Intelligent, AI-based linking and analysis of supply chain data can generate valuable information for quality management.
Vehicles are highly complex products that are developed and produced over a long supply chain. With each logistics step in the supply chain, the amount of data about the product, the production process and the delivery increases. The data volumes very quickly reach such a size that they can only be meaningfully evaluated using statistical methods and AI-based analyses.
Intelligent, AI-powered quality platform.
The AQI has identified this problem as a focal point for itself for years and has already developed a quality platform that offers numerous functionalities with the help of artificial intelligence (AI), such as:
Intelligent data analysis
Pattern recognition of faults
Tracing of production information
Prediction of production failures
The quality platform makes it possible to identify and analyze the causes of errors in data in a quality-assured manner - both within and across companies.
After many years of experience in the field of 'Intelligent Data Integration and Analysis’, the AQI has developed Q-miner, a branch-neutral platform for combining the evaluation of quality data. The Q-miner platform enables quality-assured identification and analysis of failures causes in data – both within and across companies.
Use of AI in testing processes
AI algorithms have a wide range of applications and enable solutions that would be difficult to implement using traditional approaches. In addition to the use of AI algorithms directly in products, AI algorithms can also be used to improve quality processes in companies. The current project aims to evaluate how AI algorithms can be used in testing processes in the automotive industry.
Exploration of application scenarios for the intelligent processing of quality data
The project investigates the use of AI models for structured and unstructured data in order to assess their added value for quality tasks. Suitable use cases and roles are identified and aspects for successful implementation are considered.
