Machine Learning-enhanced approaches to optimization of supply chain management at Nestlé Canada

雀巢加拿大采用机器学习增强方法优化供应链管理

• 批准号: 538626-2019
• 负责人: Lee, ChiGuhn
• 金额: $ 4.9万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=691401
• 关键词: Machine; Learning; enhanced; approaches; optimization

Nestlé Canada is handling very large numbers of claims from their retail customers for delays in shipping and receiving of products, and these claims have substantial financial and human resource impacts on the company. Advanced systems are therefore desired for evaluating these claims and for improving processes to better comply with supply agreements. This project aims to use innovative mathematical and machine learning techniques to develop appropriate decision support tools. One tool will categorise claims to improve understanding of the reasons for noncompliance, while the other tool will help manage inventory to enable improved compliance. The work will be supervised by three professors with expertise in supply chain management, advanced methods in data analysis, and business process optimization. The research team includes one postdoctoral fellow, one Master's thesis student and four Master's non-thesis students who will work with industry representatives to apply results obtained and methods developed from their research. The expected outcomes include more efficient supply chain processes at a very large business operation in Canada, which improve its competitiveness and financial returns.

雀巢加拿大公司正在处理大量零售客户因产品运输和接收延迟而提出的索赔，这些索赔对公司的财务和人力资源产生了重大影响。因此，需要先进的系统来评估这些索赔，并改进过程，以更好地遵守供应协议。该项目旨在使用创新的数学和机器学习技术来开发适当的决策支持工具。一个工具将对索赔进行分类，以提高对不合规原因的理解，而另一个工具将帮助管理库存，以提高合规性。这项工作将由三位教授监督，他们具有供应链管理，数据分析和业务流程优化的先进方法的专业知识。该研究团队包括一名博士后研究员，一名硕士论文学生和四名硕士非论文学生，他们将与行业代表合作，应用从他们的研究中获得的成果和开发的方法。预期成果包括加拿大一家大型企业的供应链流程更加高效，从而提高其竞争力和财务回报。