Process Management

Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.

Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential.

The AI Ethics Label represents a promising approach to promoting ethical AI and appropriate trust in AI systems. However, its practical application reveals some challenges due to its conservative assessment approach, limited context sensitivity, lack of benchmarks, and interpretation aids. Improvements are needed to unlock its full potential.

Demographic change is exacerbating the shortage of labor and skilled workers in the manufacturing industry, making knowledge management an increasingly important issue in many companies. Collecting and preserving tacit knowledge poses a particular challenge. This study examines the extent to which large language models (LLMs) can provide meaningful support in knowledge gathering through expert interviews. Three experts test and evaluate a personalized chatbot that has been developed using ChatGPT-5. The results of the interview are promising, but the summary shows room for improvement.

For around 15 years, German labor productivity per working hour has been increasing at significantly less than 1% per year. At the same time, more detailed productivity analyses reveal high potential in companies. The issue is that the required MTM analyses are complex and not yet employed as broadly and frequently as would be necessary. One solution is the use of digital technologies such as motion capture. These make it possible to carry out productivity analyses with little effort, as they provide data that accelerates the analysis. The MTMmotion® tool from the MTM ASSOCIATION e. V. was developed with the aim of carrying out valid and compliant MTM analyses using data provided by other technologies. This article compares the method developed for a motion capture system and MTMmotion® with a conventional MTM-1® analysis. The main result is that digital technologies can be used to create valid MTM analyses in early planning phases with little effort in order to make early ...

In engineer-to-order production systems, design and production processes are often carried out simultaneously to achieve shorter throughput times. Shipbuilding frequently adopts this approach. In practice, whilst this may lead to time savings, it can also result in efficiency losses. This article analyzes the causes of these inefficiencies and, as a counteractive measure, develops digital assistance systems for integration in the shipbuilding process chain. Digital assistance systems are based on a digital shadow of the shipbuilding process.

Decisions affecting the sustainability of products are made during the engineering process. As product engineering progresses, statements on sustainability can also be substantiated. Initially, only estimates based on related products and processes are possible, but later, operational and machine data can be used. When metrics are used for key figures, the traceability of the data should be ensured. For this purpose, relevant data quality criteria and indicators are selected and analyzed for correlations. Data availability can be increased by relying on partners within data ecosystems for product engineering. Data spaces such as Gaia-X, Catena-X and Manufacturing-X form a basis for this ambition.