“The thought of ultimate loss which often overtakes pioneers, as experience undoubtedly tells us and them, is put aside as a healthy man puts aside the expectation of death.”

– Keynes, J.M., The General Theory of Employment, Interest and Money. 2007, Basingstoke: Palgrave

Triggers for innovation – as we saw in Chapter 5 – can be found all over the place. The world is full of interesting and challenging possibilities for change – the trouble is that even the wealthiest organization doesn’t have deep enough pockets to face them all. Sooner or later, it has to confront this issue of “out of all the things we could do, what are we going to do?” This isn’t easy; making decisions is about resource commitment and so choosing to go in one direction closes off opportunities elsewhere. Organizations cannot afford to innovate at random – they need some kind of framework that articulates how they think innovation can help them survive and grow, and they need to be able to allocate scarce resources to a portfolio of innovation projects based on this view. This underlines the importance of developing an innovation strategy – a theme we explored in Chapter 4.

But in a complex and uncertain world, it is nonsense to think that we can make detailed plans ahead of the game and then follow them through in systematic fashion. Life – and certainly organizational life – isn’t like that; as John Lennon famously said, it’s what happens when you’re busy making other plans! So our strategic framework for innovation should be flexible enough to help monitor and adapt projects over time as ideas move toward more concrete solutions – and rigid enough to justify continuation or termination as uncertainties and risky guesswork become replaced by actual knowledge.

The challenge of innovation decision making is made more complex by the fact that it isn’t a simple matter of selecting among clearly defined options. By its nature, innovation is about the unknown, about possibilities, and about opportunities associated with doing something new, and so the process involves dealing with uncertainty. The problem is that we don’t know in advance if an innovation will work – will the technology actually do what we hope, will the market still be there and behave as we anticipated, will competitors move in a different and more successful direction, will the government change the rules of the game, and so on? All of these are uncertain variables that make our act of decision making a little like driving in the fog. The only way we can get more certainty is by starting the project and learning as we go along. So making the initial decision – and the subsequent ones about whether to keep going or cut our losses and move in a different direction – becomes a matter of calculating as best we can the risks associated with different options. In this chapter, we’ll explore some of the ways in which organizations deal with this difficult area of decision making under uncertainty.

8.1 Meeting the Challenge of Uncertainty

What distinguishes innovation management from gambling? Both involve committing resources to something that (unless the game is rigged) have an uncertain outcome. But innovation management tries to convert that uncertainty at the outset to something closer to a calculated risk – there is still no guarantee of success but at least there is an attempt to review the options and assign some probabilities as to the chances of a successful outcome. This isn’t simply a mechanical process – first, the assessment of risk is still based on very limited information; second, there is a balance between the risks involved and the potential rewards, which mights follow if the innovation project is successful.

Some “bets” are safer than others because they carry lower risk – incremental innovation is about doing what we do – and therefore know about – better. We have some prior knowledge about markets, technologies, regulatory frameworks, and so on, and so can make reasonably accurate assessments of risks using this information. But some bets are about radical innovation – doing something completely different and carrying a much higher level of risk because of the lack of information. These could pay off handsomely – but there are also many unforeseen ways in which they could run into trouble.

And we shouldn’t forget that under such conditions decision making is often shaped by emotional forces as well as limited facts and figures. The economist John Maynard Keynes famously pointed out the important role which “animal spirits” play in shaping decisions [1]: People can be persuaded to take a risk by convincing argument, by expressions of energy or passion, or by hooking into powerful emotions like fear (of not moving in the proposed direction), or reward (resulting from the success of the proposed innovation).

8.2 The Funnel of Uncertainty

Central to this process is knowledge – this is what converts uncertainty to risk. The more we know about something, the more we can take calculated decisions about whether or not to proceed. And in a competitive environment, this puts a premium on getting hold of knowledge as early as possible – this explains the value of an insider tip-off in horse racing or stock market dealings. In innovation management, the challenge is to invest in acquiring early knowledge – through technological R&D, through market research, through competitor analysis, trend-spotting, and a host of other mechanisms – to get early information to feed decision making. Robert Cooper uses the powerful metaphor of Russian roulette, suggesting that most people when faced with the uncertainty of pulling the trigger would be happy to “buy a look” at the gun chamber to improve their knowledge of whether or not there is a bullet in it [2]!

