Use of test data for higher throughput in production
Due to increasingly complex products and production processes as well as an increasing number of product variants, the demands on quality assurance are rising. Test procedures play an essential role in this. However, testing times are limited: Critical and error-prone parts need to be detected. This can hardly be achieved with a static testing approach. In this context, a smart quality assurance is not only important in order to compete in the market, but also has the potential to significantly increase productivity. This can be achieved by an intelligent service that reduces test efforts while maintaining quality.
The digital transformation of quality assurance: Optimizing quality and meeting requirements with the scalable three-phased approach. You decide about every step.
During operation, we continuously monitor and adjust the testing effort to meet the customer-specific risk profile. A direct link to process parameters is not necessary - the analysis of test results is sufficient. This data is used to evaluate the probability of defects based on the distribution of a test variable. These reduction potentials are updated cyclically and can be reported directly back to manufacturing. At this point, they are made available to the decision-makers on a consistent basis.
How you benefit from our Test Effort Reduction with Artificial IntelligenceArtificial intelligence and machine learning are having a huge impact on the future of the digital factory and its manufacturing process. Technologies like Big Data, Smart Data, Edge Computing and IoT are setting the pace for future industries. Leverage your existing test data and get rid of time-consuming test efforts. Optimize your production with artificial intelligence and redesign the source of value creation.
What were the customer's challenges?
High product complexity and extensive testing processes prevented continuous evaluation of process stability. This led to high efforts in quality assurance.
What did our implementation look like?
By processing the collected data, we were able to visualize quality data and inspection thresholds, which led to a quality transparency. Further processing of the data in our algorithm provides a recommendation for inspection reduction on parameter level. Continuous monitoring of test thresholds make it possible to react to changes in the production process and to adapt the test processes.
What are the benefits for the customer?
By using the Test Effort Reduction Service, it is possible to reduce the test effort and still deliver consistent quality. By dynamically adjusting test efforts and focusing on critical components, the customer can react flexibly to changing circumstances. Real-time quality transparency provides the customer with detailed information about changes on the store floor and full traceability of all decisions.