Big data based predictive analytics platform for supply chain rebates and customer analysis

基于大数据的预测分析平台，用于供应链回扣和客户分析

• 批准号: 491954-2015
• 负责人: McGregor, Carolyn
• 金额: $ 1.82万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587157
• 关键词: Big; data; based; predictive; analytics

Within Durham Region, 360 incentives was founded with a vision of dramatically improving rebate and incentive management by using the latest technology that provides leading edge analytics on the performance of their customers' rebate programs. To date they have utilised retrospective analytics to provide insights to their customer base. At present, 360 incentives lack expertise in newer approaches for Big Data analytics and knowledge discovery specifically in the area of real-time streaming analytics to create a systemic framework for new approaches for real-time performance metrics for rebate programs. Creating a new framework to support Big Data analytics of rebate programs represents an innovative technique to greatly enhance the competitiveness of 360 Incentives. One current fundamental gap in supply chain innovation is providing greater insight on the actual end customer for suppliers rather than their current limitations of analytics relating to the purchasing patterns of the companies they supply to. Another limitation of current research directions is in relation to the analytics for the performance of various rebate campaigns offered by manufacturers. Frameworks to enable the measurement of real-time customer purchasing rebates represents an open research area. Attainment of both can be achieved through new techniques McGregor wishes to explore through research with 360 Incentives that will begin with the research proposed within this NSERC Engage. We propose to build these new techniques on streaming analytics from Big Data generated by new approaches to stream sales and rebate data. Within this NSERC ENGAGE research project we will utilise McGregor experience from her Big Data analytics research in the medical domain together with her prior industry experience in the retail sector in Australia to create a new approach for streaming analytics within the supply chain context for new approaches for flow through customer analytics and rebate analytics to all members within the supply chain. We will initially focus on the design of the data acquisition and data transmission components.

在达勒姆地区，360激励成立了一个愿景，大大改善回扣和激励管理，通过使用最新的技术，提供领先的分析性能，他们的客户的回扣计划。到目前为止，他们已经利用回顾性分析来为他们的客户群提供见解。目前，360激励缺乏大数据分析和知识发现新方法的专业知识，特别是在实时流媒体分析领域，无法为折扣计划的实时性能指标新方法创建系统框架。创建一个新的框架来支持退税计划的大数据分析，这是一种创新技术，可以大大提高 360激励措施。目前供应链创新的一个根本性差距是为供应商提供对实际终端客户的更深入了解，而不是他们目前对供应商采购模式的分析限制。当前研究方向的另一个局限性与制造商提供的各种回扣活动的绩效分析有关。 框架，使实时客户购买回扣的测量是一个开放的研究领域。这两个目标都可以通过新技术来实现，McGregor希望通过360激励机制的研究来探索，这些研究将开始于NSERC参与中提出的研究。我们建议将这些新技术建立在来自大数据的流分析上，这些大数据是通过新方法来流销售和折扣数据产生的。 在这个NSERC ENGAGE研究项目中，我们将利用McGregor在医疗领域的大数据分析研究经验，以及她在澳大利亚零售业的行业经验，在供应链环境中创建一种新的流分析方法，以通过客户分析和回扣分析向供应链中的所有成员提供新的流量方法。我们将首先集中在数据采集和数据传输组件的设计。