness

Number of months between the latest sickness day and the current date

Number of sickness absences in the previous year

Number of distinct periods of sickness in the previous year

Increase in sickness absences over previous year

Percentage change in the number of sickness absences for the year to date compared with the previous year

Compensation Attributes

Compensation attributes are described in the following table.

Factor Name Description

Latest salary change

Latest salary change as a percentage of the annualized salary before the change

Reason for latest salary change

Reason for the latest salary change

Time since latest salary change

Number of months between the latest salary change or the hire date and the current date

Average salary change

Average of all salary-change percentages since the hire date

Time until next salary review

Number of months between the current date and the date of the next salary review

Time since last received options

Number of months since stock options were last granted

Ratio of vested to unvested options

Vested stock options expressed as a percentage of the worker's total stock options

Talent Management Attributes

Talent management attributes are described in the following table.

Factor Name Description

Current performance self rating

Worker's current self-assessment for the assignment

Current manager performance rating

Manager's current overall performance rating for the worker's assignment

Difference between manager rating and self rating

Difference between the manager's overall rating of the worker and the worker's self-assessment

Current appraising manager

Name of the manager performing the evaluation of the worker

Previous manager performance rating

Manager's previous overall performance rating for the assignment

Current performance rating

Current overall performance rating for the assignment

Change in current performance

Current overall performance rating for the assignment expressed as a percentage of the rating for the previous year

All performance predictions appear as percentages. To arrive at the percentage value, the predicted numeric rating from the rating model is expressed as a percentage of the maximum numeric rating in that rating model.

For example, if the predicted performance for an employee assignment is numeric rating 4, the employee's predicted performance is presented as:

  • 57.14%, if the maximum numeric rating is 7

  • 80%, if the maximum numeric rating is 5

Mapping Performance Predictions to Rating Levels

In performance documents, you rate employee performance using rating levels from a rating model. To map a predicted performance percentage to a rating level, you must first map it to the numeric rating in the relevant rating model. Once you have the numeric rating, you can identify the associated rating level.

In the following example, a predicted performance of 71.5% is between numeric ratings 3 and 4, and between Good and Very Good in the rating levels.

Performance Percentage Numeric Rating Rating Level

20%

1

Poor

40%

2

Satisfactory

60%

3

Good

80%

4

Very Good

100%

5

Outstanding

In the following example, a predicted performance of 71.5% is between numeric ratings 5 and 6, and between 10 and 14 in the rating levels.

Performance Percentage Numeric Rating Rating Level

12.5%

1

1

25%

2

4

37.5%

3

6

50%

4

8

62.5%

5

10

75%

6

14

87.5%

7

17

100%

8

20

You can view latest accuracy information for the voluntary-termination and performance predictive models on the Manage Predictive Models page.

Predictive Model Accuracy

The predictive models for both voluntary termination and performance are built using a subset (approximately 70%) of the available historical data. Oracle Data Mining (ODM) tests the accuracy of the models by making voluntary-termination and performance predictions for the remaining held-aside data (the 30% not used in building the predictive models). ODM then compares its predictions with actual outcomes.

The percentage accuracy of the predictive model:

  • For voluntary-termination predictions, derives from the percentage of correct predictions made for all employees, both those who leave the enterprise and those who remain.

  • For performance predictions, is a measure of how closely the predicted values match the actual values.

Workforce Modeling provides managers and human resource (HR) specialists with the ability to plan, model, and execute workforce changes using a graphical tool. You base your model on either the manager hierarchy or the position hierarchy. The hierarchy starts with the top manager or position and includes assignments, positions, vacancies and requisitions. They report to the top manager or position, either directly or indirectly.

You can perform the following actions in the model:

  • Promote

  • Transfer

  • Terminate

  • Change manager

  • Change location

  • Add, change, inactivate, or delete positions

  • Change position incumbents

  • Create, change, or delete vacancies

  • Create and edit Oracle Recruiting Cloud requisitions (dependent on security settings)

You can move people with or without direct reports either by dragging and dropping or using the table view and selecting a new manager. You can create vacancies in the model, and on final approval they're added to the database. Review the impact of your planned changes using the analytics. The application uses the modeled changes to create effective dated transactions when the model is approved. Vacancies are not effective dated, therefore vacancy changes or new vacancies appear when the model is approved.

