There is a lot of buzz around AI and Machine Learning, but how can you apply them in a practical way to improve your logistics? How can you create the bridge between theory and practice? In this blog, we explain four practical examples based on various use cases with customers, which can be applied in various industries. Two of them are pre-operations, used to simulate alternative scenarios and generate input for daily operations. The other two are post-operations, where we learn from the execution to continuously improve your business. By combining pre- and post-operations, you create a learning loop to improve your efficiency and service.

We have all seen the tremendous value of data analytics on the success of commercial organizations. Now, how about humanitarian and societal challenges? As one of the founders of the Analytics for a Better World Institute, ORTEC believes that analytics can also contribute to solving major challenges facing humanity - from hunger to deforestation and the extinction of flora and fauna, among others. For our ‘Jointly for Social Impact’ series, we sat down with various nonprofit partners to hear from them how data analytics contributes to a better world. Below you'll find a recap of this series, highlighting the impact stories of Analytics for a Better World, Amref, 510/Red Cross, The Ocean Cleanup, and Force For Nature.

Shell has been generating scenarios for over half a century, and Energy Fundamentals Manager Martin Haigh has played a significant part in that for the last eighteen years. “Scenarios came to fame during the OPEC oil crisis of 1973. The capability of modelling has moved on tremendously over time, with computing power and the availability of data. Nonetheless there’s been a continuous debate: to what extent can modelling play a role? There has always been a role for quantification. A company runs on numbers in a lot of ways. But there is a danger in putting too much emphasis on numbers in scenario work. It may cause you to lose sight of what is driving change in a system. There is a tension, but also a balance to strike.” Hence the distinctive, holistic Shell approach.

The manufacturing industry has been experiencing a growing complexity in the logistics of finished goods, posing significant challenges to companies seeking to reduce costs, improve efficiency, and meet the ever-changing market demands. In this article, we will examine the key trends and challenges faced by finished goods logistics in the manufacturing industry, with a focus on sustainability, process efficiency, employee and quality engagement, and digital transformation. We will also explore how data and mathematics can be leveraged to navigate these challenges, gain a competitive edge, lower costs, and improve sustainability.

As a manufacturer, you need to deliver all types of products (SKUs) with their own delivery and demand patterns to your customers, such as retailers or other manufacturers. How do you determine the right amount of each product to deliver at a specific moment in time, maximizing load utilization and minimizing handling time, while also meeting requirements like transport restrictions, demand, inventory levels, and service agreements? Add to that the added complexity of customer requirements, warehouse picking and loading strategies.

In this article, we'll explore the opportunities to create shipments with high load fill through order optimization and the significant benefits it brings. By doing so, you can revolutionize your shipping policy to your customers. Although optimizing on demand level may not be feasible for all your customers, you can start by implementing it internally for inter-company replenishments.