Data is key raw material for Industry 4.0
In Industry 4.0, one raw material plays a pivotal role. But unlike steel, plastic, or other physical components, this material is invisible and intangible. It is the invisible streams of data in connected industry. With the right tools, companies can use this material to great advantage, since it offers them a way of continuously improving their own processes. Moreover, these data are the foundation for new business models. And it is here that the real revolution in connected industry is happening. It is a revolution that is affecting large parts of the German and the global economies.
At present, the focus of interest relating to Industry 4.0 is often on the hardware. This includes smartphones, tablets, fast computers, large memories, sensors, RFID technology, and wireless connections. All these things make it possible to connect machines within factories and across national boundaries. At many of its more than 250 plants worldwide, Bosch is doing precisely that, and improving its competitiveness in the process. And the company is allowing its customers to benefit from many of the things that have proved valuable internally. This makes Bosch both a leading exponent and a leading provider – through Bosch Rexroth, Bosch Packaging Technology, Bosch Software Innovations, and many other units.
But the potential of Industry 4.0 is much bigger. More than anything else, data are revolutionizing industrial production. And they are doing so here and now. Sensors collect data relating to process times, wear and tear, oil-pressure variations from one part of a machine to another, the torque of connected tools during tightening operations, or the status of the individual parts in a hydraulic valve. This flood of data is a new raw material for connected industry. With good algorithms and the right software, it can shed light on new correlations. What we need is experts who are equally at home with data analysis, manufacturing, and the product itself.
Let’s take the example of Bosch hydraulic valves, which are composed of several parts. Until recently, final inspection of these valves involved 90 separate testing steps. But some time ago, 21 of those steps were eliminated, cutting inspection time from 177 seconds to 146 seconds. That’s a saving of 31 seconds – or 17.4 percent. Considering the extent to which modern manufacturing has been optimized, it’s also a major advance. In this case, an analysis of the data from 30,000 hydraulic valves showed that, provided the results of several earlier steps were positive, certain subsequent testing steps in the inspection process were unnecessary. The outcome of those subsequent steps could be reliably predicted by analyzing the earlier steps. Pinpointing such correlations – which are generally much more complex than the example given here – saves time and money.
Many of the solutions discovered in this way can be transferred to other plants. When the number of parts runs into millions, even savings of just a few seconds can soon add up, resulting in significant productivity gains. The keywords here are “big data” and “data mining.” It has recently become possible to evaluate these data in real time, thus making them quickly available for use in further processes. Innovations based on collecting, evaluating, and using data are the driving force behind many new business models. The ability to filter out important information and turn it into new knowledge is a key qualification for the future. It enables us to move toward new business models.
The issue of trust is also important. Who owns manufacturing data? The machine operator or the manufacturer? A machine operator could conceivably arrange for a service provider to use the data to monitor and optimize the manufacturing process. For this collaboration to be built on trust, it is essential to deal with data in a transparent way and to have clear agreements on how they are used.
Increasingly, this is putting sensors, software, algorithms, and data security on the agenda of established manufacturing companies. Electrical engineering, mechanical engineering, and software are no longer separate worlds. The change being wrought by Industry 4.0 is profound. To make the best of it, we have to find answers to two major questions.
First, we need experts who are equally at home in manufacturing and the analysis of huge volumes of data. This combination is the only way we can hope to turn our factories’ digital raw material into useful new information. With career profiles such as data scientist or data analyst moving to center stage, there is a real need to refine and rethink the subjects being taught at university.
Second, the onus is also on industry to revise its apprenticeship and further training programs. It is imperative that children learn general IT skills at school – and not just to support the vision of the future outlined above. We must not allow a shortage of IT skills becomes a barrier to innovation. Finally, people’s fears about big data and data mining need to be dispelled. Germany should therefore seize the opportunity to position itself as a location that places great value on data integrity and data security.