A Semiautomated Framework Leveraging NLP for Skill Identification and Talent Management of the Acquisition Workforce in the Department of Defense

Abstract

The Department of Defense (DoD) must address critical questions about talent management and workforce adaptability. This research introduces the potential for leveraging Natural Language Processing (NLP) techniques to address these challenges. The paper describes an NLP-based framework to analyze vast text data, including government, industry, and academic reports. The primary objective is to identify critical skills necessary within the DoD acquisition workforce efficiently and accurately. By automating this process, the DoD can swiftly pinpoint areas of expertise and allocate resources accordingly, ensuring the hiring and deploying of personnel with the right skills where needed most. With the insights derived from NLP analysis, decision-makers within the DoD can make informed choices regarding talent acquisition, training and development programs, and skill gap remediation. The ability to swiftly and accurately identify essential skills optimizes resource allocation, reduces skill gaps, and elevates operational efficiency. This newfound efficiency extends to talent management, enabling the DoD to nurture and develop critical skills proactively. Identifying and managing critical skills is pivotal for ensuring preparedness and resilience in a rapidly changing world order.