Perform position changes by dragging and dropping on the graphical hierarchy. For example, drag and drop a position including its incumbents on the hierarchy and then move an incumbent to a different position. Use the position synchronization and position defaulting features.

For the position hierarchy, you can perform the following actions for positions:

  • Create

  • Edit

  • Inactivate or delete

  • Undo inactivate

  • Undo delete

  • Move positions in the model

  • Convert a vacancy into an open position

  • Create and edit requisitions

Security and Access

Line managers and HR specialists have access to Workforce Modeling. You can access all the Workforce Modeling features if you are either an author or the top manager of a model. To access the model as the top manager, the author of the model can give you access in the model properties. HR representatives can edit the model, and other approvers can open and view the model.

The following rules define who the default approvers are:

  • If all the modeled changes occur within the top manager os position's organization, then the approvers are the author's HR Representative, the top manager's manager and one level of manager approval above.

  • In addition to the above rule, the manager who has authority to approve all the changes and one level above, are the approvers. These approvers are in addition to the author's HR Representative and two levels above the top manager.

  • If there are any changes outside the top manager or position's hierarchy, then a further rule requires the manager who has authority to approve all the changes and the manager one level above to approve the model.

Any role with position related privileges, for example, create, edit, or delete positions, can perform position related actions in Workforce Modeling. For HR specialists, this feature is ready to use automatically.

Statuses

The following table describes the Workforce Modeling statuses.

Status Description

Draft

A model is in the Draft status:

  • Until it's submitted

  • Once it's edited

  • If an approver edits a model during approval

  • If the author withdraws a pending model

Pending

A model is in the Pending status after it's submitted from the Draft, Rejected, or Returned modes.

Rejected

A model is in the Rejected status if an approver rejects it.

Returned

A model is in the Returned status if an approver requests more information.

Completed

A model is in the Completed status after it's approved by all approvers. At that point, transactions are created and assignments are updated with effective dated changes.

Effects of Reorganizing

Once the newly created workforce model is approved, the relevant assignments are updated using the model effective date. Currently, notifications aren't issued for this. Role provisioning security occurs automatically if it's set up accordingly. For example, if the line manager role is enabled for autoprovisioning, then a user who becomes a manager is given the line manager role automatically.

Note: Vacancy changes appear on the date the model is approved, irrespective of the model effective date because vacancies are not effective dated in HCM Cloud.

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

You can access Workforce Modeling actions from the following locations:

  • Model page

  • Worker assignment node

  • Vacancy node

  • Workers region in the table view

  • Terminated workers region in the table view

  • Vacancies region in the table view

  • Holding area

  • Position node

  • Positions region in the table view

  • Requisitions node

  • Requisitions region in the table view

This table lists and describes the actions available on the Model page:

Action Where Available Description

Edit Worker Assignment

Worker Assignment Node, Chart and Holding area

Move or edit the modeled attributes for a worker assignment. If you change a worker's manager, then the worker moves to that manager's hierarchy.

Create Vacancy

Worker Assignment Node, Chart and Holding area

Create a vacancy node as a placeholder for an open headcount.

Edit Vacancy

Vacancy Node, Chart and Holding area

Edit the vacancy.

Delete Vacancy

Vacancy Node, Chart and Holding area

Delete the selected vacancy node. You must confirm the deletion to remove the node from modeling.

Note: You cannot delete vacancies imported from Oracle Taleo Recruiting Cloud Service.

Edit Assignment

Model page

Select a worker assignment node in the chart, holding area, or the table and edit the assignment.

Undo and Redo

Model page

Select this option to undo and redo a change.

Edit Model Properties

Model page

Edit the model details.

Synchronize

Model page

Run the synchronization process to update any information that has changed in the live application.

