Case Studies

Predictive maintenance is fundamental to Industry 4.0. The possibility to process IoT data in real time and run machine learning algorithms to predict to optimal maintenance time is a very valuable. Predictive maintenance helps you make better use of your resources and thus increase your productivity. This gives you an advantage over those who still perform preventive maintenance.

In this case study we will show you how you can optimize your contribution margin with our platform. S&OP (sales and operations planning) has never been more important in the wake of digitization. Some companies are trying to catch up with digitalization and some want to be the pioneer. In both cases, our DSML (data science and machine learning) platform will bring them a significant step forward. It’s all about faster and easier use of data science.

Process analytical technology (PAT) is used to design, analyze and control product development processes. The goal is to identify all critical process parameters (CPPs) and quality attributes (COAs). Quality by design (QbD) targets a guaranteed quality for product developments. QbD methodology leads efficient to predict quality attributes through mathematical models instead of conducting physical tests and measures.