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Technologies for Assisting Manual Order Picking

Technologies for Assisting Manual Order Picking

From conventional pick-by systems to AI-driven manual picking assistance
Md Khalid Siddiqui ORCID Icon, Jonathan Kressel ORCID Icon, Jürgen Grinninger
Manual picking remains common due to the high initial cost of support systems. This paper reviews existing technologies, presents an exploratory vision-based prototype, and examines existing literature that explores how combining object detection with language systems could enhance manual workflows. The findings suggest a promising, low-cost direction for worker support in logistics.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 6-19 | DOI 10.30844/I4SE.25.4.6
Real-Time Monitoring of the Carbon Footprint for SMEs

Real-Time Monitoring of the Carbon Footprint for SMEs

Sustainability in real time — from operation to finished products
Henning Strauß ORCID Icon, Julian Sasse ORCID Icon
Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 102-109
Enabling the Future of Manufacturing with Digital Twins

Enabling the Future of Manufacturing with Digital Twins

Opportunities and obstacles
Javad Ghofrani, Darian Lemke, Tassilo Söldner
Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 72-81
Intelligent Digital Twins in Production

Intelligent Digital Twins in Production

Driving efficiency and accelerating agility in production planning
Cedric Kiener ORCID Icon, Steffen Schwarzer
Intelligent digital twins (IDT), as the next evolutionary stage of digital twins, have the potential to accelerate and optimize processes within companies. The intelligent twin presented here independently analyzes 3D CAD data and automatically conducts a physical simulation of the assembly. Utilizing the IDT optimized assembly, reduces production costs and accelerates the production planning process. This specific use case illustrates the broader possibilities and advantages of IDTs, offering valuable insights for their transferability.
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 84-90
Digital Supply Chain Twin: The Pathway to Success

Digital Supply Chain Twin: The Pathway to Success

A catalyst for increasing competitiveness
Gökhan Cenk ORCID Icon, Jonas Andersson, Tobias Engel ORCID Icon
Companies face a variety of challenges when optimizing global supply chains. Economic interests must be balanced with legal requirements, such as the German Supply Chain Due Diligence Act (SCDDA) and the European Sustainability Reporting Standards (ESRS). A digital supply chain twin (DSCT) enables the visualization of value creation networks and supports key business functions, such as purchasing, supply chain management, distribution, service, and sales. By leveraging immersive technologies, the DSCT helps generate sustainable competitive advantages across the entire supply network.
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 52-60 | DOI 10.30844/I4SE.25.3.52
The Core Principles of the Digital Twin

The Core Principles of the Digital Twin

Transformingorder processes and the automation pyramid
Wilmjakob Herlyn ORCID Icon
The digital twin [DT] is considered a key technology of Industry 4.0. Its basic concept is now being successfully applied in practice, as demonstrated by the commissioning of Mercedes' Factory56 in 2022. New identification technologies, tracking systems and communication solutions faciliate new ways of controlling production and managing material flows, particularly at the shop floor level. With precise technical data permanently available not only for products, but also for material availability and order fulfillment status, production processes can be managed more dynamically and efficiently. This is precisely where the concept of the DT comes into play, enabling the immediate use and evaluation of this data.Its relevance continues to grow, especially in the context of make-to-order production, the rising variety of product configurations, and the globalization of production and supply networks. This article introduces the basic concept of the DT and illustrates how it connects to ...
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 92-101
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
I4S 3/2025: Digital Twin

I4S 3/2025: Digital Twin

Innovative concepts for manufacturing, logistics, and learning environments
In the connected world, digital twins open up completely new possibilities: they virtually replicate physical systems, processes, or products. However, key challenges remain, including the collection of current product data. This issue of Industry 4.0 Science covers a wide range of topics, from the basic concept of the digital twin to its benefits in procurement and its use in supply chain management.
Digital Twins for Production

Digital Twins for Production

RAPIDZ — Resource analysis and process integration through digital twins
Christian Salzig ORCID Icon, Julia Burr ORCID Icon, Sophie Hertzog
In today’s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 6-12 | DOI 10.30844/I4SE.25.3.6
STAG — Bridging Data from Shop Floor to IT World

STAG — Bridging Data from Shop Floor to IT World

An automated mapping approach for improved access to shop floor data
Oliver Amft ORCID Icon, Dovydas Girdvainis ORCID Icon, Christoph Rathfelder ORCID Icon
Collecting data from different sources on the shop floor and making it accessible to different IT systems is one of the core tasks during the process of factory digitization. Due to the different protocols and interfaces, the data collection task comes with unique challenges. With the Sensor Technology Adapter Gateway (STAG), we present a solution that closes the gap between the shop floor and the IT system’s backend. STAG is an industry-grade middleware that automates translations between data models and protocols.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 14-22 | DOI 10.30844/I4SE.25.3.14
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