We use cookies, similar technologies and tracking services

This website uses cookies, similar technologies and tracking services (hereinafter referred to as “Cookies”). We need your consent for Cookies, which not only serve to technically display our website, but also to enable the best possible use of our website and to improve it based on your user behavior, or to present content and marketing aligned with your interests. For these purposes, we cooperate with third-party providers (e.g. Salesforce, LinkedIn, Google, Microsoft, Piwik PRO). Through these partners you can also receive advertisements on other websites.
If you consent, you also accept certain subsequent processing of your personal data (e.g. storage of your IP address in profiles) and that our partners may transfer your data to the United States and, if applicable, to further countries. Such transfer involves the risk that authorities may access the data and that your rights may not be enforceable. Please select which Cookies we may use under ”Settings”. More information, particularly about your rights, e.g. to withdraw consent, is available in our Privacy Policy .


Only technically necessary Cookies

Accept everything

Below, you can activate/deactivate the individual technologies that are used on this website.

Accept All


These Cookies make a website usable by providing basic functions such as page navigation, language settings, Cookie preferences and access to protected areas of the website. Cookies in this category additionally ensure that the website complies with the applicable legal requirements and security standards. Owing to the essential nature of these Cookies, you cannot prevent their use on our website. Details about these Cookies are available under 'More information'.

Functionality and personalization

These Cookies collect information about your habits when using our web pages and help us to enhance your user experience by tailoring the functions and attractiveness of our web pages based on your previous visits, location and browser settings. They also enable access to integrated third-party tools on our website (e.g., Microsoft Azure for single sign-on authentication). This can involve transferring your data to the United States (for information on the risks involved read Clause 1.5 of our Privacy Policy). If you refuse these Cookies, you might not be able to access the full functionality of the website. Details about the tools we use are available under 'More information'.


These Cookies are used to compile basic usage and user statistics based on how our web pages are used (e.g. via Google Tag Manager, Piwik PRO). If you accept these Cookies, you simultaneously consent to your data being processed and transmitted to the United States by services such as Salesforce Pardot (for information on the risks involved read Clause 1.5 of our Privacy Policy). Details about the tools we use are available under 'More information'.

Marketing and social media

These Cookies help third-party sources collect information about how you share content from our website on social media or provide analytical data about your user behavior when you move between social media platforms or between our social media campaigns and our web pages (e.g., LinkedIn Insights). Marketing Cookies from third-party sources also help us measure the effectiveness of our advertising on other websites (e.g. Google Ads, Microsoft Advertising). We use these Cookies to optimize how we deliver our content to you. The third-party sources and social media platforms we use can transfer your data to the United States (for information on the risks involved read Clause 1.5 of our Privacy Policy). If you accept these Cookies, you simultaneously consent to your data being transferred and processed as described above. Details about the tools we use and our social media presence are available under 'More information'.

More information

Save Settings


Feeling the heartbeat of your machines and plants.

The DXQanalyze product family allows for a comprehensive logging of all available process data with the objective of identifying possible quality defects on the product or imminent wear on the equipment in real-time. On a superordinate level aggregated data enables the system to draw conclusions about the operation of individual steps along the value chain based on documented product quality. In the future, this information will be used to automatically adjust the process to counter these changing conditions. Products within our DXQanalyze product family use artificial intelligence, i.e. machine learning, in order to identify anomalies and derive patterns.


  • Comprehensive analytics offering for all levels of data scientists (newcomers, advanced users, experts)
  • Increased equipment / plant availability and first-run rate through faster troubleshooting
  • Integrated domain knowledge in analytics solutions

Products of DXQanalyze

DXQequipment.analytics provides a deep insight into various process steps and involved equipment along the value chain. The software package aims at improving all aspects of the overall equipment effectiveness (system performance, production quality, equipment availability). In a first step, DXQequipment.analytics supports faster troubleshooting with root-cause-analysis visualizing critical situations, detected patterns, and exceeded thresholds. Secondly, an automated analysis is enabled by providing a drag-and-drop analytics builder to create own algorithms. With the deployment of such algorithms data can automatically be analyzed and a direct feedback to the machine in real-time can be realized. In future, the Advanced Analytics module will use historical data and machine learning to find the optimal parametrization of algorithms and to detect long-term trends and patterns. This AI application combines information technology with engineering expertise, identifies fault sources and determines the optimal times for scheduled maintenance. It finds correlations in the plant and adapts the algorithm using a self learning approach.

In combination with DXQplant.analytics self-learning algorithms will automatically be trained to identify quality issues. Alongside DXQequipment.maintenance further information on detected maintenance tasks is provided.

DXQequipment.analytics is based on Dürr’s expert knowledge and can be offered for various equipment types, e.g. application robots, ovens, PT / EC systems.


  • Real-time streaming analytics to ensure production quality
  • Self-learning quality anomaly detection
  • Easy-to-use frontends for data visualization and analytics model creation
  • Permanent acquisition and analytics of equipment data
  • Machine learning algorithms to evaluate the painting process and predict equipment failures


  • Faster troubleshooting to increase equipment availability
  • Increased first-run-rate
  • Decreased equipment downtimes
  • Optimized identification of root-causes

DXQplant.analytics aims at improving the first-run-rate of a production system. In a first step, quality dashboards and reports show key performance indicators in order to improve transparency of the product quality status within a quality loop. Secondly, systematic quality problems are detected using smart pattern recognition. In future, systematic quality problems will be correlated to process anomalies derived by DXQequipment.analytics enabling a root-cause-analysis as well as early troubleshooting.


  • Dashboarding for quality relevant plant KPIs
  • Smart pattern recognition for systematic quality defects
  • Visualization of product life cycle of affected workpieces
  • Indication of root-causes based on big data analytics and expert rules


  • Improved first-run-rate to increase the OEE
  • Structured overview of workpiece-related quality and process data
  • Support in finding correlations between quality defects and process causes
  • Domain knowledge linked with data analytics