Design

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.

Robotic Process Automation (RPA) stands for the software-supported operation of software solutions via their user interface. The primary goal that RPA seeks to achieve is the automated execution of routine tasks that previously required human intervention. However, the potential of RPA to improve processes in the long term is very limited. Automating processes and bridging front-end media disruptions leads to a variety of dependencies and conditions, which are summarized in this article. The path to a sustainable enterprise architecture (and the processes and systems comprised therein) requires open, adaptive systems with modern architecture that are characterized by a high degree of interoperability at various levels.

The COVID-19 crisis had an unmistakable impact on the procurement situation in global supply chains, to which companies had to adapt quickly. The effects make it clear that to reduce risks, companies must address the structure and transparency of supply chains. The following article examines what knowledge the actors have and how digitalization can lead to further improvement. The results show that companies currently have little supply chain knowledge beyond their direct suppliers, but are increasingly able to obtain the supply chain data they require. At the same time, the results indicate that there is still potential to increase transparency and the use of data.

New types of value creation characterized by extensive data use and cross-company data sharing are becoming increasingly important for companies. However, many barriers slow down the path towards data-based value creation, especially for SMEs. Companies often lack specific ideas for data usage or digitalization and data competencies. As a result, there is often untapped value creation potential in companies. By describing a real laboratory setting with real experiments, this article demonstrates support options for companies to identify their own "data treasure" and to lift it.