Purpose — Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact future pilot training efforts that lower cost and increase operator quality and proficiency.
Design/methodology/approach — This research employed a mixed-method approach. First, the authors collected data and analyzed 90 C-130J pilots' aviation records and then contextualized this analysis with interviews of experts. Finally, the authors present a modified version of Six Sigma's define–measure–analyze–improve–control (DMAIC) that identifies and reduces the variances in C-130J pilot training, translating into higher quality outcomes.
Findings — The results indicate significant statistical variances across geographically separated C-130J pilot training organizations. This leads some organizations to have higher proficiency levels in specific tasks and others with comparative deficiencies. Additionally, the data analysis in this study enabled a recommended number of flight hours in several distinct categories that should be obtained before upgrading a pilot to aircraft commander to enhance standards.
Research limitations/implications — This research was limited to C-130J pilot upgrades, but these results can be implemented within any field that utilizes hours as a measure of experience. Implications from this research can be employed to scope policy that will influence pilot training requirements across all airframes in civilian and military aviation.
Originality/value — This research proposes a process improvement methodology that could be immediately implemented within the C-130J community and, more importantly, in any upgrade training where humans advance into higher echelons of a profession.
Journal of Defense Analytics and Logistics
Slottje, J., Anderson, J., Dickens, J. M., & Reiman, A. D. (2022). Pilot development: an empirical mixed-method analysis. Journal of Defense Analytics and Logistics, 6(1), 21–45. https://doi.org/10.1108/JDAL-10-2021-0008