Thinking of innovation as a process of reducing uncertainty but increasing resource commitment gives us a classic graph (Figure 8.1). In essence, the further we go into a project the more it costs but the more we know.

In practice, this translates into what we can call the “innovation funnel,” – a roadmap which helps us make (and review) decisions about resource commitment. Figure 8.2 gives an illustration.

At the outset, anything is possible, but increasing commitment of resources during the life of the project makes it increasingly difficult to change the direction. Managing innovation is a fine balancing act, between the costs of continuing with projects, which may not eventually succeed (and which represent opportunity costs in terms of other possibilities), and the danger of closing down too soon and eliminating potentially fruitful options. Making these decisions can be done on an ad hoc basis, but experience suggests that some form of structured development system with clear decision points and agreed rules on which to base go/no-go decisions is a more effective approach [3].

Given this model, it makes sense not just to make one big decision to commit everything at the outset when uncertainty is very high but instead to make a series of stepwise decisions. Each of these involves committing more resources but this only takes place if the risk/reward assessment justifies it – and the further into the project, the more information about technologies, markets, competitors, and so on, we have to help with the assessment. We move from uncertainty to increasingly well-calculated risk management. Such a staged review process is particularly associated with the work of Robert Cooper, a Canadian researcher, who studied thousands of new product development projects [4].

This model essentially involves putting in a series of gates at key stages and reviewing the project’s progress against clearly defined and accepted criteria. Only if it passes will the gate open – otherwise, the project should be killed off or at least returned for further development work before proceeding. Many variations (e.g., “fuzzy gates”) on this approach exist; the important point is to ensure that there is a structure in place that reviews information about both technical and market aspects of the innovation as we move from high uncertainty to high resource commitment but a clearer picture of progress. We will explore this “stage-gate” approach – and variations on that – in Chapter 10.

Models of this kind have been widely applied in different sectors, both in manufacturing and services [5–7]. We need to recognize the importance here of configuring the system to the particular contingencies of the organization – for example, a highly procedural system that works for a global multiproduct company like Siemens or GM will be far too big and complex for many small organizations. And not every project needs the same degree of scrutiny – for some, there will be a need to develop parallel “fast tracks” where monitoring is kept to a light touch to ensure speed and flow in development.

We also need to recognize that the effectiveness of any stage-gate system will be limited by the extent to which it is accepted as a fair and helpful framework against which to monitor progress and continue to allocate resources [8]. This places emphasis on some form of shared design of the system – otherwise, there is a risk of lack of commitment to decisions made and/or the development of resentment at the progress of some “pet” projects and the holding back of others.

8.3 Decision Making for Incremental Innovation

When we are deciding about incremental innovation – essentially doing what we already do but better – the process of deciding is (relatively) straightforward. Since this involves comparing something new with something that already exists, we can set up criteria and measure against these – both at the outset and during progression of the project through our funnel. Systematic decision making of this kind is common in product development systems, which are discussed in detail in Chapter 9 [9,10]. While risks are involved, these can be calculated and relevant information collected to help guide judgment in a (relatively) mechanistic fashion. This is where stage-gate systems become powerful tools for innovation management – the Coloplast case in Case Study 8.1 gives an example and is described in more detail on the website.

One area where systematic management of incremental innovation becomes important is in “high involvement” systems, where a large proportion of the workforce becomes engaged in innovation [11–13]. Such kaizen or continuous improvement activities can have a significant cumulative effect – as we saw in Chapter 1. But there is a problem – if we are successful in persuading most of the workforce to make innovation proposals, then how will we manage the volume of ideas which result? (To put this in perspective, many firms with a strong tradition of high involvement innovation – for example, Toyota, Kawasaki, or Matsushita – receive several millions of suggestions per year [14] and France Telecom has around 30,000 ideas each day from across its workforce using its online suggestion scheme).

