8. Design Process and Opportunity Development
Tod Corlett is Associate Professor of Industrial Design at Philadelphia University. He directs the Master of Science in Industrial Design program, and oversees the program’s innovation-research and technology initiatives. Before full-time teaching, he was an award-winning lead designer for Cloud Gehshan Associates in Philadelphia. He maintains a design practice, Public Works, focused on design for public spaces. He holds a B.A. from Yale University and B.F.A. and M.I.D. degrees from the University of the Arts in Philadelphia.
Framing statement:
• Design is becoming known as an effective shared process, as well as a set of visual disciplines.
• This process has specific characteristics that let it help organizations understand challenges, articulate value propositions, and reach new markets.
• Now, diverse disciplines are coming to a shared understanding of design processes, one that is well suited to a fast-moving world of diverse teams, accelerated product life cycles, disruptive innovation, and social media.
“Coming Up with Good Ideas”: What’s Design?
It’s widely thought that design is the process of having new or creative ideas. Although this is not completely untrue, the process of actually having more, or more creative, ideas is just a part of design, and it’s covered elsewhere in this book, notably in Chapter 9, “Navigating Spaces—Tools for Discovery.” Design is an innovation process; that is, it’s expected to develop new value, of one kind or another. Design turns notions and opinions into value. So, if one takes a bountiful source of new ideas as a given, the real task that separates design from mere dreaming is to separate good ideas from bad ones, and to make good ideas better and more effective. Design is to ideation what the polymerase chain reaction is to DNA: It’s an amplifier that turns a precious, rare commodity into something that can be worked with on an industrial scale. If design is a way of separating good ideas from bad, it follows that design operates by making ideas real, in order to assess and manage the way those ideas fail. Failures—in particular, fast failures—are the key tools of design.
The Leverage of Upstream Innovation: Failure and Managerial Thinking
Fail early and often is a mantra used by many innovation consulting firms to remind clients of the importance of experimenting and learning earlier in the process. The notion of embracing failure is the antithesis of managerial thinking.
Failing Better
Innovation is inseparable from risk. Anything new stands a reasonable chance of failure. Evolution is built on failure. Most of us appreciate the usefulness of looking at failures in separating good innovation from bad, but we tend to see failure as a mysterious process, clear only in hindsight. Failure has a bad reputation. Certainly nobody wants it to happen to them, and only the pessimistic expect it. In fact, depending on your definition of failure, eight or nine out of ten new ventures fail—a terrifying prospect. Failures can arrive from any direction, without warning. Failures of new ventures can happen because of delivery delays, technology development problems, cash-flow issues, problematic quality assurance, and bad customer service, just to get started. It’s important to consider the difference between mistake and failure. Mistake is error without theory. Failure is an experiment tied to a theory from which you can learn and experiment again with new knowledge and insight.
Failure isn’t as mysterious as it appears, however. Like fire, it’s a frighteningly destructive force that can be used as a tool by those who are canny, cautious, and systematic enough. It also resembles fire in that small, intentional, strategic failures can guard against larger, disastrous ones. In approaching failures, however, there are some important, linked, and rather counterintuitive principles that potential users need to understand:
• Much of traditional management and business practice is based on operating according to a set of fixed assumptions, and aimed at eliminating and delaying small failures.
• In a VUCA environment (see the following section), unexamined assumptions are failures in disguise.
• Small failures prevent large failures, so preventing small failures is bad. Learning from them is key.
• Failing early is smaller and hence better than failing later; thus, delaying failures is also bad.
• Design can be thought of as a versioning and simulation system for making and learning from intentional, small, quick failures, reducing the size of failures, moving failures upstream, and converting assumptions into failures early.
