Industrial Manufacturing

The growing complexity of production processes, the increased availability of data and rising production speeds, coupled with ever stricter regulations and reduced capacities, are creating an enormous challenge in the day-to-day work of quality managers.

Not only do they have to carry out more specific tests, but they also have to document, research, keep track of and communicate more, all at the same time:

1. Increasing agility in product and development processes
2. Traceability of information
3. Rising data volumes

Standard checks are a thing of the past

When it comes to quality management, many different product variants mean a departure from parameter lists that could be applied countless times. In the past, conventional software solutions for support could be set up with the corresponding forms, images, characteristics and tolerance limits. This is no longer sufficient: manually adjusting the inspection plans consumes too much time and money and is too prone to errors.

New software solutions have the ability to create automatic inspection plans, inspection reports and forms so you don’t have to start afresh for each variant. This means that only one data import is necessary for generating the inspection plans and evaluations for the variant in question.

For manufacturers of electronic or mechatronic systems, the firmware factor is an additional parameter at this point.

Centralized data management is the key to success

In the absence of an integrated data exchange option, the quality manager has to gather the relevant characteristics for each variant himself and build an inspection plan. However, important information is often also contained in a variety of different systems, such as Excel, SAP and special supplier systems. Integrated quality management software is able to generate an inspection plan using data from different systems – which means that intelligent quality management does not have to involve a comprehensive reorganization of the IT landscape: a unified overview can be generated by the software in spite of the different systems.

Quality management has to deal with mass data in many cases. Out of this mega-data conglomerate, data needs to be prepared in such a way that it is easy to see where the problems lie and, in the case of an alert, to be able to quickly work through the data to see where the issue is arising and find any correlations. This kind of in-depth analysis is only possible when different parameters can be brought together by the software.

The application of a generic structure that clusters variants into product families provides a target-oriented approach that has become established in the field of semiconductor and PCB manufacturing. In this way, “lessons learned” can also be inherited by related variants.