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

这个SBIR一期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，在消费品制造和分销中，复杂的供应链涉及许多领域的数据分析和决策支持。如今，供应链分析中使用的工具和技术限制太大，因为它们依赖于公司的内部因素，无法有效地整合越来越多的现成的外部数据。分析的质量直接影响到产品质量、实现成本、客户满意度，最终影响到公司的财务状况。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的可用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。本研究的关键成果将包括将固有的非结构化大数据预处理为定量和定性描述符的算法和方法，这些描述符适合作为创建决策新指标的输入。更好的供应链分析的好处是明确和显著的，包括更好的产品，更好的客户服务，减少浪费和成本，提高质量。拟议创新的更广泛/商业影响是在提高产品质量和降低消费品公司成本的领域，增加产品的安全性，并根据客户反馈迅速将新产品引入市场等。拟议中的研究还将显著降低采用大数据的技术和成本障碍，同时为企业提供下一代分析能力，并将大数据的使用“民主化”给各种规模的公司。预计这将使美国企业的竞争力得到提高，从而在美国国内创造就业机会，减少海外工作岗位的流失。拟议的研究对商业社区的潜在影响是巨大的，最近发表的各种调查和研究证实了将大数据应用于商业的好处。

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.