Industrie 4.0

The Fourth Industrial Revolution has changed profoundly the entrepreneurial environment. In particular, smaller companies have difficulties putting 4.0 paradigms into practice. If one considers that small and medium-sized enterprises form the backbone of the European economy, then it becomes all the more obvious which leverage effect suitable implementation concepts for SMEs can have. The following article therefore presents an evaluation model for the definition and prioritization of Industry 4.0 implementation measures, which is based on the requirements for SMEs.

Digitalen Plattformen im Kontext von Industrie 4.0 werden zahlreiche Potenziale zugeschrieben. Allerdings birgt deren Implementierung und Nutzung einige Herausforderungen, insbesondere für kleine und mittlere Unternehmen. Der vorliegende Beitrag untersucht die Herausforderungen durch digitale Plattformen mithilfe von qualitativ-empirischen Interviews von 83 Experten aus mittelständischen deutschen Industrieunternehmen. Die Ergebnisse zeigen, dass unter anderem Herausforderungen in Bezug auf Vertrauen, Konkurrenzdenken und Koordinationsaufwand existieren. Aus den Ergebnissen können strategische Handlungsempfehlungen abgeleitet werden, wie mittelständische Unternehmen den Herausforderungen begegnen können, um die Potenziale von digitalen Plattformen zu heben.

Comparison and evaluation from research findings of 28 process models considering digitalization measures are presented. Furthermore, our own-developed process model, based upon interviews with professional managers, is reported. Our process model enables managers to deal with technology coming along with industry 4.0, such as the implementation of socio-technologies.

Opportunities arising from new technologies like smart mobile devices and augmented reality have a huge impact on the manufacturing industry but are not taken advantage of when it comes to productivity analysis. As a consequence productivity analyses are rarely used and companies cannot benefit from a systematic approach to tackle improvement processes. This paper presents a productivity analysis method that uses a web-based application for data acquisition and is designed in a way that enables production staff to perform analyses on their own.

In Germany, Big-Data is frequently associated with Industry 4.0. With regard to China, some publications focused upon the legal and ethical issues of the so-called social credit system, but offered little overview concerning the general aspects of Big-Data. This paper aims to deliver a more comprehensive outline of the Big-Data developments, which have been observed for the last seven years in China. In particular, the contribution delineates the application scenarios and governmental politics related to Big-Data and further deepens the insights by providing two concise case studies, one on mechanical engineering manufacturers and the other on Big-Data activities in the province of Guizhou. At the economic level, this article compares the recent Big-Data revenues in Germany and China.

In a previous approach of Lager et al. [1], the development of knowledge, competence and role profiles of employee groups in the course of industry 4.0 was studied. Based on that, the role of the overlapping focus IT is to be examined more closely and the analysis is extended to the tactical level using the example of production planning. In addition, the effects of the need for increased IT competence in all areas of the manufacturing industry on the erosion of current role profile boundaries are presented.

The digital interactions along the value chain pose new challenges for managing information and knowledge. The objectives of this article are to describe the changes in knowledge-based value creation induced by digitalisation and to derive fields of action for knowledge management for Industry 4.0. The “knowledge ladder 4.0” shows how digital technologies can transform strategic and operative knowledge management. Subsequently, we offer a framework for the knowledge-oriented design of dynamic digital organisations that consists of three layers of activities for the operation, reflection and design of knowledge management illustrated with leading questions and case examples in order to promote the productive, responsible and sustainable usage of digital technologies.