Creating Data-Driven Decisions in Allocating C-17 Aircrew Training
Abstract
The United States Air Force is currently in a largescale paradigm shift concerning how capability requirements are viewed and assessed. Increased emphasis on accurately measuring aircrew performance and forecasting training requirements has forced senior leaders to rethink whether the training programs currently in place genuinely achieve their desired effects. Specifically, the process of training resource allotment lacks the ability to quantify the amount of resources required to achieve a necessary level of performance or the flexibility needed to be altered with changes in resources. This study proposes a methodology to identify factors that have a significant impact on aircrew performance. Variables are identified in C-17 simulator data utilizing linear regression to develop a prediction model. Using this model, different courses of action are created where resources can be modified in order to achieve a desired capability. While the final model utilized within this study produces mixed results, the concepts, methodology, and proposed application provide insights into the value of using models like those described in this study in creating data-driven decisions in training resource allocation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2022
- Accession Number
- AD1177709
Entities
People
- Christopher M. Stevens
Organizations
- Air Force Institute of Technology