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Data moves our world, whether in everyday life or in the professional environment. Even in quality processes, ever greater mountains of data are accumulating. But having data is one thing – using it to derive targeted actions is another. Read why it is crucial for the quality to get a grip on the data and to use it sensibly.
Systems thinking and systems design are core elements of ISO 9001:2015 and are the focus of the training for quality managers. They must understand and optimize the quality processes without losing sight of the goal, namely the improvement of process and product quality. It should be clear to everyone by now that more and more data is being generated in the digital world (e.g. through increased data acquisition in production or through the product itself). The correct handling of this data requires systems thinking in many places in order to be able to use it efficiently across processes. Thinking in terms of departments, or to put it simply, silo thinking, often caused by the prevailing principle of organizational structures and the division of labor in the company, is not very helpful.
On the one hand, it seems reasonable to assume that digitalization perfectly supports the efforts in quality management, which can already look back on many years of application experience in dealing with algorithms, for example through SPC (Statistical Process Control). In this respect, quality management has historically developed mathematical procedures for tracking down non-linear processes with statistical models and methods. On the other hand, however, algorithms are nowadays quickly confused with artificial intelligence (AI), which brings with it the danger of large, expensive digitalization projects that make little sense in the end. In the process, little or no account is taken of the practical experience gained in quality assurance. AI and algorithms have a lot to do with each other; however, there is not always immediate talk of an AI project when algorithms are used.
Ultimately, those responsible must learn from their experiences with methods and algorithms – and unfortunately, the proper handling of acquired data is still a closed book for some. In the future, when more and more data points are available in the development, production process and in the field, algorithms (with but also without AI) will gain enormous importance. Whereas in the past it was all about explaining deviations, in the future we will at best use the know-how gained from the data to explore new things. So digitization doesn’t happen by the way and in one fell swoop; it is an active process that (ideally) generates added value and is really worth the effort. Unsurprisingly, this also applies to the handling of data acquired in the process.
Of course, the degree of digitalization is also a decisive factor. At this point, many like to distinguish between “datafication” and “digital transformation”. “Datafication ” means that analog information is converted and processed into digital information (data). If this information is then also made available to upstream and downstream instances, and if previous processes are also changed as a result, this is referred to as digital transformation.
Thus, much of what companies tackle in their “digitalization of skills management” is initially simple datafication. One example is the processing of complaints on the basis of the 8D report, which can be carried out manually or also transferred to an IT system (datafication). If the 8D report is then shared and processed digitally with the supplier on a common platform, this is referred to as a true digital transformation. The digitalization of quality management in the supplier network offers considerable potential here. This example also shows why it is essential not only to accumulate the data, but also to take advantage of the digital benefits and thus become the master of data.
Future-oriented companies have long since recognized that the growing volume of data can no longer be brought under control with analog as well as manual measures. Despite all the change, however, we must not forget one constant that is indispensable in the proper handling of quality data: human beings. Well, this factor is only constant to a certain extent – after all, it also requires rethinking and adaptation on the part of the employees. If, for example, quality managers want to introduce a software solution to support quality processes, this is in the interest of the employees and of quality – even if the introduction is initially accompanied by changes. This line of thought is by no means self-evident. If employees do not recognize the benefits themselves, it is the task of the quality managers to stand up for the advantages of the company with systems thinking and an overview.
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