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Predictive Quality Management

Through error prediction, you can securely ensure high quality, while also reducing your inspection and defect removal costs. Data collection and reporting form the first step in predicting defects. An early-warning system is another important element. Recording and revalidation of measures taken are likewise integral parts of the system.

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Our solution is modular and consists in collecting data from logistics and production processes for the purposes of KPI evaluations. We use the reporting tools of the QM and PM modules. We can directly hook in data collection from machines and processes for planned maintenance. Depending on their nature, the outputs from these reports are evaluated either automatically or by a manager. The response from the system or the manager is recorded for use in machine learning. The prediction system produces a defect prediction report, as well as an intervention in ongoing process nodes on various levels based on its nature, e.g. as a warning or an alarm with the blocking of further activities until the operator intervenes.

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What makes Aimtec better

Unification of reporting and KPI

The sophisticated data collection tool contains information on defects discovered during the incoming inspection and APQP processes. It registers defects found during numerous processes—manufacturing, inter-operational and exit inspections, internal audits, product requalifications, supplier audits, machine repairs and evaluations after preventive and predictive maintenance.

Displaying reports

We present the data we process both through tables and graphically in diagrams of various kinds. We typically utilize Pareto charts. In our graphical reports, you can see limitations and trends, as well as assessments of relevant statistical factors. Selected reports are combined into cockpits and displayed on a large monitor.

Revalidation, machine learning and follow-up steps

The data gathered is displayed as work lists on the screens of your managers. The managers intervene flexibly via measures with an automatic effect on the logistics processes in your system. The system automatically requests revalidation of the measures. It keeps a register of these parameters and, based on them, prepares relevant measures in similar situations on its own.

On-site intervention

One possible outcome of measures taken is an intervention at a precisely defined point in an ERP process: the worker operating the given process is immediately informed of the risk if the system evaluates process parameters as critical. In the extreme case, the system blocks the process or machine until the parameters are corrected.

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