2020 Business Case Competition – Evaluation Criteria – Applicable to Intracompany Innovation Proposals?

Dear all  - our 2020 Business Case Competition (https://open-european-innovation-network.blogspot.com/2020/05/invitation-to-university-sponsored.html) will be asking student teams to assess a case study and suggest “next practice” approaches in order to tackle them. The submitted suggestions will be assessed by a panel consisting not only of thought-leaders in the innovation space, but also be innovation leaders in high value manufacturing practice plus someone who was actually in the midst of the case study. While the panel is still scheduled to convene on the exact evaluation criteria for student team submission, one thing should be clear to us all, the proposal which arrives at its result the FASTEST will probably come out on top!

Speed of solution diffusion is of course what we are all about and we will use the Litmus Test Version 2 (https://open-european-innovation-network.blogspot.com/2020/04/innovation-diffusion-litmus-test.html) to conduct the first assessment of submissions and rank proposals by the fastest to achieve 84% diffusion to the late majority. One of the things we are also wondering about whether this “test” could be used for evaluating intracompany innovation proposals as well?

As a reminder (or cheat sheet for wannabee participants) the mathematical model and the evaluation criteria of the Litmus Test are listed below. This turned out as a bit of a long post unfortunately but the details might be helpful.

The mathematical model has two key elements:

1. Tab "Litmus Test Context Questions"

• Market Size. The relevant question is "In your opinion, how many adopters are in the market? Note that the number you enter on the right will define the market size (m) for the forecast (see tab "Diffusion Forecast"). If in doubt - enter "1000"". This setting is the basis for then determining the size of the various adopter groups where diffusion progress is forecast for (Default Settings: Innovators 2%, Early Adopters 14%, Early Majority 34%, Late Majority  34%, Laggards 16%).

• Time Periods. The relevant question is "In how many time periods are you expected to achieve market saturation (84%)?" While this variable is not integrated into the overall forecasting model yet, it needs special attention in order to interpret the forecast results. The time periods for high value manufacturing contexts are recommended to be months, and the maximum time period to market saturation is recommended to be 9 months. The model per se uses 84% market ownership as "saturation" since this encompasses all adopter groups from innovators to the late majority - this variable can however be adjusted in a later tab for experimentation.

2. Tab "Litmus Test Questions"

• Idea for Diffusion. The averages for all answers to the questions "Is your idea ready to diffuse rapidly to late adopters?" and "How confident are you in your above assessment of the idea?" are multiplied and divided by the maximum possible answers.

• Roles and Participants. The averages for all answers to the roles and participants questions are multiplied and divided by the maximum possible answers.

• Maturity Level. The score for "Idea for Diffusion" and "Roles and Participants" are multiplied.

The results of the two elements above can be “gamed” on the tab "Diffusion Forecast - Detail". This tab contains the main variables for forecasting the diffusion of innovation across the market segments. Blue shaded cells can be manipulated by the user. Amber shaded cells for p (Idea Maturity * People Maturity) and q (People Maturity) values have been transferred from other tabs.     The diffusion formula used is the Bass Diffusion Model and calculated as "=((Overall Co-Efficient of Innovation (p) + ((Overall Co-Efficient of Imitation (q)/ Total Market Size (m))*Cumulative Number of Adopters))*( Total Market Size (m) - Cumulative Number of Adopters)". The tab is designed to enable comparative forecasting of multiple ideas in a later version. Currently only one idea is captured with the p and q values transferred from the tab "Litmus Test Questions" and then subject to a weighting process which results in the p and q values used on the forecasting process.

The tab “Litmus Test Questions” captures not only the user answers but also, importantly the “certainty” of the user in respect to answering. The tab “Factor Pareto” then allows the user to sort the various variables to understand where to start getting better – please do note there is as of today no weighted correlation model available – something we continue working on in the search for game changers.

Some thoughts on the “Litmus Test Questions” and remember that the higher the score the faster the diffusion which can be expected.

Attributes of the Idea

• Degree of Innovativeness whereby a score of “0” would correspond to the innovation being a highly similar copy of an existing product used by the late majority adopters and a score of “5” would correspond to the innovation having no identifiable competitors.

• Technical Readiness Level using the NASA TRL Level structure whereby a score of “0” would correspond to a state where only the challenge by the user is known and no research had yet been launched to identify a potential solution, and a score of “5” would correspond to an innovation that has proven to create the intended value in at least one implementation in a real-world scenario.

• Budget and Resources whereby a score of “0” would correspond to no financial or human resources being committed by stakeholders and a score of “5” would correspond to full financial and human resource being committed through the innovation journey up to an including the late majority adoption.

