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The limitations of production

Steinbeiser Prof. Dr.-Ing. Martin Kipfmüller discusses possible solutions to the crisis

Manufacturing has always adhered to similar rules, ever since the Industrial Revolution. Raw materials are taken and processed, using energy to convert them into products. The value this adds – the value added – is attributed to the company that sells the product. The value added is then invested in new production facilities and workers are paid wages, who in turn are able to buy products themselves. This upward spiral is currently turning slower and slower, argues Steinbeiser Professor Dr.-Ing. Martin Kipfmüller. In our latest Steinbeis Swipe! he suggests ways to get things moving again.

The circles of production until now were successful in fueling amazing levels of affluence, because they usually met some important prerequisites: There were sufficient raw materials and energy in place to raise production levels. So the number of products that were purchased kept rising. And the global population grows continuously, also fueling the demand for products. Perhaps more importantly, however, because people make products, they earn money, which they can then use to buy products themselves. To meet this growing demand, manufacturers churn out even more products, continuously striving to use less money to do so, for example by automating processes and saving money on workers. So long as the market continues to grow, those workers won’t necessarily end up unemployed (and disappear as customers); they just find work elsewhere. And this generates even more value added.

Presently, however, the German economy finds itself in a situation whereby fulfilling these conditions can no longer be seen as a matter of course. Raw materials and energy are becoming scarce, which makes everything more expensive. Measures introduced due to COVID-19 have destroyed value in the products bought by customers, making it likely that there will be a noticeable slump in sales in the coming years. And there’s another new development fueled by the scarcity of resources. Because more and more drivers are switching to electric cars in parallel to this trend, German industry is relinquishing essential value added as a result of the coronavirus, in a core area: carmaking – or to be more specific, making engines and lots of other technologically demanding components in the powertrain.

So what can be done about this dilemma? Allow me to begin with the most tangible part: the crisis in German mechanical engineering. At the beginning of the Industrial Revolution, there was an uprising of weavers in Silesia, a signal that the spinning wheel of progress could never be turned backward. In fact whenever emerging technology bursts into a market, it squeezes out old technology and sources of profit. This is one of the reasons why mechanical engineering firms in the automotive industry should always be on the lookout for new applications for their products, or more importantly: new products. We’re very unlikely to witness a renaissance of the combustion engine.

Several major companies are leveraging their ability to make high-precision production machines to move into 3D printing – but that will never absorb an entire sector of industry. The know-how available in the industry may point to another way out, however. Similar to developing and producing robots, developing and making machines involves mastering the complex interplay between control units, drive technology, and mechanical systems – systems that epitomize product performance. So both fields – production machines and robots – involve the ultimate challenge: mechatronic systems. Japanese producers are already employing the same control and drive technology to make products in both areas – and are making a very successful job of it. A similar approach could pave the way for German mechanical engineering firms to enter the continually expanding market for robots and automation technology.

An initial stepping-stone technology could be milling robots, which you encounter in many areas. But this would still not be enough to replace crumbling markets. There is more and more potential to be found in robot applications inside and outside the factory floor – i.e. anywhere where doing business has been unprofitable until now due to the level of complexity, number of variants, or small batch sizes. Also, there are tasks in logistics that until now have been supervised by human beings for security reasons. First and foremost however, a lot more needs to be done outside factories, for example out in the fields, where we are hugely dependent on seasonal workers. Of course it’s not worth keeping a machine on hand that only digs up asparagus for a couple of weeks every year, but could the same machine be used to harvest grapes in the fall? And couldn’t this machine be owned by a cooperative that would hire its asparagus-picking or grape-harvesting device to farmers?

In the area of care for the elderly, sick, or disabled – where nursing can often be physically strenuous – exoskeletons could be used to get people moving again, and safe robots could help carry people, provide them with support, or move them to different beds.

When cleaning buildings and machinery, there are different ways to use robots for wielding vacuum cleaners, mowing lawns, or washing windows.