Managerial Thinking and Failure
A really important distinction needs to be made between what can be called “manageable failures” and “wicked problems”—because both are important, but many don’t appreciate that they require diametrically opposed strategies to resolve them and learn from them. Spinelli and others have pointed out the basic philosophical difference between “managerial” and “entrepreneurial” modes of thinking and of building and running businesses. The goal of managerial thinking is the elimination of risk and unpredictability, in the service of achieving a smooth-running enterprise. In Chapter 3, “Framing the Vision for Engagement,” McGowan notes the difference between opportunity recognition and value creation. Entrepreneurial thinking is key in the problem-finding phase of the process whereas managerial thinking is key in value creation, especially in optimization phases.
Managerial thinking isn’t bad by any means; its suitability varies by context. It’s simply better adapted to circumstances in which conditions are stable, all the learning has been done already, and efficiency of operation and optimization can be made a primary goal. Managerial thinking can make a business successful, as long as it has a model to follow, but it can’t do original innovation or adapt rapidly to change or disruption. Managerial thinking is the key to monetization of an innovation. Manageable failures are those that can be ameliorated by standard managerial thinking: failures to plan, failures to deploy the correct resources, failures to follow known best practices.
Wicked problems, on the other hand, are those stemming from volatility, uncertainty, complexity, or ambiguity within or affecting the new enterprise—the so-called VUCA factors. Traditional managerial thinking is powerless against these.
Of course, moving appropriately between managerial and entrepreneurial modes of thinking isn’t just a matter of knowing the difference between them; there must also be an institutional culture robust and flexible enough to support both modes within one organization. Ellen di Resta discusses this foundation further in Chapter 4, “Assessing Your Innovation Capability.”
Design and Disruptive Innovation
The failures of traditional managerial thinking aren’t hard to find. Sometimes they can be as simple as becoming overly complacent. As part of a business innovation course, Philadelphia University sophomore Elizabeth Benedetti researched Boscov’s department store, a family-owned regional retailer based in Reading, Pennsylvania. Boscov’s succeeded for decades, and had a sustainable competitive advantage: Their customers were stubbornly loyal. Boscov’s conscientiously learned and stocked what their customers liked. However, as the years went by, Boscov’s customer base began to skew older and older, and the company proved unable to innovate to bring in younger buyers. Its dwindling customer base forced Boscov’s to reorganize and sell out to its creditors in 2008. This enabled it to reduce its costs, but still didn’t help it to innovate; as of 2013, it was once again following its loyal customers toward the graveyard.
Disruptive innovations are an especially troubling kind of wicked problem. As defined by Clayton Christensen, these are simple, cheap new products and services that transform their industries, starting at the bottom of a market. By now, disruptive innovations are widely recognized, and means for generating them (and guarding against them) are widely sought. However, habits of managerial thinking can be hard to transcend, or to supplement with more innovative practices, even under severe corporate duress. In 2007, in an interview with Wired, Doug Morris, CEO of Universal Music Group, described his company’s response to the technological transformation that led to CDs being replaced by digital downloads: “They just didn’t know what to do. It’s like if you were suddenly asked to operate on your dog to remove his kidney. What would you do?” Universal didn’t know how innovation was done. What’s more, as Ellen di Resta discusses in Chapter 4, their corporate culture didn’t support the changes they needed to make to find out.
The Importance of Upstream
These cases share two common elements of failure: Corporate leaders failed to examine the assumptions they held about the technologies, markets, and customers they interacted with, and they found no way to act, or to build new strategies, while their problems were small. Instead of examining their assumptions about their products, value propositions, and markets—in lieu of predicting, analyzing, and testing responses to trends—they waited until change at a scale that would save them was vastly expensive and difficult, if not impossible.
However, all products and services are developed, launched, maintained, and phased out in cycles, including those at Universal Music and Boscov’s, and this cyclical nature of product and venture development gives innovators a key point where change is not just possible but necessary. At the initial, “upstream” end of a product development cycle, anything can happen, and happen cheaply. The cost of changes is low, and they can be made instantly. A product on paper can be revised in a moment. A prototype product, however, even before production tooling, can represent an investment of millions. The farther upstream in your process you can generate and test new ideas, the more quickly and cheaply you can innovate.
