Neuronal Innovation – the Next Big Thing After Open Innovation

This article shows how biomimicry can be put to effective use in designing innovative networks. It builds from similarities between the brain connectome and innovation networks to lead to a novel concept in innovation - Neuronal Innovation. This new concept shows how organizations can become proficient in deploying and using collaborative innovation.

The now well-known ‘Open Innovation’ model subsumes innovation-enabling systems & processes within a formal framework.

Most of these systems & processes, however, have been in matter-of-fact, everyday use at a number of forward-looking companies for years (Edison, Bell et al.).

The model makes the forceful point that looking for outside support is critical to a company’s innovation prowess. It has also led to a better understanding of the key mechanisms that underlie effective innovation.

But in an age of wide openness and well-nigh universal access, the capability to establish and run task-essential links, while optimizing the sundry innovation networks within and without organizations, has become the key to the effective ability to foster truly ground-breaking innovation.

To remain at that leading edge where disruptive innovation happens, companies need to acquire and maintain a clear picture of how their innovation networks are structured. Intensive collaborative innovation has become the rule.

When designing optimized networks, a striking similarity to the complex map of neurons and synapses within a brain emerges – the connectome. This is why we are hereby suggesting to name innovation network mapping the Innovation Connectome. The various nodes are linked by information flows represented by arrows (figure 1).

Figure 1: Example of an Innovation Connectome

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This may be seen as an instance of biomimicry, applied to innovation management.

The Innovation Connectome displays the connectivity patterns of two specific areas that companies trigger (activate) when they engage in innovation projects: One mirrors the firm’s internal organization, while the other one maps its outside partners’ ecosystem.

This Innovation Connectome must be seen as an intangible, but very real, company asset.

This Innovation Connectome must be seen as an intangible, but very real, company asset. The mapping of key innovation-enabling networks and cross-ties provides an overview of both the wealth and, importantly, the robustness of the multidirectional exchanges and feedback loops a company has set up with its different partners. It also gives a precise measure of just how nimble and agile a company is.

Depending on the operating parameters and the deliverables of any given innovation project, the company will have to favor some connections, and overlook or refrain from using others. This is the very capability that Neuronal Innovation delivers. Like Open Innovation, some principles of Neuronal Innovation have been in use at foresighted organizations for years.

In 2006, Thesame, a French mechatronic and innovation network, with the support of G-SCOP lab in Grenoble, launched a multi-partner innovation project named PRAXIS (Performance in Relationships Adapted to eXtended Innovation with Suppliers ).

This project arose because of a need to involve supplier partner relationships quite early on during new product development. This multi-partner innovation project is run with a community of about ten client companies representing various manufacturing sectors, as well as a suppliers’ “club”. The key goal is to help businesses –  both customers and suppliers – to cooperate better during the launch phase of new products. Thesame also developed a collaborative purchasing platform (named PEAK ®), the goal of which is to create a new set of dynamics between B2B players and their suppliers, thus leading to more shared profit. This initiative emanated from the Rhone Alps region in France.

1. Mapping the Innovation Connectome

The Innovation Connectome is made up of two complementary parts, an internal one, and an external one (see figure 1).

1.1 Designing the internal part of the Innovation Connectome

Designing the internal part begins with listing these company employees who can become involved in different innovation projects.

This occurs within the functional departments which become involved with any innovation project: Management, R&D including designers, legal & regulatory affairs, intellectual property, manufacturing, sales and marketing including communication, procurement, quality, finance.

Then, the requisite people skills can be assessed and captured through a set of weighted parameters used to select the relevant players – and the champions – suitable for specific innovation projects.

Beyond people availability and their relevant expertise in the delivery of innovation, the following additional features shall be considered:

  • Creative thinking
  • Balance of right-brain, left-brain (analysis & intuition)
  • Organizational and innovation management skills
  • Resilience
  • Execution skills: Turning ideas into viable products and services
  • Flexibility
  • Customer sensitivity
  • Networking & teamwork skills
  • Turning ideas into new businesses
  • Entrepreneurial skills, self-starters
  • Political savvy

For each attribute, a grade weighting from 1 (low) to 5 (high) is given. These grades will then be used to spot the best prospective champions, account being taken of the degree of advancement of a particular project, and of the kind of innovation sought: game-changing, incremental, and so on.

This work can also be further streamlined by the use of existing off-the-shelf methodologies such as FourSight, Belbin… which help narrow down individual profiles, with regards to the particular needs of any given innovation project.

