Demand forecasting solution

RELEX’s demand forecasting software takes full advantage of your data – internal and external – using pragmatic AI to give you the most accurate forecasts. Integrating seamlessly with your space, assortment, replenishment, promotion, inventory and capacity planning it keeps all your core processes aligned.

RELEX’s demand forecasting software makes your data work for you

RELEX’s combination of pragmatic AI and outstanding computational power allows for optimized demand forecasting that automatically factors in the impact of all known external events, such as holidays, seasons, trends and even weather forecasts, as well as planned changes in your assortment, pricing, store-space allocation, stock policy and promotional activities.

Andrew Rafferty

“We’re a lot more capable of forecasting at SKU level, line level and store level than we ever were before”

Andrew Rafferty
IT and eCommerce Director, Booths

Don’t just forecast demand, shape it

Demand doesn’t occur in a vacuum. External factors, like weather, have an impact and your demand forecasting needs to take account of them, accurately. Yet, many demand changes are self-determined. By closing gaps in how you forecast and reflect the effects of your own actions, such as planned promotions, in all other facets of your business planning, you can significantly increase forecast accuracy and, even more importantly, ensure your plans are executed coherently.

Forecasting icon

Optimal demand forecasting

Optimized forecasts for each store or channel, item and day, or even intraday, are generated by pragmatic AI enabling effective use of both internal and external data.

seasons icon

For all

Built-in functionality makes planning and managing situations such as promotions, product ramp-ups and ramp-downs, seasons and holidays a lot easier.

multi-echelon icon

Multi-echelon planning

As upstream forecasts build on the regional depots’ or stores’ order forecasts, all data about end-customer demand and planned changes in the network is included automatically.

pragmatic AI icon


The system makes use of all the latest machine learning and advanced statistical techniques without your planners having to be data scientists to understand or trust the results.

omni-channel icon

Omni-channel planning

Support for multi-channel models, such as buy online and pick-up in store. Sales can be credited to the selling channel while forecasting replenishment for the supplying location.

flexibility icon

Flexible editing of forecasts

Demand forecasts are always available on a store/channel and day/item level, but users are free to edit them at any level of granularity or aggregation and by custom groupings, such as search results.

Superior performance icon

Superior performance

RELEX has the superior computational power to run pragmatic AI on the massive amounts of data accumulated in retail to give retailers visibility hundreds of days ahead on any level of granularity.

collaboration icon

Collaborative forecasting

The sharing of demand or purchase forecasts with suppliers can be automated and partners can be invited to develop forecasts in collaboration with your planners.

Highlighted features of RELEX’s demand forecasting software

Better promotion forecasting with richer data

Promotions matter in retail, yet they are notoriously hard to forecast as so many things can affect the results. Our demand forecasting software uses pragmatic AI to consider a wide range of factors, such as timing, product and campaign type, marketing effort, in-store display, and pricing strategy to deliver the most accurate forecast for each promotion, item and sales channel or store.

Machine learning captures the impact of weather

Weather can have a huge impact – a hot weekend will cause sales of barbecue items and drinks to rocket. Our demand forecasting software integrates with weather forecast providers to take in up-to-date weather forecasts for every location. It uses machine learning algorithms to analyze the impact of different weather variables on the sales of each item and updates sales forecasts accordingly. To avoid the risk of forecast-nervousness and to allow for informed planning decisions, a baseline forecast calculated without weather forecast information is always available and the user can choose which forecast to apply.

New product introductions

In retail, thousands of new items are introduced every year, and you need to be able to generate good forecasts for all of them – for each store or sales channel. Manual forecasting methods and approaches based on manually setting reference items for new products are rarely adequate, so RELEX has built an auto-reference model that automatically identifies the best available reference product for each new item, based on attributes such as price point or brand. To further improve forecast accuracy, a ramp-up profile modelled based on past introductions can be applied. Read our Measuring Forecast Accuracy: The Complete Guide where we explain the facets of forecasting more extensively.

Case studies

RELEX uses machine learning abilities to include airport passenger numbers into the forecasts so that WHSmith can use airport traffic figures to predict demand.

Read case study

Lumene, the leading manufacturer of skincare and cosmetic products in Finland, utilizes RELEX for more efficient sales planning bringing remarkable improvements.

Read case study

The multinational manufacturer and distributor of alcohol products, Altia, has worked with RELEX to improve forecast accuracy by 9 percentage points.

Read case study

“It’s great to have found a solution provider that is eager to tackle new challenges head on. When we mentioned the opportunity to use airport traffic numbers, the RELEX team was quick to find an innovative way to utilize this data in our demand forecasts.”

– Jag Banwait, Merchandise Controller, WHSmith

Would you like to know more?

We are happy to discuss your retail optimization needs further, and show how sharpening your demand forecasting and planning would streamline your daily operations.

Contact us

Learn about implementation