A prerequisite for such a prediction is good quality data. Only if all conceivable states of production are depicted can a consolidated forecast be made. Included in these conditions should be those which are preferably avoided in practice: failures, breakdowns, disasters. If certain conditions are over- or underrepresented, expert knowledge from production is required.
Pure data-based solutions are, therefore, not considered the last word of wisdom. It is smarter to go hybrid and combine data from IT and production. Therein lies the answer to the question of numerous manufacturing companies: Do I have to become an IT company as part of the digital transformation? Only in part. Of course, building up IT expertise and manpower makes sense and is necessary. However, engineering knowledge is not curtailed in the process. Not everyone who knows how to use a predictive analytics tool can contribute the necessary expertise to the analysis.
As a result, individualization in production as well as other factors can lead to an unfavorable data situation, a so-called thin data case. In this instance as well, the increased incorporation of production knowledge is needed.
Can the algorithm make erroneous predictions even with good data? Naturally. Therefore, it is essential to design such a system with a feedback loop in order to give the algorithm – as the learning system – a chance to identify errors and their causes and avoid them in the future, in cooperation with human control and evaluation
German industry is well aware of the importance of introducing forward-looking technologies and has a good starting position. Of course, big international tech companies are generating huge amounts of data and thus have a better basis for predictions in this area. However, manufacturing enterprises have specific data in their sectors and expertise in their fields. Many German companies are market leaders in their segment and, therefore, have stable foundations.
When exchanging the necessary data, security concerns are often an inhibiting factor. The exchange of data between machine operators and IT platforms or machine operators and machine suppliers certainly fuels security concerns. Such exchanges require platforms that allow participants to confidently exchange data, ecosystems, as it were, between peers. Fraunhofer and many business enterprises have jointly founded the International Data Spaces Association. Among other things, we want to demonstrate from Germany that data can be exchanged differently than what is currently implemented at large internet companies.
The shortage of qualified specialist personnel also poses a challenge, especially since all sectors in general require expertise. We have had good experiences in this area with the development of company-specific training programs for qualifying our own employees.
It is now important for companies not to lose sight of the future, even with currently full order books: The focus on digital transformation cannot wait.
Advancing technological development will not only make predictions more and more reliable in the context of predictive maintenance, it will also open up an increasing number of areas for technology. Anomaly and pattern recognition as well as quality control are additional worthwhile application possibilities in the field of production. Even finance and retail can benefit greatly from reliable forecasts, as well as medicine and science.
The segments of transportation and energy are also areas with potential for special applications. The general limitations of traffic areas and the future emergence of autonomous vehicles make forecasts of expected traffic volumes necessary. The increased use of renewable energies, the production of which is very irregular, also requires forecasts. Once renewables make up 50 to 60 percent of the energy mix, there is no way around predictive energy. For example, energy-intensive production can be planned to start when a strong wind over the North Sea is predicted.