This is why design-based processes fare better against wicked problems: They’re based on evaluating options and innovations quickly, at the upstream end of the development cycle, using three major tools: prototyping, integrative thinking, and iteration. Risks are minimized by making a series of small, fast, intelligent bets, in order to inform the big risks that must be undertaken downstream.
Prototyping
Prototyping is a practice of making solutions as real as possible through upstream simulation. This can mean making physical representations of new products, but it can also encompass videos, storyboards, interactive mock-ups, and stories. Failures in prototype are inexpensive, instructive learning experiences, which can prevent much larger failures downstream in the development process. Steve Jobs famously said, “Sometimes people don’t know what they want until you show it to them.” This has been misinterpreted by many to mean, “Trust your instincts, ignore the naysayers, research is a distraction, full speed ahead.” To pull off this approach to new ventures, however, you pretty much have to be Steve Jobs. For the rest of us, his aphorism really means customers should be shown more versions of more products more quickly, so as to reliably learn what they want. If you use the right tools and procedures, we can all do that.
Resolution Versus Fidelity
Design prototypes, then, are representations of products or systems, produced quickly and inexpensively, for the purpose of assessing and learning from user or customer response, usability, and interaction. This is very different from the traditional engineering prototype, which is used for final validation of a system in all particulars before tooling up for production.
In 1997, Stephanie Houde and Charles Hill (interestingly, working at Apple at the time) defined the key concepts resolution and fidelity as applied to prototyping. Resolution describes how much detail is present in the prototype. To design any product, service, or business system, all the details have to be defined and prototyped eventually. But because detail tends to be both time-consuming and expensive to develop, prototypes that embody too much detail end up exiling themselves from the cheap, effective upstream end of the design process and becoming expensive, static investments in themselves. Additionally, users tend to react to the details as opposed to the broader experience and potential of the solution. Fidelity is how closely the experience of using the prototype approximates that of using the designed system—in only those areas being assessed. The classic example is a surgical handset designed by the consultancy IDEO. The breakthrough prototype—the one that allowed validation of an innovative, market-changing product—was taped together by a surgeon from office supplies on the table during a discussion (see Figure 8.1).
Resolution of prototypes should always be kept to the minimum necessary for the task at hand. Fidelity, as the example shows, is context-dependent, can be created on the fly, and should be maximized in creative ways where possible. Low-resolution, high-fidelity prototypes tend to engage users in completing the picture, offering the added benefit of user co-creation.
In-Market Prototyping
Although it’s usually true that products and systems that are ready for the market have taken too long getting there and are too expensive to realize the benefits of upstream prototyping, good businesses realize that every product is potentially a prototype—if and only if they can learn enough about their customers’ reactions and experiences with it. In addition, recent developments such as 3D printers, third-party logistics, overnight freight, e-commerce, and quick-turnaround manufacturing have made some kinds of product rollouts so fast and inexpensive that what IDEO’s Anthony D’Avella calls “in-market prototyping” is becoming commonplace. Some businesses, in fact, have built themselves around the idea of prototyping continually.
Integrative Thinking
Integrative thinking, as defined by Roger Martin of the Rotman School of Management, is the process of adding factors to a problem to achieve solutions that don’t rely on compromise. Between them, integrative thinking and prototyping are Kryptonite to unexamined assumptions. Peripheral but unexamined factors are discovered, and failures are forced to happen early.
Iteration
Iteration is the key factor in design that enables quick, effective learning based on generating and prototyping new ideas. In brief, iteration is the cyclic process of the following:
• Studying product/service users and markets to uncover new insights
• Generating credible new ideas quickly
• Visualizing and prototyping them
• Testing and evaluating them in interaction with potential users and customers
• Changing, editing, and improving them based on feedback
• Repeating the cycle
Helical Thinking
The design cycle, of course, would be useless if it just went around. As it revolves, however, it also quickly moves understanding and decision making forward. This three-dimensional dynamic has led Banny Banerjee, of Stanford ChangeLabs, to refer to it as “helical thinking.” Helical thinking and iterative prototyping can generate value in novel ways; as design teacher Hy Zelkowitz puts it, “The value lies in where you get off the bus.” User research and framing of good problems can generate new product and process ideas. However, believable, prototyped ideas can also be used as the basis for more advanced research.
