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Machine Learning in Retail Demand Forecasting

Duration: 45 min + Q&A

To ensure smooth operations and high margins, large retailers must stay on top of tens of millions of goods flows every day. At the center of this storm of planning activity stands the demand forecast. Generating an accurate forecast is actually quite simple under stable conditions, but in the inherently dynamic retail business hundreds of factors impact demand on a continuous basis.

Machine learning makes it possible to consider the impact of these factors at a detailed level, by individual store or fulfillment channel. In this webinar, our data scientists and retail planning experts will explain what machine learning is, what kind of challenges it solves, and why so many retailers today are transitioning toward machine learning-based demand forecasting.

Key takeaways: 

  • What is machine learning?
  • What retail demand forecasting challenges does it solve?
  • Case examples of how machine learning is used in retail demand forecasting
  • How to make machine learning work for your retail demand planning


Your Webinar Speakers

Henrik Aalto - 300px


Henrik Aalto
Data Scientist

Henrik Aalto is a Data Scientist at RELEX Solutions. He focuses on bridging the gap between data and people by collaborating on the development of state-of-the-art machine learning in terms of both computational and operative aspects. His experience in implementation projects, dynamic pricing, and marketing mix modelling provide him invaluable insights when building our best-in-class AI and demand forecasting solutions.



Josh Mann
Project Owner

Josh Mann is a Project Owner at RELEX who leads and assists with our largest customers in North America. He comes from a retail background, having worked in various Supply Chain roles at Marks & Spencer in the UK, and is focused on helping retailers add measurable value through the application of a pragmatic approach to AI.

Rachel Dutton Circle


Rachel Dutton
Solution Expert

Rachel Dutton is a solution expert at RELEX. During her two years with the company, she has worked closely with some of our largest Tier 1 retail customers in the US. She focuses on helping retailers across a wide range of industries improve their business outcomes through supply chain and markdown optimization.