Predictive Quality Management with QES

The user-friendly concept of the QES enables the systematic introduction of an automatic coil release system for all production lines. QES captures and pools the quality data from various process stages. The software monitors, records, and ensures product and process quality along the whole production chain – from the raw material to the surface-finished final product. Rules are used in all process stages to check the extent to which quality standards are maintained as per the customer’s specifications.

The system includes all kinds of decision-making support that is important for the operation of the plant and for the process chain as well as the delivery of the finished products. QES is the leading software for successful high-performance operation and the production of high-quality products. Its implementation creates valuable benefits for operating costs and customer satisfaction levels.

Key features

  • Give flexible, dynamic decision rules in the hands of QA engineers
  • Provide electronic quality manuals based on decision rule sets, easily transferable to other lines and worldwide
  • Generate comprehensive quality certificates
  • Uncover hidden quality issues and potentials
  • Utilize all available data and improves data quality
  • Find key influential parameters and rules driving quality performance
  • Use web-based KPI monitoring and re-assignments for all grades and rules


  • Data Integration

    Unified data landscape across the entire production route

    Each coil is represented by data from numerous data sources of the different automation levels. For bridging the physical and the digital worlds, data must be transmitted completely and seamlessly allowing the virtual coil to exist simultaneously with the physical coil. However, following the process route each coil can have a digital representative in each individual process step. The Internet of Things thus contains a comprehensive digital replica of the physical coil - with complete mapping of all product, process or specification data. The QES is able to collect, aggregate, and analyze all existing data related to a coil and thus sets the basis for the digital twin of each coil.

    By comprehensively merging all data for each coil in a digital twin, steel producers can ensure a seamless documentation of product quality and trace hidden quality potentials.

  • Automatic Product Grading and Release

    Rule-based quality and process evaluation

    Even today, quality management is not an automated process in most rolling mills. A great deal of time and effort is invested in quality monitoring by plant personnel, for example to determine the exact cause of defects. The Quality Execution System (QES) by QuinLogic is a proven and leading technology for automated coil release. It facilitates quality monitoring for plant personnel, saving a vast amount of time and money. By translating expert know-how into rules, the QES can verify if a specific product meets the customer specification while considering quality and process data from all production steps. That way it can approve and certify product quality automatically, leaving more time for process and quality experts to focus on root cause analysis, continuous process and quality improvements and quality assurance for critical products.

  • Assign Manager

    Find the best alternative order for downgraded coils

    Having both detailed information about the produced quality and all details of the customer specification at your fingertip, this application supports you finding the best alternative orders if a finished product is no longer suitable for its original purpose due to impairment in quality.

  • DataCorrelator

    Turn data into insights without data-science skills

    As a fully integrated QES application the DataCorrelator provides the user with an explorative data analysis tool.

    The primary focus are the root cause analysis, comparative analysis and predictive models. This, combined with the user’s expertise, allows the determination of the influence that different factors have on a variable, as well as the comparison of a variable under different conditions.

    In addition, it provides the user with an easy way to export the results of these operations to be utilized for the quality monitoring and to predict a quality risk.

  • Statistical Process Control

    Keep the Process Stabile

    The SPC (Statistical Process Control) aims to improve product quality through increased process stability. Within the QES, the tool uses data from the entire data pool and leverages it to monitor processes and keep them stabile.

    By utilizing control charts, you can observe how a process changes over time, identify trends, and recognize special causes of variations. Statistical methods help to recognize process deviations in an early stage automatically.

    With SPC, you can define data series based on the QES product data model and select any context data for each data series by filtering, sorting, or grouping it. In addition, the SPC application can be flexibly integrated into the QES workflow.

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