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In 2011, the German government introduced "Industry 4.0" as part of its high-tech strategy to maintain the competitiveness of the country's manufacturing sector and meet new challenges. This fourth industrial revolution relies on the Internet of Things (IoT), new technological advancements, real-time data transmission between products, customers, and manufacturing facilities, and the blending of virtual and physical worlds. End-to-end engineering and the integration of information and communication technologies are considered fundamental concepts and significant advancements in Industry 4.0. While the business sector plays a vital role in economic growth, the introduction of new technology and increased product and production complexity impact industrial companies and their workers. Critics of Industry 4.0 emphasize its technocratic focus on digitalization and new technology, despite its potential to increase economic values, competitiveness, productivity, and revenue growth. The integration of Industry 4.0 impacts all corporate operations, from purchasing and production to customer management, leading to significant changes in processes, communication, and relationships. Industry 4.0 introduces uncertainties, hazards, feedback cycles, and dynamics that pose challenges to modern production and logistics systems, supply chains, and Industry 4.0 networks. This study critically examines current applications and classifications of Industry 4.0 and proposes potential directions for future research. It suggests that interdisciplinary supply chain optimization methodologies must continue evolving, and collaboration between supply chain specialists and control engineers could enhance the realism of dynamic planning and modeling and the efficiency of supply chains, Industry 4.0 networks, and production and logistics systems. Lastly, this study examines developments in digital supply chain management and the trend toward the intellectualization of Industry 4.0. [ABSTRACT FROM AUTHOR]

In digital healthcare, data heterogeneity is a reoccurring issue caused by proprietary source systems. It is often overcome by utilizing ETL processes resulting in data warehouses, which ensure common data models for interoperability. Unfortunately, the achieved interoperability is usually limited to an institutional level. The broad solution space to achieve interoperability with different health data standards is part of the problem, resulting in different standards used at various institutions. For cross-institutional use cases like federated feasibility queries, the issue of heterogeneity is reintroduced. This work showcases how the existing German infrastructure for federated feasibility queries based on Hl7 FHIR can be extended to support openEHR without further data transformation. By utilizing an intermediate query format that can be transferred to FHIR Search, CQL, and AQL. [ABSTRACT FROM AUTHOR]