Philips Electronics doesn’t just design thousands of consumer, medical, and industrial products. They intentionally design and prototype future products that can’t be achieved at reasonable cost with current technology. Then they use these prototypes to learn which technologies, affordances, and features consumers value—not the things they want, but the things they would want—if those could be possible. Philips uses this information about its extensive nonexistent product lines to make very real, very big decisions. In 1998 Philips funded an exhibition of future products that traveled around the world; in New York it made a temporary home on the second floor of Bloomingdales. In addition to positioning Philips as an exciting leader and innovator in technology products, the show enabled Philips to gather information on consumer responses to products and technologies that it wasn’t yet able to offer—to determine corporate strategy by finding out what it did not yet know it didn’t know. Several of the prototypes featured flexible, folding display screens—an impossibility to produce, given the technology of the time. Potential customers, however, loved them. In response, Philips made major budget allocations for internal efforts to develop flexible screens; they also bought up several small flexible-display start-ups. Now, although flexible screens still aren’t commonplace, it’s clear they’re coming. For its part, Philips has assembled a formidable portfolio of flexible-display patents and technologies. When the future does arrive, it will need to go through Philips first. As one analyst observed, Philips used the design cycle to “install a tollbooth on the way to the future.” In other words, the research side of the design cycle generates prototypes—and the prototypes can be used to generate better research—to amplify the effectiveness of learning, and of institutional strategy based on it.
The Funnel, or Filter, and the Fence
The design process is commonly described as a “funnel” or “filter”—a process of editing or selecting among competing concepts, with a single surviving “best” idea at the end (see Figure 8.2).
Although this describes the convergent phase of the design process well, and a single final product often serves the needs of a project—for instance, if the objective is to develop a product—users of design processes should keep in mind their ability to “get off the bus” with more than one finished result when the desired result requires it.
In their “Heimspiel” project, for instance, IDEO Berlin wanted to define a particular “area of opportunity” they had found through user research, one based on products embodying ideas of “play” in their use. A single final designed product would have demonstrated the opportunity’s capability to support a single product, but IDEO wanted to build a “fence” around the opportunity, to show its extent, boundaries, and limits, in order to demonstrate its size and validity as a potential market opportunity (see Figure 8.3). To this end, they developed a finished collection of six products, in different product categories and customer segments, to fully define the opportunity. Because it makes use of divergent, as well as convergent, phases, and incorporates both concrete and abstract modes of thought and documentation, the design process is well suited to generate diverse products for different purposes and audiences, not just “designs.”
Opportunity Development: Design Across Disciplines
The collaborative, interdisciplinary design process described here originated primarily as a way for engineers and graphic and industrial designers to explain and structure their processes and collaborative interactions. Now, however, the process has escaped from the industries where it was born, and spread virally. In the twenty-first century, it has become anachronistic to think of design processes as the exclusive property of designers, but this broad application is a very recent development.
The Age of Agile
In the 1990s, Mark Fowler and other software programmers became dissatisfied enough with established long-term, inflexible, preplanned, and top-down methods of software development that they proposed an alternative, one that would enable them to communicate better and write more robust code more quickly, while allowing rapid responses to changing conditions. In 2001, they published the Agile Software Manifesto, which laid out a set of new development processes and principles. These were revolutionary in the software engineering field, but to industrial designers, they immediately seemed familiar: Small teams, working in fast, iterative loops, moving from divergent ideation to quick prototyping to evaluative testing and back again. The agile software revolution quickly spread across Silicon Valley and then the world during the great Internet boom. From there, entrepreneurs in the software industry adapted techniques based in their technology backgrounds to the design of entire new business ventures. Their hope was to reach better-defined markets more quickly, with better-tuned products, and to seize the prized “first-mover” advantage in the fast-changing software marketplace.
