An interview with Prof. Dr. Claas Christian Wuttke, director of the Steinbeis Transfer Center for Smart Services – Innovation and Development
Digital transformation can be particularly challenging for small and medium-sized enterprises. The question is why, especially given that SMEs are supposed to be so much more adaptable. This is the issue looked at by Prof. Dr. Claas Christian Wuttke, professor at Karlsruhe University of Applied Sciences and director of the Steinbeis Transfer Center for Smart Services – Innovation and Development. Wuttke is closely involved in both smart products and smart services, so he knows how they can be efficiently introduced to markets.
Hello Professor Wuttke. Digital transformation in industry is spawning new products, services, and thus also new business models. In what ways do you expect this to have an impact on processes at small and medium-sized enterprises?
I have the impression that lots of German companies see digitalization as a challenge – which is completely unlike the situation in places like East Asia, where any form of digital technology is greeted with enthusiasm. Of course that could also be because data and personal protection are a big priority for us. But there’s also a lot of emphasis on the cost of digitalization. It doesn’t help companies to hear all the positive predictions about the economic impact of digital transformation.
I would therefore plead for more emphasis on the concrete benefits when considering the potential of digital transformation. The question is whether – based on the available information, or information that can be gathered in the short term – a new service could be offered. Of course this is particularly appealing when a new service immediately allows new revenues to be generated. But consideration should also be given to the benefit within the company, such as improvements to value-adding and planning processes. This link between digital transformation activities and direct benefit is particularly important for SMEs because you need amortization on investments with innovations. Another challenge faced by SMEs is that they often don’t have big staff departments for developing new offers such as data-centric services. So it’s difficult for them to free up the capacity to work on new services. If they get experts in the line functions to think up and develop services, there’s a danger that these people can’t step back enough from the constellations of conventional products or certain methods.
On the other hand, SMEs are typically closer to the customer. This is a major advantage within this context because it makes it easier to involve customers in identifying and developing new services. But it’s important that everyone knows the goals and risks entailed in involving customers and that they take this into consideration.
For example, one risk is that customers want all the ideas that pop up to be implemented as soon as possible, or customers can’t think far enough into the future because they don’t really understand all the possibilities presented by digital technology. It’s only when you’re clear about the specific people that should be involved, when, how, and with which goals, that customers can be involved, efficiently and in keeping with the defined objectives – otherwise there’ll be disappointment on both sides. That said, successful digitalization projects are not just about bringing customers on board, but everyone who’s involved – especially staff. To do this, a similar approach can be used to customer integration.
One thing you just highlighted is that digitalizing production and products should, first and foremost, offer benefit – for companies and customers. But what specifically is the best approach when implementing new functions on digitalized products (smart products), or data-centric services (smart services) – especially if they’re going to be “efficient and in keeping with defined objectives”?
The only smart products and smart services we get to see in the market are the new ones, or the ones that are a success in the long term. But it would be safe to assume that up to 80% of new services introduced to the market fail. Of course there are lots of reasons why this happens. But one thing is certain: With data-centric products and services, you need different development processes and different methods compared to classic tangible goods. Naturally, at the beginning of every project an analysis of the current status has to be carried out. Digitalization maturity models are certainly a good starting point for this, such as the Industry 4.0 guidelines suggested by the VDMA, especially if you want a good overview. But if you ask me, they’re oriented a lot toward technology push, so they focus on what is technically feasible. Our method is oriented more toward market pull; emphasis is placed extremely early on the customer benefit offered by digitalization measures.
When you’re analyzing your own competences, this should not be limited to Industry 4.0 technologies in production and products. To work out some initial ideas for new products or services, it helps to ask what offers were made to customers, during which phases of the product life cycle, and what business models these were based on. I also think it’s extremely important when generating ideas to know which data-centric services have already been successful in the market. To help with this, we’re maintaining a structured collection of smart services for use in the machine and plant construction industry, which is continually expanding of course. If there’s more than one idea, it’s important to formulate it in such a way that all aspects of the offer can be understood and tried out. To do this, certain prototypes of digital services are used. Aside from a customer journey map, there’s an offering diagram, which allows you to work through the functions and sub-functions the service is supposed to offer. Many digital services are provided through an ecosystem involving several partners. There’s a motivation map which highlights the specific interests of each partner, and these are visualized on a system platform. And finally there’s a product service blueprint. This allows you to map how tangible goods interact with a service when it’s actually delivered. This makes it possible to recognize any issues before it’s too late. Using prototypes – intensively and early in the process – provides a foundation for agile development processes.
If the ideas behind the new digital services are made tangible, covering all aspects, it becomes easier to assess the customer benefit and implementation outlays – not just in terms of costs, but also of timeframes and potential obstacles. And it’s important to recognize the effort involved in implementing individual services and take this into account. Take machine and plant construction, for example: Connecting your machines to an IoT platform is a huge step – whether you’re the user or the provider – but as a rule, this also makes it possible to line up a whole range of new services. This enables you to define a digitalization strategy, which will create additional revenues step by step, or result in savings, or both, and as a result it will be financeable.
What, in your opinion, defines whether a production system is future-ready, and what requirements does it have to meet to be successful?
With all the changes that will happen, a core requirement of a production system will still be that it should offer high availability levels and reliable quality. This is also reflected in the fact that many industrial smart services aim to safeguard or enhance availability and quality with things like condition monitoring, predictive maintenance, remote servicing, smart process optimization, etc. Also, there will be increasing emphasis on the requirement to offer flexible production. There are also already services on the market for this; they revolve strongly around networking and are often based on new business models – for example performance-based contracting, smart factory as a service, or platforms for trading production capacity.
A good ten years ago, I observed a pallet cage in component production being filled to the brim with information, physically, just to satisfy product liability guidelines – data generation and storage without any actual benefit. These days, lots of companies are already protocoling all the production data they generate so they can use the information afterwards for things like quality issues. In the future, data is sure to be used more immediately and more systematically. Many of the requirements placed on production systems will still be in place but step by step, digital solutions will help us do a better job in fulfilling them.
Smart products, smart services, smart factories – what influences do you believe these developments will have on value creation within companies?
I’ll come back to my example with the machine and plant construction industry, because I know it well. The operators of smart factories can expect cost benefits through efficiency improvements, and as a result: sales growth. But sooner or later these effects will diminish because of growing competitive pressure from other smart factories. One prerequisite of commercial success will be production supported by digital technology, especially in a country like Germany with its high wages.
But I believe it will be different with smart products and smart services. The successful companies have already recognized that unlike classic, tangible goods, there’s still major potential to be achieved in this area in terms of growth and higher returns – and these things will be achieved. Some companies are already generating around a third of their turnover through services – although that’s not all through data. Nicola Leibinger-Kammüller, chairwoman of the managing board of Trumpf, expects – for the foreseeable future – half of the current revenues of around three billion euros to be generated through services and new business models. This shows where the journey is taking us.
Prof. Dr. Claas Christian Wuttke (author)
Steinbeis Transfer Center Smart Services – Innovation and Development (Karlsruhe)