Profitability

The engineer-to-order (ETO) production approach is gaining relevance in response to increasing demand for individualized products and small batch sizes. However, ETO inherently reduces the economies of scale typically achieved in series production, as each order requires tailored engineering and production steps. This loss of efficiency can be mitigated through demand-driven and context-aware information provision throughout the product development process. A recommendation system based on semantic artificial intelligence (AI) and machine learning can support this by i) analyzing historical data and prior knowledge, for example drawings or a bill of materials from previous projects, and ii) making automated suggestions, like reusing existing designs or proposing design alternatives, thus compensating for the aforementioned effects.

Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations.

Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles.

This paper deals with the concept of the Industrial Metaverse and its potential impact on the manufacturing industry. First, the possibilities of the Industrial Metaverse are explained in general and then possible resulting functionalities for production technology along the life cycle are presented. For the two topics "Synthetic Data Generation" and "Virtual Qualification" the implications of the Industrial Metaverse are considered more concretely.

An increased awareness of risks and rising incidents prompt companies to enhance the resilience of their supply chains. While various measures can be employed to increase resilience, a parallel consideration of a multitude of metrics is necessary to explicitly evaluate its impact on supply chain resilience. The paper presents approaches that facilitate the comparability of resilience across alternative supply chain designs by combining various metrics into a single numerical index. Additionally, innovative technologies are highlighted that can help to create resilient supply chains.

Global supply and value chains have become increasingly complex and interconnected, exposing companies to a range of risks caused by natural disasters, political instability, or global pandemics. The paper outlines some strategic options for companies to improve the resilience of their value chains, namely expansion of local or global supply chains, regional concentration or global diversification of production capacities, and insourcing or outsourcing activities. Data of 314 German manufacturing firms is used to investigate the influence of different digital technologies and adaptable production systems.