ML Research to Evaluate Automation and Increase Operational Efficiency and Resiliency for Fleet Readiness
Presented at the Naval Applications of Machine Learning (NAML) Conference, February 2025
In support of the Navy’s logistics, this ML research project evaluated automation opportunities to increase operational efficiency and resiliency of the navy’s readiness in a contested logistics environment. The overall focus is two-fold with an upstream and a downstream approach.
- The upstream approach investigates increased automation, improvements in quality, de-duplication in the configuration records (i.e., anomaly removal) resulting in better configuration and provisioning records, as well as potentially reducing the stock that needs to be managed.
- The downstream approach encompasses current developments in sustainment (i.e., predict supply and maintenance needs, indicate anomalies, and support deep dives into system health).
Together, these approaches result in an efficient and accurate configuration and provisioning profile in support of a spectrum of operational aspects of ships, ultimately reducing delay times and increasing uptime in alignment with one of the Navy key tenets of an 80% surge capable fleet and on-time delivery.