1.2 Designing the external part of the Innovation Connectome

This task first consists in identifying the potential external partners within the different categories who may contribute to the strengthening of the company’s innovative performance.

The main categories include:

  • Public R&D
  • Private R&D
  • Public funding
  • Private funding
  • Customers and partners
  • Manufacturers
  • Lobbyists
  • Distributors
  • Public administration
  • Regulatory authorities

Innovation Connectome, especially as relates to the external side, is never set for good – it’s not cast in stone, but continuously evolves.

Companies must cultivate relationships with potential partners with an interest in joint work. There is added value in sharing knowledge, competencies and financial resources when setting a collaborative relationship. Companies must first qualify partners of choice and then rate them within their Innovation Connectome. The way to do it is described on part 2.3.

The main factors and issues that have to be considered are summarized below.

Trust is a key factor in establishing sound relationships conducive to joint Open Innovation projects. As a P&G Open Innovation executive once put it

If a potential partner comes to us asking to have a NDA signed before talking, this is not a good start. We cannot afford to waste time. We want to increase the size of the innovation pie so that it can feed each partners in a satisfactory way. We are not interested in creating conditions that will lead to a battle to get the largest part of it.”

Because the very process of qualifying choice partners is costly and time consuming, it behooves all partners to establish and maintain full mutual trust from the very onset of the relationship. As developed on part 2.3, fruitful partnership is a relationship that relies on loyal collaboration along with a commitment and a joint responsibility towards shared objectives. Setting clear and concrete objectives is then a pre requisite. Commitment can be measured through investment level and human resources involvement. Some level of emotional intelligence is also required – the sharing of scientific and other techniques is not enough per se and can only proceed from a basis of trust, and collaborative project headway relies on well-established relationships.

2. Enabling Neuronal Innovation

2.1 The innovation value chain features

For any given project, a decision as to which elements of the Innovation Connectome network will come into play must be made at the outset.

To do so, the first step consists in analysing the innovation value chain, including needs and key drivers at every project innovation stage, from inception (concept and ideas generation) through development and innovation dissemination.

Every stage is characterized by:

  • An unavoidable level of fuzziness in the technical parameters and know-how
  • Complexity of Interfaces
  • Uncertainty as relates to process
  • Regulatory constraints
  • Safety constraints
  • Environmental constraints
  • IP issues
  • Gaps in market knowledge
  • Constraints arising from speed to market

An example is given in table 1.

Table 1: Analysing innovation project chain features

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2.2 Positioning the right internal resources along the innovation value chain

Every internal resource must be deployed at the right juncture along the innovation project value chain.

The more upstream innovation project stages mainly call for creative profiles, as does every project stage where the thinking up of new, disruptive, nonlinear, or out-of-left-field solutions constitutes the main deliverable.

Downstream stages need more profiles versed in turning ideas into viable, real products and services. These latter stages remain the ambit of traditional business development skills, rather than that of unorthodox creative skills.

Skill levels and profiles must be continuously adapted and fine-tuned, reflecting not only the degree of advancement of the project and achievements to-date, but also various pressures which may come to bear, such as rising or unforeseen levels of complexity, deadlines, costs, and time to market imperatives.

Thus, a forecast plan of the internal skills required to successfully complete the sundry project stages to completion must be drawn up from the word go. This plan will be adapted on the go, as evolving needs and constraints require.

Note that the requisite skills are not necessary related to the function. For instance, it can make sense to involve a creative in-house patent attorney at the early, R&D type stage, if and whenever the chosen strategy involves building durable competitive advantage through a proprietary position. The patent attorney may suggest specific research orientations, commensurate with the company’s legal strengths.  Samewise, a R&D specialist with a good sense of marketplace demands, attitudes, and expectations may be called upon to help at the late dissemination phase, so as to assist the sales and marketing teams with cogent technical and/or scientific arguments. Table 2 gives an idea about how such resources can be deployed.

Table 2: How to bring together the most suitable resources in an innovation project

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2.3 Identifying the right external partners

To competently select and activate the relevant elements within the partners network, a two dimensional matrix tool is used. This tool helps identify the most suitable potential partners – who will contribute the most to the innovation project and partake in their outcomes. The two dimensions here are commitment and loyalty.

The availability of suitable skill sets from within the partner network constitutes of course a first prerequisite. For any individual participant from the partner network, a requisite is his or her prior knowledge or experience within the specified innovation field.