But in the long term, couldn’t robots also replace light bulbs in the stairwell, water flowers, or take on other maintenance and janitorial tasks? There’s another trend that suggests application scenarios for robots, which is more about addressing the lack of key resources. It’s also an area in which the EU has just set an important signal by demanding that products be repairable. This poses a challenge for many business models. For smartphone makers, it can be quite damaging to business if their devices don’t stop working after two years and they can’t sell new ones. But what would happen if customers don’t buy smartphones at all, but the ability to make calls, take pictures, or use apps? Would it then be possible that devices don’t suddenly stop working, but for example only require a hardware upgrade? This would make it necessary to build production lines offering ultimate flexibility, so that Monday they upgrade devices sent in by producer A by replacing the camera, and Tuesday they insert new memory chips for devices sent in by producer B. To save resources, there will be the need to disassemble products, repair them, and overhaul them. There will be no more room for highly specialized machines. Instead, for things to be fully automated, ultra-flexible robots will be required, likely equipped with AI. And this will be essential for such a circular business model to work profitably.

So what will all the people do if their work is automated? There will be fewer low-skill jobs and society will have to start working out what to do about it. In all likelihood, it will be necessary to invest more in education to take care of complex tasks such as developing, reprogramming, and maintaining the new technology. Not every unskilled worker has what it takes to learn the complexities of engineering or computing in four years. But maybe this would be possible if the content they need to learn can be spread over six years. Also they may not need to delve too deeply into complex math. People would need to be allowed to learn at different paces or take things at a level of abstraction that is more suitable for each individual.

Also, it will not be enough to cram training in at the beginning of people’s careers. The pace of technological advancement has been accelerating at breakneck speed in recent decades, so we can expect things to stay that way. What this means for companies is that it will no longer be enough for engineers to learn all the theory at the beginning of their careers and “pick up the rest” afterward as they go along. More and more people in the world are developing technology these days, and this is accelerating the pace of development.

This accounts for the trends described above, for example in the mechanical engineering industry. Technological change will force firms to make products that no longer match the skills learned by workers at university 30 years ago. If there’s heightened demand at a company for programming skills or AI methods, it will not be enough to send people on a course for a couple of weeks. This is where the major disadvantages of using AI, robots, or automation technology actually become an advantage. Our society will have to find ways to ensure freed-up working capacity does not spell job losses. Instead, firms need to invest properly in training staff. For example, if a firm needs 20% fewer workers, either staff (especially those with technical skills) should be allowed to study for a year, or firms should try approaches like putting one day aside each week for staff to do remote studies.

Only then will it become possible for companies to achieve the transition and, for example, transform from a mechanical engineering company into a producer of robots. And business leaders will only succeed in the market if they can find ways to change their companies and staff at the rate required to keep pace with technological advancement.

To ensure firms don’t unwittingly pre-select the content of curricula, independent bodies can take on the task of training people in employment. Quite conceivably, private institutions such as Steinbeis could do this – at our Transfer Center, we are already preparing suitable offerings in the field of robotics – and these institutions could be paid directly by the companies.

It would be much better if society recognizes what needs doing and uses company levies to fund staff training, which for example can be organized at universities, online, or at specialized institutions.

If we don’t adopt this approach and we don’t come up with new products, the increasing levels of automation and plunging sales will result in companies having to shed workers and there will be further sales decline. If, however, firms are serious about investing in their workers, they can think up new products and markets. There are enough examples of markets that did not even exist at one point – online streaming, smartphones, data trading. But for this to work, firms and society will need to be both decisive and determined.


Prof. Dr.-Ing. Martin Kipfmüller (author)
Steinbeis Entrepreneur
Steinbeis Transfer Center Production Technology and Robotics (Karlsruhe)

Prof. Dr.-Ing. Martin Kipfmüller is a professor at the Faculty of Mechanical Engineering and Mechatronics at Karlsruhe University of Applied Sciences, where he heads up a research group looking at robotics and generative manufacturing. He is also responsible for the Steinbeis Transfer Center for Production Technology and Robotics, where he works on business projects with a bearing on his key areas of focus.