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Award Abstract #1346452

STTR Phase I: Using Big Data to Support Supply Chain Analytics and Optimization

NSF Org: IIP
Div Of Industrial Innovation & Partnersh
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Initial Amendment Date: December 20, 2013
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Latest Amendment Date: February 26, 2014
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Award Number: 1346452
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Award Instrument: Standard Grant
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Program Manager: Glenn H. Larsen
IIP Div Of Industrial Innovation & Partnersh
ENG Directorate For Engineering
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Start Date: January 1, 2014
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End Date: December 31, 2014 (Estimated)
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Awarded Amount to Date: $225,000.00
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Investigator(s): Vijay Hanagandi hanagandi@gmail.com (Principal Investigator)
Arun Aryasomayajula (Former Principal Investigator)
Jaroslaw Zola (Co-Principal Investigator)
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Sponsor: Optimal Solutions, Inc.
17 Kershaw Ct.
Bridgewater, NJ 08807-2595 (908)393-1316
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NSF Program(s): STTR PHASE I
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Program Reference Code(s): 1505, 8032, 8033, 8039
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Program Element Code(s): 1505

ABSTRACT

This SBIR Phase I project proposes to demonstrate the feasibility of delivering a radically game-changing Big Data - based Supply Chain Analytics Platform. A complex supply chain, in consumer goods manufacturing and distribution, for example, involves data analysis and decision support in many areas. Today's tools and techniques used in supply chain analytics are too restrictive as they rely on the company's internal factors and are unable to effectively incorporate the increasing volume of readily available, external data. The quality of analytics directly impacts the product quality, cost of fulfillment, customer satisfaction, and ultimately the company's financial health. It is proposed to introduce Big Data concepts including distributed text mining, machine learning, and scalable analytics to support supply chain analytics. The proposed innovation will integrate increasingly available structured and unstructured external data to enhance supply chain decisions. This will enable companies to be more customer-focused, make better decisions, and become more profitable. The key results from this research will include algorithms and methods to pre-process inherently unstructured Big Data into quantitative and qualitative descriptors suitable to be inputs for creating new indicators for decision making.

The benefits of better supply chain analytics are clear and significant including better products, better customer service, reduced waste and costs, and increased quality The broader/commercial impact of the proposed innovation is in the area of enhancing product quality and reducing costs for consumer goods companies, increasing the safety of products, and rapid new products introduction into the market based on customer feedback, etc. The proposed research will also significantly lower the technology adoption barriers - both technology-wise and cost-wise barriers to embracing Big Data - while delivering next generation analytics capabilities for businesses and will "democratize" the use of Big Data for companies of all sizes. This is expected to make U.S. companies more competitive resulting in job creation in the U.S. and reducing the outflow of jobs overseas. The potential impact of the proposed research on the business community is significant, and validated in various surveys and studies recently published documenting the benefits of applying Big Data to business.

 

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