Design for Complexity

Abstract

Design has traditionally been the process of moving from a problem statement, or need, to a solution.

1 Design Mindset

Design has traditionally been the process of transforming a problem statement, or need, into a solution. Design was originally the domain of the master craftsman, or architect, who translated the client’s needs into an exquisite artifact. Design thinking is a recent attempt to make the design process more accessible to a wider audience, to solve a wider range of problems, in every discipline.

Much of traditional engineering education is the development and teaching of solutions to standard problems—design and build an electrical circuit, write a piece of software, analyze a beam. These might be components of larger systems, e.g., a mobile phone or a bridge. We can break down complicated engineering artifacts into major components and those components into smaller components until the whole artifact has been designed and brought together as a working system. This divide-and-conquer strategy has placed men on the moon and spacecraft beyond the solar system, which are remarkable achievements. Systems engineering describes the systematic design process that has delivered these remarkable outcomes.

As complexity increases, design must be seen, particularly at the conceptual design stage, as a collaborative process of engagement between the client, the designer, and a wide range of stakeholders to develop effective solutions for complex problems. No one of these individuals has all the perspectives required to develop appropriate solutions. Rather, the collective wisdom must be pooled to shape the final solution.

This is, of necessity, a collaborative process where the engineer must play the role of making appropriate technology available to the co-designers, demystifying what is possible. At a later stage, they can burrow down into the detailed design of the technology component of the solution. However, if the social dimension does not work, the technology will be of little assistance.

The Apple iPod is a wonderful example of technological success, solving the human need (play music anywhere, anytime), with a beautifully designed piece of hardware. Its success comes from a different systems view, which included, not just the person listening to their music, but also the music companies, and their contracted artists. For better or for worse, Apple reorganized the music industry. By contrast, the Microsoft Zune, attempting to solve the same problem, was an abject failure, which failed to identify with the whole system, instead concentrating only on the storage and playing of music, omitting the purchasing and browsing of new music. A different systems view led to an entirely different product and music ecosystem (iPod + iTunes store).

2 Design with Systems

In the previous chapter, we discussed the concept of visual representations (maps) of systems, which illustrate system behavior, causal loops, and information flow. These diagrams support problem-framing as well as diagnosis, identify possible mitigations and solutions, and motivate stakeholders to act on those proposed solutions.

The following is a discussion of the design process that engineers use to create effective solutions. The design process is an extension of systems analysis, enabling engineers and other practitioners to move from analysis to synthesis, to satisfy client needs. The chapter continues with a brief overview of systems engineering, as a formalized design process and finishes with a brief discussion of digitalization trends in engineering design.

2.1 A Significant Paradigm Shift with Entangled Components

There is a paradigm shift underway in engineering practice, made up of two different elements. The first element of the paradigm shift is an outcome of system challenges. Although cognitive development, as well as skill building, are critical components of learning curricula of the twenty-first century, the problem-solving methodologies of the twentieth century, concentrated mainly on the technical domain, are fading away, and a gradual implementation of design engineering initiatives have become more prevalent, with a focus on innovation.

Shifts in mindset are required to keep up with the challenges and the changes of the twenty-first century and its Fourth Industrial Revolution. We note that products are not only fostering innovation to facilitate physical labor, but also are creating devices and applications to augment our cognitive capacity. Such products are well accepted by society at large. These trends in creating digital system products will be a characteristic of the twenty-first century, and engineering education must shift to enable the younger generation to fully participate in this change. This calls for significant additions of systems and design engineering to the curriculum.

But this is not the only shift that engineering education must undergo. The second element of the paradigm shift is entangled with the first one. The second shift involves using big data and intelligent machines, with humans co-designing with these machines. The new machines, not only perform mathematical simulations and analysis for the problem at hand, but also provide options for different solutions and employ big data to optimize the performance of the solutions, to enable sustainability, and to create optimum human interfaces, among many other attributes. This trend can be seen as an extension of the exponential increase in the use of engineering software, since the 1970s.

Time and soaring computational capacity have reduced our dependency on analytical solutions and calculus approximations. The hard work to ‘linearize’ physical models and apply them as special cases, is augmented, if not replaced, by nonlinear models to address complexity and chaos in a systems context. Deterministic models can be replaced by stochastic ones, and with that we come closer to more realistic investigation of some of the challenges. Given the state of progress in AI, the above-mentioned direction should be taken seriously.

In short, we observe that the engineering curriculum is going into a new paradigm of two aspects: (a) systems analysis with engineering design and (b) co-designing with intelligent machines.

