SBIR-STTR Award

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).

Benefit:
Using machine learning, we have identified a potential savings of ~$77MM (or 5% of the fleet value) for a single TAMCN (LAV25) over the life of the asset by: (1) increasing operational availability by 6.5% and (2) increasing confidence in asset availability by reducing excess inventory management. An increase in LAV25 readiness is a result of improved maintenance productivity (identifying components with the highest probability of failure), optimized equipment procurement/inventory management and a tool that enables improved replace vs. repair decision making. Under conservative assumptions, this scales to $1.15 billion over 236 critical TAMCNs in USMC MAGTF, a small force relative to the USMC Aviation division, broader Department of Navy, USAF, Army and generally DLA/LOGCOM operations.

Keywords:
asset management, asset management, Data science, Failure Prediction, Machine Learning, Data Analytics, survival analysis, internet of things

Risk-based time-to-event (TTE) modeling across the LAV fleet will be used to improve strategic mission-critical MAGTF scenario planning. This project seeks to model, quantify and forecast LAV operational availability as a function of time. Consolidated key intelligence data is used to estimate the probability of an event occurring in the future (given all past maintenance related information) and predict the associated time-to-event as a function of maintenance status (e.g. deadlined, operational-degraded, etc.), cost and failure mode (parts). By integrating service request records and supply activities at scale and leveraging complex computational principles, Tagup has developed methods of extracting and structuring large volumes of existing data while creating methods to predict asset availability outcomes more reliably. This modeling/technical approach leverages a wealth of maintenance and supply data in the existing Global Combat Support System (GCSS-MC) database [rated Controlled Unclassified Information (CUI)] therefore mitigating installation of expensive sensor equipment. During Phase I, Tagup developed and evaluated ML based techniques that could be used to forecast LAV availability as a function of historical use, health and reliability (all captured in GCSS-MC). Tagup’s technical approach successfully demonstrated a means to improve Marine Air-Ground Task Force’s (MAGTF) scenario planning capability (e.g. deployment to a remote desert environment), by: Providing a means to increase operational availability of the LAV through a reduction in short parts, Logistics Response Time (LRT) and overall Customer Wait Time (CWT) Providing a method to increase confidence in asset readiness and inventory posture Through a series of three (3) in-person meetings held during Phase I with key USMC stakeholders (LOGCOM and SYSCOM), Tagup confirmed the primary research objectives, analytic needs, product capabilities and proposed methods to advance the research developed during Phase I for productization, implementation and validation in Phase II. Phase II will operationalize and validate these analytic methods as defined in the Technical Objectives. In order to validate the technical approach and related benefits, a continuation to Phase II would allow Tagup to continue working with MARCORSYSCOM and MARCORLOGCOM to: Build and deploy two (2) new simulation/forecasting tools on streaming GCSS-MC data (as confirmed by the technical approach demonstrated in Phase I) OPerational Readiness & future Availability (OPRA) Demand-Driven LOgistics Simulation Tool (LOST) Validate and operationalize the analytics through block building, deployment planning and maintenance and supply optimization Certify Tagup’s software meets DOD cybersecurity requirements Savings realized due to increased asset availability will be quantified in detail. These savings will support commercialization plans across additional TAMCNs and users.