Linked Innovation: 5 Keys to Success in Open innovation Challenge Management

Open innovation crowd sourcing methods, when applied to the right problem, can effectively extend the solution provider search beyond the boundaries of an industry. This article presents the application of a targeted broadcast crowd sourcing method to identify unobvious solution providers for a German chain-drive industry consortium. The majority of solutions submitted through this method were previously unknown to the consortium. This evaluation demonstrates the power of open crowd sourcing to provide solutions from discontinuous industries and how effective crowd sourcing can be in open innovation.

FVA, or “Forschungsvereinigung Antriebstechnik”, is a German industry association consisting of medium sized enterprises providing components and systems in the field of drivetrain technology. Many of FVA’s members are world market leaders in their segment and typical representatives of strong German engineering. FVA is part of VDMA, an association representing 3,000 companies in the German engineering industry, representing a total turnover of roughly €160 billion and 920,000 employees.

Each year, FVA provides substantial funds to source and support technical research and development in pre-competitive domains as a means of accessing the latest technical solutions, sharing risk and financial investments by performing this research for participating members. For a typical project, a number of member companies form a consortium, define a common research interest, and then find a research institute to perform this task in the form of contract research. In most cases, contractors are either German university laboratories or dedicated research institutes such as Fraunhofer or Helmhloz, but also small engineering companies or specialized labs can be approached. The results are shared as joint IP among the members.

Recent research in the chemical industry has demonstrated the effectiveness of using open innovation methods and especially the “Request for Proposals” method (Jeppesen & Lakhani 2010). This paper presents the evaluation of an open innovation targeted crowd sourcing method developed by NineSigma, known as Linked Innovation, as a means to enlarge the challenge broadcast beyond the FVA consortium borders, the capacity to identify unobvious solution providers and share benefits within a group of industry members.

Methods

The project set-up was straight forward  and involved taking technical challenges from FVA’s members which had been already subject of internal and consortium research according to the traditional contract research model, but had not produced sufficient or adequate solutions, and to crowd source these challenges to identify solutions to these problems in an open innovation network. After an extensive analysis of open innovation intermediaries (Diener & Piller 2010), the decision was made to partner with NineSigma, given their strong experience in the mechanical engineering domain and their sophisticated, but still flexible open innovation approach. The NineSigma targeted crowd sourcing Linked Innovation method was applied as it provided the means to federate the consortium resources while avoiding conflict or competition amongst the members. Five challenges were identified by FVA. Two came directly from individual member companies; three were taken from the list of topics for consortium research.

For clarity, the definition of open innovation was taken as the formal discipline and practice of leveraging the discoveries of unobvious others as input for the innovation process through formal and informal relationships. Crowd sourcing was the underlying mechanism that enables this relationship, defined as the act of taking a task and outsourcing it to a large, undefined network of potential contributors in form of an open call. The NineSigma targeted crowd sourcing method goes beyond simple broadcasting through a web supported community, to actively identify and solicit the highest potential solution providers in their community data base, effectively increasing the quality and relevance of solutions received.

The following NineSigma method to crowd sourcing technical problems was applied to the FVA project. First, the five FVA problems were translated with the help of senior program managers at NineSigma’s European office in Leuven, into “requests for proposals” (RFP), an open document that describes the problem statement, indicates the performance criteria of the expected solution, and also provides the business proposition for collaboration in form of potential benefits or royalties for a successful solution provider. Then NineSigma defined the specific audience of solution providers (up to and above ten thousand) relevant for the specific problem by applying search tools to its proprietary network of over two million potential solvers and public data bases. After the selection of potential solution provider population, the RFPs were broadcasted by NineSigma. Responses were handled via the NineSigma helpdesk and program managers. After the deadline for the solutions passed (about 4-5 weeks from the initial posting), results were evaluated and discussed with the FVA partners.

Results

Table 1 presents the data on the solutions received for 4 of the challenges (we are not permitted to reveal the outcomes for one challenge for confidentiality reasons) and illustrates the power of crowd sourcing a technical problem. Consider as an example RFP 66198, a search for materials with specific characteristics to enable the fabrication of gearing systems without lubricants. For this RFP, 26 solution proposals were submitted from providers around the world. Of those, the majority of solution providers were entirely new to the companies (which is remarkable as this in a highly specialized field where before we often heard “we know everyone relevant in our industry”). Also, most of the technologies behind the solutions (16 out of 26) were entirely new to the consortium. For four solutions, more information was requested. Just six solution technologies were known before – but often coming from new potential solution providers.

Table 1 Results of piloting open innovation at FVA

Click to enlarge

Practical implications

Innovation performance depends to a large extent, from the ability of an organization to access new knowledge sources and connect those with previous knowledge in an innovative way. A core activity to achieve this goal is to establish broad networks with external entities. Exactly this process has been facilitated by the open innovation approach described here. Its main effect is to enlarge the base of information that can be accessed and utilized for the innovation process.

In a conventionally “closed” system of innovation, only information about solutions that is in the domain of a firm can be used as creative input for the innovation process, a problem that has been called the “local search bias”. In an innovation system more open to external input, this knowledge stock is extended by the large base of information about needs, applications, and solution technologies that resides in the domain of users, suppliers, experts, universities, SMEs and other external parties. Thus, just by increasing the potential pool of information, better results are becoming possible. Also, frequently this process provides solutions which can be “ready to use” or “off the shelf”. The innovation project then just becomes a standard purchasing or in-licensing process.

