Process assessment, with the help of calculated statistical values and graphics, serves to increase the overall vision of the process. Process weaknesses (as well as strengths) are clearly highlighted, allowing improvement measures to be initiated systematically. Continuous and systematic control of production processes minimises the risk of scrap parts, and results in meaningful and quantifiable cost savings. Real-time visualisation and evaluation of the recorded data is a vital tool for successful continuous process improvement.

O-QIS - Operator Quality Information System

The O-QIS suite of software tools provides users with a range of process assessment tools for easy visualisation and monitoring of a manufacturing process. Data can be presented in a variety of ways to suit the individual process, such that machine operators can quickly respond to process issues.

Q-DAS Monitoring

With Q-DAS Monitoring, process data from multiple gauging systems can be visualised online simultaneously.

The "view" of each process can be defined in multiple layers, from the overall plant view right down to individual modules, cells or even individual values from one machine. The user can easily navigate from layer to layer at will, putting essential process data at their fingertips.

Individual alarm messages are broadcast through each layer to inform the user about the current process status. In this way, critical processes can be observed in great detail from a distance, and problems located quickly. Q-DAS Monitoring empowers the user with the ability to follow the status of multiple processes easily, and precisely.

MCA/CMM Reporting

Complex assemblies with many characteristics are easily managed with the MCA/CMM Reporting System, which provides a complete analysis and overview of the measured part, along with historical measurement data.

An array of powerful filtering and sorting functions allow the user to focus on the important characteristics of the part, while still maintaining effective control over all features.

For example, a data set from a CMM can be automatically sorted to display results for critical characteristics only, according to the amount of tolerance used. This totally removes the need for users to print out reams of paper reports, with the highly likely result that process problems will be missed as too much information is being presented at one time.

Programmable levels of operator interaction with the measured data ensures that important information such as process traceability data, events, causes and corrective actions are recorded with the measured values. Suspected faulty measurements (e.g. parts that have not been cleaned correctly) can be easily eliminated and re-measured, guaranteeing the validity of each measurement set.