Terminate

Worker Assignment Node

Select a termination action, reason and notification date.

Cancel Termination

Terminated Worker Assignment Node

Select this option to cancel the termination within the model.

Export to Excel

Terminated Workers, Workers, and Vacancies, in the table view

Select this option to open and save the information in a spreadsheet.

This table lists and describes the actions available on the Model page for positions:

Action Where Available Description

Create Child Position

Model page, Position node

Available for existing positions only. Enter basic position information. The application creates the new position in the position hierarchy and that position reports to the position from which the action initiated.

Edit Position

Model page, Position node

Edit the position and if required, the parent position details. If you edit a parent position, then you also have the option to move or leave the children with the parent position.

Inactivate Position

Model page, Position node

Available for positions that do not have active or suspended incumbents as of the effective date or a date in the future.

Undo Inactivate Position

Model page, Position node

For positions that have been inactivated in the model.

Delete Position

Model page, Position node

You can delete a position if it does not have incumbents in the past, present, or in the future.

Undo Delete Position

Model page, Position node

Select this option to reverse the deletion and all the associated changes. Available for positions that have been deleted in the model only.

Convert to Position

Model page, Vacancy node

Edit the vacancy and review the default values for the position. This option is available only if the parent of the vacancy is an incumbent of a position in the hierarchy.

You can use workforce modeling to create or edit a model to terminate workers. You can use existing models to terminate workers, and these models can also include other planned updates, such as promotions and transfers. Any workers you terminate will display as grey in the model. The terminations affect the analytics which you can use to review the impact of your planned terminations. The termination changes are only applied to the transactional system when the model is approved.

Canceling Terminations

Cancel a termination, if, for example, you realize that the planned termination could harm the business or you terminated a worker as part of a planned restructure, which has been canceled. Canceling a termination also affects the analytics.

Analyzing Terminations

The impact of the termination or canceled termination in terms of headcount, salary cost and predicted effectiveness is calculated and displayed, providing you with immediate feedback on your modeled changes.

Terminating Managers with Subordinates

If you terminate a manager with subordinates, then you can decide to terminate them also. If you cancel this termination, then you must cancel the termination of each subordinate separately. If you decide not to terminate the subordinates, then the subordinates are assigned to the terminated manager's manager. If you cancel the termination, then you must manually move each reassigned subordinate back to their original manager.

Planning workforce changes can take time, and changes can occur in the live transactional application that affect the model. Therefore, Workforce Modeling runs a synchronization process that checks the attributes in the live transactional application against the attributes in the model, and makes some automatic updates, and other recommendations. The synchronization process ensures the assignment and vacancy information in Workforce Modeling are up to date with any changes made in the live transactional application.

The synchronization process runs automatically when you:

  • Open the model for editing.

  • Open the Review page.

  • Open the Approval page.

You can also run the process manually from the Actions menu on the Model page.

Synchronization Rules

When you open a saved model, the synchronization process runs automatically to review all application changes that occur after the date the model was last updated, and on or before the model effective date. If there are any changes in the live application, for example, one of the workers in the model has been transferred and another worker has been terminated, then the application synchronizes each attribute using the following rules:

  • If the live application has changed but the model hasn't, then automatically update the model.

  • If the same attribute has changed in the model and in the live application, and if the live application changes are irreversible, then automatically update the model with the live application change. Irreversible changes include any moves in or out of the hierarchy, such as, hires, transfers, and terminations.

  • If the same attribute has changed in the model and in the live application, and you can update the live application change, then the application recommends the modeled value. If this scenario occurs, then the application recommends the model changes for all attribute updates.

Synchronization Alerts

When you reopen a saved model, and the synchronization process finds changes in the live transactional application and in the model, then you can review the automatic updates and recommendations in the Alerts page. If you confirm the changes, the application updates the analytics and the information in the hierarchy.