The solution to this is to make use of approaches that have been termed “policy deployment” (sometimes called “hoshin planning”) – essentially devolving the top-level innovation strategy to lower levels in the organization and allowing people at those levels to make the decisions. This provides a strategic focus within which they can locate their multiple small-scale innovation activities. But it requires two key enablers – the creation of a clear and coherent strategy for the business and the deployment of it through a cascade process that builds understanding and ownership of the goals and subgoals [13,15].

Policy deployment is a characteristic feature of many Japanese kaizen systems and may help explain why there is such a strong “track record” of strategic gains through continuous improvement. In such plants, the overall business strategy is broken down into focused three-year mid-term plans (MTPs); typically, the plan is given a slogan or motto to help identify it. This forms the basis of banners and other illustrations, but its real effect is to provide a backdrop against which efforts over the next three years can be focused. The MTP is specified not just in vague terms but with specific and measurable objectives – often described as pillars. These are, in turn, decomposed into manageable projects that have clear targets and measurable achievement milestones, and it is to these that workplace innovation activities are systematically applied. Case Study 8.2 gives an example.

This challenge raised by the need to manage a high volume of innovation ideas is exacerbated by the trend toward opening up the “front end” of innovation to people outside the organization through innovation contests and other cocreation approaches. The result is a pressing need for idea management systems to sort and filter the many creative possibilities – and to make sure that these ideas contribute in a positive direction. The risk is that this absorbs an increasing amount of time in the selection and resource allocation process; one solution increasingly used in online suggestion schemes and crowdsourcing approaches is to engage the community itself in rating, commenting, and supporting promising ideas.

8.4 Building the Business Case

Even though projects may be incremental in nature and build on established experience, the development and presentation of a persuasive business case is important and much can be done with tools and techniques to explore and elaborate the core concept. The purpose here is to move an outline idea to something with clearer shape and form, on which decisions about resource commitments can be made. As we move to more radical innovation projects – which are by definition higher risk – so the “business case” needs to be more strongly made and to mobilize both emotional and factual components to secure “buy-in” from decision-makers.

Many organizations make use of approaches based around making the underlying “business model” explicit. In essence, these are representations of how an innovation will create value – examples include the widely used “business model canvas” and variations on storytelling approaches [16]. The value of such approaches is that they involve thinking through the innovation from different angles and asking questions, which help sharpen and shape it. These challenges can also be raised from the perspective of different stakeholders – for example, bringing in customer information; again the idea is to explore the proposed innovation thoroughly. Case Study 8.3 gives an outline of business model thinking.

Table 8.1 gives some examples of business models and the information needed to represent the “value proposition” to others in securing support for the investment decision.

8.5 Concept Testing and Engaging Stakeholders

Despite the presence of formal decision-making structures choices about which options to select are subjective in nature – leading to political and other behaviors [21,22]. Many of the problems in product and process innovation arise from the multifunctional nature of development and the lack of a shared perspective on the product being developed and/or the marketplace into which it will be introduced. A common problem is that “X wasn’t consulted, otherwise they would have told you that you can’t do that …” This places a premium on involving all groups at the earliest possible stage, that is, the concept definition/product specification stage. Several structured approaches now exist for managing this, including quality function deployment and functional mapping [23].

For entrepreneurs trying to start a new venture, the problem of resource limitations often means that they need to develop expertise in building coalitions and networks of support.

And as we saw in Chapter 5, users play an increasingly important role as a source of innovative ideas [24]. Working with them from an early stage helps refine and elaborate the concept and crucially also builds in their support. A key principle in innovation diffusion is the compatibility of the innovation with the context into which it is being introduced – in other words, how well it fits the world of the user. By engaging users early, these issues can be surfaced and designed into the innovation and downstream acceptance accelerated.