Another key requirement is the setting up of effective communication channels – project-critical information must be shared at once among the relevant players.

Broadly speaking, the commitment of partners to project success can be measured by how they deploy costs and resources, and by their readiness to share risks inevitably associated with such projects. In other words, proof of commitment lies not only in the non-cash resources that a partner is ready to deploy, but also when applicable in the level of funding made available by the partner to the joint project.

Loyalty can be assessed as follows:

  • History of business relationships: Analyzing past business relationships tends to be a good predictor of how a relationship will unfold. It helps anticipate partner strategies and behaviors, based upon past relationships within innovation projects. For new partners, scientific and technical networks are wontedly well-versed in observing potential partners’ policies and practices. Due diligence must always be conducted before a new partner is accepted as such.
  • Business strategy and objectives: A key issue here is that there is an inescapable and intractable tug-of-war between the competing requirements of transparency and of confidentiality. It is a sensitive issue, especially since  partner willingness to get into an alliance sometimes may hide ulterior motives, and perhaps, at worst, a hidden agenda. There has been cases where a partner were moved by their own narrow objectives and did not care much about anything else. Through opportunism, for example, they may be looking to acquire new knowledge or new skills, at the least cost. If the partner happens to be struggling financially, they may view a partnering innovation project as a lifeline, especially if the project is on track to receive public funding. Such a partner will abandon cooperation as soon as they have recovered, which will prejudice, and might kill, the innovation project. An unethical partner may also be tempted to unilaterally appropriate the results of an innovation project. Should an incident of this kind occur crosswise between members of the network, then the entire network may soon become aware of the situation, and the reputation of the guilty party would be impugned. There is therefore a certain safety in numbers, in this case in the number of partners taking part in the project.
  • Values and culture: Cultures can be starkly different. There seldom is such a thing as a better business culture, however different cultures can swiftly lead to misunderstandings, misread cues, a different specialized vocabulary where the same words mean different things, and so on.  Leo Wildeman, Director at KPMG partner, estimates that fully 21% of failed technology alliances stem from misunderstandings arising from cultural differences.

How the process works.

This is a two stage process. Its onset is constituted by defining the partners of choice to be deployed along the project value chain (see table 3).

Table 3: External partners selection: Phase 1

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Once preparation is completed, project-suitable potential partners must be selected from within the wider Innovation Connectome. This step amounts to triggering and activating the appropriate external nodes.

For instance, during the launch phase of the “BlueBuster” project, the company will be seeking suitable public R&D partners.

These partners are deployed as per their commitment and loyalty as evaluated by company staff (figure 2). Simple aid tools such as the ones shown in table…  are applied.

Table 4: Evaluating partners loyalty

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Table 5: evaluating the commitment of partners

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Figure 2: Selection of External Partner: phase 2

These tables will permit the drawing of a graph identifying the partners of choice (figure 2).

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On figure 2, bubble sizes represent the level of investment the different partners are willing to commit to the “Innoblue project”. On that basis, Partner A is a strong candidate to be the partner of choice. Using the Neuronal Innovation methodology results in this connection being activated.

3. Concluding remarks

Nowadays, collaborative innovation has become the rule.

Even though companies nowadays run complex organizations dedicated to the enablement of innovation – which includes both internal resources and external partners of choice, seldom do they know how to bring these organizations optimally to bear.

Often, we see barriers, dams and bottlenecks –  all kinds of hindrances, which prevent the relationship flows from properly nurturing and delivering innovation projects.

Innovation Connectome and Neuronal Innovation are two fully complementary concepts describing the dynamics that companies have to put to use and master when they engage in collaborative innovation.

Depending on the specific parameters of any given innovation project, Innovation Connectome nodes from within a wider pool are activated, and information flows through these nodes are enabled and fostered. This is Neuronal Innovation. The winners in the disruptive innovation stakes – who not only know how to generate great ideas but also how to execute them and bring them to market on time – will be those who have learnt to deploy this methodology properly.

“Open Innovation”  is a constitutive element of the Neuronal Innovation process. Companies have to acquire potential partners whose profiles include some of the following features:

  • Convergence of interests and objectives
  • Reliable history of business relationships
  • Commitment
  • Complementarit
  • Shared culture and values

Neuronal Innovation remains an ongoing process. Companies will be able to continuously update their stores of knowledge regarding processes, partners, and skills.

