Proactive Risk Monitoring Using Predictive Analytics

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-18-C-7326
Agency Tracking Number: B2-2612
Amount: $992,341.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: MDA16-T002
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-30
Award End Date (Contract End Date): 2020-04-29
Small Business Information
1270 North Fairfield Rd, Dayton, OH, 45432
DUNS: 004475216
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dr. Nilesh Powar
 (937) 266-3774
 Nilesh.Powar@udri.udayton.edu
Business Contact
 Joe Sciabica
Phone: (937) 426-2808
Email: DSuchecki@utcdayton.com
Research Institution
 University of Dayton Research Insti
 Claudette Groeber
 300 College Park
Dayton, OH, OH, 45469
 (937) 229-2919
 Nonprofit college or university
Abstract
Presently the missile defense systems are using a reactive and program-centric framework for assessing industrial base risk. The Phase I effort focused on developing a wide and deep network algorithm using Industrial Product-Support Vendor (IPV) Gen II program for predicting the probability of failure. The proposed Phase II effort leverages the Phase I work and will focus on three main goals:(1) Inclusion of Bayesian networks to the existing wide and deep network algorithm to extract risk correlations;(2) Implementing a secure cloud based module for ingesting new data sources - weather, financial and event data;(3) Implementing an application that integrates the algorithmic and data sourcing components in an adaptive framework that can predict the likelihood of supply chain failure and identify the key risk factors that create those failures. Phase II will result in a software program that will adaptively gather relevant industrial supply data and provide an almost real time prediction of industrial risks. UTC/UDRI team includes a commercialization partner, Lockheed-Martin (LM), capable of providing data and transitioning this technology to several targeted DoD platforms. Our team includes members with relevant experience in advanced machine learning algorithms, data analytics, and supply chain management.Approved for Public Release | 18-MDA-9522 (23 Feb 18)

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

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