As first part of the research analysis, an extensive market study was carried out. Initially, we employed the list of exhibiting companies of two of the most important trade fairs for plant, machinery, tool and die making: the "Exposition Mondiale de la Machine Outil" - EMO in Hannover and the "International Exhibition for Metalworking Technologies" - METAV in Düsseldorf, which have more than 500 exhibitors each. To analyze this list in detail, we collected secondary data on each exhibiting company from its website, such as its product portfolio, and from additional websites where annual reports and company publications are presented. From this analysis, 202 German companies were identified that offer data-driven products and services in addition to their core products. By analogy with cyber-physical systems, these are suppliers of physical products such as machinery, systems or components that could be enhanced with computing and communications capabilities. Trading and software companies were eliminated from the analysis. In a second part, these 202 companies were analyzed according to their size, measured in annual turnover and number of employees, as well as their portfolio of Industrie 4.0 offerings. To classify these offerings, we first had to develop a scheme based on established Industrie 4.0 maturity models (see Chap. 2.1).
Finally, to deepen the analysis and identify successful practices of pioneering companies in providing data-driven products and services from the machine and plant engineering sector, we analyzed more than 130 pubicly available publications, ranging from press releseases to marketing podcasts as well as conducted interviews with experts from some of these pioneering companies. The interviews resulted in 20 hours of written conversations. The detailed results and analysis of these interviews and data are presented in Chap. 3, where we also describe the patterns and conclusive results of all Fast Movers and the most interesting individual expressions of the expert interviews.
2.1. Scheme for Market Data Analysis – Level of maturity
Industrie 4.0 is often described as a step-wise evolution of systems and processes. In 2017, a development path and model for the maturity level and business value of such systems, as depicted in Fig. 1, was introduced by the National Academy of Science and Engineering acatech [18]. The technological basis is set through computerization followed by connectivity. Together, these two elements create visibility as the lowest level of Industrie 4.0 maturity. In a second step, understanding is developed before predictive capability is achieved. The highest level of maturity is seen as the ability to adapt based on a prediction.
We employed this model of Industrie 4.0 maturity, aggregated the steps where technically feasible (see Fig. 2) and translated it into offering levels (see Fig. 3):
On the lowest level, local computing power provides data aggregation, transparency, or even some feedback based on data from the automation level. Technologically, this is mostly reached through well-studied and established technologies and implemented in solutions such as SCADA.
On the second level, aggregated data are available online through an internet connection. Technologically, this requires understanding and mastering the IIoT stack from an edge level to TCP/IP, MQTT, and others, but also includes aspects such as data stream architectures, network security, data privacy and safety, among others. Typically, these data streams are used to feed dashboards to provide information for domain experts.
On the third level, these data streams are connected to models and very specific domain knowledge to pursue specific goals, from process optimization to better maintenance and improved plant operations of even lower energy consumption. In all these cases, domain expertise needs to be matched with data processing capabilities, very often requiring sophisticated models, data science approaches and specifically designed applications.
On the fourth level, services are provided on top of these models and data streams. In addition to mastering the different technology stacks and domain expertise, this also requires redesigning the organization towards the provision of services, which typically includes most internal processes and even the basic business model.
It is important to realize that all lower steps must be mastered in order to reach a higher step. E.g., local data aggregation (level 1), data provision through IIoT (level 2) as well as machinery wear and tear models (level 3) need to be well understood before providing offers such as Equipment as a Service or Performance Improvement.
All of the companies included in our study were analyzed with respect to their offering portfolio and then classified according to this maturity level model. As domain-specific value generation and application cases varied across the different actors.
2.2. General findings of the market data
The researched companies ranged from the very small, with a single-digit number of employees, to those with a global presence. Of the 202 under scrutiny, 92 hat actual software or data-driven products and services in their offering portfolio, representing 43% of the total.
Following the EU definitions for the sizing of companies, 12 of these 92 companies fit into the category of micro or enterprises with less than 50 employees. On average, these companies employed 19 employees and had an annual sales volume of 2,7 million €. In the category of medium-sized companies (mostly referred to as SMEs) with fewer than 250 employees and less than 50 million euros in annual turnover, there are 26 companies with an actual offering of software or data-driven products and services. On average, these companies employed 122 employees and had an annual sales volume of 16 million €. As the remaining group of large companies had a very disparate number of employees, we decided to classify them into two subgroups: Companies with less than 1000 employees, which are represented for 34 companies in this subgroup. On average, these companies employed 489 employees and had an annual sales volume of 93,2 million €. the second subgroup, conformed by large-scale enterprises (corporations) with more than 1000 employees, making up 20 companies in total. On average, these corporations employed 6.100 employees and had an annual sales volume of 1,2 billion €.
When ranging the companies according to their number of employees, a clear correlation between company size and Industrie 4.0 maturity level was observed: While offerings at the highest level could be found in any subgroup, the number of large companies offering data-based services was much higher than that of small companies (see Fig. 4).
Figure 4 also illustrates that this correlation trend has a high range of variation. Even some small or micro enterprises were found to offer products and services with a high Industrie 4.0 maturity level. Still, the overall correlation between company size and maturity level is positive. So, when it comes to the ability to develop new, data-driven products and services, size does matter.
2.3. Detailed view on maturity level in product portfolio
When looking at the different company sub-groups, the distinction discussed above became even clearer: For micro and small enterprises, 75% of all companies with digital offers stayed on level 1, thus offering products and services based on classical technologies with local applications only. In the same group, only one company offered data-driven services, thus exhibiting the highest maturity level. On the other side of the size scale, a large number of mature offerings could be found. More than half of all large companies with more than 1000 employees had data-driven product and service offerings at maturity level 4, and almost the same number of companies showed offerings at level 3. Maturity levels 1 and 2 are very rare among this subgroup. Very often, small and medium-sized companies (as per EU definition) are described as the backbone of the German economy as well as a key driver for innovation and technological progress [19]. Several research and funding lines as well as further support is guided towards this group of companies [20]. When looking at the maturity level of the data-driven offerings, this innovativeness can be seen: More than half of all companies from this sub-group had offerings of maturity levels 3 and 4 in their portfolios, with an almost equal split between these two. In this respect, SMEs had an even more mature portfolio than the next group of larger companies ranging from 250 to 1000 employees (Fig. 5).
2.4. Product portfolio in high maturity levels
Services based on IoT and data can take many forms. A navigation model of the St. Gallen University differentiated 66 patterns and more than ten different offerings in the area of Equipment-as-a-Service alone. These range from the service-based provision of software or consumables to hardware components, and equipment or even a complete fleet [21].
The companies under scrutiny in level 4 offered a comparable wide range of services: While Equipment-as-a-Service certainly is the most prominently advertised and discussed in many publications, the overall number of this pay-per-use offer was comparably small (33%) in comparison to performance packages (~ 60%). At the same time, Software-as-a-Service was often found even with company offerings on maturity level 3. It became clear that the offer of equipment, i.e. machinery on a per-use base highly depended on the complexity and exchangeability of the equipment, or its opposite – the customer's level of specification. Generally speaking, companies offering machines that could be more easily exchanged and transported (compressors, lathes, etc.) are more likely to finance on a pay-per-use basis. On the other hand, even for highly customer-specific equipment, performance enhancement and other services could be offered as-a-service.
While this market study gave an overview of what was on offer, we wanted to understand how the fast movers with a high level of Industrie 4.0 maturity conducted their data-driven services and products. Thus, we analyzed these few companies in more detail, which will be discussed in the next chapter.