Case study: Rossmann
RELEX features enabled Rossmann to increase efficiency, cut back on in-store shelving work, and relieve some of the pressure on logistics while reducing inventory and out-of-stocks.
Improve demand forecasting accuracy by factoring in weather impacts
Weather-based forecasting is challenging, but those retailers who can accurately forecast when the weather will affect sales can capitalize on increased sales opportunities and prevent excess stock and costly spoilage.
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.
Machine learning in retail demand forecasting
In this webinar, our our data scientists and retail planning experts 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.
The long-term strategy: Transforming operations for sustained online growth
During the coronavirus crisis many businesses saw an increase in online ordering. This trend won't go away once the crisis has passed - people are discovering the convenience of online ordering.
The short-term strategy: Managing an increase in your online orders
While a sudden growth in online sales presents an opportunity to recoup lost in-store sales, it also poses a significant operational and logistical challenge.
Strategies to address demand variation between essential and nonessential goods
RELEX’s best practices for managing demand for essential and nonessential items within a single environment during these unusual demand patterns.
Study highlights four grocery businesses improving retail sustainability
Fresh items attract customers, yet grocers lose over 1,5 percent of revenue to food waste. Waste reduction is not just about sustainability, but it also contributes to a healthier balance sheet.
Make a difference: Cutting food waste and carbon footprint through optimized forecasting and replenishment
This study outlines how our four customers have led the charge toward environmentally responsible business practices while significantly improving both operations and cost-effectiveness.
Case study: Europris
Norway’s largest discount variety retailer achieved over 17% cut in DC inventory in just 18 weeks.
Case study: Suomalainen Kirjakauppa
Finland’s leading bookseller uses RELEX's Markdown solution to optimize stock levels and largely automate the markdown process.
Case study: Coop Värmland
By linking shift planning to demand forecasts and the incoming delivery schedule, the Swedish grocer has been able to reduce personnel costs by 6-8 %.
Stochastic planning for more resilient and cost-effective supply chains
Stochastic planning has been presented as a result for tackling uncertainty in supply chains. Our whitepaper explains what it is and when and how to use it.
Machine learning in the context of retail demand forecasting
Retail is impacted by many factors, such as weather and cannibalization, that can be taken into account with the help of machine learning.
Case study: Arena
RELEX has played a key role in supporting the evolution of Arena’s supply chain processes towards a more holistic control of its entire customer-fulfilment chain.
Case Study: Oda with RELEX
With RELEX, the largest Norwegian online fresh food retailer Oda has seen a 49% reduction in spoilage value and a 25% increase in inventory turnover.
Case study: Musti Group
RELEX helped the leading pet supplies retailer in the Nordics to integrate its complex and ever-changing supply chain, which is now managed by a single super-user.
Case study: Akademibokhandeln
On top of Akademibokhandeln's list of requirements were full centralization of its replenishment and more effective control of the Christmas sales peak.
Case study: Saarioinen
RELEX combines demand forecasting with financial and production planning to support Finnish convenience foods manufacturer's unified S&OP process.
Case study: WHSmith
WHSmith uses RELEX's machine learning to optimize weekday profiles and manage multiple daily deliveries, resulting in decreased spoilage and increased availability.
Case study: Apoteksgruppen
RELEX has provided the Swedish pharmacy chain with highly effective tools that support decision making, optimize reordering and improve the organization’s control over its entire product range.
Case study: Hankkija
Finland’s premier agricultural store chain Hankkija overhauled its entire procurement process, set up a new DC and built its purchasing organization around it.
Case study: Galexis
The Swiss pharmaceutical wholesale dealer, implemented RELEX to increase automation, maintain availability at close to 100%, and at the same time lower inventory.
Case study: Granngården
Swedish retail chain in the pets and gardening sector balances stock between online and stores through virtual ring-fencing.