• Number of Competitors whereby a score of “0” would correspond to no competitors being known (which often correlates to a high level of innovativeness although replacement products and services do need to be considered) and a score of “5” would correspond to at least 40 specific competitors having been identified (which enables the application of statistical regression methods for covariate analysis).

• Degree of Complexity whereby a score of “0” would correspond to the innovation being “disorderly” where human collaboration dominates in order to determine what value to the late majority adopter is envisaged in the first place and a score of “5” would correspond to the innovation being “simple” with low levels of human collaboration needed for its implementation.

• Compatibility with Existing Ways of Work whereby a score of “0” would correspond to the innovation falling within a heavily regulated environment which would need to be changed in order to permit the innovation to be used by the late majority adopter and a score of “5” would correspond to the innovation being fully within processes and procedures established in the late majority adopter context.

• Ease of Understanding: Whereby a score of “0” would correspond to the late majority user needing dedicated classroom / lab training in order to understand how the innovation generates value and a score of “5” would correspond to a late majority user being able to use general common sense to understand how the innovation works.

• Ease of Use: Whereby a score of “0” would correspond to the late majority user needing dedicated classroom / lab training in order to understand how use the innovation and a score of “0” would correspond to someone totally unfamiliar with the context being able to use the innovation in the right way and.

• Ease of Adaptation: Whereby a score of “0” would correspond to the innovation being so specialized for use in a specific domain that it really can´t be used elsewhere and a score of “5” would correspond to the innovation easily being used in different domains.

• Ease of Trialing: Whereby a score of “0” would correspond to a test use of the innovation requiring significant funding and resource or process changes and a score of “5” corresponds to a late majority user being able to quickly and freely “play” with the innovation in its final setting.

• Observability of Impact: Whereby a score of “0” would correspond to the effect of using the innovation not being visible within normal business planning timeframes and a score of “5” would correspond to the effect being immediately apparent.

• Urgency of Need: Whereby a score of “0” would correspond to the impact of the innovation being desired but not required and a score of “5” would correspond to the impact of the innovation being needed in real-time.

• Degree of Certification: Whereby a score of “0” would correspond to the use of the innovation requiring changes to laws and a score of “5” would correspond to the innovation already complying with all relevant laws.

Attributes of Roles and Participants

All roles MUST be populated by at least one (ideally unique) individual. Core roles (key user, inventor, product owner and business sponsor) are needed to bring the idea to life and accelerating roles (investor, influencer, super user and moderator) give the diffusion of the innovation that acceleration needed in order to arrive at 84% of the late majority adopters. For each populated role as assessment is made in regard to their behavior in the innovation diffusion web (https://open-european-innovation-network.blogspot.com/2020/01/the-narrative-for-generic-diffusion-of.html ) therefore:

• Urgency: Whereby a score of “0” would correspond to the individual not seeing any need to implement the innovation in the short term and a score of “5” would correspond to the individual needing the innovation implemented with relevant benefits generation “yesterday”.

• Priority: Whereby a score of “0” would correspond to the individual not having the implementation of the innovation at the top of their “to do” list and a score of “5” would correspond to the individual having this as the next thing that must get done.

• Motivation: Whereby a score of “0” would correspond to the individual not being particularly internally motivated and a score of “5” would correspond to the individual “burning with a passion” to support the innovation diffusion.

• Domain competency: Whereby a score of “0” would correspond to the individual having no familiarity with the area where the innovation is intended for implementation and a score of “5” would correspond to the individual being a “super user” in the relevant context.

• Collaboration preferences: Whereby a score of “0” would correspond to the individual preferring to work on their own and a score of “5” corresponding to the individual getting energy from collaboration.

• Degree of voluntary engagement: Whereby a score of “0” would correspond to the individual being “made” to involve themselves against their will and a score of “5” would correspond to the individual voluntarily supporting the innovation diffusion.

P.S. If you are interested in learning more please visit us at www.innovation-web.eu, our LinkedIn Group at https://www.linkedin.com/groups/8779542/, our blog at https://www.innovation-web.eu/entov-hvm-blog, our Researchgate project page at https://www.researchgate.net/project/Open-European-Network-for-Enterprise-Innovation-in-High-Value-Manufacturing-ENTOV-HVM, our Sourceforge page at https://sourceforge.net/projects/entov-hvm/  and our Facebook page at https://www.facebook.com/groups/2014779865300180/. You can also follow us via Twitter: @owschwabe (#innovationweb) and the LinkedIn Group page https://www.linkedin.com/company/entov.