For the U.S. Navy Naval Air Systems Command (NAVAIR), keeping aircraft operational and ready for flight is an integral part of its role in protecting national security. The issue of how to make maintenance more efficient, conserve resources, and deliver increased uptime for the time and effort expended is of some urgency for NAVAIR; for several years, it has struggled with an “RBA gap” that has constrained its readiness and capability. Data analytics capability is a major barrier—and a crucial opportunity—for NAVAIR’s efforts to close this gap.
For each Type/Model/Series (TMS) of aircraft, NAVAIR has set a goal of maintaining a set number of Ready Basic Aircraft (RBA) at any given time. An RBA gap emerges when it falls short of these goals. In partnership with NCMS and the SAS Institute, NAVAIR is exploring the adoption of SAS’s Results as a Service (RaaS) environment to assess the quality of maintenance performed at different levels of the Naval Aviation Maintenance Program (NAMP). The goal is to close the RBA gap by adopting strategies to better allocate and deploy maintenance resources. This relies on having accurate, detailed data about the current state of NAVAIR aircraft maintenance and the ability to assess thousands of data points.
NAMP divides maintenance tasks into three levels—O, I, and D—which differ based on their extent and complexity. Tasks of each level are conducted at different sites, by different sets of personnel, each using different documentation, and the subsequent data stored in separate databases. Unable to integrate data, NAVAIR currently struggles to gauge the quality of maintenance performed at each level and, in some cases, if it is being performed at all. Using RaaS in a proof of concept project, SAS will use historical data to calculate descriptive statistics which will serve as baseline metrics for maintenance quality. With baselines in hand, SAS will be able to identify outliers and discrepancies between actual and scheduled maintenance times.
This project promises immense benefits for NAVAIR, providing data and findings that will allow it to maintain its fleet with improved safety, decreased downtime, and greater efficiency within each of its maintenance levels, at decreased cost. It will also provide a useful model for the future—ultimately, this type of data analytics solution will prove invaluable to fleet maintainers, whether they service assets belonging to a local government, commercial airlines, or any organization trying to control costs while keeping an aging, diverse fleet ready, safe, and operational.