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

该SBIR第一阶段项目旨在展示提供一个彻底改变游戏规则的基于大数据的供应链分析平台的可行性。例如，消费品制造和分销领域的复杂供应链涉及多个领域的数据分析和决策支持。当今供应链分析中使用的工具和技术过于严格，因为它们依赖于公司的内部因素，并且无法有效地结合不断增加的随时可用的外部数据。分析的质量直接影响产品质量、实施成本、客户满意度，并最终影响公司的财务健康。建议引入大数据概念，包括分布式文本挖掘、机器学习和可扩展分析，以支持供应链分析。拟议的创新将整合越来越多可用的结构化和非结构化外部数据，以增强供应链决策。这将使公司更加以客户为中心，做出更好的决策，并变得更有利可图。这项研究的关键结果将包括算法和方法，将内在的非结构化大数据预处理为适合作为输入的定量和定性描述符，以创建新的决策指标。更好的供应链分析的好处是显而易见的，包括更好的产品、更好的客户服务、更少的浪费和成本以及更高的质量。拟议创新的更广泛/商业影响是在提高产品质量和消费品公司成本、增加产品安全性、根据客户反馈快速将新产品推向市场等方面。拟议的研究还将显著降低技术采用障碍--拥抱大数据的技术障碍和成本障碍--同时为企业提供下一代分析能力，并将使所有规模的公司对大数据的使用“大众化”。预计这将使美国公司更具竞争力，从而在美国创造就业机会，并减少就业机会在海外的外流。拟议的研究对企业界的潜在影响是显著的，并在最近发布的各种调查和研究中得到了验证，这些调查和研究记录了将大数据应用于企业的好处。

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.