Beschreibung:
<jats:title>ABSTRACT</jats:title><jats:p>During the last decade, industries in advanced economies have experienced significant changes in their engineering and manufacturing practices, processes, and technologies that have the potential to create a resurgence in their engineering and manufacturing activities. This phenomenon referred to as the Fourth Industrial Revolution or Industry 4.0 ‐ <jats:italic>a term used commonly in Europe about high digitization in manufacturing, loosely equivalent to advanced manufacturing used in the United States</jats:italic>. The term's basis comes from advanced manufacturing and engineering technologies, such as massive digitization, big data analytics, advanced robotics and adaptive automation, additive and precision manufacturing (3D printing), modeling and simulation, artificial intelligence, and the nano‐engineering of materials. This revolution presents challenges and opportunities to the systems engineering discipline. For example, virtually all systems will have porous and ill‐defined boundaries and requirements. Under Industry 4.0, systems will have access to large types and numbers of external devices, and enormous quantities of data, which must undergo analysis through data analytics. It is, therefore, the right time for enhancing the development and application of data‐driven and evidence‐based systems engineering. One of the trends in data analytics is the shift from detection to prognosis and predictive monitoring in systems testing and maintenance using prognostics and health monitoring (PHM). Also, it is proposed to practice evidence‐based risk management as a more effective approach for managing the systems’ risks.</jats:p>