Robotics

In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced.

Warehousing is a very labor- and cost-intensive task in many companies. Digitization and automation of manual warehouse processes can increase efficiency, reduce costs and relieve employees. Collaborative robots that share work tasks with employees are increasingly used in warehouses. However, the pure techno-centric use of such robots can negatively influence the acceptance of human-robot collaboration. Various influences such as fear of job loss, higher cognitive stress, expected extra effort, or concerns about injuries can hinder human-robot collaboration and negatively impact economic benefits. This paper presents possible barriers to the acceptance of collaborative robotics in warehouses and discusses recommended actions for human-centered, sustainable human-robot collaboration.

This article aims to develop a model for the acceptance of RPA technologies based on the presentation of the state-of-the-art. The model is the basis of an empirical study on the acceptance of robot-assisted process automation (RPA) at a global technology company. The results of this study are presented in a corresponding publication "An Empirical Analysis of Technology Acceptance of Robotic Process Automation (RPA) (Part 2)".

Data plays an important role in our world - including production technology. Businesses are faced with rising customer demands and competitive pressure. Furthermore, the trend towards smaller batch sizes and increasing variant diversity requires quick reactivity and agility. In order to make the right decisions under these circumstances, data must be generated and analyzed to derive insights. AI technologies are suitable to address the growing uncertainty and complexity. In the following, methods are described that are vital to master data processes for high-quality AI technologies.

Collaborative robots (so-called cobots) enable human-robot collaboration without security fences and are regarded as a promising future technology for manufacturing companies. However, actual market penetration has fallen behind estimations in recent years. A lack of market transparency makes it difficult to identify current barriers, development trends and the potential of cobots in practice. Will this technology become a game-changer or turn out to be a mere hype topic without significant impact on production practice? In this article, we present findings from an empirical market analysis based on secondary data and qualitative interviews with German market experts from business and academia.