Read the transcript of an audio interview with Lynne Maher, who describes her work in involving patients to improve innovation concepts in health care.

The availability of prototyping and simulation technology, especially computer-aided design, has helped facilitate this kind of early discussion and refinement of the concept. In the process of innovation, early involvement of key users and the incorporation of their perspectives are strongly associated with improved overall performance and also with acceptability of the process in operation. This methodology has had a strong influence on, for example, the implementation of major integrated computer systems which by definition cut across functional boundaries [25,26].

As organizations move to increasing use of “open innovation” approaches, the potential for engaging a wider community of stakeholders such as suppliers, users, and so on, increases. Bringing in the ideas of end-users not only improves the quality of the final design but can also help accelerate diffusion of the innovation [27]. Early involvement of suppliers means that their specialist expertise can often provide unexpected ways of saving costs and time in the subsequent development and production process. Increasingly, product development is being treated as a cooperative activity in which networks of players, each with a particular knowledge set, are coordinated toward a shared objective. Examples include automotive components, aerospace, and electronic capital equipment, all of which make growing use of formal supplier involvement programs [28,29].

Interaction with outsiders also needs to take account of external regulatory frameworks – for example, in product standards, environmental controls, and safety legislation. Concept testing can be helped by close involvement with and participation in organizations that have responsibilities in these areas.

8.6 Spreading the Risk

Even the smallest enterprise is likely to have a number of innovation activities running at any moment. It may concentrate most of its resources on its one major product/service offering or new process, but alongside this there will be a host of incremental improvements and minor change projects which also consume resources and require monitoring. For giant organizations such as Procter and Gamble or 3M, the range of products is somewhat wider – in 3M’s case around 60,000. With pressures on increasing growth through innovation come challenges like 3M’s, “30% of sales to come from products introduced during the past 3 years” – implying a steady and fast-flowing stream of new product/service ideas running through, supported by other streams around process and position innovation. Even project-oriented organizations whose main task might be the construction of a new bridge or office block will have a range of subsidiary innovation projects running at the same time.

As we have seen, the innovation process has a funnel shape with convergence from a wide mouth of possibilities into a much smaller section, which represents those projects to which resources will be committed. This poses the question of which projects and the subsidiary one of ensuring a balance between risk, reward, novelty, experience, and many other elements of uncertainty. The challenge of building a portfolio is as much an issue in noncommercial organizations – for example, should a hospital commit to a new theater, a new scanner, a new support organization around integrated patient care, or a new sterilization method? No organization can do everything, and so it must make choices and try to create a broad portfolio that helps with both the “do what we do better” and the “do different” agenda.

There are a variety of approaches that have developed to deal with the question of what is broadly termed “portfolio management.” These range from simple judgments about risk and reward to complex quantitative tools based on probability theory [10,30,31]. But the underlying purpose is the same – to provide a coherent basis on which to judge which projects should be undertaken, and to ensure a good balance across the portfolio of risk and potential reward. Failure to make such judgments can lead to a number of problem issues, as Table 8.2 indicates.

Portfolio methods try to deal with the issue of reviewing across a set of projects and look for a balance of economic and nonfinancial risk/reward factors. Descriptions of a variety of portfolio-based tools are available on the website and Chapter 10 discusses such approaches in more detail.

8.7 Decision Making at the Edge

When the innovation decision is about incremental innovation (“do what we do but better”), there is relatively little difficulty. A business case with requisite information can be assembled, cost-benefits can be argued, and the “fit” with the current portfolio demonstrated. But as the options move toward the more radical end so the degree of resource commitment and risk rises and decision making resembles more closely a matter of placing bets. Uncertainty is high and emotional and political influences become significant. At the limit, the organization faces real difficulties in making choices about new trajectories – in moving “outside the box” in which its prior experience and the dominant technological and market trajectories place it [33–35].

