collaborative, and cooperate between people and systems (Kevin Reilly, 2020). To both identify existing risks and project into the future, AI‐based models mimicking human behavior and learning will be necessary, as part of the development ecosystem. As HSE matures, these models will be a common component of HSE to design and build highly adaptive and resilient systems needed to keep pace with the velocity of change in business. This chapter presents a framework for defining and identifying the elements for the next evolution of SE, Epoch 4.
3.2 Changing Business Needs Drive Changes in Systems Engineering
SE, and more recently HSE, seeks to deliver successful systems that realize programs’ targeted outcomes and the value derived from realizing those outcomes. Early in the evolution of SE, complexity drove the need for stability and control in engineering practices in order to reduce development risks and improve quality in operation that led to the definition of a normative set of processes, frameworks, and guidelines to increase stability and control as well as address risk in system development (Jamshidi, 2008).
Over the next 50 years, the continual drive for greater speed, agility, and innovation as well as the tremendous increase and integration of information technology (IT) into systems necessitated an evolution – if not a revolution – of SE. By the early 2000s, advances in computing power and SE tools allowed engineers to move the field from a process‐driven, traceability‐focused discipline to a discipline based on speed to value. This shift in addressing business needs was also driven by the realization that systems rarely existed in isolation but were part of a larger ecosystem of systems of systems, where systems of systems engineering codified the modeling of this interconnectedness into an evolving set of principles (Jamshidi, 2008).
Concurrently, systems engineers also recognized that the sociotechnical aspects of system development are key to success. Various methodologies (Scaled Agile Framework [SAFe] [Alexander, 2019; Leffingwell et al., 2018], Agile [Guru99, n.d.], SCRUM, etc.) were developed to increase the amount of communication between stakeholders to continually align perspectives as well as shorten the time to delivered value. Agile methodologies have migrated from domains that were primarily for software development to being widely used in the broader SE development environment. Model‐based approaches and digital engineering provide for high‐fidelity concept exploration, rapid integration, and continual verification during all phases of system development through the integration of digital engineering (Baldwin, 2019; Zimmerman and Gilbert, 2017), model‐based systems engineering (MBSE) (Ramos et al., 2012), modeling and simulation, and DevOps constructs, where DevOps is defined as a set of practices combining development practices and IT operations with the goal of shorten the system development life cycle. This digital foundation has expanded the development of the ecosystem (Tolk and Hughes, 2014; Tolk et al., 2017) to integrate stakeholder perspectives, further reinforcing the value of exploration and experimentation of the sociotechnical solution space.
There is an emerging realization that the success or failure of a system development effort is often driven by the sociotechnical aspects of the development environment. By explicit quantitative modeling of sociotechnical factors, with a focus on delivering capabilities, stakeholder alignment and coordinated action can be engineered. Sociotechnical factors can describe the quality of stakeholder alignment in a complex, multi‐stakeholder environment. These factors assess the degree and strength of alignment between stakeholders and are combined with traditional SE evaluation criteria to create a complete picture of the project environment, allowing for a holistic risk analysis, early warning of anti‐pattern presence, and success determination. AI makes it feasible to deal with the complexity and dynamic nature of adding temporal sociotechnical factors across large numbers of stakeholders. Critical aspects of the projects often have significant, dynamic uncertainties that challenge integrating evidence from stakeholder behaviors with traditional project management techniques. Innovative use of AI to assess the implication of new evidence can more fully identify risks and emergent trends and suggest corrective action or where continued actions are unwarranted.
As business owners realize the benefits of HSE and AI, the fourth epoch will integrate HSE and AI into a system development ecosystem that can effectively address the development and release of incremental value for complex, human‐technology integrated systems while promoting innovation and effectively managing risks.
3.3 Epoch 4: Delivering Capabilities in the Sociotechnical Ecosystem
As discussed above, the serial approach to SE from phase 1 has morphed into an almost continual delivery of value, particularly in IT. For example, the increased popularity of DevOps has provided a basis for continual improvement in enterprise IT systems as exemplified by Netflix (Netflix Technology Blog, 2017). DevOps is “a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production, while ensuring high quality” (Mersino, 2018), which can be viewed as accelerating continual improvement, but perhaps at the cost of thoroughly investigating the implications of changes across the entire stakeholder base. Epoch 4 will:
Provide the ability to explore the implications of changes across the stakeholders, as well as experimentation to better understand the effects of the integration of possible enhancements and modifications.
Augment the continuous development cycle of DevOps and the rapid delivery of value in SAFe with persistent quantitative sociotechnical validation through AI. It can be viewed as accelerating continual improvement while concurrently investigating the implications of changes across the entire stakeholder base.
Provide the ability to explore the implications of changes across the stakeholders as well as experiment to better understand the effects of the integration of possible enhancements and modifications.
Augment the continuous development cycle of DevOps and the rapid delivery of value in SAFe with persistent quantitative sociotechnical validation.
3.3.1 A Conceptual Architecture for Epoch 4
Epoch 4 builds on the prior epochs, particularly in the areas of Agile methodologies, DevOps, and MBSE and adds the sociotechnical components as shown in Figure 3.2. For purposes of this discussion, we have highlighted four areas of interest for Epoch 4:
Temporal sociotechnical measures: Measures over time of the complex organizational interactions between people and technology in workplaces.
SE frameworks: Incorporates a variety of SE methods and techniques that work together as a cohesive set for development activity.
Sociotechnical models: Illuminate the multidimensional interconnections between humans and technology.
Digital twins: A digital replica of an entity, human, or system.
Figure 3.2 A conceptual architecture for Epoch 4.
3.3.2 Temporal Sociotechnical Measures
System development has been based on measures for decades. Historically, measures could be traced to the “iron triangle” of cost, schedule, and performance used in traditional project performance management. If the system development effort was expending resources in accordance with a predetermined plan, value was realized when expected, and the functionality of the emerging system was