A new curriculum should provide engineering students with new content, processes, and training to establish competency in the following areas:

• (a) Systems Thinking

  • Knowing the foundations of systems thinking

  • Understanding the functioning of systems dynamics: feedback loops and delays

  • Being able to identify, explore, and map system relationships for interventions, while leveraging flexible and divergent thinking practices

• (b) Design Process

  • Knowing the basic elements of the design process

  • Use design methodologies to understand critical design requirements and unexpressed needs, and to implement innovative and relevant solutions

• (c) Interdisciplinary Collaboration

  • Effectively participate and lead in multidisciplinary teams to accomplish significant objectives

  • Understand team dynamics and apply tools to maintain optimum team performance

  • Use planning tools to complete projects within time and other constraints

  • Professionally documenting and communicating design outcomes

• (d) Communications

  • Provide constructive feedback

  • Deliver crisp verbal presentations

  • Create compelling visual presentations and representations.

These elements (a) through (d) are chosen based on the anticipated needs of future work and the expectations of employers. An example of those needs is listed in the World Economic Forum’s ‘Future of Jobs Survey’ (World Economic Forum, 2020).

Table 3.1 compares the learning outcomes with the top ten required skills from that survey.

Table 3.1 Top ten required skills and learning outcomes

Further, the World Economic Forum (WEF) presented a set of literacies, competencies and character quality that are critical for 21st-century successful persons. These are listed in Table 3.2. It should be noted that most of the qualities indicated by the WEF are consistent with the USA-ABET accreditation requirements (ABET, 2022).

Table 3.2 Literacies, competencies, and character quality needed to succeed in the twenty-first century

2.2 Design as a Creative Process to Address Human Challenges

If there is a word that integrates the essence of humanity, it is ‘design.’ Human beings have been designing their environment for thousands of years. The word design stands for several meanings in different contexts. As a noun, it may mean a plan to achieve a business, a chemical or a manufacturing process. It can stand for an architectural plan or an engineering drawing. It is also an action-oriented verb for creating or achieving a goal. It suggests the notion of a process through which a physical or a digital object is achieved. Some try to generalize the creation aspect of design by indicating that it is about technologies that are lifesaving; others state design must have an impact. But these are limiting notions too.

Design is a process for problem definition and solving, that intentionally brings a human system from an inferior state to a higher performing state. (Simon, 1968)

Thus, design connects the artifacts to economics and to sociopolitical dimensions. It also connects to scientific discovery, and business innovation. Design connects to our cognition and emotions and allows integration to form implicit and explicit information.

Design, as an intellectual branch of knowledge, formally started almost 100 years ago, perhaps because of the complexity of the artifacts created beyond craftsmanship. In the 1920s, Theo Van Doesburg predicted a ‘new spirit’ that can construct new objects (designishistory.com, n. d.). This was followed by assertions that there is a need for methodologies and an ‘objective system’ to teach and assess the value of the artifact. In addition, there were attempts to create connections to science as a source of inspiration and discovery. For example, Cross (2018) pointed out that there was 'a desire to produce works of art and design based on objectivity and rationality, that is, on the values of science.’ This theme was developed during the 1960s (Baldwin, 1997) and voiced at the Conference on Design Methods, held in London in 1962.

The feeling was that ‘design’ should have solid epistemology that can be taught and developed through methodologies not too different from the scientific methods. Buckminster Fuller called for a ‘design science revolution’ based on science, technology, and rationalism, to overcome human and environmental problems (entreVersity, 2018).

Herbert Simon in ‘The Sciences of the Artificial’ argued for the development of ‘a science of design’ in universities; ‘a body of intellectually tough, analytic, partly formalizable, partly empirical, teachable doctrine about the design process.’ Simon saw this new science as suitable for addressing human challenges, the sort that (Rittel & Webber, 1973) described as ‘wicked’ problems, a different class of human challenges of high complexity. Others, like Schön (1983), noted that science is analytical while design is constructive, that designers engage in reflective practice, and that the epistemology of this practice, implicit in the artistic and intuitive processes, brings insights to situations of uncertainty and instability. These notions make design methodologies especially well-suited for addressing large human challenges (Cross, 2001).

As Rittel and Webber articulated, most human challenges are open-ended, broad, ill-defined, and normally originate from conflicts within interdependent and interacting human systems. In general, linear techniques are not suited for addressing such problems. The design process approach has attractive attributes that are useful in addressing these challenges.

