Uwe Kobbert

Since 1989 I have been working on a single question:
How do you make production processes measurable, controllable and better? Back then as a consultant at SAS, later as Head of Industry at STERIA, where I was responsible for process control systems and Manufacturing Execution Systems for the food and beverage industry.

In 1999 I founded SYMESTIC in Dossenheim near Heidelberg.
The conviction back then was the same as it is today: manufacturing companies need transparency about what happens on the shopfloor. Without real-time data, downtime stays invisible, losses are estimated instead of measured, and decisions are based on gut feeling.

In the early years we delivered classic MES projects – on-premise, individually configured.
Server rooms, lengthy implementations, high-maintenance operation. It worked, but it was slow and expensive. Especially for mid-sized companies, getting started with professional production data collection was often too complex and too costly.

The strategic decision that transformed SYMESTIC came in the mid-2010s.
We stopped moving existing software into the cloud and instead built a new platform from the ground up. Cloud-native, on Microsoft Azure, with a microservice architecture and an API-first approach. Not lift-and-shift, but a fresh start.

The result: the SYMESTIC Cloud MES platform.
Today it is in use in around 20 countries across five continents. More than 15,000 machines are connected – from injection moulding machines in the automotive industry through CNC machining centres in metalworking to packaging lines in the food industry. The platform is fully self-funded, without external investors.

What drives me:
Over more than three decades I have seen hundreds of production floors from the inside. Every company believes it knows its own production. But almost every time we switch on the first automatic data collection, a different picture emerges than expected: availability is lower, micro-stops are more frequent, changeover times are longer than assumed. Not because employees work poorly, but because without data, perception is systematically distorted.

Solving this problem – that is what I work on every day.