Production Planning

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Computer Use in Industrial Business Processes

Computer Use in Industrial Business Processes

A systematic literature review of the last 40 years
Norbert Gronau ORCID Icon
The most important value-adding industrial business processes are product creation and order processing. For 40 years now, Industry 4.0 Science and its predecessor titles have supported the development and implementation of the software used in these industrial business processes. In honor of the journal’s 40th anniversary, a systematic literature review is carried out to identify trends that played a role in the past but no longer do today. The research provides some surprising insights into the topics that have been important in the last 40 years – as well as those that will remain relevant in the future.
Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 8-14 | DOI 10.30844/I4SE.25.1.8
Digitization of Raster Drawings with Deep Learning

Digitization of Raster Drawings with Deep Learning

Framework outperforms OCR software in extracting data from mechanical drawings
Xiao Zhao, Marko Weber, Jan Schöffmann, Daniela Oelke ORCID Icon
A new look into the depths of technical drawings: A deep learning framework reads CAD drawings more accurately than ever before, recognizing geometrical dimensioning and tolerancing, dimensions, and every other detail. What used to be tedious manual labor is now carried out by an AI that understands the special features of every line and label. This promising technology not only increases accuracy but also speeds up the processing of drawings considerably. The system thus opens up new avenues for precision in production.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 10-17
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
Setting Up Assembly Assistance Systems

Setting Up Assembly Assistance Systems

System for the efficient configuration of assembly instructions and assistance functions
Dennis Keiser, Dario Niermann ORCID Icon, Michael Freitag ORCID Icon
In industrial assembly, humans are working more closely with machines due to assembly assistance. However, despite their great potential, the implementation of digital systems is time-consuming, which entails high training requirements. Small and medium-sized businesses, in particular, are reaching their limits. A newly developed setup system is designed to facilitate the introduction and use of such assembly assistance systems and increase their acceptance.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 32-39
Large Language Models (LLM) in Production

Large Language Models (LLM) in Production

An analysis of the potential for transforming production processes in modern factories
Pius Finkel ORCID Icon, Peter Wurster ORCID Icon, Robin Radler
The rapid development of generative artificial intelligence is opening up new avenues for the manufacturing industry amid a shortage of skilled workers. Large language models can potentially make production processes in medium- sized businesses more efficient. But how exactly is this potential measured? Key areas of application such as communication, training and knowledge management show why a lot depends on employee acceptance.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 48-55 | DOI 10.30844/I4SE.24.6.48
Intelligent Shopfloor Assistants

Intelligent Shopfloor Assistants

Increasing productivity through the use of generative AI
Eckart Uhlmann ORCID Icon, Julian Polte ORCID Icon, Christopher Mühlich ORCID Icon, Yassin Elsir
In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 64-71
Analyzing Work Processes with Motion Capture Systems

Analyzing Work Processes with Motion Capture Systems

Solution and implementation principles
Hermann Lödding ORCID Icon, Silas Pöttker ORCID Icon, Tim Jansen ORCID Icon
The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 43-49 | DOI 10.30844/I4SE.24.5.42
AI-Assisted Work Planning

AI-Assisted Work Planning

Extracting expert knowledge from historical data for streamlined efficiency and error mitigation
Jochen Deuse ORCID Icon, Mathias Keil, Nils Killich, Ralph Hensel-Unger
Global competitive pressure is forcing companies to use resources efficiently, especially in high-wage countries. This is further intensified by market and legislative pressure for sustainable products and processes. In the face of digital and ecological change, holistic approaches to optimizing manual work processes are essential. An AI-supported assistance system for work plan creation is intended to remedy this and thus enable more efficient process design.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 74-80 | DOI 10.30844/I4SE.24.5.74
Bakery 4.0

Bakery 4.0

Development of an IoT framework for the automatic collection of machine, process data in medium-sized bakery operations
Abderrahim Ait Alla ORCID Icon, Frida Köning, Heiner Alsen, Joshua Coordes, Michael Freitag ORCID Icon
While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 3 | Pages 36-40
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
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