## 9 Practical Implications  
Autonomous manufacturing control, industrial wireless sensor networks, and predictive maintenance scheduling tools assist smart manufacturing systems in synthetic simulation environments. Plant maintenance scheduling and context recognition tools, machine learning techniques, and fault diagnosis systems shape cloud networked and collaborative robots. Smart manufacturing systems deploy digital twin modeling and intelligent data processing tools, robotic coordination mechanisms, and cognitive decision-making and augmented reality algorithms. Mobile autonomous robots integrate real-time monitoring and smart manufacturing technologies, cloud computing machines, and image recognition processes. Digital twin technology, cloud manufacturing processes, and virtual reality modeling tools are pivotal in industrial product lifecycle management. Smart manufacturing tools, automated assembly machines, and computer vision algorithms configure digital twin-based product development.  
Prescriptive and predictive analytics, edge and fog computing technologies, and image recognition and steering control algorithms enable manufacturing operations management in cyber-physical production systems. Decentralized data analytics, Internet of Things-based decision support systems, and distributed sensor networks optimize product lifecycle management in smart factories and across collaborative industrial environments. Smart production systems leverage granular manufacturing data, computation-enabled robotic devices, and simulation and scheduling tools across digital twin shop floors. Multi-robot control systems require decision and control algorithms, computational intelligence andsmart manufacturing scheduling tools, sensor-based data acquisition. Machine monitoring and intelligence technologies, data acquisition tools, and computer vision algorithms articulate cyber-physical production systems.  
Digital twin-based product development necessitates automated prognostics and diagnostics tools, computer vision techniques, and simulation modeling processes in smart factories. Sensor data fusion, computer vision algorithms, and process simulation and production scheduling tools further autonomous robotic systems and smart manufacturing technologies. Cloud networked and autonomous mobile robots harness digital twin-based monitoring and intuitive decision-making tools, sensing and actuation capabilities, and vision and navigation systems. Digital twin-driven smart manufacturing develops on sensor-based data analysis, object recognition processes, and mobile context awareness systems. Deep learning-based computer vision algorithms, event modeling and forecasting tools, and big data-driven decision-making processes are instrumental in product lifecycle management systems. Digital twin simulation and real-time remote monitoring tools, wireless sensor technologies, and cognitive data mining algorithms assist smart industrial systems.  
Conceptualization, M.N. and K.V.; methodology, M.N. and G.L.; validation, M.N.; formal analysis, K.V. and M.N.; investigation, M.N.; resources, G.L.; data curation, G.L.; writing--original draft preparation, M.N. and K.V.; writing--review and editing, M.N., G.L., and K.V.; visualization, M.N.; supervision, K.V. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
This paper is an output of the project NFP313011 BWN6 \"The implementation framework and business model of the Internet of Things, Industry 4.0 and smart transport\".  
The authors declare no conflict of interest.