Typeset

The political and social request for environmentally compatible products is putting companies under increasing pressure. Small and medium-sized companies (SME) in particular have to quickly find or develop solutions to these demands. This paper presents a methodological approach that combines the proven strategies of agile development with the specific activities of so-called ecodesign. The methodology is first discussed theoretically and then experimentally evaluated and discussed by means of a case study in a real company context.

The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.

Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.

A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.