Under such “discontinuous” conditions – triggered, for example, by the emergence of a radical new technology or the emergence of a new market, or a shift in the regulatory framework – established incumbents often face a major challenge. Heuristics and internal rules for resource allocation are unhelpful and may actively militate against placing bets on the new options because they are far outside the firm’s “normal” framework. As Christensen argues, in his studies of disruption caused by the emergence of new markets, the existing decision making and underlying reward and reinforcement systems strongly favor the status quo, working with existing customers and suppliers. Such bounded decision making creates an opportunity for new entrants to colonize new market space – and then migrate toward incumbent’s territory [36]. In similar fashion, Henderson and Clark argue that shifting to new “architectures” – new configurations involving new knowledge sets and their arrangements – poses problems for established incumbents [37].

8.8 Mapping the Selection Space

As we saw in Chapter 6, there is a balance to be struck between “exploit” and “explore” behavior in the ways organizations search for innovation triggers. But there are also limits to what is “acceptable” exploration – essentially organizations have “comfort zones” beyond which they are reluctant or unable to search. In a similar fashion, their decision making, even around radical options, is often constrained – this gives rise to the anxiety often expressed about the need for “out of the box” thinking. Stage gate and portfolio systems depend on using criteria which are “bought into” by those bringing ideas – a perception that the resource allocation process is “fair” and appropriate even if the decisions go the “wrong” way. Under steady-state conditions, these systems can and do work well, and criteria are clearly established and perceived to be appropriate. But higher levels of uncertainty put pressure on the existing models – and one effect is that they reject ideas that don’t fit – and over time build a “self-censoring” aspect. As one interviewee in research on the way radical ideas were dealt with by his company’s portfolio and stage-gate systems explained, “around here we no longer have a funnel, we have a tube!”

One way of looking at the innovation selection space is shown in Figure 8.3. The vertical axis refers to the familiar “incremental/radical” dimension in innovation, while the second relates to environmental complexity – the number of elements and their potential interactions. Rising complexity means that it becomes increasingly difficult to predict a particular state because of the increasing number of potential configurations of these elements. And it is here that problems of decision making become significant because of very high levels of uncertainty.

Zone 1 is essentially the “exploit” domain in innovation literature. It presumes a stable and shared frame – “business model”/architecture – within which adaptive and incremental development takes place. Selection routines – as we saw earlier in this chapter – are those associated with the “steady state” – portfolio methods, stage-gate reviews, clear resource allocation criteria, project management structures, and so on. The structures involved in this selection activity are clearly defined with relevant actors, clear decision points, decision rules, criteria, and so on. They correspond to widely accepted “good practice” for product/service development and for process innovation [4,10,45]. As the sector matures so the tools and methods become ever more refined and subtle.

Zone 2 involves selection from exploration into new territory, pushing the frontiers of what is known and deploying different search techniques for doing so. But this is still taking place within the same basic cognitive frame – “business model as usual.” While the “bets” may have longer odds the decision making is still carried out against an underlying strategic model and sense of core competences. There may be debate and political behavior at strategic level about choices between radical options, but there is an underlying cognitive framework to define the arena in which this takes place and a sense of path dependency about the decisions taken. Often there is a sector-level trajectory – for example, Moore’s law shaping semiconductor, computer and related industry patterns. Although the activity is risky and exploratory, it is still governed strongly by the frame for the sector – as Pavitt observed there are certain sectoral patterns that shape the behavior of all the players in terms of their innovation strategies [46].

The structures involved in such selection activity are, of necessity, focused at high level – these are “big bets” – key strategic commitments rather than tactical investments. There are often tensions between the “exploit” and the “exploring” views and the boardroom battles between these two camps for resources are often tense. Since exploratory concepts carry high uncertainty, the decision to proceed becomes more of an “act of faith” than one which is matched by a clear, fact-based business case – and consequently emotional characteristics such as passion and enthusiasm on the part of the proposer – “champion” behavior – or personal endorsement by a senior player (“sponsorship” behavior) play a more significant role in persuading the decision-makers [47].

