Design

Short delivery times are essential in competitive markets. In addition to product selection and quality requirements, customers are also demanding more and more from logistics. However, high product variance complicates the situation, as it involves complex material flows and therefore leads to long throughput times. However, a four-step process analysis and modeling can help to reduce throughput times and strengthen the competitiveness of companies from within.

Alongside sustainability, sustainability management is also gaining in importance. At the same time, digitalization continues to advance, embedded in a complex interplay of economic, ecological and social challenges. A sustainability platform that not only records relevant company data, but also ensures the necessary security, can unleash synergies between transformative trends to foster sustainable and efficient corporate development.

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.

In order to cope with growing customer requirements and the associated increase in complexity, companies are opening up their value chains, reducing their vertical integration and increasingly entering into collaborations. Cross-company data exchange along the supply chain is thus becoming a key component for competitiveness and the realization of customer-specific solutions. For this reason, the European Union has launched the GAIA-X project, which aims to create the next generation of data infrastructure for Europe and its companies. The GAIA-X maturity model offers an approach for classifying companies into different development stages and provides concrete requirements for further development along a predefined development path towards becoming a fully-fledged participant in the federated GAIA-X data infrastructure.

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.