Current production industry is designed for large lot sizes. Due to the shorter product lifecycles, manufacturing industry needs to shift its focus. Rapidly changing market requirements demand efficient changeable production processes. Furthermore, customers are increasingly attracted to tailored products that yield a much higher value creation than mass-produced goods. A major challenge for process changes is the necessary reprogramming of Programmable Logic Controllers (PLC). PLCs implement automation programs that execute pre-programmed sequences, optionally supporting a pre-defined amount of flexibility.
Walking around the shop-floor to manually query machine states is inefficient and prevents operators from efficiently obtaining a holistic view on the complete production process. Streaming of live production data to IT on the office-floor remains a challenge that prevents efficient monitoring and understanding of production processes. The BaSyx Virtual Automation Bus (VAB) enables machine to machine communication across the layers of the automation pyramid; shop-floor devices can directly interact with enterprise resource planning (ERP) systems, even if they use different protocols.
Industrie 4.0 is about more efficient mass-production processes, and mass-customization, the efficient and inexpensive production of small lot sizes. Industrie 4.0 is also the end-to-end digitalization of manufacturing and enables new business models. The following exemplary Industrie 4.0 business models can be implemented with Eclipse BaSyx. Data BrokeringData produced in manufacturing processes is extremely valuable for equipment manufacturers and other third-party businesses since it enables prediction and optimization of manufacturing processes and product quality.
Every factory device creates a continuous stream of data. Data analytics can provide valuable insights into production processes: Which variables have impacts on production cost and quality? How can we predict production performance? Where is hidden potential for optimization in my manufacturing processes? Data analytics requires the combination of data from different sources, i.e. production devices, as well as IT servers. The ability of Eclipse BaSyx to structure machine and product data in Asset Administration Shells, enriching both with semantic information, then combining them, is the enabler for big data analysis.
Repairing a device once it’s ‘broken’ can lead to unpredictable and expensive downtime. Scheduled maintenance improves the situation, but a static maintenance schedule can cause unnecessary interruptions. In the worst case, required parts need to be ordered, extending downtimes significantly. Predictive maintenance solves these issues: Combining live sensor data and past experience enables prediction of necessary maintenance activities and schedules. Production processes can then be adapted to synchronize and integrate maintenance activities with the least possible impact.