Application of MES for Generating Data

Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ...

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.

The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems.

With the advent of digitalization, prevailing paradigms – such as product centricity, face-to-face collaboration and hierarchical structures – are giving way to the vision of data-driven business models, digital, collaborative ecosystems and an agile, holacratic way of working in flat hierarchies and self-managing teams. Collaboration is made possible through the use of software solutions. In addition to adapted management concepts, the digital space also requires a digital cultural understanding on part of the companies involved. Lean empowerment is a pioneering approach to collaboration based on cultural values. In expert workshops, ideas were developed to explore how these values can be lived in a digital culture and thus in terms of global digital collaboration. This article presents concrete solutions from which requirements for digital collaboration and for implementation within IT structures and software solutions in particular can be derived.