Technologie: Training

In remanufacturing, used goods are brought up to the quality level of a new product. This distinguishes it from recycling, which recovers materials and converts them into new units or products. Various uncertainties, such as the condition, quantity and time of return, can be minimized with Industry 4.0 approaches and artificial intelligence. A special learning concept ensures that employees have the required competence profiles in this context.

The typical problems of a medium-sized company, coupled with the new requirements for sustainability, harbor the potential for economic tension. Learning factories can counteract this: they simulate production processes and offer an environment where participants can develop knowledge and skills in a realistic production setting. Establishing an innovation factory not only increases productivity, but also significantly reduces land consumption.

The volatility of economic conditions and rapid technological progress require production sites to be constantly adapted and improved. This needs highly qualified factory planners who can use digital planning tools efficiently. The best qualifications emerge from closely interlinking the teaching of planning methods and the application of these methods in a planning project according to the principle of “learning by doing”.

Learning factories promote digital transformation through an interdisciplinary approach between lean management, Industry 4.0, energy efficiency, training center or research farm. SME centers are characterized by the on-site integration of small and medium-sized companies. Such a regional strategy, combined with learning factories, promotes a goal-oriented dialog between science and practice where students can put their theoretical knowledge to the test.

Digital transformation processes have a high tendency for delay, exceeding costs, and failure. This poses a significant risk in competitive global markets and shifting business models of entire industries. Successful companies have a different approach to new technologies than more traditional incumbents. Including the workforce in the transformation via change leadership in a digital transformation coaching process can reduce fear and resistance and can lead to a paradigm shift of approaching the digital transformation itself: as an agility driven, infinite game with high potential gain.

Transformative forces present companies with enormous challenges. At the same time, new forms of collaboration and new roles and responsibilities are emerging. Due to the risks associated with change, many employees hold on to old habits and work processes, which can slow down positive developments. The challenge for managers is to recognize this resistance, prevent it and turn it into acceptance or even proactive support.

Industrial manufacturing offers a wide range of opportunities for a more ecologically compatible and socially just organization. Sustainability-oriented HR management can competently support industrial manufacturing companies in the design and implementation of sustainable manufacturing. It is important to integrate ecological, social and economic requirements into the respective areas of responsibility. In addition, employees must be motivated and qualified to adopt environmentally friendly working practices in order to be able to implement sustainable manufacturing in practice. Sustainable incentive systems and employee-oriented personnel management must also be taken into account.

Despite the fact that SME innovation is critical to a country’s economic success, SMEs spend less than 50 % of their budget on R&D when compared to large companies. “Open innovation”is seen as helping SMEs to improve their competitive position. For regions dominated by SMEs it is important to develop an ecosystem that supports open innovation processes. Universities can be key enablers within these ecosystems. They support SMEs with their expertise in science and engineering as well as in innovation and project management. In this article we present a case study to demonstrate the role of a university of applied sciences in an open innovation ecosystem.

Implicit experiential knowledge will be lost through the retirement of the babyboomer generation. This know-how is difficult to capture and transfer. The KI_eeper project aims to develop an efficient AI-based system that automatically identifies and stores knowledge in the work process. The resulting knowledge base will provide assistance to all employees. The system will be designed in cooperation with employees according to their needs to gain high user acceptance.