Within the manufacturing domain, we combine our customer’s factory knowledge with our knowledge of data/AI, cloud, software, security, and manufacturing systems. This way, we automate factories, reduce downtime, save energy, predict maintenance, reduce waste, and increase the productivity of factory employees. In a pilot with Omron and the Brainport Innovation Center (BIC), we were challenged to automate & improve a specific part of the Omron factory in ‘s Hertogenbosch.

Omron aimed to increase the productivity of a production line, so that they could meet their product’s growing demand without investing in expansion. In this pilot, we took a first step together by creating production line insights.

Open standards

In order to create meaningful insights, we made use of open standards, forming a universal framework that enables seamless communication across all machines. A typical factory environment houses a diverse array of machinery and data streams, often integrated over time, each with its unique communication protocol or "own” language.

To effectively analyze machine data and draw meaningful comparisons, it is crucial to have equivalent parameters across all machines. For instance, when comparing uptime, we must guarantee that 'uptime of machine A' is defined and measured identically to 'uptime of machine B.' These open standards not only facilitate the automation of individual machines but also pave the way for elevating the performance of entire production lines.”

Our solution

For Omron’s production line, we first defined a blueprinting method to translate original PLC states to standard PackML machine states. PackML is a common language for machine parameters, so that we can compare ‘uptime’ or ‘idling’ across machines. Then, we applied this to a machine via one-time manual mapping. On top of the PLC states, we also applied PackML tags on sensor metrics related to the output and quality of the production line. This way, we could start analyzing and optimizing the expected and actual uptime, performance, and output across machines and production lines.

We used an OPC-UA server on an IoT edge device to gather and publish the standardized data and transmitted it to the cloud via MQTT. In the cloud, we further processed the data into OEE (Overall Equipment Effectiveness) KPIs and visualized them in a dashboard.

The OEE dashboard provides the line and factory managers with actionable insights into availability, quality, and performance. This allows them to tune their processes and settings in such a way that they can increase yield, reduce costs, and improve quality.

Tim Foreman, R&D Omron Europe: “If there is one thing that I’ve learned from this pilot, it’s that, when you’re planning to start with factory automation, always start smaller. Each time you think: this is small enough. But most of the time, the complexity of a small part is bigger than you think. Important, however, is that you focus on your biggest challenges, try to balance and learn, and use your learnings to scale up and apply it to other parts of the factory.”