Workforce Retention Modeling - Acquisition Research Program
Will They Stay or Will They Go? Using Data Analysis to Predict–and Improve–Acquisition Workforce Retentionby ARP staff | Aug 26, 2020
How can the Department of Defense (DoD) and Department of the Navy (DoN) grow and shape the civilian acquisition workforce to deliver world-class warfighting capabilities for the U.S. military? This question has plagued DoD, DoN, and the federal government for decades. Two Naval Postgraduate School (NPS) faculty members are developing a data-driven approach that will allow DoD leadership and human resources managers to forecast an acquisition professional's choice to stay in the workforce or to leave based on his or her socio-demographic, academic, and professional background. This approach will enable leadership to make strategic, informed policy decisions about recruiting, retention, and incentives that cultivate an efficient, diverse, and motivated workforce for the long run.
NPS faculty members Tom Ahn and Amilcar Menichini are developing a Dynamic Retention Model, which uses a powerful mathematical technique called dynamic programming. In their 2019 report “Retention Analysis Modeling for the Acquisition Workforce,” Ahn and Menichini explain how this tool can predict where DoD and DoN can most effectively focus energy and resources to retain and develop members of the acquisition workforce not only for the immediate future, but also for decades ahead. This study addresses a known problem in the defense acquisition workforce – a high percentage of employees (over 50%) are currently within 10 years of retirement age, and there are not enough younger workers to balance this seniority and fill the gaps that will come with the upcoming surge in retirement.
Using data from Defense Manpower Data Center (DMDC), the authors tracked over 13,000 DoD acquisition professionals across four decades. They find two factors that have substantial impact on retention: prior military experience and education level. If an acquisition professional comes to the workforce from military service, they are more likely to remain. Their conclusion: “leadership should augment recruiting from active duty to seamlessly transition them into the civilian workforce.” Additionally, more education equals longer careers. This is especially true for employees who earn master’s degrees mid-career; this group has the longest career. Here, the key takeaways are that leadership “should be ambitious in recruiting advanced degree holders” and "invest in the workforce by encouraging/subsidizing education without worrying about brain drain.”
The study also finds that while the acquisition workforce is fairly diverse, it is more heavily weighted toward female and white. As retention rates hold steady across all demographic groups, DoD leadership may be able to improve diversity by ramping up recruitment of traditionally under-represented socio-economic groups.
The authors propose ambitious additions to the model, which "can be extended in different important directions. For instance, we may incorporate the effect of the Federal Employees Retirement System pension and Thrift Savings Plan on retention behavior. We can also extend the model to help leaders make hiring/firing decisions at various points of the employee experience distribution to achieve a target shape of the force in a certain number of years.” The fully constructed model will allow leadership to use the information and analysis generated to make smarter, more cost-effective talent management decisions to improve the acquisition workforce. For example, as implied in the current study, DoD may be able to cut costs, increase retention, and improve worker quality by putting more funds toward subsidized education rather than into higher compensation or retirement benefits.
As noted in this report, the DoD and DoN strategic workforce plans note the need for a diverse, adaptable, and well-managed acquisition workforce for the U.S. military to remain competitive in an age of rapidly changing technologies, business processes, and threats. Using the power of data analysis to understand what motivates today’s workforce – and what is likely to predict workforce behavior in the near (and far) future – is an invaluable contribution to DoD and DoN.
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