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In theory, the Open Innovation (OI) concept makes a lot of sense: present tough, unsolved problems to extremely large numbers of the world’s most inventive minds and chances are someone, somewhere may either already have a solution or the wherewithal to deliver a solution. Look beyond a few well chosen ‘low-hanging fruit’ examples, however, and the distance between theory and practice begins to look like a rather large chasm. This article discusses the extent and form of that chasm and reports on how some of the Systematic Innovation tools and strategies may be used to bridge it and thus increase the likelihood of Open Innovation success. During the course of an internal and collaborative programme of research to combine the principles of Open Innovation with a range of other inventive problem solving strategies, the main problems encountered during open innovation initiatives have been identified as follows: 1) The initial problem posed to the open innovation community is the ‘wrong problem’. 2) Lack of objective means to determine whether a ‘new’ solution is better than existing solutions. 3) Failure to adequately solve the inevitable ‘yes, but’ problems as an external solution is imported into the specific context of the organisation posting the challenge. 4) Failure to adequately transfer the surrounding tacit knowledge from domain to domain. The article discusses these four issues, in each case suggesting potential remedies through real case study examples taken from a range of different industry sectors. Having discussed the main Open Innovation problems, we go on to outline a number of solutions. Building from this description, then, a final section examines an overarchingprocess for overcoming the problems that frequently occur when Open Innovation solutions are transferred from one domain to another. We show that while the Open Innovation concept has great potential for accelerating the creation of novel solutions, it is by itself fundamentally insufficient. Tools and strategies for systematically overcoming the weaknesses and difficulties are proposed and a prototype Systematic Open Innovation roadmap is presented. The Wrong Problem Based on our research, the first of the four problems – companies defining the wrong problem – is both prevalent, and the problem most likely to damage the reputation of the OI cause. The ‘defining the wrong problem’ issue is also the most contentious of the four problems. In order to explore both why this is, and, more importantly, how to set about solving the problem, the following discussion examines the parallel problem of why so many innovations appear from outside the incumbent companies in an industry. Based on historical evidence, a breakthrough solution is almost 99% likely to come from either a new player or a new entrant to a market (Reference 1). Does this happen because incumbents fail to see the new solution coming? Or is it more likely because they have so much money invested in doing things the current way, they have neither the skills nor resource to make the transition? Either way, historically, incumbents will almost never disrupt themselves. From anopen innovation perspective, knowing they are unable to make the transition, incumbents thus tend to pose open innovation questions that are about improving matters in the current business model. Henry Ford is famously quoted as saying if he’d asked his customers what they wanted, they would have asked for a faster horse. In the same way that customers are often unable to ask for something that doesn’t yet exist, in-company problem solvers are equally likely to ask for ‘faster horse’ solutions rather than disruptive step changes. In a survey of open innovation RFPs taken from the last twelve months, over a third of all of the problems being presented have been shown to fall into this ‘wrong problem’ category. Here are a few exemplar case studies of the problem: * A company asking for solutions to improve temperature retention in soda cans by incorporating an internal insulating layer. * A company asking for solutions to maintain bread with a crunchy crust and soft crumb for 5 days. * A company seeking monomer technologies which will chemically modify the internal structure of human hair fibers to modify mechanical attributes such as strength, fiber size, and fiber rigidity. * A sausage manufacturing company seeking technologies to allow consumers know how well-done their sausages have been cooked. In none of these cases, it may be argued, are the true desired outcomes of customers – either tangible or intangible – being addressed. Sure, for example, it is good to be able to offer consumers soda that feels cold to the touch, tastes cold and stays so for longer. But that outcome may well be served in far better ways than adding an internal insulation layer. The way the project has been presented, however, precludes other, more ideal, solutions. Albert Einstein is famously quoted as saying that no problem can be solved from the same level of consciousness that created it. Adding something to a sausage to make it tell the consumer when it is cooked and ready to eat is a classic sausage-industry solution to a problem better solved elsewhere in the value chain. Sausages with under-cooked middles and burned-skins are a symptom of poor cooking and poor cookers. There will always be poor cooks, but there is no reason why this problem couldn’t be solved at the higher level of designing barbeques that have better heat release control. Nor is there any reason why the skin of the sausage couldn’t be re-formulated in such a way as to conduct heat better to the centre of the sausage. Except that the problem owners have decided that they want to solve the problem at a level they understand. The template illustrated in Figure 1 is a simple yet effective means of determining the different levels of a problem. In the large majority of cases, what this template highlights is the fact that ‘best’ problem to solve is one other than the one that is originally specified. If the problem owner, however, has no authority to solve the problem at a different level, or – worse – has no domain knowledge to be able to judge whether a proposed solution at one of those levels is better, then the opportunity is lost. Making a colour-changing indicator sausage is a gimmick to temporarily increase sales; teaming with a barbeque manufacturer to produce a ‘nomistakes’ cooking device is a way to potentially climb the value chain and re-invent the business. open innovation figure 1

Figure 1: ‘Why-What’s-Stopping Problem Definition Template

Customers only ever buy solutions that allow them to achieve outcomes better than they do currently. Figure 2 is another simple template, this time designed to help map those

outcomes. The figure includes a description of the bread problem as an exemplar. The template divides the world into four outcome quadrants, each focusing on tangible or intangible, individual or collective dimensions. The posed open innovation problem of bread with a crusty-crust and a soft middle is very much about trying to solve tangible level problems associated with the purchase and consumption of the bread. But a solution to these tangible problems goes against the majority of the intangibles present in the consumer relationship. Alas, when it comes to fast moving consumer goods like bread it is increasingly the case that the intangibles are the most important part of the equation. “A man makes a decision for two reasons – the good reason and the real reason,” so said advertising guru J.P.Morgan. The ‘real’ reason is almost always the entry in the top righthand corner of the Figure 2 template. And with that in mind, this particular crusty-andcrumbly bread open innovation project most likely again falls into  he ‘wrong problem’ category.

Figure 2:  Outcome Mapping Template And Bread

The second area where open innovation initiatives may be seen to go wrong has in

common with the ‘wrong problem’ story the issue of lack of outside-domain knowledge. As soon as an open innovation problem owner goes to the world with a problem like ‘find better ways to join component A and B together’ it is theoretically possible to very quickly identify other ways of delivering the required function (Reference 2). From a practical standpoint, however, firstly few scientists and engineers are familiar with the concept of functionally-classified knowledge databases, and secondly, even those that do make use of such knowledge, almost invariably lack the out-of-domain knowledge required to adequately and effectively compare one candidate solution with another. Give a mechanical engineer.

Figure 3.

Figure 3: Looking For Solutions In Domains That Are Known

If that mechanical engineer doesn’t understand, say, solutions coming from the chemical domain, they will tend to be rejected. Irrespective that is of whether they hold the key to a solution that is ultimately stronger.

Although unable to solve this out-of-domain-knowledge psychological inertia problem, one thing that can be done to help ease the transfer of solutions from one domain to another is not just arrange knowledge in functional terms, but also then to map solutions within each function in terms of how well a given solution performs certain key attributes. Figure 4, for example, illustrates how a database of solutions to a ‘join’ function might be classified in terms of two attributes that are known to be important – strength of join and adaptability/re-usability of the join.

Figure 4: Attribute Mapping Of Different Join Methods

Obviously the same basic attribute-mapping strategy can be extended to include dimensions describing other attributes of the system. Ultimately, though, this type of function-attribute domain map can only go so

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