Artificial Intelligence

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
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
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
Artificial Intelligence in ERP Systems

Artificial Intelligence in ERP Systems

Development potential and benchmarking
Marcus Grum ORCID Icon, Nicolas Korjahn
The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 100-105 | DOI 10.30844/I4SE.23.1.100
Why AI Relies on Data

Why AI Relies on Data

Uwe Müller
Artificial intelligence has the potential to bring companies and entire industries to a completely new technological level. The prerequisite is data with a high degree of maturity, with which companies can automate complex processes, calculate forecasts or create analyses. With the right data strategy, structuring and achieving the necessary data quality are no longer dreams of the future.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 63-66
Trends and Challenges in Factory Software

Trends and Challenges in Factory Software

Norbert Gronau ORCID Icon
Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.
Industry 4.0 Science | Volume 39 | 2023 | Edition 1 | Pages 114-119 | DOI 10.30844/I4SE.23.1.114
Use Artificial Intelligence for Internal Videos

Use Artificial Intelligence for Internal Videos

Michael Kummer
Huge amounts of information are stored in the memory of every employee. How a product works, best practices in departments, customer-specific information, general market and competitive knowledge, etc. This expertise is a blind spot in most companies. So how do you make in-house know-how available? And in a time when many employees work remotely, in home offices, even across countries or continents?
Industrie 4.0 Management | Volume 38 | 2022 | Edition 5 | Pages 61-63
Multidimensional Maturity Model for Digital Twins

Multidimensional Maturity Model for Digital Twins

Method for Systematic Classification and Assessment
Michael Lütjen ORCID Icon, Eike Broda, Jan-Frederik Uhlenkamp, Jasper Wilhelm, Michael Freitag ORCID Icon, Klaus-Dieter Thoben ORCID Icon
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.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 5 | Pages 7-11
Smart Connected Solutions

Smart Connected Solutions

Status quo, challenges and recommendations for industrial companies
Jonas Peter
As a result of dynamic markets, industrial companies often reach their limits to remain competitive. Smart connected solutions (SCS) comprise data-based and service-oriented offerings to stay successful. This paper provides practice-oriented insights into SCS maturity, challenges in building SCS business models and recommendations for action for industrial companies.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 5 | Pages 57-60
Artificial Intelligence and Future of Work

Artificial Intelligence and Future of Work

Changes and Possible Approaches
Andreas Heindl, Alexander Mihatsch
Artificial intelligence (AI) is already an important part of business models and processes of many companies. In the future, AI systems will pro- foundly change our working environment. AI systems can develop completely new potential for companies in a wide variety of sectors and domains - especially in industry. Existing busi- ness models can be optimized along the value chain by optimizing production flows and processes or avoiding production downtimes with predictive maintenance. At the same time, AI systems can enable completely new business models and thus radically change existing mar- ket structures through new players. The AI economy of tomorrow will be more individual, more precise and more sustainable: Competitive value creation without AI will not be possible in many areas of industry.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 4 | Pages 10-14
1 7 8 9 15