## 2 Literature Review
Manufacturers can adjust the type and amount of their products to meet the demands of a rapidly changing market by harnessing digital twin-based industrial data, resource and production scheduling, and cognitive artificial intelligence across distributed interoperable environments [20]. Cyber-physical production systems, which are the essential elements of smart factories [21], are characterized primarily by decentralization and autonomy, integrating data-driven technologies with physical systems [22]. Cyber-physical production systems deploy Internet of Things-based real-time production logistics and robotic device capabilities across industrial processes [23] in order to transfer and gather factory floor data by use of predictive maintenance and computer vision detection technologies, spatial mapping and reinforcement learning algorithms, and virtual simulation modeling tools. Data-driven Internet of Things systems powered by artificial intelligence technologies cover how cyber-physical systems communicate by integrating autonomous navigation systems, computer vision capabilities, and remote interaction sensors [24]. Enhanced connectivity enables the collection and sharing of real-time data pertaining to all aspects of industrial processes [25]. Cyber-physical production systems are self-governing, with the ability to make decisions based on real-time data and virtual simulation algorithms, inspection of prior actions' outcomes, and learning from by-products [26; 27]. Enterprise resource planning applications, cyber-physical production networks, artificial intelligence-based decision-making algorithms, and manufacturing execution systems are software control providers in decentralized smart factories [28]. Permanent Wi-Fi access is necessary for data-driven machine-to-machine interactions on the shop floor. Industrial Internet of Things enables machine interaction and continuous connection by use of virtual simulation algorithms [29], being developed on cyber-physical system-based real-time monitoring, big data-driven decision-making processes, and wireless sensor networks [30]. In smart manufacturing, human-machine interaction is necessary since certain tasks cannot be fully automated [31]. Intelligent factories can produce intelligent items with sensors that aid in localization and life cycle monitoring in addition to manual manufacturing, considerably simplifying maintenance [32]. Product lifetime management is the business operation responsible for managing the manufacturing process in cyber-physical production networks by integrating intelligent decision-support systems, virtual reality mapping tools, and digital twin technologies [33]. Data perception and sharing are pivotal in product lifecycle management throughout smart factories by use of digital twin simulation modeling, sensing and actuating devices, and situational awareness and spatial cognition algorithms towards the Industry 5.0 environment [34] (Table 1). Big data analytics and cloud manufacturing are important components of Industry 4.0. In addition to their physical representation, machines operating in smart factories may learn from and interact with their surroundings in real time [35], having a virtual identity that is stored in the cloud. By combining cloud-based systems with cyber-physical systems, it is feasible to provide continuous data exchange with cloud-based data collecting systems [36]. Internet of Things sensor networks, real-time big data analytics, and industrial artificial intelligence collaborate to promote the merging of physical and digital worlds. Data gathering and analysis enable real-time threat identification and decision making by collaborative operation mechanisms [37]. Shop floor manufacturing processes that reduce errors by leveraging data mining and context awareness tools, real-time event analytics, and deep learning-based image classification algorithms may increase production across synthetic simulation environments [38].