Life is for learning and we all try to continuously improve and learn from our mistakes. In a company that really works together as a team, we can also benefit from the experience of others. Learning together and getting better together – continuous improvement can achieve this.

Most companies are already aware of the potential for improvement. But how can optimization potential be identified and quantified? This is where optimization comes in and helps to replace gut feeling with numbers.

We like to treat solvers as magical black boxes, but it’s worth understanding their basic principle. In this episode, find out more about how solvers can be used to find our optimum incredibly fast.

A good set of data is the core of every optimization project. But why is so crucial, to use real data already when still implementing the software? Let us share some insights we gained on data during our optimization projects.

Creating tournament schedules seems simple, but in practice it can be quite complex. It’s also a great example of optimization. Learn more about the details and tricks of creating tournament schedules in this episode.

At first glance, infeasible problems present a major challenge. Where do the problems come from and how can you recognise and solve them? In this episode, we will explain why you shouldn’t panic about such problems and what options there are for solving them. Have fun with it!

The management of complex production networks is demanding. Often, the necessary transparency is lacking to coordinate demand and production capacities in such a way that goods availability and production costs are in an optimal relationship. How production networks work and how they can be optimized with the help of advanced analytics is what our colleague Marc Johann talks about in his premiere as the second host of “One more thing about…”. Enjoy!

It seems virtually impossible to find a model for our decision-making which takes all details into account. In this episode we demonstrate how data abstraction can help to model a problem, thus making it the core of successful optimization.

“Let’s use the cloud to solve it faster…” Sounds easy, but does it really meet the expectations when it comes to solving optimization problems? It actually can, but there are some aspects to think about before and we show which ones they are.