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(1) LAV25 LOGISTICS OPTIMIZATION USING MACHINE LEARNING

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0160
Agency Tracking Number: N193-A01-0399
Amount: $128,144.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A01
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
28 Dane Street, Somerville, MA, 02143
DUNS: 032909891
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Will Vega-Brown Will Vega-Brown
 Chief Data Scientist
 (856) 889-5139
 will@tagup.io
Business Contact
 Charlotte Ward
Phone: (513) 262-0159
Email: charlotte@tagup.io
Research Institution
N/A
Abstract
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).

* Information listed above is at the time of submission. *

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