These first two zones represent familiar territory in discussion of exploit/explore in innovation selection. By contrast Zone 3 is associated with reframing. It involves searching and selecting from a space where alternative architectures are generated, exploring different permutations and combinations of elements in the environment. This process – essentially entrepreneurial – is risky and often results in failure but can also lead to emergence of new and powerful alternative business models (BMs). Significantly, this often happens by working with elements in the environment not embraced by established BMs – but this poses problems for existing incumbents, especially when the current BM is successful. Why change an apparently successful formula with relatively clear information about innovation options and well-established routines for managing the process? There is a strong reinforcing inertia about such systems for search and selection – the “value networks” take on the character of closed systems which operate as virtuous circles and – for as long as they are perceived to create value through innovation, act as inhibitors to reframing [48].

The example of low-cost airlines here is relevant – it involved developing a new way of framing the transportation business based on rethinking many of the elements – turnaround times at airports, different plane designs, different Internet-based booking and pricing models, so on – and also working with different new elements – essentially addressing markets like students and pensioners which had not been major elements in the “traditional” BM. Other examples where a reframing of BM has taken place include hub and spoke logistics, digital imaging, digital music distribution, and mobile telephony/computing. The critical point here is that such innovation does not necessarily involve pushing the technological frontier but rather about working with new architectures – new ways for framing what is already there.

Selection under these conditions is difficult using existing routines that work well for zones 1 and 2. While the innovations themselves may not be radical, they require consideration through a different lens and the kinds of information (and their perceived significance), which are involved may be unfamiliar or hard to obtain. For example, in moving into new underserved markets the challenge is that “traditional” market research and analysis techniques may be inappropriate for markets which effectively do not yet exist. Many of the “reasons” advanced for rejecting innovation proposals outlined in Table 7.3 can be mapped on to difficulties in managing selection in zone 3 territory – for example, “it’s not our business” relates to the lack of perceived competence in analysis of new and unfamiliar variables. “Not invented here” relates to similar lack of perceived experience, competence or involvement in a technological field, and the inability to analyze and take “rational” decisions about it. “It’s not a business” – relates to apparent market size, which in initial stages may appear small and unlikely to serve the growth needs of established incumbents. But such markets could grow – the challenge is seeing an alternative trajectory to the current dominant logic of the established business model [43,49].

Here the challenge is seeing a new possible pattern and absorbing and integrating new elements into it. This is hard to do because it requires cognitive reframing – but also because it challenges the existing system – something Machiavelli was aware of many centuries ago.² Powerful social forces toward conforming – groupthink, risky shift, and so on – come into play and reinforce a dominant line at senior levels [34]. This set of emotionally underpinned views is then rationalized with some of the statements in Table 7.3 – the “immune system” we referred to earlier. Significantly where there are examples of radical changes in mindset and subsequent strategic direction these often come about as a result of crisis – which has the effect of shattering the mindset – or with the arrival from outside of a new CEO with a different world view.

Zone 4 is where new-to-the-world innovation takes place – and represents the “edge of chaos” complex environment where such innovation emerges as a product of a process of coevolution [44,51,52]. This is not the product of a predefined trajectory so much as the result of complex interactions between independent elements. Processes of amplification and feedback reinforce what begin as small shifts in direction – attractor basins – and gradually define a trajectory. This is the pattern we saw in Chapter 1 in the “fluid” stage of the innovation life cycle before a dominant design emerges and sets the standard [53,54]. It is the state where all bets are potentially options – and high variety experimentation takes place. Selection strategies here are difficult since it is, by definition, impossible to predict what is going to be important or where the initial emergence will start and around which feedback and amplification will happen. Under such conditions, the strategy breaks down into three core principles – be in there, be in there early, and be in there influentially (i.e., in a position to be part of the feedback and amplification mechanisms) [51,55].

