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None of us are as smart as all of us. The application of Open Innovation

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 far towards facilitating the transfer of ideas from one industry to another. Extrapolate the idea of one mechanical engineer not wanting to get outside their comfort zone to consider potential solutions from other domains to a whole company full of similarly biased mechanical engineers and it becomes possible to see why so few companies are successfully able to make the highly disruptive shift from doing things by a different means. From an organizational perspective, to make the world’s best screws you need to employ the best screw-designing talent. You fundamentally don’t employ the world’s best, say, glue chemists. Glue may well turn out to be a better way of joining component A to component B, but your screw-designing talent is highly likely to tell you that this is not the case whether it is true or not.

‘Yes, But’..

Even if incumbent designers and engineers can be convinced of the potential merits of a solution from another domain, the almost inevitable next problem is that the specific context of the originating domain is inherently different from the context of the domain looking for a new solution.

By way of example, the author recently had the opportunity to work on the problem of removing coriander seeds from their shells. The coriander industry has traditionally solved the problem by using a rotating drum to mechanically fragment the shells. Extraction efficiencies using this kind of mechanical solution can sometimes drop as low as 20%, which basically means that 80% of otherwise good coriander seeds get thrown away with the husks. The coriander process engineers, however, understood rotating drums and were basically looking for a better mechanical system. They were not looking for a system using rapidly changing pressures or ultrasound, but it turned out that here were a pair of potential ways to lift the extraction efficiency to the high 90s in percentage terms.

After overcoming the initial out-of-comfort-zone shock of considering non-mechanical solutions to the problem, the next hurdle arrived when the specifics of the transfer were examined. Getting pistachios out of pistachio shells frequently uses the rapidly changing pressure solution to achieve its desired outcome. The porosity of a pistachio shell and the porosity of a coriander seed husk, however, are different. Directly attempting to transfer the pistachio solution to the coriander context would likely result in the need to ‘soak’ the coriander at the high pressure for a much longer period of time. Given the importance of speed in any production process, this was obviously a problem for the coriander process engineers. It could very easily, in fact, have been used as an excuse for rejecting the pressure-based solution – ‘pressure sounds interesting, but the process is too slow’. The ‘yes, but’ expression is very often used in this kind of solution-rejecting mode. In the majority of cases, ‘yes, but’ is allowed to kill many potentially very good solutions much too quickly. Any ‘yes, but’, however, is merely the expression of a contradiction – we want something, but something stops us from achieving it. According to TRIZ, someone somewhere will already have solved such problems (Reference 3). In order to tap into such solutions, TRIZ requires problem solvers to translate the desired outcome and the thing preventing that outcome from being achieved into a Contradiction Matrix tool. For the coriander problem, that contradiction centres around the need to increase the speed of the process and the thing preventing the speed from being increased relates to the pressure and the difficulty of getting the high pressure outside the husk through and onto the inside of the husk. Figure 5 illustrates how this conflict can be mapped onto the 2003 version of the Matrix (Reference 4):

Figure 5: Mapping The Coriander Problem Onto The Contradiction Matrix

The ‘Suggested Inventive Principles’ shown at the bottom of the figure represent the generic solutions used by others to resolve similar problems elsewhere. It is beyond the purpose and intention of this paper to discuss how those generic solutions were translated into actual solutions to the coriander problem (needless to say; they were). Rather the point is that unless the coriander process engineers had known about the Contradiction Matrix tool, any candidate solution with a ‘yes, but’ was highly likely to have been rejected prematurely.

Tacit Knowledge

To an extent, nearly all open innovation projects seek to resolve tacit knowledge problems by introducing a development and/or validation programme into the contractual relationship they form with a solution provider. Such validation programmes are designed to transfer the knowledge from technology owner to problem owner. The commercial agreements made between the various parties is required to ensure that all are aligned in terms of their rights and obligations. The fourth reason that open innovation initiatives go wrong is that, by definition, tacit knowledge is knowledge that the domain experts are unable to formally communicate to third parties. The open innovation scenario tends to double the extent of the tacit knowledge transfer problem since it involves two parties, both with their own tacit knowledge from their respective domains, and both unlikely to understand the context and conditions within the other operates. Tacit knowledge transfer is thus the most difficult of the four problems discussed here. On the plus side, it is a problem that only tends to appear after the other three have been successfully overcome.

