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Systems Engineering, Data Analytics, and Systems Thinking: Moving Ahead to New and More Complex Challenges
Author(s) -
Kenett Ron S.,
Zonnenshain Avigdor,
Swarz Robert S.
Publication year - 2018
Publication title -
insight
Language(s) - English
Resource type - Journals
eISSN - 2156-4868
pISSN - 2156-485X
DOI - 10.1002/inst.12208
Subject(s) - digitization , analytics , prognostics , big data , predictive analytics , automation , predictive maintenance , process development execution system , industry 4.0 , engineering , data science , computer science , manufacturing engineering , risk analysis (engineering) , systems engineering , advanced manufacturing , data mining , business , reliability engineering , mechanical engineering , telecommunications
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 ‐ a term used commonly in Europe about high digitization in manufacturing, loosely equivalent to advanced manufacturing used in the United States . 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.