Process efficiency to save time
Before TrendMiner, data scientists would manually download and process data, plot results and confirm the last coating layer was uniform. Now machine operators can do all this in one minute.
Get to know ZEISS
ZEISS, founded in 1846, is a manufacturer of optical systems and optoelectronics. With revenues of more than 10.0 billion Euros, ZEISS is active in four business segments: Industrial Quality and Research, Medical Technology, Consumer Markets and Semiconductor Manufacturing Technology. With its headquarters in Oberkochen, Germany, ZEISS is represented in 50 countries, with 30 production sites, and 30 research and development facilities worldwide.
Challenges
- Increased global demand for ZEISS products created pressure to enhance production speed and efficiency.
- No live monitoring of the machines and processes was possible.
- Existing analytics tools were cumbersome and time-consuming to use on the shop floor.
Outcomes
- Improved process understanding, optimized manufacturing workflows and maintenance through data visualization and analysis.
- Empowered engineers and shop floor experts by combining process and analytical expertise for comprehensive overviews, preventing failures, mitigating losses, and enabling efficient shift handovers.
- Condition monitoring increased the productivity, availability, and efficiency of the machines.
- Predictive maintenance has led to a decrease in expensive repair operations and to a better planning of service activities.
Solutions
TrendMiner Self-service Industrial Analytics, including:
- Time-series Data Analytics
- Event Analytics
- Condition-based Monitoring
- Dashboarding & Reporting
- Machine Learning modelling
“TrendMiner has delivered tangible results for us in the industrial sector. The operators and engineers are now empowered to build dashboards that are tailored to their needs and requirements without any assistance from IT, which has resulted in an increase in productivity and efficiency.”
Engineer from ZEISS
Condition-based monitoring reduces downtime
The ZEISS Group manufactures precision optical, medical and microchip technology using some of the most expensive and unique machinery in the world. With production and other data scattered across four business units and multiple unique machines, ZEISS had limited insight into productivity and lacked the ability to predict and prevent downtime.
ZEISS turned to TrendMiner for help. Their engineers wanted to view all the machine data, dig deeper and be able to prevent unwanted events, reduce downtime, boost machine output, and improve product quality.
The executive team was easily convinced by TrendMiner’s proof of concept which enabled visualization of time series data across the entire organization. And use cases showed engineers and operators that they could solve most problems themselves.
“It’s not just about the technology,” the engineer adds, “at TrendMiner we have a customer success manager and in-house we have a data analytics engineer who gives training and holds use case sessions. We are all in this together.”
Some Use Case Examples
Condition monitoring to increase customer satisfaction
Using pattern recognition tooling, ZEISS can identify the data associated with production scrap to save lenses from manufacturing errors. Thus, the high quality standards of the customers can be met and delivery delays caused by faulty production runs are kept at a minimum.
Real-time monitoring of custom-made machines
With TrendMiner, ZEISS engineers can monitor in real time how the machine is performing—reducing downtime with faster fault detection.
Predictive maintenance to reduce reliance on field services
Using TrendMiner, engineers can now spot when the parameters are ‘off.’ They can use indicators to predict the state of the machine’s cleanliness, for example. If a cleaning is needed, it can be scheduled and performed before the problem occurs.
TrendMiner: behind ZEISS technology all the way
Following the successful proof of concept, ZEISS has begun the ramp-up phase where it will harness the power of machine data to create value across the board. Together, ZEISS and TrendMiner are paving the way for a future where data-driven insights drive decision-making processes and propel ZEISS to even greater success.
Value-driven self-service analytics is also expected to promote a culture of empowerment and innovation across various levels at ZEISS, from managers to shop floor personnel.
This effort is supported by a dedicated rollout team, an active TrendMiner user community, and regular, fruitful exchanges between ZEISS and TrendMiner.
The data scientists have also started using TrendMiner’s MLHub to extract even more technical insights, find anomalies and move toward prescriptive maintenance.
But, for now, ZEISS’ priorities are to continue to drive condition monitoring and to champion explorative industrial data analytics.
Blog Posts
Low-code and No-code AI & Analytics Solutions Yield High-Value Use Industrial Use Cases13 September 2024 - 15:19- Building a Foundation for AI & Analytics with an Industrial Data Fabric30 August 2024 - 23:12
- TrendMiner’s 2024.R2 Release; A Complete Platform to Facilitate Event Analytics and Operationalize Industrial Data8 August 2024 - 16:38