Interestingly, design has organic links to both the arts and engineering. The boundaries between art and design are blurred. For example, applied arts is a narrow example of the connectivity between arts and design. Perhaps more important is that design entails integrating aesthetics in addition to functionality.

Engineering as a problem-solving method uses scientific and mathematical principles, creating an inaccessible language for non-engineers. Design reaches out through the need for functionality and joins engineering with arts while humanizing the solution. Connection to products, industrial applications, and optimization are most successful when design interjects and is successful in creating gratification of the senses and sensuous delight.

When issues are complex such as in cases of open-ended human challenges, connections between design and engineering are critical. Design is forward looking and explores what can be, it joins with engineering, which translates design solutions into realities. Therefore, the concept of design engineering encompasses both imagining the future and building it.

An integral part of design engineering is innovation; without new syntheses and solutions, there are no transformative outcomes. Innovation is a mindset, a methodology, and a process, all in one. It derives new behaviors and outcomes and creates system transformations at scale. Thus, design creation is innovative; a repetition of a solution without new synthesis does not represent design. Through innovation, we can incorporate universal design and meet people’s needs without stereotyping. The design process also manages the emotional challenges and logistical issues.

2.3 Design Connections

Design, along with the arts, sciences, and engineering, is a facet of human cognition and inquiry. Finding differences among the above-mentioned domains is easy; finding complementary parts is where opportunities arise. Science focuses on understanding natural phenomena and uncovering patterns and similarities among them, design deals with the artificial. Both science and design aim to solve problems and test solutions. However, there is a major difference between the philosophical underpinning of the ‘hypotheses’ of science and design. In science, the goal is to uncover what is, while in design the goal is to reveal what might be.

Modern engineering is analytical and uses mathematics as a language. It is founded on scientific principles, and illustrations and visualization are central to engineering methodologies. Through synthesis, and often through inspiration from nature, engineering improves human life.

The organic connections between design and engineering provide a holistic perspective. Through design, an examination and interpretation of systems leads to mitigations, if not solutions, which are sensitive to the overall human situation. The intuitive nature of design integrates well with the rigor of engineering. Engineering provides the rational, analytical, and theory-guided approach. The aesthetics and interpretation of the arts feed design and engineering with human compassion and humanistic interpretations.

3 The Design Process

In a previous chapter, we discussed the types of challenges that our graduates will encounter in the future practice, using the UN’s SDGs as examples. Whether the problem is profoundly complex or lends itself to simple cause and effect, the design process encourages a systematic understanding of client needs plus a search and trial of suitable solutions. The design process has been popularized as the ‘design thinking process’ (Brown, 2008).

3.1 Design Thinking

Over the years, the term design thinking has acquired multiple meanings and become trendy and is sometimes viewed as a new construct invented in Silicon Valley. Not only is ‘design thinking’ not new, it is also sometimes executed in a pedantic way, with little understanding of its limitations.

Nevertheless, it is encouraging that design thinking has been adopted by academics, as well as the public, and viewed as a method for solving any issue in industry, administration, and government. Under the banner of design thinking, some reduced design to a few steps done in a mechanical way, devoid of the creative process. The design thinking steps can be simple, and the systematic method is helpful for creating needed artifacts. However, the current simple approach needs to be carefully examined when addressing human challenges where significant innovation is required and which cannot be prescribed by ‘users’ or ‘stakeholders.’

Expert guidance on design thinking has been provided by Rowe (1986) at the Harvard Graduate School of Design, and also von Hippel (1986) from the MIT Sloan School, who discussed ‘user-driven innovation’ and the steps for successful design. The design thinking approach starts by researching and defining the problem, first by empathizing with the client and stakeholders (Fig. 3.1). The simple process of customer interviews and the focus on end users to achieve a satisfactory design is important, leading to an agreed problem definition, acceptable to all stakeholders.

Fig. 3.1

Design thinking process (after interactive design foundation)

The next step is ideation, for which design thinking does not provide any explicit direction. Hence, expecting design thinking to be a process for innovation has limitations. However, there are many innovation techniques that can help at this stage, depending on the nature of the problem [e.g., TRIZ, the Theory of Inventive Problem Solving, Orloff (2006)] (Doblin, 2021). To achieve the radical innovation necessary to create ambitious solutions for wicked problems, the end user is the ultimate judge of success. Many innovative products have failed in the marketplace because they have failed to connect with genuine human needs (Destination Innovation, 2022).

