WORKFORCE DEVELOPMENT
deeper—such as understanding employee behavior, employee- related decision-making, characterizing employee movement, understanding factors that influence job changes, behavioral patterns and environmental effects.
In August 2020, the U.S. Army Acquisition Support Center entered into a partnership with George Mason University (GMU) to conduct research on workforce dynamics. Tis research effort, which will run through August 2025, has been established and funded via the U.S. Army Research Institute (ARI) for the Behav- ioral and Social Sciences—the Army’s lead agency for research, development and analyses for the improvement of Army readiness and performance. ARI’s mission is to drive scientific innovation to enable the Army to acquire, develop, employ and retain profes- sional Soldiers and enhance personnel readiness.
Even though workforce forecasting strategies have been in use for decades and are utilized across a multitude of companies and organizations, predicting future workforce requirements is typi- cally performed by data collecting and models. Currently, this data is collected and modeled manually. Trough the GMU part- nership, the DACM Office looks to develop reliable workforce forecasting capabilities and understand workforce and employee patterns with state-of-the-art big data processing and machine learning methods.
Machine learning is a type of artificial intelligence that involves the use of data to build computer systems that will learn from the data provided. Essentially, machine learning techniques use specially designed algorithms to discover patterns and relation- ships in data to be used for analysis and to make predictions. Trough the GMU study, this is being accomplished with a high level of detail and with algorithms tailored to the acquisition workforce with the use of command structure, job description, geographical and environmental information.
GMU has developed several incremental proof-of-concept models—evidence that demonstrates a concept is achievable— that provide individual-level resolution of the Army Acquisition Workforce. Tese are based on conceptualizing the workforce in a multiscale, integrated network built with nearly a decade of the personnel and organizational micro-level data. Te result is a high-resolution longitudinal picture of the workforce that has not been available. Tis is a flexible and scalable quantitative framework that is expected to provide answers to difficult ques- tions about organizational effectiveness. Improvements to these initial prototypes will be tailored to proactively address specific leadership questions about individual-level career forecasting,
management and composition of teams, team performance and the effects of incentives and external influences on individual career decisions.
In the near future, the DACM Office expects this effort to improve our ability to understand personnel movements, work- place diversity, the effects of incentive programs and to build more effective teams in pursuit of the Army mission. By gaining a deeper understanding of these factors through machine learning, the Army will be able to make more informed decisions regard- ing training, recruitment and retention strategies.
CONCLUSION When the need arises, agencies will need to know if there will be enough trained personnel in the right place at the right time. Accurate workforce forecasting will be crucial for the Army to effectively manage and plan to support future mission require- ments. Developing the workforce of tomorrow involves investing in education, skills training, preparing individuals for an evolv- ing workforce and recruitment and retention. Implementing machine learning in workforce forecasting can aid the Army by more accurately predicting workforce trends and eventu- ally assist in making more informed decisions and optimize its human resources. Machine learning technology can help plan for having the right number of trained personnel available, ulti- mately enhancing mission success.
Douglas R. Bush, assistant secretary of the Army for acquisition, logistics and technology, said in the Spring 2022 issue of Army AL&T, “We must ensure the appropriate processes and tools are in place—particularly in the areas of recruitment, develop- ment and retention—for effective talent management.” Taking proactive steps by investing in education, building stronger lead- ership and fostering a healthy work environment can result in a knowledgeable and devoted workforce of the future. We must start today to properly prepare the Army Acquisition Workforce of tomorrow.
For more information, contact Daniel Stimpson, Ph.D., at
daniel.e.stimpson2.civ@
army.mil.
REBECCA WRIGHT is a writer and editor with Army AL&T and the U.S. Army Acquisition Support Center at Fort Belvoir, Virginia. She has over 14 years of experience writing and editing for the Department of Defense and the Department of Justice.
https://asc.ar my.mil
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