Beyond Buyouts and RIFs: A More Effective Approach to Workforce Management

On April 12, the Office of Management and Budget issued a memorandum requiring agencies to develop plans to reduce their workforces. This has led many agencies to search in their talent management toolkit for help.

The usual tools employed—reductions in force, Voluntary Early Retirement Authority/Voluntary Separation Incentive Payments, and managed attrition—are being dusted off. But federal executives can do themselves a favor by turning to two neglected talent management tools: data analytics and mobility programs. When combined together, they can help agencies manage workforce reductions in the most effective way.

I say this from experience. In 2013, I was in charge of reducing my office’s headcount following the American Recovery and Reinvestment Act’s surge at the Energy Department, which left us with too many people with too little to do. We performed a workforce restructuring study that relied heavily on data analytics and then used the usual tools (VERA/VSIP in particular) in combination with a mobility option that helped us retain the talent we needed for future operations. This option involved the use of managed relocation programs, including employee relocation assistance programs. (These are distinct from recruitment, retention and relocation bonuses and telework/flexible work arrangements.)

Blunt instruments like RIFs and VERA/VSIP do not allow agencies to retain their highest performers or manage critical skill gaps. Instead, the rules of a RIF or VERA/VSIP typically favor tenure at the expense of workforce composition requirements.

But it is a fact of 21^st century workforce dynamics that federal workers are part of a highly competitive international marketplace. Highly talented workers who leave government service are going to be snapped up quickly by the private sector. Thus, it is in the interest of every agency’s leaders to ensure they retain critical workers even as they reduce the overall size of their workforces.

Using predictive data analytics, agencies can tailor mobility programs to make them more flexible to account for generational and work preference characteristics. This involves the applied science of merging employee requirements with agency mission and workforce needs.

Combining analytics with mobility programs involves considering the following six factors:

• Strategic: Determine how mobility programs address agency restructuring requirements—for example, by moving individuals from the field to headquarters or realigning field operations to include new capabilities.
• Administrative: Assess workforce demographics (including staffing ratios and productivity measures) and identify trends that impact overall agency restructuring efforts.
• Policy: Analyze mobility and restructuring policies to ensure alignment to agency restructuring plans.
• Return on Investment: The mobility program, if properly analyzed and administered, will pay a significant ROI to the agency in terms of productivity, mission accomplishment and employee retention (thus reducing recruitment, hiring and training costs).
• Cost: Data analytics will explain variances in budget and ensure that scarce mobility funds are effectively and efficiently utilized.
• Service: A key data point is determining whether or not the balance of service versus costs is appropriate and meets the overall needs of the organization and restructuring requirements.

Federal agencies have an opportunity to be on the cutting edge of combining mobility programs with data analytics. A 2016 study found that only 10 percent of companies worldwide have moved their global mobility function directly under the talent management area.

If agencies use the opportunity of OMB’s restructuring directive to take advantage of this advanced talent management tool, it might be time to say that business should be run like government.

Bill Valdez is president of the Senior Executives Association. He served at the Energy Department for over 20 years, 15 as a member of the Senior Executive Service, and led six different program and management offices.

Photo: Flickr user Kate Ter Haar