Deep Machine learning for risk Analysis and Prediction (D-MAP) in supply chains

Deep Machine learning for risk Analysis and Prediction (D-MAP) in supply chains

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-18-C-7325
Agency Tracking Number: B2-2645
Amount: $997,308.00
Phase: Phase II
Program: STTR
Awards Year: 2018
Solicitation Year: 2016
Solicitation Topic Code: MDA16-T002
Solicitation Number: 2016.0
Small Business Information
15400 Calhoun Drive, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Yi Shi
 (301) 294-2468
 yshi@i-a-i.com
Business Contact
 Mark James
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 Randy V. Bradley, Ph.D.,
 Randy Bradley, PhD
 304 Stokely Management Center
Array
Knoxville, MD, 37996
 (865) 974-1761
 Nonprofit college or university
Abstract
Globalization and digitization have been driving the recent economic growth at the expense of raising the risk level in the supply chain related to fraud, security, and safety, while current practice of supply chain management and risk assessment is lagging far behind. Therefore, commercial industries and government agencies are seeking advanced supply chain risk assessment solutions, which can effectively assess and predict the risks through advanced analytics on various novel data sources and suggest/recommend mitigation strategies to help better manage supply chains in the face of potential risks and support high fidelity and timely decisions. To address this critical need, IAI proposes to design and implement the D-MAP system that provides a unified framework with the following novel capabilities: 1) Data Management that automatically collects raw data from multiple data sources, improves data quality, processes raw data to obtain both features and ground truth to facilitate further analysis; 2) Risk Prediction that applies advanced analytics to predict future risks in a supply chain building upon risk identification from realistic risk scenarios; (3) Risk Mitigation that recommends approaches to mitigate risks such that a supply chain can be improved to meet its mission requirements.Approved for Public Release | 18-MDA-9522 (23 Feb 18)

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

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