## 3 Materials and Methods
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. Machine intelligence and digital twin technologies harness situational awareness and remote sensing algorithms, cloud-based cyber-physical systems, and image acquisition devices in Industry 4.0-based networked environments. Production process modeling develops on predictive maintenance tools, remote sensing algorithms, and data fusion technologies. Smart interconnected devices, swarm robotic and machine learning algorithms, and process simulation and scheduling tools articulate smart manufacturing systems and digital twin-based virtual factories. Smart networked factories are configured by computer-assisted technologies merging cyber-physical production systems and leveraging Internet of Things-based real-time production logistics. In addition to product decision-making information systems, Industry 4.0 integration depends on organizational structure, IT proficiency, and strategic-minded management. This research followed the five stages of digital evolution in firms [63] (see also Figure 1):  
1. The company has a production management information system in place, but its Internet visibility is limited. The organization is starting to think about putting into practice autonomous production processes, Internet of Things sensing networks, and predictive maintenance systems while lacking a coherent digital strategy. It can partially participate in supplier-customer relationship information flows. Simple economic software makes communication with other state administration bodies possible. 2. A company with a software-controlled, dynamic Internet presence begins to recognize the importance of data. Currently, automation and first integration activities are being implemented. Consideration is being given to establishing a digital strategy and engaging in supply and demand chain information flows, including collaborative virtual archives, real-time big data analytics, semi-automatic ordering, etc. 3. The third level consists of the company's utilization of online and mobile technology, smart tablets, and social media. The company has a comprehensive digital strategy and solid data culture foundations, such as initiatives for integrating data culture, real-time absorbed automation, and customized products with virtual components. 4. The next stage may be characterized by the organization's scattered and individualized digital approach, in addition to the multichannel activity that is digitally merged through digital twin modeling. The data architecture is included into each phase of the manufacturing process, including contact with customers and subcontractors, and data exchange. Digital diagnostics forecast system breakdowns and problems. 5. The company is a digitizing platform that integrates the Internet and real worlds into a single, financially viable entity. By utilizing virtual goods that interact with clients throughout their life cycles, it offers customers a unique, personalized experience. The most modern and effective solutions (full automation, real-time sensor networks, 3D printing, etc.) employ a cybernetic system that can independently implement the product's physical component. Figure 1: Five phases of digital transformation for businesses. Source: Authors’ compilation. After briefly discussing the core components of Industry 4.0, it is essential to address SMEs in order to comprehend their interactions with Industry 4.0 wireless networks, cyber-physical system-based smart factories, and cognitive automation. The fundamental requirement for qualifying SMEs, according to the European Commission, is presented in Table 3 as the number of employees and revenue. These requirements only apply to particular firms [64].