Design

Agile methods are extremely useful in solving complex problems. This is particularly beneficial in market environments where the routines of traditional corporate management are constantly being questioned. Agility is closely linked to the core ideas of Lean Management, as evidenced by the focus on processes and people. Lean factories facilitate a hands-on engagement with Lean principles, thereby promoting agile process management in various production contexts.

The success of technologies depends not only on their innovative strength and acceptance, but also on their management. Decision-makers evaluate factors like technical framework conditions and organizational requirements, with the demand for flexibility adding to the complexity. Industry 4.0 addresses this with networking, transparency and decentralized decisions. Digital twins, which can be implemented with open source software, play a key role.

Digitalization opens up new forms of operational training. A growing focus is on smart services, which allow for proactive engagement with customer demands and empower businesses in times of digitalization. While traditional learning environments are rarely tailored toward individual needs, smart services offer new opportunities. Decentralization, previously only a utopic vision, is becoming the reality now.

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.

Establishing “smart” production processes that are focused on sustainability and based on aggregated data requires a great deal of exchange at various levels. The vertical integration of different production components provides companies with an important basis for achieving their sustainability goals. Digital twins can play a decisive role in driving this process forward.

While generative artificial intelligence has gained more visibility and achieved initial successes, it is largely unused in the industry context. In contrast, its development and versatility point to a promising application for industrial manufacturing – especially in cases where complex challenges such as decisionmaking or process optimization are present. Showcasing the various development horizons and several example case studies provides a particularly illuminating illustration of its potential for the field of technology management.

The planning and implementation of automated warehouses is characterized by high investments and risks. The FMEA (Failure Mode and Effects Analysis) currently used to reduce risks requires a great deal of effort to conduct, as it has deficits in terms of design and implementation support. These deficits include a predominant focus on the process view without linking this to the design FMEA for automation objects, an insufficient structure for the use of similar repetitive processes and technologies, a lack of automated, parameterized generation of activities, failures and causes, and a lack of integrated test scenario derivation. These deficits lead to unrecognized failures and increase the effort required to carry out the FMEA and develop test scenarios. In this article, we present a generic FMEA model which, among other things, is able to access extensive practical data in the form of knowledge bases and thus resolve the aforementioned deficits.

Digital product passports are becoming increasingly mandatory for monitoring saving targets within the framework of EU-wide emission reductions. DPPs can enable a standardized exchange of emissions data along the entire product life cycle. A framework for systematic introduction and targeted use can simplify their often difficult practical integration, especially for small and medium-sized companies.

The crises of recent years have highlighted the importance of robust business processes. Even if the concept of robustness is often not clearly defined in the context of entrepreneurial activity, it can certainly be defined on the basis of various factors such as agility, adaptability and resilience. A systematic analysis of robustness and its prerequisites in the corporate context is therefore highly relevant, especially in times characterized by uncertainty.