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

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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.