A risk analytics model for strategic workforce planning: readiness of enlisted military personnel

Leo MacDonald () and Jomon Aliyas Paul ()
Additional contact information
Leo MacDonald: Kennesaw State University
Jomon Aliyas Paul: Kennesaw State University

Annals of Operations Research, 2024, vol. 338, issue 1, No 19, 513-533

Abstract: Abstract We develop a dynamic stochastic model of military workforce planning that incorporates uncertainties about personnel gains and losses across ranks. We then apply it to determine the probability of not meeting required targets as well as the resulting shortages and overages in the short, medium, and long terms along with the evaluation of policies to mitigate these risks. Our model allows decision makers to adjust recruiting and training practices to minimize the risk of not meeting target personnel levels as well as to value retention and reenlistment policies by calculating the expected marginal value of retaining additional service members. Moreover, it allows us to create a penalty function to optimize recruiting and training levels. The outcome is a tool to evaluate and ensure comprehensive force readiness.

Keywords: Stochastic programming; Workforce planning; Military; Risk analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05567-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:338:y:2024:i:1:d:10.1007_s10479-023-05567-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05567-0

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().