Branche: SME

Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers.

In recent years, learning factories have evolved into key experimental environments in the context of the Fourth Industrial Revolution. In addition to their role as training centers for skilled workers, they also serve as real-world research laboratories. This issue of Industry 4.0 Science examines learning factories as venues for exploring new approaches and technologies—whether digital assistants, cobots, serious games, or digital twins.

AI ethics in the workplace is everyone’s responsibility. It requires accountability from companies as a whole and conscious action from individuals—whether developers or users, managers or employees. Key issues revolve around ethical AI skills and questions of governance and employee representation. How will the world of work change, from radiology and speech therapy to assembly and quality control?

European companies are investing heavily in AI. But many AI projects remain stuck in the pilot phase. The fault does not lie with the systems, but no one can explain their results. “The algorithm said so” is not a basis for costly decisions. Backwell Tech’s AI is both smart and transparent. This is how AI can give you a competitive advantage.

The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework.

This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems.

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 transformation has been a central focus of scientific discussions for years. Questions relating to data-driven decisions, artificial intelligence and resilient supply chains are at the heart of current research. The articles in this issue explain key trends and present scientific findings and practical solutions - from automation and the circular economy to cloud computing.

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.