# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 2 Enablers of the Fourth Industrial Revolution  
The digital transformation induced by the advancement of 1 4.0 has led to the establishment of new enabler technologies in the manufacturing industry [29; 30]: (I) Industrial Internet of Things (IIoT); (II) Cyber Physical Production System (CPPS); (III) Cloud Computing (CC); (IV) DM, DS, and DT; (V) AI and ML; and (VI) Big Data. Due to the interconnected nature of I 4.0, the fundamental requirement for the successful and sustainable implementation of I 4.0 technologies requires an appropriate Information and Communication Technology (ICT) infrastructure [29]. The IIoT (I) interconnects physical entities through the internet, enabling real-time data exchange [30]. Generally, a CPPS (II) refers to a system composed of interconnected physical and virtual components. A more detailed definition of CPPSs, according to [31; 32; 33], concludes five characteristics, defining them as (i) superordinate systems within systems; (ii) consisting of cooperative components, capable of adjusting data transfer between multiple production environment layers; (iii) acting situationally appropriate and supporting decision-making processes; (iv) providing Human Machine Interfaces (HMIs); and (v) showcasing resilient design. CC (III) provides on-demand computing resources separated from the production site by the CC providers [34]. Hereby, different CC models provide different services, from data storage and processing, up to AI and ML [29]. The DM, DS, and DT (IV) describe digital depictions of physical entities, however differ depending on the automation of data transfer between digital and physical entities [35; 36]. Here, the DM has no automated data transfer between both entities, whereas the DS features unilateral and the DT features bilateral data transfer [36]. AI (V) attempts to replicate human intelligence with suitable algorithms, enabling autonomous decision-making, and thus situationally appropriate acting. As a sub-category of AI, ML (V) aims to develop learning data-driven computational algorithms with the goal of linking data, recognizing correlations, drawing conclusions, and optimization [29; 37; 38; 39]. Big Data (VI) refers to large volumes of heterogeneously structured data being gathered, analyzed, and disturbed at high speeds in order to extract valuable and trustworthy information [40; 41]. These characteristics are summarized in the 5 Vs of Big Data: Volume, Velocity, Variety, Veracity, and Value [42]. In order to fully profit from the benefits of I 4.0, the interconnection of value chain participants is imperative, enabling the optimization of products and processes along the entire product lifecycle, thus reducing waste along the value chain and improving the sustainability of production [29; 43]. Per definition, a value chain includes all value creating steps in order to create a final product [44]. As for I 4.0 enabler technologies, suitable ICT infrastructure serves as a fundamental requirement for the networking of enterprises. Henceforth, transparency concerning data governance has to be emphasized, allowing transparent data sharing between enterprises in a value chain [45]. As a result of the transparent data sharing, the analysis of product and process data can be used to find correlations and draw conclusions, leading to the optimization of both [43]. Furthermore, the I 4.0 enabled omnipresent real-time monitoring leads to a drastic change of traditional QC methods [43; 46]. Whereby traditional QC uses statistical methods to project the quality of a product sample, onto the entire production population, the new omnipresent data-driven QC allows the evaluation of each individual product, also incorporating advanced technologies, such as AI and ML [43; 46; 47; 48].