Verganti (2009) challenged the notion of human-centric design, arguing that for true radical change, the designer must do more than translate user requirements. A truly innovative design, according to Verganti, must not only ask users about the ‘product,’ but inquire about the social, cultural, and environmental contexts of the challenge. It must consider not only the pragmatic need, but the reason(s) why people do things and how systems and their feedback loops interact. Designers with this orientation become interpreters in the discovery stage and more critical than the users.

Even in simple product design, Verganti asserts that people buy meaning, and the designer must understand, anticipate and influence how users will attach meaning to design. This discussion has been well extended by Eklund et al. (2021) and Sinek (2009). An example of a design-centric product is Swatch, which transformed the watch from being a commodity instrument indicating time, to a fashion statement. Currently, we observe a variety of digital wearable devices that have been created with a narrow application-based thinking and others, such as smart watches that cover broad contexts.

After the first step of the design process, designers continue to conduct their investigation but with an emphasis on creating their own vision and purpose, developing their particular language and new meaning (which ideally should be radical). In generating new meaning, designers must continue to explore and investigate their aim by working with users who define the sociocultural dimension. Ultimately, users need to actively participate in the creation of the product/solution. There is a design push approach that is complementary to the technology push and the market pull.

Innovation is one of the major sources of long-term competitive advantage (for individuals, organizations, and economies) and design is a tool for innovation to create economic value. Innovative design is broad and extends to business models as well as to human organizations. Human organizations are complex. Design helps translate visual and physical symbols and aesthetic experiences to an organization’s values.

In the 1990s, Gagliardi (1990), Alvesson and Berg (1992) discussed organizational symbolism, and Strati (1999) wrote on aesthetics and organizations. This work suggests that organizations struggle while dealing with the ambiguity of knowledge work, and this struggle diminishes if employees perform their work as ‘design practice.’ That is, employees engage in finding the root cause of issues and being innovative in creating solutions that are sustainable and progressive.

Boland Jr. and Collopy (2004) in Managing as Designing suggested that since designers relish the lack of predetermined outcomes, managers as designers are better equipped to handle business uncertainties, and Dunne and Martin (2006) connected approaches for managerial problems to approaches of design, stating that ‘we are on the cusp of a design revolution in business … today’s business people don’t need to understand designers better, they need to become designers.’

From the above discussion, we may conclude that design does not predetermine outcomes, and it is a mindset and an attitude that create opportunities for making the ‘remarkable.’ Design is a social endeavor and there are two social aspects that influence design through a cultural connection: vocabulary and functionality. Frank Gehry (philosophy-question.com, n. d.) warned about the influence of a certain ‘vocabulary’ while attempting to create high-impact design. Vocabulary creates the boundary, he stated. For example, words like cost–benefit analysis and discounted net present value, stifle innovation.

In addition to vocabulary, functionality is another aspect that needs to be understood. Functionality is normally viewed as ‘how things work,’ but some functionality evokes human and emotional dimensions of hopes and dreams of new possibilities. So, in its broadest sense, functionality connects to society and its aspirations. Thus, there is a dialectic dialogue between the outcomes of a design (the product) and culture. When the design is successful, a new language is adopted and, possibly, a new culture emerges leading to enhanced awareness. This is particularly true when a wicked problem is successfully addressed.

3.2 Systems Engineering—Beyond Design Thinking

Systems Engineering is a formalized and rigorous approach to engineering design, which is essential in complex industries such as the aerospace (INCOSE, n. d.). It can also be used in all engineering design tasks in a simplified manner, providing much more structure than design thinking offers, enabling students to deal with more complex design tasks. Typically, students need this simplified version to learn design over a range of tasks of different difficulties. Students may start designing smaller simpler tasks in their formative years, leading to larger, more complicated, and then complex tasks as they approach graduation.

Why is Systems Engineering necessary? Problem complexity is growing faster than our ability to manage it. Complexity is growing in terms of problem scope, the number of components, the number of interactions with adjacent systems, and the number of people involved in both the implementation team and in the number of stakeholders and customers. Systems engineering now needs to take account of the solution in context, including the business environment, the natural environment, and social and political aspects that may impact the solution’s future uses and impacts.

Too often, the system engineering design emerges gradually, rather than from an overall system model, e.g., in transport and telecommunication networks, which tend to grow as one component is bolted onto the last. An overall system model is essential to guide these new additions. The overall system engineering model must also be regularly updated to recognize the changing requirements of the system, e.g., the introduction of 5G technology in telecommunications will lead to significant revisions in the communication protocols. Another example is the way Google Maps has transformed how people use transport systems, and the future impact of increasing use of artificial intelligence by system owners and system users.

