## 4 Discussion  
Since 2013, the term Industry 4.0 made its appearance and spread across industries worldwide. \"Industrie 4.0\" is the name formally adopted by the German Federal Ministry of Education and Research to identify a strategy aimed at enabling industries to transition towards future ways of production [45]. It refers to a set of recent innovations with the potential to disrupt value chains, with a substantial impact particularly on the manufacturing sector. Industry 4.0 gravitates around one core rationale: recent progress in a variety of digital technologies has created unprecedented possibilities that result in huge improvements in operational effectiveness for manufacturing industries. The magnitude of this paradigm shift justified referring to Industry 4.0 as being the \"Fourth Industrial Revolution\". There is a widespread consensus among the different stakeholders in the industry, at research institutions and in academia, that Industry 4.0 consists of essentially nine clusters of key enabling digital technologies that can radically innovate and disrupt the current manufacturing paradigms: big data and analytics; autonomous robots; simulation; horizontal and vertical system integration; the industrial internet of things; cybersecurity; the cloud; additive manufacturing; and augmented reality [46]. Industry 4.0 enables several synergies among different elements of current production and new digital technologies. These synergies can be translated into initiatives and levers with the potential to result in considerable improvements in manufacturing processes. Industry examples have shown that real-time process optimization can lead to yield improvements (output/input) of up to 10-15%; predictive maintenance can decrease total machine downtime by 30 to 50% and increase machine life-time by 20 to 40%; human-robot collaboration can increase throughput by up to 40% while maintaining the same employee base; supply chain innovations and optimization can reduce costs for holding inventory and time to market by 20 to 50%; real-time data analytics and advanced process control for real-time error corrections can minimize rework and scrap, decreasing costs related to suboptimal quality by 10 to 20%; crowd forecasting based on advanced analytics can increase the accuracy of demand forecasting by more than 85%; concurrent engineering, simulation, digital manufacturing, and additive manufacturing can reduce the new product development cycle and lead time, resulting in a 30 to 50% reduction in time to market; and remote and predictive maintenance can reduce maintenance costs by 10 to 40%. These figures represent measured gains from the real-world implementation of Industry 4.0-related innovation [47]. Therefore, the main trends that capture the digitization of the manufacturing systems are the inevitable evolution of the way goods are produced and the services that are offered. Small, medium, and large organizations are continuously researching how to control and develop their processes, services, and operations. They strive to optimize efficiency, improve quality, and reduce scrap and waste, ultimately to increase their agility in creating, executing, and adjusting strategy. This unveils the need to digitalize, interconnect and link the production processes. Regardless of their size, many companies are already looking towards the integration of products, machines and people using digital technologies. The present paper focuses on how different business functions apply data analytics and intelligence to enable the next industrial advancement in production engineering and particularly in precision manufacturing. This research work presents different case studieson how to integrate digital technologies within existing manufacturing realities rather than what technology or tools to use. In this respect, the Acatech model is applied to highlight examples of products capable of being constantly monitored across the different phases of their process chain and their process life cycle.