https://dair.nps.edu/handle/123456789/12 This collection currently includes academic research conducted on defense-aquisition topics by faculty from the Naval Postgraduate School and by expert researchers from US universtities and think tanks funded by the OUSD (A&S) grant program. 2026-01-31T10:32:09Z https://dair.nps.edu/handle/123456789/5454 Title: Case Study: International Burden Sharing in Alliances & Coalitions Authors: Philip J. Candreva, David Kaczorowski Abstract: There are several normative theories for how the costs of running a coalition military operation or sustaining an alliance should be distributed among the members. The reality is that this becomes a matter of diplomacy and negotiation. This case study has students allocate the burden of a five-member military coalition using three approaches: ability to pay, capacity to contribute, and a complex qualitative approach based on a risk-sharing theory and incorporating descriptions of the member nations and their leaders. The case could be used in courses in national security policy, public budgeting, international affairs, or political science. It illustrates theories of alliances, conceptions of fairness, monetary and non-monetary costs of participation in such endeavors, and the effects of political ideologies on such decisions. Description: Case Study 2025-08-15T00:00:00Z https://dair.nps.edu/handle/123456789/4976 Title: A Historical Review of the Navy’s Enlisted Personnel Distribution Process Leading to MyNavy HR Authors: William D. Hatch II Abstract: This research is a historical account of the Navy’s enlisted personnel distribution and career management system. Its origins are principally based on conscription policies in preparation for a major theatre war. This has resulted in a labor-intensive system that has been modified only in the margins by numerous information technology add-ons and manual policy injections since the disbandment of the Cold War draft and inception of the volunteer force. Although it has been over 40 years since the volunteer force was implemented, the policies and processes have not changed at the same rate when compared to the advancements in lethality and capability of executing assigned missions in support of the National Defense Strategy. The Navy is operating under a significantly different personnel demographic than the conscription force. The conscription manning and assignment process and how it supports a workforce of volunteers with a significantly larger married workforce, stronger economy, effectively full employment, and other factors must be evaluated. Specifically, there have been only marginal attempts at best to wipe the slate clean. Never has a zero-base volunteer force distribution process been designed and implemented. The notion of a “marketplace” process of assigning Sailors may address a clean slate process. The new process should address the Navy’s evolving occupational jobs and career needs, while also meeting the desires, preferences, and aspirations of individual Sailors. This research identifies the allocation, placement, and distribution process prior to MyNavy HR. Description: Faculty Research 2023-07-14T00:00:00Z https://dair.nps.edu/handle/123456789/4789 Title: Contractual Safety of Model-Based Requirements Authors: Alejandro Salado, Niloofar Shadab Abstract: This report describes recent research in support of acquisition programs using requirements as contractual mechanisms. Requirements form the backbone of contracting in acquisition programs. Requirements define the problem boundaries within which contractors try to find acceptable solutions (design systems). At the same time, requirements are the criteria by which a customer measures the extent that their contract has been fulfilled by the supplier. Therefore, requirements are instrumental in the success of acquisition programs. In this context, the quality of a requirement set is determined by the level of contractual safety that it yields. From a technical perspective, contractual safety is driven by the accuracy, precision, and level of completeness of the requirement set. Achieving accuracy is necessary to guarantee that the requirements capture the real needs of the customer. Achieving precision is necessary to guarantee that the supplier interprets the requirements exactly as the customer intended when writing them. Achieving completeness is necessary to avoid gaps in the problem formulation. If requirements are missing, a supplier may reach contractually acceptable solutions that do not fulfill the needs of the customer. Unfortunately, textual requirements do not provide acceptable levels of contractual safety, as they remain a major source of problems in acquisition programs. This is partly caused by the inherent limitations of natural language to statically capture written statements with precision and accuracy. In addition, natural language is difficult (often impossible) to parse into consistent logical or mathematical statements, which limits the use of systematic and/or automated tools to explore completeness. Model-based requirements have been proposed as an alternative to textual requirements, with the promise of enabling higher accuracy, precision, and completeness when eliciting requirements. However, this promise has not been demonstrated yet. Description: Systems Engineering / Grant 2022-11-21T00:00:00Z https://dair.nps.edu/handle/123456789/4713 Title: Machine Learning in AWF Talent Management: New Approaches to Prediction of Workforce Retention and Promotion Authors: Tom Ahn, James Fan Abstract: The Department of Navy (DoN) and Department of Defense (DoD) Acquisition Workforce (AWF) Strategic Plans call for a restoration and strengthening of the civilian AWF after more than two decades to contraction. To reform and reshape the workforce to improve the acquisition process and delivery of world-class warfighting capability for the military, the AWF leadership must understand how attrition and retention will impact the “size, composition, and skill” needs of the workforce in “parallel with technology advances and global trends.” To achieve strategic talent management of the workforce, it is critical to have the ability to predict which workers are most likely to leave the AWF. Forecasting attrition will aid the leadership by identifying 1) which workers to target for retention via incentives and 2) which areas will need to increase or decrease recruitment to quickly fill personnel gaps that may arise. This technical report is the first to evaluate whether Machine Learning (ML) can be a useful tool for the AWF leadership to make attrition forecasts. We first show that ordinary least squares (OLS) which is the tool most-often associated with statistical modeling of worker behavior performs poorly, especially given the sparse administrative dataset we have access to. We then test a variety of ML algorithms and find that they can predict worker attrition with a higher degree of accuracy. Our conclusion from this exploratory analysis is that, as algorithmic effective increases with dataset size (in terms of more worker and job/task characteristics), there may be many use cases for these algorithms in future predictive modeling for manpower and retention. Description: Faculty Report 2022-06-24T00:00:00Z