DRIVING SYSTEMS ENGINEERING TRANSFORMATION

While complex systems transform the landscape, the Systems Engineering discipline is also experiencing a transformation to a model-based discipline. In alignment with this, one of the International Council on Systems Engineering (INCOSE) strategic objectives is to accelerate this transformation. INCOSE is building a broad community that promotes and advances model based methods to manage the complexity of systems which seamlessly integrate computational algorithms and physical components across domains and traditional system boundaries. This paper covers contextual drivers for transformation as well as challenges, enablers, and INCOSE resources aligned with accelerating the transformation of Systems Engineering to a model-based discipline. INTRODUCTION Today’s systems are more instrumented, interconnected and intelligent than ever before which is changing the way we develop, manage and interact with them. The number of internal and external interactions is growing at an increasing rate placing significant demands on organizations to improve system safety, security, and reliability. The challenges and opportunities of today’s engineered systems which are built from, and depend upon, the seamless integration of computational algorithms and physical components are both significant and far-reaching. To address the challenges presented by these systems, the Nation Science Foundation has called for methods to conceptualize and design for the deep interdependencies inherent in these systems within aerospace, automotive, energy, medical, manufacturing and other sectors. While system complexity advances and transforms the landscape, the Systems Engineering discipline is also experiencing a transformation—moving from a document-based to a model-based approach. This transformation, led by the International Council on Systems Engineering (INCOSE), is necessary to advance the discipline, manage complex emergent behaviors and meet stakeholder needs. CONTEXTUAL DRIVERS Figure 1 below outlines internal and external drivers influencing systems and their context. Collectively these drivers influence both the discipline of Systems Engineering and the target systems, or systems of interest that Systems Engineering produces. Internally, both digital transformation and systems complexity have a significant influence on the need and ability to transform the discipline of Systems Engineering and delivery Proceedings of the 2018 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) Driving Systems Engineering Transformation Page 2 of 11 systems. Likewise, externally the market context is pushing for more customization and adopting technology more rapidly while connecting systems more readily as well. These interrelated systems and contextual influencers cut across all sectors, and they’re driving profound change all around us. Figure 1: Growing internal and external system and contextual complexity and rate of Change. Consider the following quotes which are put forward as appropriate descriptions of the challenges we experience with our programs and systems. “Today more and more design problems are reaching insoluble levels of complexity. ” “At the same time that problems increase in quantity, complexity, and difficulty, they also change faster than before. ” COMPLEXITY DIGITAL TRANSFORMATION S ys te m C o n te xt TECHNOLOGY PROLIFERATION S Y S T E M C O N T E X T CONNECTIVITY INTERNAL EXTERNAL Proceedings of the 2018 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) Driving Systems Engineering Transformation Page 3 of 11 “Trial-and-error design is an admirable method. But it is just real-world trial and error which we are trying to replace by a symbolic method. Because trial and error is too expensive and too slow. ” These quotes nicely summarize our context today, yet they were expressed over 50 years ago by Christopher Alexander in his book Notes on the Synthesis of Form. These bullets are likely more applicable today than they were 50 years ago, and they will probably be even more applicable 50 years from now. The Role of Digital Transformation Digital Transformation is changing the fundamental nature of how organizations use and adopt digital technologies to create order of magnitude changes in value creation. This value can be expressed in more desirable products or in reduced resources such as time and cost to produce value. The lower left graphic of Figure 1 shows the rise in interest in Digital Transformation as given by Google Trends for the term “Digital Transformation. ” Given this dramatic growth and interest, there has been a similar growth in publications covering the topic. Many of the Digital Transformation publications, however, emphasize the anticipated impact of Digital Transformation on business systems such as corporate websites and marketing strategies, sales efficiencies, front office processes, and the customer experience as outlined in The Digital Imperative, Boston Consulting Group 2015[2]. In another publication, The Digital Business Imperative from Forrester, 401 global executives in companies with 250 or more employees identified e-commerce, marketing and IT as the top three functions to be impacted most by digital technologies[3]. This same report from Forrester charted insurance and retail as the most likely to have digital technology drive strategy and disrupt their business. It also mapped engineering as the least likely out of a dozen different sectors to have digital technology drive strategy and disrupt their business. Many publications also noted that the most significant impact of digital technologies is when they are leveraged within the most substantial value creation aspects of an enterprise i.e. when Digital Transformation impacts an institution’s core capabilities. For many organizations, the most significant value creation area and core capability is new product development or Systems Engineering – the aspect of the business most directly impacting innovation. Unfortunately, the cited publications and many other articles and publications do not address the significant impact and value of a broader systems approach, and its effect on the innovation process and role of modeling and engineering. This paper proposes that transforming Systems Engineering and the application of systems modeling is the essential element to value creation and the area of necessary and revolutionary change in today’s hyper-connected dynamic world. To be at the heart of this revolution requires that organizations understand contextual drivers as well as leverage the major technological shifts as noted by INCOSE’s Vision 2025, the World Economic Forum and other sources. System modeling provides a virtual system synthesis and an integrated view of how major digital technology trends impact designs. A model-based approach, however, requires a deep culture shift, and a new mindset to operate within a data-centric digital environment. The paper “Deep Shift from the World Economic Forum” highlights Six Megatrends affecting the global economy. It points out the convergence of IoT, AI, 3D printing, and other megatrends which are truly reshaping the industry. The trends identified by the World Economic Forum and others are supported and enabled through model-based systems engineering methods providing both new opportunities as well as ways to mitigate key risks. Below are a few example trend areas and associated challenges and opportunities for how model-based systems engineering methods can help. Proceedings of the 2018 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) Driving Systems Engineering Transformation Page 4 of 11 Internet of Things (IoT) has created a rapid explosion of interrelated systems and is a significant contributor to the increase in interconnectedness and system complexity. IoT impacts are still only in the formative stages and it continues to have exponential growth. This hypoconnectivity is ubiquitous, occurring across domains and with systems we use every day. Systems Engineering is fundamentally about designing and managing interactions to drive state change and desired behaviors. IoT is driving up interactions, so the application of model-based systems engineering is a necessary and logical method to fully express and manage internal and external interactions and their potential risks and opportunities. Data Science. The amount of data from instrumented and connected devices covers the full lifecycle from stakeholder needs to disposal. The amount of data organizations receive from systems is immense; in fact, it’s often overwhelming. It’s been said that many firms are drowning in data, and starving for information, knowledge and shared understanding. The use of systems models to put data in context, however, provides a great opportunity to improve engineering with robust feedback loops and ongoing learning and continual refinement of systems models and the overall systems engineering effort. Cyber Security is another a rapidly growing need and trend. As our system boundaries expand and more and more systems and subsystems become interconnected new vulnerabilities and failure modes are often introduced. Here multidisciplinary models that bridge the cyber and physical domains are essential. Cyber security is often viewed from an enterprise IT perspective rather than an engineering viewpoint of a CyberPhysical System. As more and more elaborate systems models are produced integrating both the cyber and physical portion of a design a more complete understanding of Cyber Security and Safety is provided. System models can easily be traversed to assess vulnerabilities, propagation paths, identify broker components and more. Artificial Intelligence (AI) may be the megatrend with the most hype given the strong movement toward autonomy. AI is being incorporated very rapidly into more and more domains and products. In 2009, Garry Kasparov concluded that “weak hu