RELEX Named a Leader in the 2025 Gartner® Magic Quadrant™ for Supply Chain Planning | Read the report

Case study: One Stop

With RELEX's machine learning forecast, One Stop has enhanced its daily forecast despite the complex weather-driven demands and increased in-store availability without spoilage corresponding.

Case study: Atria

Atria significantly improved their forecast accuracy and stability after implementing RELEX machine learning-based forecasting capabilities.

Case study: DOUGLAS

DOUGLAS, the leading beauty platform in Europe, has significantly improved its forecast accuracy and product availability with RELEX's AI-driven forecasting and replenishment solutions.

The RELEX product roadmap

In this video, our expert Josh Mann gives a view into the RELEX product offering and explains how we help retailers plan better, sell more, and waste less.

Retailers must centralize their data to thrive

It is more important than ever that retailers collect data from all channels and use it to manage the flow of goods through the supply chain to the point of customer interaction.

Breaking down functional silos

By now, everyone knows that “Silos” is the new bad word in business as it creates critical weaknesses. Find out how to break out from silos to empower greater transparency, collaboration, and information sharing.

A pragmatic guide to AI & machine learning

Our expert Josh Mann explains what machine learning is, what kind of challenges it solves, and why many leading retailers are starting their transition toward machine learning-based demand forecasting.

Introducing RELEX Labs

Welcome to RELEX Labs, our research engine at the core of our company working closely with our customers to develop the future of retail optimization.

Grocery store

Case study: S Group

A leading Finnish retailer piloted RELEX’s AI-based main delivery day optimization functionality to take replenishment to the next level.

Pragmatic AI for better retail decisions

With AI hype going on, many forget that AI doesn't matter, but the results do. We now use pragmatic AI when describing how we use data for better decisions.

Decision science and pragmatic AI in retail

Most retailers are rich in data, but access to data does not automatically enable retailers to make better decisions faster. This is where decision science steps in.

Future-proof your technology strategy

Retailers are no longer interested in major system replacements. Instead, there's a trend toward implementing in parts rather than as a whole.

Where should a retailer spend its technology budget?

With spending on IT coming under more pressure, not necessarily because of reduction in budgets but more from the increasing number of projects a CTO is faced with, where should a company focus its attention?

Beehive

Big data – Big talk or big results?

Big Data isn’t a magic bullet but there are some impressive gains to be made in traditional operations, such as supply chain management.