The transition from Industry 4.0 toward the humancentered vision of Industry 5.0 creates new opportunities for AI systems that augment rather than replace human expertise. This paper presents the integration of Large Language Models (LLMs) and AI copilots into industrial engineering workflows through multiple cases within the EU AIMS5.0 project. Eight use cases span system-of-systems engineering, support and aftersales services, regulatory self-assessment, product engineering, factual correctness checking, robot programming, ontology and knowledge-graph applications, and AI-assisted testing. The cases range from conceptual designs and proofs of concept to pilot implementations and therefore provide implementation experience and design insights rather than directly comparable performance results. Across the cases, recurring architectural patterns include Retrieval-Augmented Generation (RAG), semantic search, multiagent planning, natural-language interfaces, code generation, and verification mechanisms. The experiences also reveal common deployment challenges related to heterogeneous and evolving data sources, multimodal content, access control, factual reliability, legacy-system integration, and evaluation. Viewed through the Industry 5.0 dimensions of human-centricity, sustainability, and resilience, the cases illustrate how LLM-based systems can support knowledge access, engineering assistance, system adaptation, and safer interaction with industrial AI. The findings emphasize that successful industrial adoption depends not only on model capabilities, but also on high-quality domain content, appropriate safeguards, transparent system behavior, integration with existing infrastructure, and continuous human oversight.
This chapter reviews recent advances in the application of Deep Reinforcement Learning (DRL) to industrial scheduling and supply chain optimization. We first introduce foundational RL concepts and leading DRL algorithms, as well as various other machine learning algorithms. A central component of the chapter are various case studies, where DRL and other methods are applied to company use cases. The chapter concludes with perspectives on scalability, deployment, and research opportunities within Industry 4.0 and 5.0 contexts.
Industry 5.0 extends the productivity-oriented perspective of Industry 4.0 by placing greater emphasis on humancentricity, resilience, and sustainability in manufacturing. Although deep neural networks dominate much of current AI research, traditional machine learning (ML) methods remain widely used in industrial practice. Their continued relevance stems from their interpretability, data efficiency, and suitability for deployment on resource-constrained hardware. This paper examines their role through the European AIMS5.0 project. More than 40 traditional ML, optimization, and signal-processing methods are mapped to 13 industrial use cases covering machine tools, intralogistics, semiconductor manufacturing, supply chains, and other production settings. We describe the main concepts behind the different method families and summarize the observed selection logic in a decision diagram based on task type, label availability, and the need for operator-facing explanations. Across the use cases, a common human-in-the-loop pattern emerges: expert feedback is used to refine the models, while operator actions connect model outputs back to the physical process. The reported outcomes include an increase in overall equipment effectiveness and reduction in electricity cost, as well. At the same time, the use cases show that strong technical performance does not necessarily translate into scalable deployment. Based on these findings, we derive a research agenda centered on dataefficient learning, controlled adaptation to concept drift, decisionoriented and resource-aware ML, and evaluation criteria that extend beyond predictive accuracy.
As industrial systems become increasingly complex, dynamic, and data-rich, organizations face growing pressure to enhance efficiency, flexibility, and decision-making through intelligent automation. This work examines how robotics, automation, and process management enable these transformations in modern industrial operations through data-driven and machine learning–based approaches. It first reviews the current state of research and industrial practice to establish a foundation for understanding technological and methodological developments in highly automated manufacturing environments. Building on this foundation, the article presents real-world industry use cases demonstrating how advanced analytics and machine learning improve material handling, robotics, intra-logistics automation, fleet management, and production operations. Particular attention is given to the operational deployment of machine learning, including machine learning operations (MLOps), and to the integration of engineering and management perspectives required to achieve robust and efficient manufacturing systems. The presented applications highlight improvements in system performance, operational efficiency, and decision support in complex industrial settings. By synthesizing practical insights and methodological advances, the article shows how these approaches can be transferred and extended across industries, contributing to more adaptive, intelligent, and resilient production systems.
Diagnostics and prognostics are crucial components of Smart Factories. Their continuous development is driven by the Industry 4.0 and 5.0 revolution, which relies on Artificial Intelligence (AI), Internet of Things (IoT) and Industrial Internet of Things (IIoT). This survey provides a comprehensive review of AI-enhanced Anomaly Detection (AD), Fault Detection (FD), and Predictive Maintenance (PdM) frameworks, considering both state-of-the-art methodologies and practical applications across diverse industrial domains. Key aspects covered include benchmark datasets, the integration of IoT/IIoT infrastructures, and the economic implications of diagnostics and prognostics in smart manufacturing. Moreover, the emphasis on the humancentric vision of Industry 5.0, the significance of Explainable Artificial Intelligence (XAI) and Uncertainty Quantification (UQ) for fostering transparent and trustworthy industrial AI systems is highlighted. Finally, research gaps and future research directions are considered, notably in model transferability, computational efficiency, and human-centric AI development for realworld deployment.
