In the third and last blog post within the “Tackling your Industry 4.0 challenges” series, Frank Piller, an award-winning professor and future manufacturing expert, addresses two of the biggest worries of manufacturing executives. Namely, how to improve the skill base in your company, and whether algorithms will be able to replace the work of executives.
One of the things executives are often afraid of is upscaling the labour base, especially in times of labour shortage. Just try to hire a data scientist with an understanding of industrial manufacturing, and you will find that it is almost impossible to do so.
Despite this being one of the biggest challenges, there are a few strategies to address it, and although we will explore this in more details in our Masterclass, let us make a few points now.
- On one side you can see big companies with centralised plants top-down strategies. For instance, a large automotive company setting up a vision for their engineers in 2030, hence starting to hire and train young people that will have those capabilities in 10 years.
These significant investments in education, which are often made by partnering up with universities, are definitely something you should get involved with. However, what I find more interesting are bottom-up opportunities to build the skill base ready for industry 4.0.
Technology enabling bottom-up opportunities
Let me provide you with two examples;
- When visiting a local factory by Siemens, a company that has a lot of top-down strategies for upscaling, I saw that a work cell had one cobot there, which had been gifted by the cell manager for Christmas, for them to play around with. This enabled the work cell to gain skills of robot programming, as nowadays basic robotic programming works and looks very much like a computer game. A couple of months later that cobot was now integrated into the production line, as by playing with it they’d found a good use for it.
- There were a few engineers who had some machine learning and advanced statistic in university, and they looked at the data they now had organised in excel. They took a couple of classes on coursera to train themselves, they have got some open source algorithm from the internet and managed to make great achievements in the maintenance environment without having to hire expensive consultants.
This is the beauty of the technologies we are working with today. They have much lower level entry, they are easily accessible, and they also are a much cheaper to implement and experiment with.
Trusting your team
For me, one of the core success factors, when talking about skills in industry 4.0, is to give freedom to your people, to trust them and to enable them to learn by themselves. If you are a busy executive, you know that this is easier said than done. However, I think it is easier to execute this, rather than trying to hire hundreds of data scientists from an empty market. My advice here is to trust and work with the people you have, give them the tools to play but also provide them with time to play.
Let us look at one last example for a reference;
- Last time we were at one of the BMW factories we visit with the “Leading the Factory of the Future” Masterclass we saw that they now had a small innovation lab for the blue collars close to the production line, which I found striking, as companies that size usually have fancy innovation labs at their headquarters designed only for the top people. In this factory, however, management had created innovation spaces close to the line with the idea that complementary to the continuous improvement cycles on that plant; the workers would get some extra time and tasks to explore new technologies that might enter the line in the future. Making this not only an innovation initiative but also a cleverly designed education initiative.
Will executives be overruled by algorithms?
Our last challenge in this series is almost a philosophical one. Although executives do not say it to me directly, I can often sense that they fear being outsmarted by an algorithm in the future.
Although some of the writing regarding artificial intelligence in a factory environment might suggest that this could be possible to some extent, I would not be too afraid of it. If you ask me who is going to win; a man, or a machine, in the end, I believe that both will be the winners.
Decision Making with Big Data
One of the prominent segments we are exploring in the Masterclass is going to be decision making with rich industrial data and algorithms. There is one perspective that says that we as managers are biased, as we often go with our gut feeling and that is not good, whereas algorithms do not have bad Mondays and always stay sharp.
However, where executives’ strength lies is in coming up with new ideas of implementations and making sense of the data discovered and transferring it to actionable insights. To do this, you need domain-specific knowledge which is what humans have.
Ultimately you need to understand the algorithms, just like you need to understand the hardware when you run a factory. There is no need to become a data scientist, but you need in-depth knowledge of these algorithms.
Also, you need to find a way to combine your expertise with the algorithms, so that instead of fearing it you can work alongside it hand in hand. This is the true smart factory, where humans and machines work smoothly, alongside each other.
If you would like to discuss these issues in more detail and to get your own roadmap for the factory of the future, attend our "Leading the Factory of the Future" Masterclass, run in association with BMW, Porsche and Siemens.