The NineSigma approach empowers this process by two unique characteristics: First, the open call for solutions enables a self-selection by potential solvers from any field. Often, the general class of a problem can be known and understood in different, disconnected domains. A company, however, has a tendancy to seek for the “usual suspects” within its own network, biased by the seekers own assumption about the character of the solution. The process of defining the need and the open request for proposals transmits the problem to actors from different domains – and with different levels of the state of the art. Secondly, NineSigma has a number of search specialists who use broad, unbiased search practices to find potential solution providers around the world. Both strategies lead to the identification of “unobvious” others – explaining the striking success of the application of this NineSigma targeted open innovation method to the FVA open source challenges. What is more remarkable is that normally open innovation broadcast cahllenges provide up to a 30% rate of solution success (Lakhani et all 2007)  where as in the case of Linked Innovation, 100% of the challenges identified solutions.

“The utilization of [open innovation specialists like NineSigma] clearly pays off. As long as there is a fair interaction between seekers and solvers, the problem broadcasting method will be successful and improve companies’ problem solving activities in terms of quality and efficiency” This is how one of the industry partners summarized the FVA experience. But in the end, crowd sourcing for technical problems can also enable important change within a company: “In the course of the project with NineSigma we have learned a lot about potential partners, but more importantly, we have learned a lot about ourselves and our own company,” another industry partner said. This “learning about one selves” may be one of the largest benefits of networking and open innovation crowd sourcing.

Conclusions

Open innovation is not an automatic success but one that demands thorough preparation and rigid implementation. From our experiences in with FVA and similar projects in different contexts, we could derive a number of key success factors to profit from open innovation and crowd sourcing of technical problems.

The 5 keys to turning Open innovation challenges into success are:

  1. Clearly defined problem definition, ownership & objectives. Perhaps the most important factor is to have a problem or task that is suited to being crowd sourced. Start with the problem and challenge owner. Determine the objective of your open innovation venture before hiring an intermediary. Intermediaries differ, and not all are equally suited for the same kind of task.
  2. Create a good open innovation environment. A dedicated team is critical to make open innovation a success. This starts by installing a central project competence for your open innovation initiative. Successful companies have nominated an internal OI champion who is passionate outwardly focused and capable to coordinate different crowd sourcing projects. Equally important, is to educate other team members and employees about the objectives and principles of the open innovation project so that they understand what is expected from them and what they should expect of open innovation..
  3. Decide about the span of control. Open innovation is about opening up to the periphery of your firm. But you can decide about the control you want to keep during the knowledge transfer process and the exploitation of the results by selecting an appropriate method and intermediary. For example: Shall potential solution providers learn who you are and see your name and company logo on the RFP? We know that RFPs that reveal the challenge owner’s identity can receive significantly more and sometimes better proposals. But revealing who you are also may inform your competitors. Knowing about these trade-offs is crucial for open innovation success.
  4. Create realistic time-lines and budgets. First of all, be fast. RFPs and also solution proposals will get to competitors sooner or later. So before you start, get the buy-in for the implementation of returned solutions that match your requirements. Reserve a budget for this. Be sure about the human capacities and resources required for the review process of the solution proposals and for making a technology sourcing decision.
  5. Decide about your resource allocation. Consider what’s next in the short term: Are you seeking for an intermediary that also provides support before and after the RFP broadcasting, e.g. with evaluating the solutions or negotiating the licensing terms? Or do you want to perform these activities in-house. Both options are available, and have their distinctive pros and cons. Also consider what’s next in the long term: Think of, e.g., community management. Shall the intermediary help you to manage a community of potential solvers? Or are you prepared to take these tasks by yourself?

By Frank Piller and Rick Wielens 

About the authors


Frank Piller is a chair professor of management and the director of the Technology & Innovation Management Group at RWTH Aachen University. He also is a founding faculty member and the co-director of the MIT Smart Customization Group at the Massachusetts Institute of Technology, USA. Frequently quoted in The New York Times, The Economist, and Business Week, amongst others, Frank is regarded as one of the leading experts on mass customization, personalization, and open innovation. Frank’s recent research focuses on innovation interfaces: How can organizations increase innovation success by designing and managing better interfaces within their organization and with external actors.

Rick Wielens, CEO, NineSigma Europe. Rick Wielens joined NineSigma in 2010 and is responsible for NineSigma Europe. Previously, Rick worked with his own company in open innovation and expert services mainly in the High Tech area in the Netherlands and Germany. Rick brings international experience working in Germany for SAP and in the Netherlands for Royal Philips Electronics in various roles and industries. Rick holds a M.Sc. in Transport Planning and Management from the Westminster University in London and a BA in Traffic Engineering from the University of Applied Science in Tilburg.

 

References

Jeppse, L.B. & Lakhani K.R. (2010) Marginality and Problem-Solving Effectiveness in Broadcast Search. Organisational Science Vol 20(5)
pp 1016- 1033.

Diener K. & Pillzer F. (2010) The Market for Open Innovation. A Study of the Intermediaries and Brokers for Open innovation, available at
www.study.open-innovation.com.

Lakhani K., Jeppesen L., Lohne P & Panetta J. (2007) The value of Openness in Scientific Challenge Solving. HBS publications.

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