Retail is impacted by many factors, such as weather and cannibalization, that can be taken into account with the help of machine learning.
Using AI to optimize main replenishment days enables more efficient goods handling and store operations in retail distribution.
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.
Great data processing delivers great business value. Still, RELEX is one of very few solution providers that has opted for developing our own database and database engine. Why is that?
As a retailer grows and matures, they will inevitably find themselves, at some point, needing to invest in merchandising systems. But which capabilities to prioritize?
How RELEX combines time-series, regression and machine learning forecasts for best results? Take a look at the different forecasting approaches that can be used to achieve great levels of accuracy in diverse situations.
In this whitepaper we show how our business rules engine works, and introduce best practices for using it.
A typical mid-market retailer must wrestle with all the retail supply chain planning complexities that call for a top-notch solution.
Where should a CTO prioritise his spending? Lets look at the opportunities and risks for the company.
The RFP-process is not well suited for selecting SaaS-solutions for a number of reasons.
According to The McKinsey Global Institute applying big data solutions to supply chain management can add between 5% and 35% to operating margins.