Whether its industry or government agencies indirect/support activities tend to get the short end of the stick with regards to analytic rigor. Much of my dissertation research focused on injecting more analytic rigor to better understand the economics of, and policy impacts, to these activities. This was one of my papers that assessed the potential application of bayesian networks to provide decision support.
Current constraints in the fiscal environment are forcing the Air Force, and its sister services, to assess force reduction considerations. With significant force reduction comes the need to model and assess the potential impact that these changes may have on support resources. Previous research has remained heavily focused on a ratio approach for linking the tooth and tail ends of the Air Force cost spectrum and, although recent research has augmented this literature stream by providing more statistical rigor behind tooth-to-tail relationships, an adequate decision support tool has yet to be explored to aid decision-makers. The authors of this research directly address this concern by introducing a systematic approach to perform tooth-to-tail policy impact analysis. First, multivariate linear regression is applied to identify relationships between the tooth and tail. Then, a novel decision support system with Bayesian networks is introduced to model the tooth-to-tail cost consequences while capturing the uncertainty that often comes with such policy considerations. Through scenario analysis, the authors illustrate how a Bayesian network can provide decision-makers with (i) the ability to model uncertainty in the decision environment, (ii) a visual illustration of cause-and-effect impacts, and (iii) the ability to perform multi-directional reasoning in light of new information available to decision-makers.