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Proactive Risk Monitoring Using Predictive Analytics

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-17-C-7615
Agency Tracking Number: B16C-002-0016
Amount: $124,897.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA16-T002
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-03-15
Award End Date (Contract End Date): 2017-09-14
Small Business Information
1270 North Fairfield Rd
Dayton, OH 45432
United States
DUNS: 004475216
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Nilesh Powar
 Principal Investigator
 (937) 266-3774
Business Contact
 Robert Klees
Phone: (937) 426-2808
Research Institution
 University of Dayton Research Insti
 Linda Young
300 College Park Array
Dayton, OH 45469
United States

 (937) 229-2358
 Nonprofit College or University

In a pilot study effort in 2013, the Secretary of Defense for Manufacturing and Industrial Base Policy (ODASD (MIBP)) developed a methodology that goes beyond legacy reactive and program-centric frameworks for assessing industrial base risk. This proposed effort leverages the pilot studys work with the fragility and criticality (FaC) assessment to develop a predictive and proactive tool to assist in the analysis and categorization of industrial base risks. The risk mitigation tool will use advanced data warehouses to ingest identified sources of relevant data and will employ unsupervised deep learning algorithms to identify fragility and criticality patterns in the dataset. Other untapped sources of data will be automatically gathered by the tool to assess industrial base risk. This innovative way of predicting supply risks will be extremely efficient and accurate and will require less direct human interaction than the traditional FaC process. UTC/UDRI team includes a commercialization partner, capable of providing FASI-G (Fleet Automotive Support Initiative - Global) data and transitioning this technology to several targeted Department of Defense (DoD) platforms. Our technical team includes members with relevant experiences in advanced machine learning algorithms and data analytics.Approved for Public Release | 17-MDA-9219 (31 May 17)

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

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