IT/OT Convergence for Increased Productivity and Sustainability
ERLANGEN, GERMANY – How do you collect, understand, and use data from modern machines and legacy equipment? How do you turn data into greater productivity and sustainability, and ensure that every employee can use data in their job? We’ll show you how you can benefit by sharing specific examples from our own production at the Siemens Electronics Factory in Erlangen, Germany.
Innovative ways to use production and energy data
IT/OT convergence plays a huge role in connecting automation with IT technologies like edge and cloud computing. It’s the foundation of comprehensive data use to optimize production as well as improve sustainability. At the Electronics Factory Erlangen, our experts are working on making the connection of modern and legacy equipment as easy and as efficient as possible.
Discover the use cases and benefits in detail
IT/OT convergence is an important step for companies on their way to becoming a sustainable Digital Enterprise. The Electronics Factory Erlangen has successfully focused on use cases that promise the greatest value and created practical solutions for daily use as well as proofs of concept for future solutions.
Production and productivity optimization
As a Digital Enterprise, the factory is constantly working to optimize its availability and efficiency. Using Industrial Edge devices, it’s possible to evaluate log files from machines and determine operation modes, availability, quantity and quality. With a simulation model running alongside the actual plant, allowing to compare malfunctions and other KPIs between machines by using real production data. Dashboards, which can be also easily created with our low-code platform Mendix, show the actual disturbances that have affected productivity and allow to target these bottlenecks. It’s no longer just data scientists that can use the data with the citizen analytics approach. Thanks to Mendix, everyone is able to combine their domain expertise with IT capabilities to make confident decisions. The first realized process optimization already yielded a productivity increase of 4% with many more to come.
Integration of Artificial Intelligence to continuously optimize cleaning rates
Our closed-loop vision involves using current process and quality data to dynamically control our production processes to reduce non-productive times, for example by adaptively adjusting the cleaning rate in solder paste printing. The question is how often to clean. If you don't clean often enough, you'll have quality problems; if you clean too often, you'll have problems with uptime and system efficiency. This example applies to many other applications. Therefore, the right cleaning rate must always be chosen in order to clean as often as necessary, but as little as possible. The solution is using Industrial Edge devices to run the AI model and to capture process data along the entire value chain.
Reduction of manual work in quality inspection
The Electronics Factory Erlangen uses automatic optical inspection (AOI) in the SMT production to monitor the quality of the production process. If the AOI detects that a component is offset, it is ejected. An operator then evaluates the board to determine if it is good or if it needs to be repaired. There are cases where the AOI detects an anomaly, but the operator still judges it to be good quality – so-called false calls. Industrial AI can reduce the number of false calls and send good parts directly to the next manufacturing step, reducing the operator's workload. AI algorithms can identify 60% of false calls, reducing manual labor by 60%.
