# The Future of Manufacturing and Industry 4.0
## 2 An Overview of Published Articles
The proposed framework uses KGs to model essential operator-related factors, such as movement tracking, working conditions, ergonomics, and interactions with robotic systems. It incorporates semantic reasoning and ontology-based modeling, enabling real-time data exchange and adaptive decision-making within cyber-physical manufacturing environments. A case study on wire harness assembly definitively demonstrates the practical application of the HCKG framework in an industrial environment. The findings clearly show how knowledge graphs enhance resource allocation, optimize workflows, and provide real-time operator support through the integration of sensors, AI-driven analysis, and KPI-based insights. The study's key contributions are threefold: extending existing automation standards (ISA-95, B2 MML, and AutomationML) to integrate human-centric processes, using graph-based data analysis to assess operator-machine collaboration and performance metrics, and semantically integrating monitoring systems, human activity recognition, and digital assistance tools. The research also validates the HCKG concept through a replicable industrial case study. It emphasizes the importance of human factors in cyber-physical systems and advocates for a balanced integration of human expertise and automation in future manufacturing ecosystems. Future research will focus on integrating DT technology, automating data retrieval processes, and enhancing decision-support mechanisms to further advance I5.0 applications [54]. Reconfigurable mechanisms are essential in complex manufacturing systems. They enhance adaptability and efficiency. The next study examines the impact of multi-spindle reconfigurable machines (MRMTs) within reconfigurable manufacturing systems (RMSs), focusing on various reconfiguration policies. The primary aim is to assess how modular and adaptive systems affect manufacturing performance under different conditions, such as machine failures, process time variability, and fluctuations in part inter-arrival times. Using discrete event simulation models, the study evaluates multiple system configurations, comparing single-spindle and multi-spindle machine setups under both periodic and continuous reconfiguration policies. The analysis considers key performance metrics like throughput, system stability, machine utilization, and production delays. The results are clear: integrating well-balanced multi-spindle machines significantly improves system performance and stability compared to setups with a single multi-spindle machine. Additionally, continuous reconfiguration policies offer greater flexibility and resilience to failures compared to periodic approaches. The study also reveals that failures and variability in processing times are critical factors influencing overall performance, with multi-spindle machines showing greater robustness in handling these disruptions. The research emphasizes the crucial role of simulation-based decision-making in optimizing RMS configurations, providing valuable insights for industry leaders considering investments in multi-spindle machines. Future research will focus on refining module allocation strategies, conductingeconomic feasibility analyses, and exploring integration with DT technologies to further enhance system adaptability and efficiency [55]. Another study explores data-driven business model innovation (DDBMI) and its significance for incumbent manufacturers. The study focuses on assessing organizational readiness to integrate data-driven technologies into business models, identifying key enablers, value creators, and expected outcomes that influence successful implementation. Nine topics central to DDBMI are identified through topic modeling and thematic synthesis: digitalization, the IoT, AI, big data, mobile networks, platformization, servitization, the sharing economy, and sustainability. They are organized into three hierarchical levels.