Industrie 4.0

Smart Service Lifecycle Management

Smart Service Lifecycle Management

Rahmenkonzept und Anwendungsfall
Mike Freitag, Stefan Wiesner
The growing amount of available data due to the digitalization of value creation is accelerating the transformation of manufacturing industries into providers of customer-oriented services. Smart services, currently the most highly developed level of data-based digital services to complement physical products for specific customer expectations, are an example of this. However, the analysis of expert interviews as well as of use cases from business practice shows that the knowledge of how such smart services can be developed is still rudimentary. This article presents a framework for Smart Service Lifecycle Management that supports the systematic development of Smart Services, taking into account business models and the value network. The framework concept will be implemented and validated based on an application example from the textile industry.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 5 | Pages 35-39 | DOI 10.30844/I40M_19-5_S35-39
Virtual Production

Virtual Production

A study on the use of digitalization in the manufacturing industry with focus on AR
Axel Wellendorf, Felix Kottenbrock, Sebastian Trampnau
In times of increasing globalization, international capital and consumer markets get more and more dynamic. To remain competitive, companies have to respond to new requirements and move away from traditional manufacturing concepts. Digitalization offers different technologies and methods to provide a remedy. The following article describes the status quo, as well as future possibilities of Virtual Production with a particular focus on Augmented Reality in the production environment. It gives a comprehensive overview of the current market situation and facilitates strategic investment decisions.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 25-29
Industrie 4.0 and Lean Production Systems

Industrie 4.0 and Lean Production Systems

Interdependencies and Use-Case Analysis
Uwe Dombrowski, Thomas Richter, Fabian Loerwald
The complexity of the production process and the higher demand for quality and individualization leads to structural changes and challenges in the production process. The analysis of interdependencies of Lean Production Systems as an industrial standard und Industrie 4.0 is able to determine new spaces of action and alternative solutions to optimize the production processes. The analysis shows that the technologies and systems of Industrie 4.0 does not compete but rather support the principles of Lean Production Systems.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 43-46
Humans in Cyber-Physical Production Systems

Humans in Cyber-Physical Production Systems

A Method for Evaluation of Design Principles for User Interfaces
Hendrik Stern ORCID Icon, Till Becker
Due to the change of work in manufacturing caused by the introduction of Cyber-Physical systems, there is a need for adequate design principles for user interfaces between humans and machines. Within a research project, a method for the determination and evaluation of such design principles was developed. The method can be used to create a catalogue of rules regarding the successful integration of human factors into Cyber-Physical production systems.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 51-54
Implementing Digitization Potential

Implementing Digitization Potential

An approach using apps for the industrial shop floor
Christian Knecht, Andreas Schuller
Small and medium-sized enterprises can hardly exploit the potential of digital transformation. In the BMBF research project »ScaleIT« an Industry 4.0 platform was developed with which individual process steps can be improved with the help of apps. There are both ready to use apps and open source tools that make it easy to develop new apps. Companies do not run the risk of a profound change in their IT processes, but can optimize their value chain step-by-step by implementing and installing new Industry 4.0 apps. A methodology helps to uncover the greatest digitization potential in companies.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 51-54 | DOI 10.30844/I40M_19-3_S51-54
Industry 4.0 Platforms from the Perspective of SMEs

Industry 4.0 Platforms from the Perspective of SMEs

How to tackle managerial challenges
Julian M. Müller, Johannes W. Veile, Kai-Ingo Voigt
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.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 63-66
Industry 4.0 Assessment – A Guide for SMEs

Industry 4.0 Assessment - A Guide for SMEs

Bewertungsmodell zur Festlegung und Priorisierung von Industrie 4.0-Umsetzungsmaßnahmen in KMUs
Dominik T. Matt, Erwin Rauch, Marco Unterhofer, Bozen, Michael Riedl, Riccardo Brozzi
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.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 7-10
Process Model for the Industry 4.0

Process Model for the Industry 4.0

Structured Introduction and Implementation of Digitalizations Measures in the Manufacturing Industry
Simon Hennegriff, Sebastian Terstegen, Sascha Stowasser, Holger Dander ORCID Icon, Patrick Adler
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.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 47-50
Web-based Productivity Analysis

Web-based Productivity Analysis

A Data-Driven Approach for the Design of Production Systems
Constantin Grabner, Robert Glöckner, Hermann Lödding ORCID Icon, Nils Barck
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.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 3 | Pages 30-34
Big-Data in China: An Overview

Big-Data in China: An Overview

Christoph Mingtao Shi, Martin Lechner
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.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 2 | Pages 61-65
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