Systems Engineering, Data Analytics, and Systems Thinking: Moving Ahead to New and More Complex Challenges
Authors: Kenett Ron S. , Zonnenshain Avigdor , Swarz Robert S. 
Published: August 2018

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 is often referred to as the Fourth Industrial Revolution or Industry 4.0,1 and is based on advanced manufacturing and engineering technologies, such as massive digitization, big data analytics, advanced robotics and adaptive automation, additive and precisions manufacturing (e.g., 3‐D 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 to enormous quantities of data, which have to be analyzed 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.

System properties like reliability, safety, and security are very important systems attributes. So, it is proposed to integrate such “ilities” into the systems engineering processes by introducing modern methodologies based on data analytics and systems theories. Modeling and simulation are going through impressive developments in this era. It is imperative for the systems engineer to exploit these advancements for system design, operation, and demonstration. Companies which are striving to introduce the advancements of the Fourth Industrial Revolution may apply the maturity level assessment tool proposed in this paper. We therefore provide here a context, an assessment tool and a roadmap for enhancement in system engineering designed to meet the challenges of industry 4.0.

The paper also evaluates the INCOSE VISION 2025 with a critical view to see if it meets the challenges and opportunities of the Fourth Industrial Revolution.

Finally, it is suggested herein that systems engineering needs a new model which we refer to as “the double helix.” This model emphasizes the tight connection that systems engineering must have with applied systems thinking, as well as the new properties and processes, such as data analytics, security, safety, reliability, risks, and resilience, that must be integrated into the process. Following an introduction, the paper includes 13 sections that map different aspects of challenges for systems engineering in the context of the Fourth Industrial Revolution. The roadmap this presents is designed to provide both researchers and practitioners with a practical agenda for addressing emerging Industry 4.0 challenges and opportunities.