- Complex factory with 100+ different CNC machines and multiple controllers
- Litmus connected to most CNC machines out-of-the-box and developed two custom drivers
- Visualized the data with Litmus dashboards for real-time machine status monitoring
- Large Andon board shares machine status with all stakeholders on the shop floor
- Achieved condition monitoring, machine optimization and OEE improvement
A large tool manufacturer has a complex factory with more than 100+ different CNC machines and 5 different CNC controllers including FANUC, Heidenhain, Mitsubishi, Roeders and more. With no standard way to connect to the shop floor, they were leaving important data intelligence locked in machines. The customer aimed to fix that problem by implementing a CNC monitoring system across all facilities to determine both machine status and part quality.
The customer chose Litmus Edge as the edge infrastructure and data connectivity solution to connect to all their CNC machine types and capture complete machine data. Litmus connected to most out-of-the-box, and rapidly developed two new drivers in a matter of weeks to connect to another two machine types to meet the customer’s specific needs. Litmus allowed the customer to integrate the data to a Grafana visualization platform they built for machine status monitoring and they began to successfully visualize data immediately.
Litmus Edge Manager, allowed the customer to easily scale the solution with a centralized management platform for bootstrapping, data consolidation, dashboarding and visualizing all aggregated data in one location. The platform also handled license and firmware management, application deployment and data storage in Litmus Edge Manager.
The customer achieved condition monitoring, machine optimization and OEE improvement for 100+ different CNC machines, with plans to expand the use case to additional plants. They display all visualization on a large Andon board to share machine status with all stakeholders on the shop floor. The early phase of the use case focuses on monitoring, and they plan to continue to expand on utilizing the data to improve the health of machines, reduce scrap and improve utilization rate.