FAQs for Generate Workforce Deployment Intelligence

The attribute values that you change for a worker while performing a what-if analysis are saved as a worker plan using a plan name that you supply. View the associated predictions later or rerun a saved scenario by selecting the plan from the list of saved worker plans for a worker on the What-if Analysis tab. You can also select a worker plan from the list of all worker plans for all workers.

Note: The voluntary termination and performance predictions in a worker plan are those that applied when you last saved the what-if analysis. To update the predictions, you must rerun the scenario.

When you select a worker plan, only the attributes in the saved what-if analysis appear; attributes that you didn't change in the original what-if analysis aren't saved. To make further changes to the scenario (for example, to include different attributes), you must reset the scenario and then perform the what-if analysis.

Saving a what-if analysis has no effect on the worker's records. If you want to make any of the changes permanent (for example, promoting a worker), then you must do so explicitly.

The effect of any changes made to the what-if-analysis attributes on current performance and voluntary termination predictions is calculated, and new predictions appear. The attribute changes are not applied to the worker's records; however, you can save the what-if analysis as a worker plan for later retrieval.

A worker plan is a saved what-if analysis for a worker. When you view worker plans, you display a list of all saved what-if analyses for all workers. Each worker plan is identified by the name you supplied when you saved the what-if analysis. The predicted performance and voluntary termination values for each scenario are those that applied when you last saved the what-if analysis. To update the predictions, you must rerun the what-if scenario.

If you have never saved a what-if analysis for a worker, then the list of worker plans includes no entry for that worker.

If you move a worker assignment in the organization chart, the Manager and Department fields in the Edit Assignment dialog box default to the new manager and the new manager's department.

All the other fields show their most recent values as of the model effective date. The current values are secured to ensure that people with no access to a worker's grade and salary can't view them.

Why can't I inactivate a position in Workforce Modeling?

Because the position you want to inactivate either has an active or suspended incumbent as of the effective date or in the future. To inactivate a position with incumbents, you can either move incumbents out of the position or terminate the incumbents and then inactivate the position. If you implement a model with inactivated positions, then this position will not appear in the HCM position hierarchy.

How can I convert a vacancy into a position?

Open the vacancy to edit, and review the default values for the position. The option to convert a vacancy into a position is available only if the parent of the vacancy is an incumbent of a position hierarchy. The position headcount defaults from the number of openings, therefore, if the vacancy has unlimited openings, then the position headcount defaults to 1. You create the new position and it reports to the parent position.

FAQs for Evaluate Workforce Deployment Performance

The hierarchy is based on completed transactions. Incomplete transactions, such as transfers or new hires awaiting approval, don't appear. However, terminations that are withheld from publication until a specified date are evident in your organization hierarchy from the termination date rather than the publication date.

A person's readiness for promotion is based on the time since the person's last promotion relative to the average time between promotions for people in the same job or position and grade. For example, if the average time between promotions for people in the same job and grade is 5 years, a promotion appears due if the time since a person's last promotion is within range of 5 years. If the gap is outside this range, the promotion appears as either not due or overdue. Additional factors, such as performance and length of service, determine whether you decide to promote a person whose promotion appears due or overdue.

Each entry in the hierarchy is an assignment. If a person has more than one assignment, and each assignment reports to a manager in the hierarchy, then the hierarchy contains an entry for each of the person's assignments.

Yes, when terminating a manager you can select an option to also terminate his or her subordinates. The default behavior is not to terminate a manager's subordinates, but to automatically reassign them to the terminated manager's manager.

Yes. Edit the assignment and select the Promotion or Location Change action. You can update a worker's salary information if the worker has a current salary record, their assignment does not have a grade ladder and if their salary basis does not use components or payroll rates.

You can search for workers, place them in the holding area, and then terminate them. Predefined approval rules ensure that the appropriate manager approves such changes. For example, if you terminate a worker who does not directly report to you, then the first manager in the hierarchy who you both report to, must approve the termination. The approver's manager must also approve the change.

Yes you can cancel a termination. For example, if you terminate a worker in the model and then cancel that termination, there is no change in the model. You can select to cancel the termination within the model without affecting the live transactional system.