Being ready for combat is the responsibility of each military service. And it is almost certain that all commanders worry about it. They want to be sure their command is ready. But how can they measure readiness? To what degree are they ready? Is there a measure they can use with confidence to indicate that their unit, if called upon, is prepared to go to war?
Maj Andrew “Rip” Lipina, an AFIT IDE student in the Department of Operational Sciences, last month briefed senior leaders on just such a study he had conducted under the direction of his research advisor, Maj Shay Capehart from the Department of Mathematics and Statistics. The study, in support of USAFE/A9, analyzed combat mission readiness (CMR) data to determine the key factors affecting CMR rates.
Several metrics have been proposed and used in the operations and maintenance community to indicate whether a command meets wartime requirements. CMR is one such metric. However, a serious issue when studying CMR rates arises from the tension between the priorities of two different groups, such as maintenance and operations, giving inputs on what to measure for success. For example, maintenance would consider a single-ship instrument flight, where the gear fails to retract, as effective, while the pilots within operations would obviously consider this sortie ineffective, since no effective readiness requirements were achieved.
An initial study was conducted using Air Force Smart Operations principles. This week-long workshop with, among others, operators and maintainers identified the most affecting factors. Focusing efforts on these categories scopes the problem for further statistical analysis. In this case, multivariate regression analysis provides a systematic approach to analyzing the CMR rates to determine which factors have a statistically-significant impact on explaining the overall variation. Based on the guidance from senior leaders, a more exhaustive follow-on study might be next. One senior leader was especially impressed, saying, “This is a great example of our Advanced Academic Degree programs and their graduates benefiting the Air Force in an immediate and critical manner.”
To complete the cycle, the data made a return trip, as it were, to AFIT for further use. Not only did the study provide a great research opportunity to engage real-world operations, but it also provided an exceptional teaching tool in Maj Capehart’s Applied General Linear Models class. As a final project, each of his students analyzed this authentic dataset, giving them a taste of the difficulties encountered in moving from the standard classroom example to an actual operational problem.
