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Digitalisation - generating new solutions

Complex systems, machine learning and co-creating work systems
Prof. Dr. Uta Wilkens
Prof. Dr. Uta Wilkens
Chair for Work, Human Resources and Leadership, Ruhr University Bochum
Prof. Dr. Uta Wilkens, Ruhr-Universität Bochum
Prof. Dr. Uta Wilkens, Ruhr-University Bochum

In issue 31, Prof. Dr. med. Uta Wilkens, Head of the Chair for Work, Human Resources and Leadership at Ruhr-Universität Bochum addressed our topic of "value creation networks", discussing her perspective on the connection between digitalisation and new value-added communities. sat down with her once again to discuss her perspective on how digital processes are changing working environments and complex networks.

Digitisation is changing the form of cooperation between product development and sales via continuous feedback– also including customers. What does this change mean for an SME in terms of work processes and the impact on the individual?

Prof. Dr. Uta Wilkens:

SMEs are somewhat wary of taking further steps in digitisation since they’re not exactly sure how things will continue to develop for them if their employees have to work in a completely different way both within the company as well as with their customers. Changing forms of cooperation necessitate different ways of working, different relationships of trust and forms of coordination. Once people get their bearings with the new forms of coordination and communication, better and faster solutions are created, and the skills required for this can be thoroughly developed. When employees learn to think more from the perspective of others and to incorporate their own expertise against this background, they have a good chance of understanding each other across disciplines and thereby working out and implementing a common solution with others.

What is your view on dealing with complexity in this context?

Prof. Dr. Uta Wilkens:

Complexity is a bigger challenge since it’s a cognitive ability of the individual to create and develop certain information-processing strategies. In order to help him or her cope with certain forms of complexity, one must gradually familiarise oneself with new worlds of experience and e.g. first create trial areas, similar to a simulator. This boosts the certainty that solutions can be found also given increased complexity. The aspect of learning is crucial for this viewpoint: experiential learning was always an important area for optimisation in industrial processes. Today, many experiences are stored in machines, which process them differently and also suggest better solutions due to the amount of data processed. Humans are often initially empowered by machines, however they need to increasingly question and re-optimise the supposedly good, digitally generated solution. That's the real complexity challenge. This discursive questioning and ongoing development presupposes that the learning process from machine to human will also be expanded to include the learning process from human to machine. And, a human being will only ally himself or herself with this process positively if he or she has the certainty that their own problem-solving contributions and thoughts will also contribute to optimisation in addition to the machine knowledge.

Isn’t the danger of machine learning that people don’t think along with things as much as they should?

Prof. Dr. Uta Wilkens:

The acceptance of machine learning is a vital point. It should not lead to a person being downgraded in terms of his or her expertise, but rather that they be able to define their new role. It‘s important that he or she realise that they’re a key player in that type of event. They can work better and with more reflection if they more strongly factor in the machine's suggestions and then optimise their own work on it. But if individuals get the impression that their own actions are no longer important or that their own thoughts and expertise are being questioned, it becomes increasingly blunting to them. So it’s urgently necessary to promote acceptance and to take action so that humans aren’t devalued in their expertise over time, e.g. where is their thinking, reflection, their critical contribution still required? If machine learning and individual learning are not developed jointly, the capability for evolving is reduced. If no new types of solutions are added to existing data, then the solutions overall won’t be able to continue to develop.

In your opinion, what importance is accorded to digitisation in highly complex networks?

Prof. Dr. Uta Wilkens:

Digitisation always includes digitalisation which means the effects in the socio-technical system. Digitalisation affects different levels in very different ways at the moment – as far as I can tell. If we talk about manufacturing or assembly at the execution or implementation level, it can actually lead to workers receiving instructions in more pronounced digital form and in their entirety. In a sense the machine then acts as a substitute for management, because the worker is also controlled by the machine's specifications. However, I think that digital management substitutes can never completely replace the interaction with leaders because supervisors are important contacts for the feedback processes. So they‘re still needed, but they have to redefine their role – not necessarily in terms of instructions and co-thinking, but rather, they take on a coordinating position with regard to specific problem-solving, goal orientation and inspirational motivation with respect to the overall aims and mission. On other levels such as product development and sales basically, I think that digitalisation leaves more scope for self-control and creative solutions in highly complex networks. The solution scope gets bigger, as does the variety of solutions and certainly also creativity, provided that the new work systems are well designed.

Digitalisation leaves more scope for self-control and creative solutions in highly complex networks.

Prof. Dr. Uta Wilkens, Ruhr-Universität Bochum
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