Management

Artificial Intelligence (AI) can make a major contribution to the future viability of our economy and society—whether by improving existing processes or new products and services that promise greater efficiency, more robust structures and more climate protection. At present, however, SMEs in particular are still reluctant to use AI systems. The frequently cited reason is that data protection hurdles appear to be too high. This article discusses the opportunities of data-based value creation. The central question is how AI applications in industry can generate economic added value from data while maintaining data protection and security.

Climate neutrality and digitisation are two future-relevant and interlinked topics that are gaining in importance for manufacturing companies. However, especially for small and medium-sized enterprises (SMEs), it is often difficult to get an overview of the concepts and practical measures in these fields. This article presents a maturity model that offers companies practical assistance in combining the goals of climate neutrality and digitisation and in identifying suitable (digitisation) measures for the company to support the transformation towards climate-neutral production. (Only in German)

Digital twins are an important part of the Industry 4.0 idea. They mirror physical goods in the digital world and enhance them with additional capabilities and functions for analysis, forecasting and decisionmaking. This paper contributes to the classification and assessment of Digital Twins using a multidimensional maturity model. The presented method "DT-Assess" enables an application-specific assessment of Digital Twins. The developed maturity model consists of seven categories with a total of 31 characteristics to be evaluated. The systematic evaluation in five application scenarios allows, for the first time, a classification of the respective "digital twin" implementation or concept with the aim of identifying further development options and weaknesses.

Manufacturing companies are currently facing many hurdles. It is becoming increasingly difficult for them to find qualified workers, so-called blue collar workers. Technology adoption and system usability also pose challenges in the industry, as digital time tracking and shift scheduling are far from the norm in the industrial sector. Human resources software should facilitate the daily tasks of departments in order to achieve business goals. The use of such technologies saves valuable time as processes are automated. The needs of employees are put in the foreground to improve both resource planning and communication of manufacturing companies.

Technology is disruptive when it leads to fundamentally new business models, products and production concepts. It is not just about the technology. Disruption only occurs when new technology is used in the context of a new business model or in fundamentally changed processes. Cyber-physical systems can be used disruptively. (Only in German)

Plant construction and engineering companies have to go to great lengths to present their highly complex products that weigh tons. Because sales staff cannot take them to the customer. Exhibiting them at trade fairs has proven its worth. But with the Corona pandemic, this sales channel has disappeared. That’s why digital alternatives are needed, such as a virtual showroom that supports salespeople in presenting and selling equipment. An API management platform ensures the smooth flow of data between various source systems and the virtual showroom app.

Against the backdrop of industrial transformation, medium-sized supplier firms can use digital technologies to optimize their work processes. New digital production systems offer one opportunity for organizing factory processes more efficiently. However, their introduction requires a “corporate culture 4.0” that is characterized by trust in decisions concerning the future design of such systems, despite the involved high uncertainties. Using the example of a digital tool management system, this article presents measures for strengthening such a corporate culture.

With the advent of People Analytics Big Data are made operative on the level of personnel management. An algorithm-based screening and analysis of all kinds of employee-related data and behavioral traces is expected to guarantee evidence-based decisions in an organizational field once considered ‘soft’ and subjective. The article summarizes the objectives and challenges of such data-driven personnel management, as well as its implications for employees and corporate labour policies.