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How to avoid pitfalls in data interpretation in the context of process optimization and quality management

: Dinkel, Thomas

Wittmann, J.:
Quality management in technology 2018
Luxembourg: CreateSpace Independent Publishing, 2018
ISBN: 978-1-985895-13-3
ISBN: 1-985895-13-7
Aufsatz in Buch
Fraunhofer IFF ()

In the present time, companies are unlocking an ever-increasing information flood of process data, while at the same time the possibilities for data analysis become more versatile and more sophisticated. However, especially with automation or semiautomation of data analysis still the data interpretation is the key to effective and sustainable process management. With more manifold possibilities, the problem that not everyone can be a methodical expert becomes more severe. Thus, the room for misinterpretation increases, which leads to wrong decisions. This article proposes four basic principles for the avoidance of pitfalls in data interpretation in the context of quality management and process engineering. Overall, it seems advisable to introduce a quality assurance process for data interpretation whenever the latter is central for an important decision. Similar to a classical FMEA1 there could be a formalized procedure of checks (or even additional investigation) to perform in order to minimize the risk in a decision.