Artificial intelligence - Industry 4.0 Science

Artificial Intelligence for Rent

Artificial Intelligence for Rent

A new Fraunhofer study shows how small and medium-sized companies can use AI

To be able to use artificial intelligence, a company does not necessarily need a qualified specialist. The Fraunhofer study “Cloud-based AI Platforms - Opportunities and Limits of Services for Machine Learning as a Service” shows how small and medium-sized companies can proceed instead. This article summarizes the arguments and results of the study, citations from it are therefore not marked accordingly.

Industrie 4.0 Management | Volume 37 | 2021 | Edition 5 | Pages 44-48

Quantum Computing: A Brief History

Quantum Computing: A Brief History

With applications of quantum computing in automotive

David von Dollen, Daniel Weimer, Florian Neukart

In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?

Industrie 4.0 Management | Volume 37 | 2021 | Edition 4 | Pages 34-36

A Machine Learning Compass for Product Development and Production

A Machine Learning Compass for Product Development and Production

Identification and planning of machine learning algorithms in manufacturing companies

Alexander Jacob, Carmen Krahe, Rebecca Funk, Gisela Lanza

Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.

Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 7-11

Status Report Industry 4.0

Status Report Industry 4.0

An analysis of adoption barriers for industrial maintenance in Germany

Jonas Wanner, Lukas-Valentin Herm, Kevin Fuchs, Axel Winkelmann, Christian Janiesch

Industry 4.0 is a political concept intended to help German manufacturing companies to exploit data potential. Today, maintenance activities are not proactive by current approaches. Decision support systems based on artificial intelligence allow a change here by even foresighted machine maintenance. However, AI’s opaque decision-making process represents a barrier for users, which endangers its effectiveness. Therefore, this article sheds light on both: the technological as well as social factor for the adoption of AI in Industry 4.0.

Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 39-43

Man and Digital Technology

Man and Digital Technology

A roadmap for the digital transformation of an Alpine region

Dominik T. Matt, Guido Orzes, Giulio Pedrini, Mirjam Beltrami, Erwin Rauch

We are currently experiencing rapid transformation in technologies and society. Due to the convergence of various megatrends, these changes have considerable impacts on everyday life. Our study aims to identify relevant strategies for the digital future of a macro-region (Tyrol, South Tyrol and Veneto). The study conducts semi-structured interviews with representatives of companies, universities and local governments, using the approach of a triple helix model. Based on the empirical analysis, we develop an action plan for the digital transformation of the macro-region.

Industrie 4.0 Management | Volume 36 | 2020 | Edition 3 | Pages 11-15

Artificial Intelligence in Visual Quality Control

Artificial Intelligence in Visual Quality Control

Using intelligent algorithms to improve product quality, increase efficiency and reduce costs

Stefanie Horrmann

Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.

Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 57-60

Artificial Intelligence for the Future Economy

Artificial Intelligence for the Future Economy

How to develop competitive business models from data

Johannes Winter

Artificial intelligence (AI) and self-learning systems have immense economic potential and are a driving force for digitalisation. Artificial Intelligence is radically changing value chains, business models, and employment in industry. Data-driven services are added to traditional products in almost all industries. Integrating Artificial Intelligence in products and services as well as using data from the production process provides opportunities for new business models in an increasing competitive international environment.

Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 43-46

Process Stability Prediction with Machine Learning

Process Stability Prediction with Machine Learning

The potential of artificial intelligence for the early detection of deviations in pharmaceutical filling

Matthias Mühlbauer, Hubert Würschinger, Nico Hanenkamp, Moritz Schmehling, Björn Krause

Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.

Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 34-38

Machine Learning in Production

Machine Learning in Production

Application areas and freely available data sets

Hendrik Mende, Jonas Dorißen, Jonathan Krauß, Maik Frye, Robert Schmitt

Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets. This article therefore answers the following questions: Which application areas in production offer the greatest potential for the use of ML? Which freely accessible data sets are suitable for gaining experience and which learning tasks are associated with them? What are best practices for the application areas?

Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 39-42 | DOI 10.30844/I40M_19-4_S39-42

Common Sense Instead of MBA

Common Sense Instead of MBA

How to recognize sustainable leaders

Hans Rosenkranz

Management tools are a dime a dozen. The US-American strategy consultancy Bain & Company, for example, analyses regularly the 25 most popular of them worldwide. However, the best tool is only as good as its user. The proper and efficient utilization requires common sense. If a manager has it or not can be identified by the following qualities: He knows that others see him different from how he sees himself. He sets high value on a respectful feedback culture in his company, and he counts on the power of cooperation.

Industrie 4.0 Management | Volume 35 | 2019 | Edition 2 | Pages 57-60 | DOI 10.30844/I40M_19-2_S57-60