We’ve partnered with the This Week in Machine Learning & AI podcast for a 7 part series on Industrial AI. Check out Episode 6 below and download our latest paper exploring the unique challenges and requirements of Industrial AI.
In Part 6 of TWIML AI’s Industrial AI podcast series, Sam Charrington sits down with research scientist, Calvin Seward, to discuss how he used deep learning to optimize warehouse operations at one of the largest e-commerce companies in Europe.
At Zalando, Calvin used machine learning to determine the optimal way for workers to move about a warehouse filling carts with goods to be shipped to customers. Calvin discusses these techniques in great detail, including how they progressed from data analytics to deep learning, and how they used simulations to generate the data with which they trained their neural networks. He also touches on how his team leveraged the cart picking solution to create new products, and how managers can start integrating machine learning technologies within enterprises.
“There’s a progression in companies. The first thing they do is use all the data they have sitting around to drive efficiency. They take existing processes and make it a little bit more efficient with data science and machine learning. And then you can go to the next thing where you create new processes that drive efficiency with data science.” - Calvin Seward
Listen to the full conversation with Calvin in the link to the AI podcast below. To learn more about how you can build reinforcement learning models for warehouse and route optimization in your own organization, visit bons.ai.