Design

My Colleague Is a Robot

My Colleague Is a Robot

Acceptance of collaborative robotics in warehouses
Frederic Jacob, Eric Grosse ORCID Icon, Stefan Morana, Cornelius J. König
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.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 23-26
Determining Sustainable Application System Architectures

Determining Sustainable Application System Architectures

EAM as enabler for the design of transferable AI solutions
André Ullrich ORCID Icon, Norbert Gronau ORCID Icon
The need to sometimes respond very quickly to changes requires companies to have a high degree of flexibility and speed of reaction. Application system architectures, which usually consist of old and self-developed systems, often do not allow companies to meet these requirements. However, investment funds for new software are limited, so priorities must be set when it comes to replacing legacy systems. An adaptability analysis is an efficient analysis method for planning the renewal of the application system landscape. This article describes the procedure and results of an adaptability analysis, using the example of an internationally active automotive supplier.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 46-52 | DOI 10.30844/I4SE.23.1.46
Methods for Designing Enterprise Architecture in Manufacturing Companies

Methods for Designing Enterprise Architecture in Manufacturing Companies

EAM as enabler for the design of transferable AI solutions
Arno Kühn, Arthur Wegel ORCID Icon, Jonas Cieply ORCID Icon
A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 37-42 | DOI 10.30844/I4SE.23.1.106
Digitalization of Logistics Processes on Construction Sites

Digitalization of Logistics Processes on Construction Sites

Concept for the creation and use of a digital shadow for construction site logistics in mechanical and plant engineering
Sigrid Wenzel ORCID Icon, Daniel Vössing ORCID Icon, Deike Gliem ORCID Icon, Christoph Laroque ORCID Icon, Wibke Kusturica ORCID Icon
The planning of logistics processes and their efficient implementation are decisive competitive factors for customized plant construction. On the construction site, however, the collection of logistics data is often neglected, preventing the project planner from building a reliable database. Related information gaps can be closed with the help of a digital shadow that collects logistics-relevant data (partially) automatically, stores them in a consistent manner and makes them available to project management. This article describes the first important results of a research project on information and communication processes in construction site logistics and explains their vital role in the development and use of a digital shadow.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 53-58 | DOI 10.30844/I4SE.23.1.53
Optical Detection of Measured Values

Optical Detection of Measured Values

Machine Learning Methods for Digitalizing Manual Reading and Measuring Processes
Matthias Mühlbauer, Hubert Würschinger, Nico Hanenkamp, Svyatoslav Funtikov
In factory operations, measuring equipment is often used without automatic storage or further processing possibilities of the measured value. In this case, employees must capture and process the measured values manually. In this article, an approach for the optical detection and digitization of measured values with the help of machine learning methods is presented. This aims to reduce the workload of the employees, avoid reading errors and enable automated documentation.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 43-47
Fire Department Action Patterns for IT Support?

Fire Department Action Patterns for IT Support?

Norbert Gronau ORCID Icon, Eva-Maria Kern
Emergency organizations such as fire departments or technical relief organizations are expected to react very quickly – sometimes to unknown situations – and provide the appropriate assistance. Can principles used in these organizations be transferred to IT support, e.g. for ERP systems? An experiment in an IT service unit investigates this question – with surprising results.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 59-63 | DOI 10.30844/I4SE.23.1.59
Predictive Manufacturing

Predictive Manufacturing

An intelligent monitoring system to detect anomalies in 3D printing
Benjamin Uhrich, Martin Schäfer, Miriam Louise Carnot, Shirin Lange
In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 27-31 | DOI 10.30844/I4SE.23.1.88
Integration of Artificial Intelligence into Factory Control

Integration of Artificial Intelligence into Factory Control

Norbert Gronau ORCID Icon
With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 95-99 | DOI 10.30844/I4SE.23.1.95
How to Gaia-X?

How to Gaia-X?

Erik Konietzko, Cansu Tanrikulu, Florian Schwarz, Kai Lindow ORCID Icon, Christoph Heinbach, Henning Gösling, Oliver Thomas
How can organisations successfully participate in interoperable and decentralised data ecosystems? To answer this question, this paper presents a process model using the transport logistics industry as an example, which methodically describes the collaborative and interdisciplinary development of services in the decentralised federated data ecosystem Gaia-X [6]. The model supports evaluation and decision-making processes within the development of decentralised data ecosystems in practice and helps IT decision-makers and participating stakeholders to identify the relevant communication flows in a use case. It can be used independently for specific use cases, data spaces and connector technologies and ensures that the communication and alignment of individual development statuses in a decentrally organised framework is comprehensible and understandable for the overall context.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 54-58 | DOI 10.30844/IM_22-6_54-58
Industrial Subscription Business Models

Industrial Subscription Business Models

How Several Players Benefit in a Subscription Ecosystem
Markus Burger, Julia Arlinghaus ORCID Icon
Following the success of subscription models such as Netflix or Spotify in the IT and multimedia sector, the implementation of Industry 4.0 is increasingly creating the conditions for offering comparable models in the industrial context. Accordingly, pioneers are offering subscription models for printing machines, compressors or locomotives, for example. Providers do not act alone, but are supported in the design and implementation of the subscription offer by various players such as financiers, insurance companies or digitization service providers. This creates what is known as a subscription ecosystem. This article sheds light on these ecosystems and shows to what extent which players can participate in industrial subscription models. Depending on customer acceptance of these models, a wide variety of companies have the opportunity to benefit from the subscription trend and to tap into new markets and customer groups
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 63-66
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