Industrie 4.0 is being "pushed" from many sides, and horror scenarios describing what will happen if the reluctance of the German medium-sized industry continues are being spread. This article deals with some of the basic contradictions in the Industrie 4.0 hype.
According to Wikipedia: "A disruptive technology is an innovation that has the potential to completely supplant an existing technology, an existing product or an existing service." In this way, the continual development of the existing product is disrupted. The new proposal generally comes from an unexpected direction. In the discussion about Industrie 4.0, the fact that there will be disruptive changes is stressed again and again. It is then pointed out that, in order to handle this is, all one needs to do is pay attention to new business models. The best thing is to employ an innovation consultant, who will then take care of everything. Unfortunately, this is a part of the Industrie 4.0 illusion. Disruption cannot be planned in advance, nor arranged by consultants – otherwise it wouldn't be disruption. Essentially, the company's task is to effectively monitor the market, so as to be able to quickly detect any disruptive change that threatens the company's own business model. By means of intensive customer orientation, the attempt will also be made to discover potentially new products, which will themselves then also be able to bring about a disruptive change. It would, however, be foolish to concentrate solely on finding new business models.
Big data would appear to be the fuel on which the Industrie 4.0 engine runs. In the eyes of many Industrie 4.0 promoters, it is the blessings of large data quantities that make progress possible. However, it should not be forgotten that the success of German medium-sized industrial companies is largely due to the fact that the requirements of the individual customers are met in the requisite high quality, and that as a result, premium prices can be secured. Individual details, however, cannot be seen in a big data analysis. There, it is all about patterns relating to the mass. Further, data analysis is always a look back with subsequent extrapolation. But updating past developments is perhaps not really the best approach in times of rapid transition. It is much more a question of finding out what the future will "demand" of the company in terms of Otto Scharmer's "Theory U". Third, data analysis indicates correlations but no cause and effect relationships. In our increasingly networked economy, there are no longer any direct cause and effect relationships that can simply be approximated through correlation. Whether the type of complex feedback systems that exist today can still be measured by rational models remains to be seen. Many medium-sized companies attribute their success to an intuitive rather than a rational analytical understanding of the market, and, in increasingly complex systems, this will probably continue to promise future success.
Great improvements are expected as a result of intensive data exchange. One characteristic of digitisation is that control over data is crucial for economic success. Companies which only manage data (Google, Facebook, Uber, Airbnb…) make large profits or at least achieve incredible market capitalisation. Companies that continue to produce hardware, need to invest, and therefore bear great risks, must consequently make do with smaller margins. How does the question of data handling need to be organised for a medium-sized company to achieve the best possible results? This is the question to which every company must give serious attention. There are no patent solutions, everyone must find the way that suits them best. Can it be good for all production companies to disclose large amounts of production know-how through machine operating data, just to make data-driven preventive maintenance possible? Is it not the case that a production worker has often gathered so much experience that he knows best of all how to keep "his" machine up and running? Should we really stop making use of this knowledge? There is certainly no such thing as good or bad data exchange. What every company has to do is to make a conscious decision with regard to data handling. To this end, core areas of responsibility must be defined, and in these rigorous safeguards must be applied to know-how, and no data exchange be allowed. In the other fields, the attempt can then be made to secure economic success by means of the selective disclosure and acquisition of data.
You can read about how to get your company on the right track in the next EDAG newsletter.