A well-documented systems engineering approach counters some serious issues. For example, the loss of knowledge at project lifecycle phase boundaries, such as feasibility, conceptual, detailed design, construction, operations, maintenance, decommissioning stages, where there is regularly a significant turnover in team membership (Watson et al., 2020). Similarly, knowledge and investment are lost between projects, so a well-documented system enables new teams to learn from earlier projects. A systematic approach is required. This will be discussed next.

3.2.1 Lifecycle

Engineering projects move through a predictable lifecycle, from feasibility assessment, through conceptual design, detailed design, construction or manufacture, operations, and maintenance, to decommissioning. This is not necessarily a smooth pathway, usually requiring constant iteration and refinement, through conversations within the design team and back and forth with the client, to ensure that the right problem is being solved and to make sure that the final product will address all the client’s requirements.

Most of what follows relate to the design stages of feasibility assessment, conceptual design, and detailed design, when the form and function of a product or service are being shaped. A model-based systems engineering approach (MBSE) then continues to support the product in operation, maintenance, and in its eventual decommissioning.

That does not mean that design does not occur at the other three stages. It is just that the nature of the products and services differ. For example, designing temporary formwork for construction, or a launch beam for a bridge, is also design. It may not use the same level of rigor as for the bridge itself.

3.2.2 The V-Model

In terms of systems engineering, the six lifecycle stages mentioned above are elaborated with some additional steps that pay attention to the key design stages and to the need for constant validation and verification (Fig. 3.2):

Fig. 3.2

V-diagram

• 1. Concept of Operations (User view of the system intention)

• 2. System Requirements

• 3. Conceptual design

• 4. Detailed design

• 5. Implementation

• 6. Component testing (verification and validation)

• 7. Subsystem testing (verification and validation)

• 8. System testing (verification and validation)

• 9. Customer Acceptance testing (final verification and validation).

In traditional project management (often called the waterfall model), this is seen as a linear process, where action cascades from one step to the next until the project is complete. Unfortunately, this has led, at times, to the wrong product being delivered, since verification and validation, as a formal step, is left too late (de Bruijn et al., 2010).

At this stage, customers find themselves with a product that does not match what was requested or it contains obsolete technology, or it no longer matches business requirements. To counter this, verification and validation become part of every step, so that as the design proceeds, the design team is constantly checking with the client and stakeholders to ensure that what will be delivered is still in line with client expectations and requirements.

Consequently, systems engineering bends the process into a V, as shown in Fig. 3.2, introducing continuous checking processes across the V, to ensure constant alignment between user needs and the final delivery. Think of these steps as regular meetings with the client. The V-model was originally developed in Germany in the 1990s, with this focus on verification and validation, and was later adopted in the UK and US (Chapman, 2021). This iterative approach also aligns nicely with Agile Project Management, which has been widely adopted in technology companies (Atlassian, 2019).

The V-model begins with the Concept of Operations (ConOps), which is a high-level statement of what is to be delivered. It must address user needs and it may take quite some time for it to emerge. The clearer this statement is, the more likely that a successful project will be delivered. The Concept of Operations states the goals and objectives of the proposed system as well as the process to realize the system, including the stakeholders who must be involved in the process (ISO/IEC/IEEE, 2018).

What helps here is to be able to validate the Concept of Operations. This could mean building a rough prototype that can be tested in the field. Often, it’s only when the end users see and touch a prototype that they can think clearly about what is required. Agile methodology has arisen from this build-test-build-test approach (Workfront, n. d.), where the intent is to co-develop the system with the end users, using a series of sprints and stand-ups to develop the system incrementally (Chapman, 2021).

A simple example is buying a home. Typically, we all go and view several homes before we get to the stage of signing a contract. As we proceed, we consciously or unconsciously change our specification of what we want. This is a process of convergence where certain needs may become more important in our minds and others less so. Ultimately, the problem becomes better understood through this process of successive refinement. Eventually, we have the confidence to sign a contract on our final choice.

This is true of large hardware and software projects as well. The sooner an end user can begin to interact with a system, the sooner they can tell whether it will do the job for which it is intended. Likewise, the user is more clearly able to articulate the nature of the problem to be solved.

Validating the system early helps to ensure that the next stage, system specification (requirements), is proceeding from a solid foundation.

