Deep learning for online retail - from convenient mobile to smart IoT shopping
Daniel is the CTO of Picnic, the world’s fastest growing online supermarket that makes grocery shopping simple, fun, and affordable for everyone. We migrated recently our systems landscape to a ML-driven farm-to-fork platform. Check out more here. Previously, he was Director R&D of Fredhopper, responsible for the product and technology roadmap, and led engineering teams located in Amsterdam and Sofia. Daniel holds a PhD in Computer Science and an MBA. He published papers, edited journals, and chaired international workshops on software correctness.
Deep learning for online retail – from convenient mobile to smart IoT shopping
Why should you see this?
In this talk, we provide a view behind the scenes of our deep learning based behavioural analytics and prediction engine. We will talk you through the ups-and-downs of product, category and promotional recommendations of FMCGs and do not shy away from demoing also the failures. Now we are able to predict with >95% likelihood the top 12 articles of the next order of each customer.
In the second half of this talk we will make a deep-dive into the main challenge of e-commerce fulfilment: deliver under uncertainty (real-world physical operation) with ultra-high accuracy (99% on-time) in narrow time windows (20 min). We will demonstrate how our fleet of real-time connected vehicles, smart planning algorithms, precise monitoring tools and predictive distribution models create an unprecedented distribution logistic system for groceries. Then we provide a look behind the scenes of our smart planning algorithm that broke world records of the famous travelling salesman problem (TSP). Finally, we deliver insights into the architecture and design of our distribution eco-system (planning, monitoring, execution, control components) and explain how we made them ready for autonomous distribution and self-driving vehicles.