How to Improve your Supply Chain with Deep Reinforcement Learning

How to Improve your Supply Chain with Deep Reinforcement Learning

How to Improve your Supply Chain with Deep Reinforcement Learning. Using Ray and DFO to optimize a multi-echelon supply chain.

What has set Amazon apart from the competition in online retail? Their supply chain. In fact, this has long been one of the greatest strengths of one of their chief competitors, Walmart.

Supply chains are highly complex systems consisting of hundreds if not thousands of manufacturers and logistics carriers around the world who combine resources to create the products we use and consume every day. To track all of the inputs to a single, simple product would be staggering. Yet supply chain organizations inside vertically integrated corporations are tasked with managing inputs from raw materials, to manufacturing, warehousing, and distribution to customers. The companies that do this best cut down on waste from excess storage, to unneeded transportation costs, and lost time to get products and materials to later stages in the system. Optimizing these systems is a key component in businesses as dissimilar as Apple and Saudi Aramco.

A lot of time and effort has been put into building effective supply chain optimization models, but due to their size and complexity, they can be difficult to build and manage. With advances in machine learning, particularly reinforcement learning, we can train a machine learning model to make these decisions for us, and in many cases, do so better than traditional approaches!

optimization data-science deep-learning supply-chain machine-learning

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