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Despite advanced supply chain planning and execution systems, manufacturers and distributors tend to observe service levels below their targets. This can be explained by unexpected deviations from the plan or systems that are not properly configured. Quite often it is too expensive to have planners continually track all situations in supply chain systems at a granular level to ensure that no deviations or configuration problems occur. We propose to develop a machine learning/artificial intelligence (AI) system that predicts service level failures a few weeks in advance and alerts the planners. It will help save organizations millions of dollars by preventing service failures and provide an optimal recourse action.
Apurva Narayan
Pradeep Kumar Mahato
Rich Products Of Canada
Computer science
Manufacturing
University of British Columbia Okanagan
Accelerate
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