production

Production Control in Space

Production Control in Space

An AI-supported approach for industry in orbit
Dominik Augenstein, Lara Jovic
Production in space, of semiconductors for example, offers many advantages for companies. At the same time, high transport costs mean that careful consideration must be given to the production materials being transported. The use of Kalman filters enables (real-time) control from Earth, making space production a cost-efficient option. Machine learning could make it a viable approach even for highly complex production systems.
Industry 4.0 Science | Volume 41 | 2025 | Edition 6 | Pages 22-29
Digital Twins for Production and Logistics Systems

Digital Twins for Production and Logistics Systems

Challenges and focus areas in implementation and use
Deike Gliem ORCID Icon, Nicolas Wittine ORCID Icon, Sigrid Wenzel ORCID Icon
For a successful implementation as well as sustainable use and maintenance of digital twins for production and logistics systems, it is necessary to identify relevant use cases and master the associated challenges. This paper analyzes scientific literature on common applications and challenges in the implementation of digital twins for the planning and operation of production and logistics systems. To confirm the practical relevance of the results, the results of an empirical survey have also been included. The findings are used to derive key focus areas for the successful implementation and long-term use of digital twins in production and logistics.
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 42-49 | DOI 10.30844/I4SE.25.3.42
Error Management in Production

Error Management in Production

Current situation and challenges in the industry
Johannes Prior ORCID Icon, Milan Brisse ORCID Icon, Nikita Govorov, Robert Egel ORCID Icon, Bernd Kuhlenkötter ORCID Icon
This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 38-45
Introduction of Machine Learning in Production

Introduction of Machine Learning in Production

An SME-specific, holistic guide
Manuel Savadogo, Malte Stonis ORCID Icon, Peter Nyhuis ORCID Icon
Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 88-95
Double Transformation as the Key to Sustainability

Double Transformation as the Key to Sustainability

Methodology for evaluating an AI application in manufacturing companies
Jennifer Link ORCID Icon, Markus Harlacher, Olaf Eisele, Sascha Stowasser
EU regulations demand more intensive and transparent sustainable practices from companies. Industry needs to adapt many processes and products to take charge of this responsibility. Artificial Intelligence (AI) in particular offers innovative potential. Firstly, however, this technology needs to be evaluated focusing on weak AI—market-ready systems that perform specific tasks using algorithms and data-supported models efficiently.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 82-89 | DOI 10.30844/I4SE.24.5.82
Warehouse Inventory Detection with Airship Drones

Warehouse Inventory Detection with Airship Drones

(Semi-)autonomous aircraft for inventory and quality inspection of pallets in block storage facilities
Dmitrij Boger, Michael Freitag ORCID Icon, Britta Hilt, Michael Lütjen ORCID Icon, Benjamin Staar ORCID Icon
The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 56-63
Efficient Production Simulation

Efficient Production Simulation

A method for software-supported collaboration between production and simulation experts
Marec Kexel, Walter Wincheringer
Production simulations involve considerable effort, among other things, due to the knowledge transfer between the domain expert and the simulation specialist. For small and medium-sized companies, this often represents an economic hurdle in the use of simulation. In this article, a method for a software- supported cooperation between the production expert and the simulation specialist is presented, which leads to a considerable reduction in effort. This means that the advantages of simulation can be used economically even with low optimization potentials.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 6 | Pages 46-50 | DOI 10.30844/IM_23-6_46-50
Assessment of Technical Cleanliness in the Production Process of Lithium-Ion Battery Cells for Automotive Applications

Assessment of Technical Cleanliness in the Production Process of Lithium-Ion Battery Cells for Automotive Applications

Laura Meusel, Bernd Rosemann, Michael Morawiec
Technical cleanliness as a quality feature in the automotive industry is continuously growing in importance. In this context, particularly high cleanliness requirements are placed on battery cells for electric vehicles, which must be adhered to along the value chain. This paper will introduce an assessment method for the analysis of technical cleanliness in the production process of lithium-ion- cells as well as revealing potential failure causes.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 19-23
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 ORCID Icon
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
Tool Management of the Future – A Practical Approach to the Use of Digital Twins

Tool Management of the Future - A Practical Approach to the Use of Digital Twins

Praxisorientierte Ansätze zur Nutzung Digitaler Zwillinge
Anja Wilde, Stefan Wiemers, Jan Theissen
A fast flow of information throughout the entire supply chain is unavoidable for risk minimization and is not subject of a discussion in volatile times or crisis situations. The flow of information within the supply chain is characterized by various forms of transmission: EDI, cloud applications or other system interfaces are manifold in the areas of value-added networks for digital risk monitoring and process efficiency increase. If corporate processes are examined more closely, one area remains digitally underrepresented at the moment: The digital twin of a production tool. The handling of these production tools must now be taken to a new level.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 6 | Pages 39-42
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