3.2.3 Requirements Modeling

The next stage of the design process is to articulate the business requirements for the new system. Tools include naming the assumptions, defining design objectives, brainstorming, and in design, a technique that uses an easily constructed matrix to correlate objectives with proposed solutions, quickly highlighting those that are easily achieved and those that might also create the greatest long-term value (Fleming, 2021).

Requirements must then be documented and checked for completeness (AcqNotes, 2021; Koelsch, 2016). This may take several iterations. Requirements must be analyzed, refined, and decomposed, ready for validation. Again, this is an iterative process until everyone has agreed with the requirements.

3.2.4 Conceptual Design—Concept Generation and Selection

Deciding on which of the many solutions available is the most appropriate for a particular problem requires objective methods that enable stakeholders to have trust that the decision has been made fairly and honestly. Many tools are available, such as decision trees, multicriteria analysis, strategic risk analysis, investment logic mapping, business cases, cost–benefit analysis, cost-effectiveness analysis, and the choice of yeses (Fleming, 2021). The Theory of Inventive Problem Solving (TRIZ) also provides a broad set of heuristics to aid in concept generation (Belski & Belski, 2008; Orloff, 2006; Petrov, 2019).

3.2.5 Model-Based Systems Engineering (MBSE)

Systems engineering has evolved from a mostly paper-based process to a computer-based process, where the system under development becomes represented by a series of increasingly complex models. This has become known as model-based systems engineering. MBSE adds rigor and precision, enhances communication between team members, manages the complexity of systems, is in line with other engineering disciplines, which use models, and supports the entire product lifecycle.

The system model joins together a series of subsystem models and component models, in computer-readable as well as human-readable forms. Component models might include the structure, the thermal model, the elevator model, the cost model, the construction sequence model, and so on.

One emergent example is what has become known as digital twins, where a proposed system, e.g., a building, is represented as a series of three-dimensional objects, including all its services, enabling the client to take a virtual walk-through to examine each space (Koerner, 2021). The building could be furnished, walls painted, floors carpeted, and so on, providing the client with an authentic view of the final product. Similarly, all the mechanical, electrical, and telecommunication systems can be run as simulations.

Such a model enables the process of verification—are the spaces all as originally specified? It enables sightlines to be checked and, with the right simulation tools, congestion in corridors or elevators at various times of the day could be evaluated.

The model could also include construction sequence, so that every component of the building can be assembled in sequence, virtually, before the real artifact is constructed or manufactured (Constructible, 2022). Such a digital process can demystify complicated construction sequences, e.g., in major bridge projects, where launching of large bridge beams must be carefully rehearsed to ensure trouble-free completion.

At the heart of MBSE is a structured approach to storing the complete system description, which begins with requirements, including interfaces, components, etc. The other three major components of the system model include the system structure (usually a hierarchical breakdown of the system into subsystems and components), behavior (rules for how components behave and interact), and parametrics (the quantitative models of system behavior, including constraints).

These four key components are considered the ‘four pillars of SysML,’ the Systems Modeling Language (Hummell & Hause, 2015; SysML.org, 2021). SysML captures the complete system model and connects to subsidiary engineering models that are used to describe complex subsystem behavior (electrical, mechanical, structural, etc.). The future of systems engineering is an integrated set of digital models that describe both the form of the system and its complex behavior.

3.2.6 Digital Modeling

Digital modeling has been a part of engineering since at least the 1940s, when the finite element method was developed (Hrennikoff, 1941). Engineering computer software became readily accessible in the 1970s and accelerated through the 1980s as access to mainframe computers became readily available and affordable. The availability of cheap and powerful desktop computers has accelerated this trend in the last two decades.

In the early 70s and 80s, there was significant in-house program development, for specific purposes. However, it was not long before software houses emerged to service engineering applications in the major disciplines, e.g., mechanical, electrical, civil, chemical, etc. Many of these have evolved into comprehensive suites that address a wide range of engineering applications, e.g., Dassault, Siemens, and Bentley. Others are more specialized but also widely available, such as MATLAB, Ansys, Aspen, COMSOL, and others.

Engineering graduates need skills in using software relevant to their discipline. These should probably include one of the general systems software, plus one or more of specialized ones suited to their career path. Current progress on Python is facilitating designing and testing different software. The language is modular and flexible, and it is not difficult to master. In the next section, the history of computing tools is considered, including emerging trends such as AR and VR, and how these might be integrated into future engineering curricula.

