Typeset

Software development is fundamental to digital transformation. A good foundation of data is required for developers to tailor software to the needs of the commissioning department. Unfortunately, the data models required for this are incomplete, often created unilaterally by the development department and not embedded in the business context. This makes it difficult for both developers and AI to find the right algorithms. The present approach increases understanding and exchange between the specialist and development departments and offers digital assistance with data modeling as a basis for software development. Furthermore, AI approaches can help to increase the quality and completeness of the data.

Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due to the generic nature of this approach, it can be applied for any use case and be adapted or enhanced in case of need.

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.

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.

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.

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 ...

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.