This movement, called “lean start-up,” was evolved by the technologist and venture investor Steve Blank, in his book Four Steps to the Epiphany. Blank added another vital element that united lean development with user-centered design. Frustrated with the failure rate of new ventures, most of which were predicated on “if you build it, they will come” approaches that put technologies first, Blank advocated that entrepreneurs instead “get out of the building,” widen their focus, and start with product users instead—study their potential customers’ lives, work, and needs closely, in order to frame better-understood, simpler problems for technology start-ups to solve. In other words, Blank completed a process of convergent evolution, as best practices for new business opportunity development became analogous in principle and process with product development processes. Thus, we refer to this interdisciplinary, collaborative process as “opportunity development.” Whether the opportunity is a product, a system, a technology, or a business, the steps, the roles, and the kinds of thinking required are the same.
Modes of Thinking in Opportunity Development: A Shared Process
To work effectively in a shared design process, design, engineering, and business disciplines have to understand the modes of thought that lead to success in different parts of the iterative cycle, and the ways diverse disciplines can contribute (see Figure 8.4). Although traditional disciplinary stereotypes are not always true or applicable—the creative designer, the linear businessperson, the analytical engineer—it’s really useful to remember that design is as much about editing, organizing, and analyzing as it is about creating. And if there are team members who are strong in a particular thought process, it’s good to know where to deploy that thinking within the design loop, as Dr. Beckman notes in Chapter 5, “The Role of Learning Styles in Innovation Team Design.”
Diagram based on Sara Beckman and Michael Barry, “Innovation as a Learning Process,” California Management Review, vol. 50, no. 1, Fall 2007.
Figure 8.4 Design cycle: modes of thinking.
As Beckman explains, the process begins in the lower-left quadrant with opportunity finding and framing, which calls for skills in analytical thinking, applied to concrete situations. The next phase, ideation, is much more abstract—this is where the brainstorming happens, and serious divergent thinking is required. It’s still analytical, though; these ideas can be about visualizing or responding to pieces of a problem clearly, and at this phase it’s important that there is no requirement that an idea represent a perfect solution or respond to every identified need. It’s enough to get all the ideas out into the world where they can be compared and evaluated in the next phase, called integration. This is a phase that calls for good skills working in abstract, convergent modes, and it’s also synthetic—in this phase the whole is more important than the parts, and the team is striving to fit things together. The process then shifts to prototyping and testing, which is concrete and synthetic; it’s about making something in and for the real world.
Iteration is simple in principle and powerful in its effects, but can be difficult to build into organizations that are used to linear processes. The usual fear is that iteration will result in needless repetition and cost, as decisions are examined over and over again, or that the process will come to a halt without having generated meaningful results. In fact, this is hardly ever the case. Iteration, in many cases, beats planning, especially if you don’t know what you don’t know. However, Jump Associates, a design and business consultancy, has found that this fear is consistent enough that they’ve been able to graph it (see Figure 8.5). Design always requires taking a risk, persevering in pursuit of insights and solutions, and having faith in the process and the abilities of the team members using it. As you use design in your own work, keep this knowledge in mind, and you’ll be better able to predict your own, and other stakeholders’, reactions and state of mind.
Conclusion
This chapter has introduced design as a set of processes, procedures, and ways of thinking, all aimed at taking good ideas and making them better. Although the details will always be evolving, all good design is based on two major principles. The first is what the entrepreneur Steve Blank calls “getting out of the building”—finding new insights directly, by observing and talking to the people who will buy and use the systems being designed. The second is iteration, the process of making small, fast, informed, sequential, and very intentional failures as a way of putting ideas into the real world, while managing the attending risks.
For decades, these simple ideas were thought of as somehow the property of inherently “creative” people—but in fact, those people were probably more creative just because they learned to follow a few simple design processes. As Jump Associates has shown, design still has the capability to discomfit conventional thinkers, because it’s impossible to predict the process’s end result. In the end, though, the results are generally well worth the discomfort.