Your search did not match any results.
We suggest you try the following to help find what you’re looking for:
To reduce risk and exposure, higher education institutions must ensure there are no compliance gaps across statutory requirements, such as unions, collective bargaining agreements, and federal mandates. With Oracle Modern Best Practice, you can leverage mobile, social, analytics, big data, and the cloud to create an efficient and compliant faculty and staff separation process that supports tenure, adjunct, fellow, and other complex employment scenarios. Easily determine involuntary separation factors, such as budgetary and disciplinary reasons. Quickly and accurately finalize payout options with verification and adjustments for union-dictated severance policies. Effortlessly connect relevant employee details to talent profiles and talent pools for future rehire considerations. Predict potential workforce movement and prevent attrition by analyzing turnover factors using data points, such as job classification, department, and location.
Select the employee for involuntary termination based on budgetary considerations or disciplinary and performance issues noted in employee records. Ensure compliance with union and legal policies. Create talent pools for desirable rehires.
Complete the employee separation process incorporating the department or organization, union/collective bargaining agreement, as well as compliance board or government policies.
Gain insight into pending faculty and staff departures and discuss off-boarding matters using a checklist.
Automate the processing and posting of the employee’s final pay to payroll for scheduled or off-cycle payroll runs. Automatically perform verification and adjustments for severance policies dictated by unions, compliance boards, or government.
Predict future workforce movement and prevent attrition by analyzing turnover factors using a variety of data, such as department, location, and job classification (tenured, adjunct, or staff). Maintain the talent profile and exit interview data in order to continually identify rehires.