On the negative side, there are few established formal ways and means for eliciting tacit knowledge. Perhaps the best of these ways is something that again forms a part of the systematic innovation toolkit. By encouraging the various different stakeholders in a problem to construct function and attribute analysis (FAA) models, for example, individuals are forced to break-down the complex relationships present in any situation down to the constituent parts. Very often even people from within the same domain find that the FAA model highlights the existence of perspective differences that, while they continue to exist, make improvement of the system difficult.

Putting It All Together

Open Innovation as a concept makes considerable sense. Because that concept is still relatively new, companies are still finding their way when it comes to capitalising on the potential. The main theme of this paper is that too often at this point in time, open innovation is in effect being used as a means of solving the wrong problem faster. Failing faster is preferable to failing slowly, but better yet would be to find ways and means to encourage problem owners to define better problems. In turn ‘defining better problems’ means resolving some of the potentially enormous cultural problems within organisations.

Someone that has been employed and rewarded by an organisation for doing one thing is very unlikely to disrupt him or herself (Reference 5). Until this problem is resolved, the open innovation community is likely to find itself in a downward spiral whereby less and less solution providers participate because they see none of their previous solutions being successfully commercialised.

Once it can be resolved (there are typically business contradictions that prevent it from happening – in which case Reference 6 is a resource that in effect says ‘someone, somewhere already solved this kind of problem too), Figure 6 charts a process that offers the opportunity to overcome the other Open Innovation problems identified through the course of this paper. The left hand side of the figure illustrates the required process steps;

The right hand side identifies the tools and strategies designed to allow each step of the process to be completed in a systematic and reproducible manner.

Figure 6: (Systematic) Open Innovation Protocol

While it is probably a truism that ‘none of us is as smart as all of us’, it is also true to say that the problem solving part of the innovation story is very often the easy part. Especially when compared with defining the ‘right’ problem to go and solve. Until the Open Innovation community successfully grasps and manages that conflict, it will continue to be yet another big idea that remains just that. While it remains to be seen whether Open Innovation is a ‘necessary’ part of the innovation equation, it is already clear that it is not ‘sufficient’ in its own right. Open Innovation needs to open itself to the idea that someone, somewhere already solved the problems it currently faces.

References

1) Utterback, J., ‘Mastering The Dynamics of Innovation’, Harvard Business School Press, 1993.

2) Function Database, www.systematic-innovation.com, Links.

3) Mann, D.L., ‘Hands-On Systematic Innovation’, IFR Press, 2nd Edition, 2007.

4) Mann, D.L., Dewulf, S., Zlotin, B., Zusman, A., ‘Matrix 2003: Updating The TRIZ

Contradiction Matrix‘, Creax Press, 2003.

5) Christensen, C.M., Johnson, C.W., Horn, M.B., ‘Disrupting Class: How Disruptive Innovation Will Change The Way The World Learns’, McGraw-Hill Professional, 2008.

6) Systematic Innovation E-Zine, ‘Some Contradictions Are More Important Than Others: Managing Conflict Complexity’, Issue 22, November 2003.

About the Author

Darrell Mann

Chief Technology Officer

Prior to joining blackswan, Darrell spent 15 years working at Rolls-Royce in various R&D related positions, and ultimately becoming Chief Engineer responsible for the company’s long term future military engine strategy.

He left Rolls-Royce in 1996 to first help set up a high technology company before entering a programme of systematic innovation research at the University of Bath.

He first started using Systematic Innovation in1992, and by the time he left Rolls-Royce had generated over a dozen patents and patent applications………

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