3.2.7 Future Systems Engineering

The International Council for Systems Engineering has articulated a vision for the future of systems engineering (INCOSE, 2022). This vision recognizes that engineers operate within increasingly complex business, community, and natural environments. Engineering systems are evolving and are more sophisticated. Most of the engineering systems have systems nested inside them. Thus, systems engineering needs to be able to model these systems of systems, especially as they represent many social and economic systems that are the embedded within the challenges within the United Nations Sustainable Development Goals (United Nations, 2021). Systems engineering is then essential for addressing human goals.

Engineering is also using increasingly complex technologies, notably the rapid rise of artificial intelligence systems that are transforming formerly dumb infrastructure systems into smart systems that can respond to levels of demand, time of the day, and so on. These systems are increasingly transdisciplinary, turning traditional civil, electrical, mechanical, and chemical systems, into adaptive data engineered systems.

The INCOSE Vision statement maps out the skills that all engineers will need as they work on these increasingly complex and integrated systems. A summary of a typical project is contained in chapter three of the vision statement. It demonstrates how a socially integrated approach is used to develop a new product through several stages. These stages include (a) concept definition engaging all relevant stakeholders, (b) systems definition with the application of a range of digital tools, (c) systems realization of the hardware, software, and AI-ware, into systems production using digital twins, and (d) finally systems support, and utilization based on the digital systems tools and models that have been built during the development phase. This process will become fully integrated and become the standard for engineering design.

4 Digitalization Mindset

One of the amazing achievements of the digital age is the set of flexible and adaptable Internet protocols. Over the life of the Internet, there have been several major shifts in device connectivity as well as changes in hardware platforms and yet, the Internet continues to operate well regardless of the changes in the software, hardware, and network systems. The creativity of the engineering of the Internet protocols made the Internet a universal device (Internet Society, 2022). The Internet will probably continue to operate as is and serve humanity for many generations. Internet applications and its portals, the smartphones, are not the only transformations of the past 20 years. Advancements in renewable energy, medicine, public health, precision agriculture, aviation and space travel, robotics and AI made significant impact. We note that most of these advancements were achieved at a systems level and are outcomes of interdisciplinary engineering that took many iterations to reach its current stage. There is no question that the necessary conditions were provided by the ability to retrieve information over the Internet.

In fact, fast and easy access to the Internet facilitated the development and deployment of several search engines, with Google being the most popular search engine covering a worldwide market, followed by Microsoft’s Bing, Yahoo, Baidu (China), Yandex (Russia), Duckduckgo, Contextual Web Search, and Yippy Search; a huge variety of searching engines are available to people across the globe.

These engines perform flawlessly on all browsers with easy-to-use interfaces, quality search results, and a personalized user experience. However, most of the platforms catalog the browsing habits of users and share information with advertisers. Such privacy issues have been a subject of discussion, but the practices of the companies that are providing the services free of charge, are tolerated; it is often said that if the service is free, we are the product!

In general, surveillance, tracking thoughts through searches, and communication, are now part of 21st-century practices. Citizens across the globe resent such practices, but so far there are no voices to support implementing legislation similar to the legislations used to control news media and similar agencies. These practices have implications on several aspects of human life including security and sociability, products and business practices, education, and the future of work, among several others.

With the numerous search engines and the massive number of websites that span research and education, data and information are readily available. Thus, these digital technologies present incredible opportunities for learning and creative entrepreneurship. With the stable Internet infrastructure, it was thought that online information will take away from the important role of the educational institutions and need for their teaching faculty and will render on-campus courses less critical. Such notions were rebutted by Herman (2020). Human beings are social animals; socialization and peer-to-peer learning must be part of the learning process. Group discussions and faculty-student interactions continue to be of prime importance.

In addition to the presence of knowledge online, there have been tremendous advancements in AI and robotics. These are facilitated by advancement in hardware and firmware which have been facilitated by miniaturizations in MEMS and CMOS electronics. In all digital fields, semiconductors continue to play a major role, and new silicon fabrication technologies led to great advancements that reached less than 8nm line definitions for electronic circuitry, which meant that nano-MEMS became useful for many applications. In addition, several software techniques are paving the way for new types of robotics, virtual reality, and augmented reality. Through apps residing and distributed using Cloud and Edge-computing, powerful applications will become available to various devices including autonomous cars and drones. Blockchain is becoming one of the popular techniques, and multiexperience, as well as others, delivers immersive experiences.

Machines are designing other machines, and such innovation is opening new dimensions. With that, great expectations are looming. How far can we advance AI and to what extend can it complement, if not substitute, human intellect? Such questions have been with us since Al-Jazari designed and built the first robots (Wikipedia, 2022). There is still a fundamental obstacle that we need to conquer, which is machines that understand context.

