(1) LAV25 LOGISTICS OPTIMIZATION USING MACHINE LEARNING

Current USMC logistics information systems do not possess the predictive modeling and simulation tools required to support strategic mission critical MAGTF planning efforts. Standard maintenance and supply information (service requests, spare parts requisitions, NIIN inventories, fleet readiness metrics, etc.) is readily available in an ERP system and is visualized via custom-built asset health/fleet readiness dashboards. The dashboards are dynamic; however, they lack critical scenario planning capability (e.g. deployment to a remote desert environment) by integrating key intelligence data. Tagup is proposing to build and validate risk-based asset survival models on key LAV25 maintenance and supply data. Survival models will be used to estimate probability of failure and model time to event as a function of maintenance status (e.g. deadlined, operational degraded, etc.), cost and failure mode. Potential savings will be identified as a result of model accuracy (as a function of increased asset availability) with a plan to validate model outputs on live streaming data across target USMC functions/users (Phase II).