Examples here might be the emergence of product innovation categories for the first time – for example, the bicycle that emerged out of the nineteenth-century mix of possibilities created by iron-making technologies and social market demands for mass personal transportation [56]. The emergence of new techno-economic systems is essentially a process of coevolution among a complex set of elements rather than a reframing of them. Change here corresponds to what Perez calls “paradigm shift,” and examples include the Industrial Revolution or the emergence of the Internet-based society [57].

Once again this zone poses major challenges to an established set of selection routines – in this case, they are equipped to deal with uncertainty but in the form of “known unknowns,” whereas zone 4 is essentially “unknown unknowns” territory. Analytical tools and evidence-based decision making – for example, reviewing business cases – are inappropriate for judging plays in a game where the rules are unclear and even the board on which it is played has yet to be designed! An example here might be the ways in which the Internet and the products/services which it will carry will emerge as a result of a complex set of interactions among users. Or the ways in which chronic diseases like diabetes will be managed in a future, where the incidence is likely to rise, where the costs of treatment will rise faster than health budgets can cope, and where many different stakeholders are involved – clinicians, drug companies, insurance companies, carers, and patients themselves.

Table 8.5 below summarizes the challenges posed across our selection space and highlights the need to experiment with new approaches for selection in zones 3 and 4.

Research Note 8.1 offers some tools to help with high uncertainty decision making.

Summary

In this chapter, we have looked at some of the challenges in making the selection decision – moving from considering all the possible trigger signals about what we could do in terms of innovation to committing resources to some particular projects. This quickly raises the issue of uncertainty and how we convert it to some kind of manageable risk – and build a portfolio of projects spreading this risk. Tools and techniques for doing so for incremental innovation are relatively straightforward (though there is never a guarantee of success) but as we increase the radical nature of the innovation so there is a need for different approaches. The problem is further compounded because of the simplifying assumptions we make when framing the complex world – and the risk is that in selecting projects that fit our frame we may miss important opportunities or challenges. For this reason, we need techniques that help the organization look and make decisions “outside the box” of its normal frame of reference.

Further Reading

The theme of innovation decision making, risk management, and the use of the stage-gate concept is extensively covered in the work of Robert Cooper and colleagues [2,4,32,66,67]. Tools for portfolio management and related approaches are discussed with good examples in Goffin and Mitchell’s book [68] and policy deployment approaches in Bessant [13] and Akao [69]. Gann, Dodgson, and Salter [20] and Schrage [64] explore the growing range of simulation and prototyping tools that can postpone the commitment decision point, while von Hippel and colleagues expand [70,71] on the user involvement theme [72,73]. Peter Koen’s work provides useful insights on fuzzy front end tools and methods (a good source is the PDMA Handbook [9]) and Julian Birkinshaw explores the challenges in developing “ambidextrous” decision-making structures [74]. A detailed review of the psychological issues and problems around reframing can be found in Hodgkinson and Sparrow [34], while the work of Karl Weick remains seminal in discussing the ways in which organizations try and make sense of complex worlds [75,76].

Useful websites include Innovation Excellence (http://innovationexcellence.com/) and http://www.innovationmanagement.se/ that provide case examples and links to a wide range of innovation support resources and the Product Development Management Association (www.pdma.org) that covers many of the decision tools used with practical examples of their application in the online “Visions” magazine. NESTA (www.nesta.org.uk) and AIM (http://aimresearch.org/) provide reports and research papers around core innovation themes including many of the issues raised in this chapter.

Case Studies

You can find a number of additional downloadable case studies at the companion website, including:

• ABC Electronics exploring the implementation of portfolio management and a stage-gate system
• Coloplast describing the operation of a typical stage-gate system – AIM – for Accelerating Ideas to Market
• Philips Lighting describing how the transition in the underlying mindset when faced with radical innovation was managed
• Lufthansa Systems and Liberty Global using different evaluation approaches in the context of online innovation platforms
• Eastville Community Shop and Lifeline Energy as examples of innovation concept development in the field of social innovation

You can also find a wide range of tools to help work with concepts introduced during this chapter, again at the companion website.

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