The principles set forth in this article constitute the basis for additional research impacting different fields, such as multi-criterion decision tools, in which Qiventiv Systems prides itself on being a thought leader.

The complexity of any Innovation Connectome primarily depends on the internal organizational structure and on the scope and depth of the external network of partners.

Neuronal innovation tools, such as those proposed above, are particularly useful for companies who have to deal with a large number of partners, and/or many innovation projects. Neuronal Innovation entails some level of complexity when dealing with innovation projects and with many different partners.

It is, however, but a small price to pay to successfully innovate in a fiercely competitive marketplace.

I would like to extend my thanks to Chris Ransford, a partner with Eumerica AP, an organization which helps international projects happen. Chris has been my sounding board and has helped me clarify some of the concepts discussed here

By Jean-François Lacoste-Bourgeacq

 

About the author

Jean-François Lacoste-Bourgeacq, PhD, is the CEO of Qiventiv Systems, a company specializing in boosting sustainable innovation. Jean-François has 25 years of business experience in innovation management and deployment. He has published a number of scientific papers as well as two books on “Agile Innovation” and innovation risk intelligence. (AFNOR editions, 2007, 2009.). Jean François is part of the experts committee working at the European level on innovation management guidelines. He also teaches food innovation at EPITA college. For more information, please visit www.qiventiv.com.

Further reading:

- Sandra Cheriti & al.  “Measuring the supplier’s performance in collaborative design: Proposition of a framework” R&D Management, vol 41, n°1, p 61-79. (AERES B, CNRS 3).

- Sandra Cheriti & al.  ” Developing an approach for design-or-buy-design decision-making”, Journal of Purchasing and Supply Management,

vol 16, n°2, p. 77-87 (Aeres B, CNRS 4).

- Vijay Govindarajan and Chris Trimble “10 Rules for Strategic Innovators: From Idea to Execution”,  (Harvard Business School Press, 2005)

- Humphreys, P. K., G. Q. Huang, et al. (2007). “Integrating design metrics within the early supplier selection process.” Journal of Purchasing and Supply Management 13(1): 42-52.

- Humphreys, P. K., Y. K. Wong, et al. (2003). “Integrating environmental criteria into the supplier selection process.” Journal of Materials Processing Technology 138: 349-356.

- Jean-François Lacoste Bourgeacq, “Innover avec succès”, 2009, AFNOR publisher

- Lars Hakanson  (1993), « Managing Cooperative Research and Development: Partner Selection and Contract Design », R&D Management, 23, 4, 273-285

- Morten T. Hansen and Julian Birkinshaw,” the Innovation value chain”, Harvard business review, June 2007

- Miron, et al. (2004) “Do personal characteristics and cultural values that promote innovation, quality, and efficiency compete or complement each other?”. Journal of Organisational Behaviour 25, 175-199.

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  • Sebastian Sigloch

    Thank you for your article, Jean – Francois. Since I am researching on Innovation Networks, I do agree on the importance of mapping internal as well as external relations to rout out valuable associations between actors in a network.

    However, Neuronal Innovation won’t be the “Next Big Thing after Open Innovation”, but rather an extension to foster and measure innovative capabilites within a network along-side and besides the common value-chain.

    Open Innovation teaches us, how to open – up our organizational boundaries to let in -or outflow ideas, information, technologies, IP ..etc. This, of course, creates some kind of a network, where an organization is necessarily embedded and operating in. Hence, relations in a network have to be analyzed and measured regarding to innovation’ performance. These methods are extending Open Innovation, triggering another view on this long – lasting “buzzword”.

    I hope to contribute to this interesting topic and am open for any discussion.

  • Jean François Lacoste Bourgeac

    @sebastiansigloch:disqus Dear Sebastian,

    Thank you for your feed back and comments. As you know, Open Innovation has been existing almost forever. François the 1st, a French king from the XVI century was already using technology scouting when he invited Leonardo da Vinci to stay in France. Think also about electrical bulbs and keypad stories… What is really new about innovation is that its pace is accelerating dramatically. Innovation has always been a “social” process and the result of this process. Adressing properly and quickly the issues that innovation can solve has become key. Networks are the core center of any innovation process. But to be efficient, you need to light up the right network nodes depending on the innovation project type. These nodes are first made of people. You need to connect the right “internal” and “external” people together and make them happy to work together. This is quite challenging for many reasons (behavior, culture, technical or business background…). Some promising innovations failed because of that. Neuronal innovation is all about this.

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