Human beings can understand their context quickly. In fact, a 2-year-old child knows a lot about themselves (Rochat, 2003) and by age of 4 they relate to the context of their environment and its rewards and risks (Moore & Corbit, 2019; Tummeltshammer & Amso, 2017). For machines, such as robots, as well as other artificial intelligence (AI), it is very difficult to train them to create solutions within context. This is a consequence of the fact that the human ecosystem has a very complex context. However, the effectiveness of an AI solution is highly influenced by its implementation in each human and social context. Such ability to recognize context as well as context integration within the social, biological, ecological, and organizational foci is a human trait, and it might be very difficult to create AI that can be successful in addressing broad complex interventions (Brézillon, 1999). Recently, Chat-GPT has shown significant progress in this direction. Context is acquired from people as they chat with AI.

In addition, there are notions that co-design is the way to integrate context within different AI modalities. Thus, human and machine would operate collaboratively, and each would perform the best they do in each domain. Previously, machines were expected to perform well under the supervision or the assistance of human beings. But can human beings be assisted by a machine or set of collaborating machines? And how much independence should such machines be given?

These proposals and questions might be viewed as part of the quest to create completely autonomous AI, or a general-purpose AI. An example of autonomous AI is driverless vehicles. Although great progress has been established, the driverless car is still far from being error-free and will require more sensors to provide data to enhance reliability and increase the ability of the AI to understand context and address complexity.

On the other hand, specific-purpose hardware–software systems show very promising outcomes. For example, recent advancements in robotics made it possible to create significant applications. Humanoid robots benefited from the advancements of machine learning, natural language processing, and imaging (Trend Max, 2020). There are many examples of such robots successfully performing specific goals such as interacting with toddlers and elderly persons and performing specific tasks in motor control and neurorehabilitation. Application areas include enhancing the mobility of healthy individuals, restoring the mobility of patients with gait deficits, and assisting those with upper extremity weakness to perform activities of daily living. In addition, wearable robotics for rehabilitation is a successful intervention.

There is a range of interesting devices; some are used to help persons with severe walking difficulties, a loss of balance with an increased risk of falling, as well as muscle fatigue that quickly sets in during exertions. Also, robotic exoskeletons have been used with significant success to help stroke survivors with hemiparesis of varying severities and types of impairments (Gagnon & Aissaoui, 2020). Soft programmable mechanisms with new flexible mechanical meta-materials, for example, can augment soft robots that can safely and effectively interact with humans and other delicate objects. In general, robotics can be viewed as part of the overall AI technologies that will have a role in education and learning.

Virtual reality (VR) is another technology that has started to hold traction in the last few years, and soon it will be part of the educational technologies that we need to engage with. VR provides simulated experience of a situation that can be similar to or different from a real-world one. These might include augmented reality or a mixture of realities and, in some situations, it may extend reality to create immersion experiences.

With such possibilities, using VR for learning¹ has limitless applications including training and experiencing and creating outcomes as part of active learning. These applications, of course, may cover different disciplines of different complexity. VR could create immersive experiences and benefit students by creating interesting experiences and deeper and lasting experiences. Applications of VR could spread as an extension of the human brain to encompass broad applications in business, games, and security.

Although we attempted to provide some insights of the technology that has been developed over the past 15 years, there are many undiscussed topics. Thus, the digital mindset should be viewed as orienting the readers to the important topics that are being invented and the speed at which inventions are made. Both the different inventions and their speed are important factors that are influencing education in general, and engineering in particular.

In the future, we will also see more and more digital learning and blended formats for engineering students, and this will create even more possibilities for active learning methodologies and applied blended learning modes. One way to respond to the situation of students sitting at a distance is by raising the awareness of how the students can improve their own learning. When learners must organize their own learning process virtually, they need ideas, imagination, peers, and structures for how they can organize, reflect, and improve these processes. Therefore, meta-cognitive skills for progress and learning become an important part of future skill sets.

5 Conclusion

The design process can reasonably be considered the engineer’s universal problem-solving process. It begins with client request and proceeds through a process of problem definition, solution generation, prototyping, testing, and implementation. Effective engagement with stakeholders in the early stages is critical to ensure that the problem is properly defined in its full context. Systems engineering provides a structured approach to design, which ensures that what is finally constructed or manufactured also meets the client and customer needs, through a process of continual validation and verification. The future of design and systems engineering lies in digital tools and digital twins. This is a key area of competency required for all graduates.