Did the pandemic significantly hurt the spread of the Internet of Things (IoT) in Germany or not? Industry experts gave a clear answer to this question at a COMPUTERWOCHE roundtable: No. Lockdown and short-time work have often put the implementation of projects temporarily on hold, but in addition to the basic technologies of machine learning and artificial intelligence, IoT obviously now plays an indispensable role in the digitalization roadmap of most companies.
The reason is obvious: While in recent years mainly the new possibilities of networking production plants and products using sensors in the form of initial prototypes or on a project basis have been evaluated, innovative and scalable business models must now follow in a second phase. And these are essential for most companies. It is about cost advantages and once again about the decisive competitive advantage. Relevant studies – also by COMPUTERWOCHE – show that more than two thirds of all companies actively pursue an IoT strategy and see the Internet of Things as the basis for new networked products and future data-based business processes.
- Ralph Schneider-Maul, Capgemini
Fast failure and thus the time of experimentation is over in the manufacturing industry. In some projects that failed, you paid a lot of tuition. Now it’s all about the return on investment and scaling the successful lighthouse projects.
- Thomas Kanzler, A1 Digital
Companies whose machines could not be serviced due to travel restrictions are likely to see IoT in general or predictive maintenance differently.
- Vincent Ohana, Concept Reply
One cannot simply lump Industry 4.0, machine learning and artificial intelligence together in the debate about IoT. Depending on the industry, these topics have very different relevance. For example, AI issues currently play a significantly larger role in the insurance industry than in plant engineering, where the focus is more on Industry 4.0.
- Dominic Rüchrat, PTC
IoT is currently able to replace lack of human resources in remote service, especially in connection with predictive maintenance on augmented reality.
- Alwin Schauer, Software AG
With IoT, many job profiles in production change. The individual machine operator has considerably more responsibility and design options because he can read out machine and maintenance data as well as production plans directly on his industrial PC and thus significantly intervene in workflows on site.
- Florian Bogenschütz, Wayra
For many of our B-to-B customers, however, the crisis has accelerated the IoT topic in many cases to a “crash course in a simple way”.
- Frank Föge, Zuora
We have customers who have been going in the wrong direction for a year and then, with the possibility of iteration and testing, we have taken the right path and successfully implemented IoT applications.
Alwin Schauer, Senior Vice President DACH at Software AG, also underlines that COVID-19 was perceived more as a catalyst than as a braking factor: “The past few months have shown who drove digitization most in companies. Was it that? CIO? Was it the CDO? Or was it COVID-19? Just as people have generally opened up to digitalization and overcome their critical distance from new technologies, this will also help us in the context of the Smart Factory, for example, because in many IoT scenarios are crucially about the willingness of the employees to accept the new applications and processes. ”
The question is no longer about the technical feasibility and meaningfulness of IoT in corporate use, but about whether and how digital IoT platforms are developing as serious business models. Ralph Schneider-Maul, Head of Center of Excellence for Digital Manufacturing at Capgemini, also sees the current market development as fundamentally positive: “In the production environment, COVID-19 has already left its mark. Wherever factories were closed or short-time work is still taking place, you will not find any employees that you can use to implement IoT projects. But we only see a pause in implementation, not a fundamental stop. “
However, the interpretations of IoT and the approaches in the individual industries are still different. While many IoT initiatives have already started successfully in the local manufacturing sector, but skepticism regarding a fundamental transformation remains unchanged, other branches of industry are already more advanced in terms of IoT business models.
Smart home solutions available in the hardware store, the tracking app for the expensive e-bike, smart city as a real concept for planning new living and working environments – both in the B2C and B2B sectors. Not to forget the insurance industry, where the policyholder, within the framework of so-called “Car-Telematic” policies, declares that a series of speed and acceleration data are sent to the insurer by means of sensors in his motor vehicle, which gives him an evaluation of the driving style and thus derive the risk content. All of these scenarios are based on one thing: Pure technology aspects such as sensor technology, machine learning or IT security only serve the actual purpose – namely to generate new, data-based business models.
Study “IoT 2021”: You can still participate!
COMPUTERWOCHE is currently conducting a multi-client study among IT decision-makers on the Internet of Things. If you have any questions about this study or if you want to become a partner, Ms. Regina Hermann (firstname.lastname@example.org, phone: 089 36086 384), Mr. René Krießan (email@example.com, phone: 089 36086 322) and Mr. Bastian Wehner will help you (firstname.lastname@example.org, phone: 089 36086 169) gladly further. Information on the Internet of Things study can also be downloaded here (PDF).
But back to plant and mechanical engineering, the backbone of the German industrial landscape: here too, on the one hand, you can feel a sharp rise in customer demand for networked products and digital solutions. On the other hand, as mentioned, there is great uncertainty with regard to what is technically possible. If consumption-based rental models are possible for entire production lines or individual machines using IoT sensors and machine learning, this “as-a-service” business cannibalizes the traditional sales to date. To date, however, it has not been easy for many manufacturers to question the tried and tested and at least partially reinvent themselves.
