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Supply Chain data Acquisition and Narrative analysis (SCAN)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-17-C-7605
Agency Tracking Number: B16C-002-0006
Amount: $99,952.00
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
1400 Crystal Drive
Arlington, VA 22202
United States
DUNS: 036593457
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Peter David
 Director of Analytic Products Divison
 (703) 414-5009
Business Contact
 Dana Ho
Phone: (703) 414-5016
Research Institution
 George Mason University
 Dr. Daniel Barbara
 (703) 993-1627
 Nonprofit College or University

Supply chain risk management relies on expensive manual processes that are performed infrequently and with a limited set of data. Supply chain managers often operate in a reactive mode, scrambling for solutions to issues that could have been anticipated if more data and continuous monitoring of the supply chain were practical. The supply chain analysis problem is one where the quantity of data drives the quality of the results. Acquiring more data from more sources exposes more industrial entities, more relationships, and a wider range of activity that can be monitored. DECISIVE ANALYTICS Corporation (DAC) proposes an automated solution to supply chain monitoring and analysis that learns how to find relevant data from open and proprietary sources, automatically extracts industrial relationships from unstructured text, and fuses the extracted data into a comprehensive model of the supply chain. Our solution uses known risk criteria and anomaly detection techniques to detect unusual or risky phenomena in the supply chain, providing analysts with a triaged and prioritized set of possible risks to evaluate. An interactive visualization of high-risk paths through the supply chain allows users to isolate and drill into the high-risk pathways to explore the identified risks. Approved for Public Release | 17-MDA-9219 (31 May 17)

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

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