This work presents novel integration strategies for incorporating thermally induced errors into Digital Twins of machine tools alongside conventional geometric error modeling. A high-fidelity Digital Twin is developed using a unified simulation framework that combines geometric errors, kinematic coupling effects, and a newly defined method for embedding thermal error behavior directly into the Digital Twin structure. A systematic thermal error injection and sensitivity-based parametrization approach is proposed to identify the dominant geometric and thermal contributors to Tool Center Point deviations. Validation using standardized measurement strategies demonstrates strong agreement between simulation and experimental results across varying operating conditions and workpiece materials. The proposed integration approach enables accurate prediction of combined geometric and thermal errors and provides a robust foundation for calibration, optimization, and compensation of machine tools.

Existing supply chain risk classifications rely on broad risk labels that obscure how risks actually emerge, propagate, and affect performance, limiting their usefulness for analytics and decision support. This article proposes a morphological approach that shifts risk definition from labels to explicit, multidimensional configurations. The morphological analysis systematically captures key supply chain risk characteristics, including affected supply chain areas, involved actors, severity, and temporal dynamics. Established supply chain risks from literature and expert interviews are mapped to this morphological box, enabling a consistent comparison and a precise specification. Building on this mapping, a selection mechanism identifies risks that are relevant for specific analytics use cases. An automotive use case demonstrates how the approach supports targeted selection of relevant risks.

The Fourth Industrial Revolution enables manufacturers to improve agility through IoT sensing and big data analytics. However, small and medium-sized enterprises (SMEs) continue to struggle to implement real-time performance measurement systems due to the lack of structured, end-to-end implementation guidance. While enabling technologies are widely available, organisations often lack practical methodologies for translating business key performance indicators (KPIs) into cohesive real-time architectures and decision support. This paper proposes an implementation methodology comprising a five-component technical framework covering KPI-driven sensing, secure connectivity, real-time data ingestion, stream processing, and role-based decision support. The methodology was developed through literature synthesis and validated through semi-structured expert review representing technical, strategic, and operational perspectives. Findings confirm that technical architectures alone are insufficient for SME adoption and must be paired with business-level guidance addressing scoping, budgeting, resourcing, and phased deployment. Accordingly, the framework is complemented by a six-step phased implementation roadmap that integrates these organisational prerequisites into the overall methodology. The result is a practical approach that combines technical architecture and adoption guidance for real-time performance measurement implementation in Industry 4.0 contexts.

Physical complexity of products including production equipment has been a major driver of cost in recent years. Replacing this physical complexity with virtual complexity by relying on Software-Defined Product Features can prevent or mitigate the impact of these complexities on various aspects of the product creation process while at the same time delivering capabilities necessary for concepts such as Software-Defined Manufacturing. In this paper, a new approach for implementing Software-Defined Product Features based on traditional product development methods is proposed. By applying it to two selected use-cases, this new product development method is validated.

The Cyber Resilience Act (CRA) introduces new regulatory obligations for products with digital elements, creating significant challenges for scalable and explainable conformity assessment. This paper presents a proof-of-concept system for ontology-based pre-market conformity triage under the CRA, operationalizing the previously proposed OntoCRA-NS reference architecture. The system combines structured evidence extraction, ontology population, and symbolic reasoning to infer regulatory admissibility outcomes based on a representative subset of CRA requirements. Ontological modeling and SWRL rule-based reasoning are used to evaluate the presence and adequacy of key conformity artefacts, such as technical documentation, software bill of materials, vulnerability status, and security update support. In the present prototype, evidence extraction is implemented using deterministic heuristics to validate the end-to-end reasoning pipeline. The system produces explainable triage outcomes, including compliant, non-compliant, and cases requiring human assessment. Evaluation is performed using curated product dossiers from the CURIUM project and an expert-defined reference labeling protocol to verify reasoning correctness and consistency. The results demonstrate the feasibility of ontology-driven regulatory triage and highlight the role of symbolic AI in supporting transparent and human-centric regulatory decision-making..