For companies (SMEs and global players)
Product defects must be detected at an early stage – ideally before they occur – and remedied in good time. A fragmented IT infrastructure with decentralized data sources, be it internally or beyond the supply chain, will hardly be able to meet these requirements.
Q-miner develops processes for digital transformation for both internal and cross-company interfaces and their analog equivalents.
Q-miner is a central online platform for digital data exchange for linking and evaluating quality data.
Q-miner digitizes and checks data, processes it qualitatively, recognizes patterns and automates them for improved data optimization and security.
Q-miner enables the use of quality-assured data to identify and analyze causes of errors, both within and across companies.
› Read moreThe intelligent platform is the direct access for companies to a central source of product and quality data. It additionally offers a range of modules, such as machine learning methods, digital transformation, forecasting and visualization of error analysis.
Companies receive a uniform database for all involved in the process; with regards to data quality and data structure as well as uniform data interfaces (APIs) to the platform.
Q-miner is already being successfully put to use in the automotive industry. The intelligent platform enables simultaneous access to data, promptly recognizes product defects and resolves them across the entire supply chain.
In this approach, the data is first checked according to the pre-defined quality criteria before being imported. Data is then processed, digitized and made available on the intelligent platform. This creates a uniform, fast and high-quality data pool for various processes.
Using the high-quality data as a foundation, individual data analyses are carried out using AI algorithms. The results are then utilized to identify specific patterns as well as potential causes for errors and respective measures to be taken. As a next step, automated processes are prepared and implemented in a partially or fully automated manner.
The data is managed centrally over the entire product life cycle. That way information can be tracked throughout all processes as well as the complete product history: Procurement, manufacturing, assembly, logistics, quality management, sales, usage, controlling, etc.
Usage data on the product is stored anonymously via pseudonyms so that compliance with data protection regulations regarding user data can be ensured.
The data generated by systems and tools is continuously tracked. Customers are immediately informed about system failures that have previously occurred for components during utilization or in case tools fail during production.
System failures that have already occurred will be examined to determine their root cause with the help of artificial intelligence and static models. Dependencies between system failures and system or environmental properties will be identified accordingly.
Furthermore, combinations of system/environmental properties that cause system failures can be determined.
Predictive analytics makes it possible to forecast future events. For this, mathematical models are created on the basis of historical data in order to detect trends and patterns. Current data is then applied to these models so that predictions can be made about specific developments.
The platform relies on artificial intelligence (AI) to calculate and predict potential system failure times in the most accurate way possible.
Company processes are viewed holistically in all phases in order to describe interrelations between and effects of changes to individual parameters. The same holistic approach is also applied to systems in order to identify the effects of parameter changes on the system design.
Likewise, the development of external frameworks (i.e. legal regulations, changes in awareness on the topic of sustainability, more flexible client-centric solutions) is analyzed. This includes the prediction of possible changes to customer demands and the overall market situation.
All necessary data is stored centrally in a complete and correct manner for effective data analysis. The aim is to ensure a uniform and clean data structure with respective access (multi-client capability).
Data digitization is a prerequisite for the use of Q-miner.
AQI provides in-depth consultation together with Q-miner as well as first-class project management to support the successful implementation of the jointly developed solutions.
Listening is an integral part of the consulting process, which takes place in dialogue with experts and the company’s management. Needs for action are identified and prioritized in workshops as well as through interviews, while implementation is accompanied by reviews and honest feedback.
Q-miner is a modular technology platform with AI-based analysis
This enables:
Q-miner Portfolio
Our platform is designed to cater to businesses of all sizes, whether you are a small startup, medium-sized enterprise, or a global player. We offer a comprehensive range of services, including consultation, application-specific AI modules, and Big Data technology, all of which can be tailored to your specific needs.
Q-miner Modules
Our platform is composed of a wide range of versatile components and functionalities that can be used individually or in combination, depending on your specific use case.
Our modular architecture has been intentionally designed to provide our customers with maximum flexibility and adaptability.
Each component and functionality of our platform has been carefully crafted to seamlessly integrate with your existing IT infrastructure, ensuring easy scalability and minimizing any potential disruptions.This allows you to easily build and customize your own solution, tailored to your unique needs and requirements.
Data pools within the company and from different locations, plants as well as departments in order to analyze quality processes.
Data pool within a group of companies and its subsidiaries in order to manage joint quality management processes.
Data pool within a supply chain across multiple companies, multiple suppliers or multiple partners in order to optimize the supply chain and quality assurance.
Multi-client capable data pool between companies in the same industry in order to securely share common data, collaborate more effectively via standardized interfaces and benefit from insights.
AQI is a company of the German automotive association VDA and acts as an economically independent limited liability company. AQI conducts research and carries out application projects. Due to its affiliation with the VDA and its Quality Management Center (QMC), AQI has direct access to a comprehensive network of experts in the automotive industry.
Q-miner is a product of AQI.
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