Nevertheless: Many IoT application scenarios were also tackled there, for example in the area of predictive maintenance and with interactive dashboards for monitoring and controlling production plants. These beginnings were not always successful. According to experts, there was often a lack of not only the willi
ngness to change, but also the necessary digital competence at the crucial levels. One of the main causes is likely to be that two worlds still meet in this industry: on the one hand, classic IT with its hardware, software and network components, and on the other hand, Operations Technology (OT), which to date has been almost all in one closed ecosystem without internet connection.
Both the automation specialists and the classic IT providers have long recognized this shortcoming and offer corresponding IoT services from the cloud. The solutions range from pure computing power through IT infrastructure to complete IoT platforms. The latter appear on the market under their own labels such as Ability, Adamos or Axoom, use some of the infrastructure-as-a-service (IaaS) offers of the major hyperscalers and then “refine” them with their own value-added services. This includes analysis tools with which all process and production data can be recorded synchronously with the machine cycle, or machine learning algorithms and AI functionalities for comprehensive predictive maintenance scenarios.
Information on the partner packages of the study ‘Internet of Things 2021’
Predictive maintenance is already the most common IoT application in the production environment at the moment, because, according to experts, the benefits of digital production can be illustrated most concretely. But Vincent Ohana, Managing Director of Concept Reply, warns against the assumption that industry-specific approaches of this type of digital factory of tomorrow can already help to make a breakthrough: “I rate alliances and common platforms such as ‘Adamos’ in mechanical engineering with skepticism, because in case of doubt every manufacturer is over has its own proprietary operating system and hardware. As long as there is no interoperability, such an approach can only serve simple use cases. We’re still a long way from a one-size-fits-all solution. “
Regardless of the success or failure of various platform approaches in mechanical engineering, critics also complain about the still missing fundamental digital mindset of this industry. In addition to the willingness to question the traditional product business, there is often a lack of concrete project visions that would focus on the future beneficiaries of an IoT solution, i.e. employees and customers. At the same time, in the confusion of competence disputes between IT and OT, the wrong choice of platform and insufficient attention to a previously required data standardization often occur.
- Facebook faces
Computers can learn to distinguish human faces. Facebook uses this for automatic face recognition.
- Machine learning
Contrary to what the picture suggests, machine learning is a sub-area of artificial intelligence – but a very important one.
Machine beats human: In 2016, Google’s machine learning system AlphaGo defeated the world champion in the game Go.
- GPUs GPU Nvidia
The leading companies in machine learning use graphics processors (GPUs) for parallel processing of data – for example from Nvidia.
- Deep learning
Deep learning methods first learn low-level elements such as brightness values, then elements on the middle level and finally high-level elements such as entire faces.
- IBM Watson
IBM Watson integrates several artificial intelligence methods: In addition to machine learning, these are algorithms for natural language processing and information retrieval, knowledge representation and automatic inference.
In addition, there is often the root of all evil in failed IoT projects: a lack of staying power. It is not without reason that Thomas Kanzler, Head of Digital Services at A1 Digital, states: “Strategic IoT projects deserve a very long-term perspective. The problem is that users and providers often do not have the patience to do this. I also notice that The success of IoT solutions and business models depends largely on how the top management of companies is composed. It makes a big difference here whether the company is owner-managed or salaried managers are in charge. ”
Frank Föge, Country Manager Germany & Austria at Zuora, also campaigns for patience and above all for a strategic approach from a slightly different perspective: “In order to introduce IoT in a mechanical engineering company, the objective was to largely promote the business model, as-a-service “It takes a solid change management process to change. Above all, you have to take sales with you and integrate it into the new strategy at an early stage. We have customers who have been going in the wrong direction for a year and have the option of Iteration and testing then went the right way and successfully implemented IoT applications. ”
It is not only the business perspectives of the Internet of Things that are exciting, but also the challenges that IT organizations in companies face here in general. As in the manufacturing industry, this is not just about merging classic IT with OT, but generally about a powerful IT infrastructure. Because technology plays a decisive role in the development of sustainable IoT products and business models. It must be there and function as a “commodity”. Connectivity based on 5G and edge computing are required to a certain extent. And what’s even more crucial: Big Data is now becoming a reality.
Information on the partner packages of the study ‘Internet of Things 2021’
So anyone who has neglected topics such as data management, data governance or the classification of data creates huge problems. If you have not implemented a well-founded cloud-first strategy, you are at a standstill for larger IoT scenarios without a balanced infrastructure. Alwin Schauer sums it up: “IoT has long ceased to be rocket science. In many service projects, appropriate use cases have been implemented right from the start. And if possible, IoT should be seen as a holistic approach – as a combination of networking, machine learning and artificial intelligence. Especially in the early days of IoT, many projects failed because the use case was defined too isolated and the entire application scenario was ultimately not worth it. “
In classic mechanical engineering too, the time for thinking, waiting and piloting is over for Capgemini manager Schneider-Maul: “Where no solutions are now being implemented due to the pandemic, the strategy is often checked again. If the platform is right, we have it Defined the right use cases? Resilience is now also at the top of the agenda. So you have to put everything back to the test because you want to start again when the crisis is over and business is picking up again. ” But: “Fast failure and thus the time of experimentation is over in the manufacturing industry. In some projects that have failed, a lot of tuition has been paid. Now it’s about the return on investment and the scaling of successful lighthouse projects.” (mb)