Virtualization have to be used where dataVirtualization have to be used where data

Virtualization technologies (Virtual Reality and Augmented Reality): Virtualization technologies are based on AR and VR tools that are entitled the integration of computersupported reflection of a real-world environment with additional and valuable information. In other words, virtual information can be encompassed to real world presentation with the aim of enriching human’s perception of reality with augmented objects and elements (Syberfeldt et al, 2016). For this purpose, existing VR and AR applications associate graphical interfaces with user´s view of current environment. The essential role of graphical user interfaces is that users can directly affect visual representations of elements by using commands on appeared on the screen and interacts with these menus referenced by ad hoc feedbacks.Simulation: Before the application of a new paradigm, system should be tested and reflections should be carefully considered. Thus, diversified types of simulation including discrete event and 3D motion simulation can be performed in various cases to improve the product or process planning (Kühn,2006). For example, simulation can be adapted in product development, test and optimization, production process development and optimization and facility design and improvement. Another example could be given that handles assembly line balancing and machining planning that requires to calculate operating cycle times of robots and enables design and manufacturing concurrency.Data Analytics and Artificial Intelligence: In consequence of the manufacturing companies start to adopt advanced information and knowledge technologies to facilitate their information flow, a huge amount of data related to manufacturing is accumulated from large amount of multi-source, heterogeneous and real-time data, which is occurred during R, production, operations and maintenance processes and the accumulated data is increasing at the exponential speed (Zhang et al., 2016). In particular, data integration and processing in Industry 4.0 is applied for improving an easy and highly scalable adaptation for dataflow basedperformance analysis of networked machines and processes (Dai et al., 2011). In addition to that, data mining techniques have to be used where data is gathered from various sensors, information about current state and configuration of different machinery, environmental and other counterpart conditions that can affect the production as seen in smart factories. Data appears in large volume, needed to be processed quickly and requires the combination of various data sources in diversified formats. The analysis of all such data may bring significant competitive advantage to the companies that they are able to be meaningfully evaluate the entire processes.Communication and Networking (Industrial Internet of Things): Communication and Networking is a link of physical and distributed systems that are individually defined and can interact to achieve given targets and focus on embedding intelligent sensors in real-world environments and processes and seen as the application of Industrial Internet of Things. IoT relies on both smart objects and smart networks and enables the physical objects are smoothly integrated into the network where these devices become active components in manufacturing and service processes. In other words, the major aim of IoT is to provide computers and machines to see and sense the real world applications that can enable the connectivity from anytime, anywhere for anyone for anything.RTLS and RFID technologies: Smart Factory has some of the key requirements such as smart logistics, transportation to achieve efficient coordination of embedded systems and information logistics by identification, location detection and condition monitoring of objects and resources within and the organization and cross company with using AutoID technologies. This provides the aggregation and processing of the real data gathered from production processes and various environmental resources that assists the integration of organization functions and enables self-decision making of the machines and other smart devices. Thus, radio-frequency identification (RFID) and real time location systems (RTLS) may generate value in manufacturing and logistics operations as Uckelmann (2008) described the basic concepts of real time monitoring systems in the following way: