Benefit from experimentation for effective innovation
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Benefit from experimentation for effective innovation

Let's look at the following story. After setting up the innovation team in the company and setting the goal of improving the distribution chain, the leadership team, together with some experienced employees, brainstorm and a great idea to create a new distribution channel has emerged, it has unanimous consensus in the group.

The senior management team has evaluated the idea and agrees, they have a lot of experience in the sector. After a rigorous elaboration of the project plan, they manage to approve the budget and are ready to work on a twelve-month project. After a lot of hard interdisciplinary work, the project is finished and the employees are pleased with the result.

The launch takes place, and after a few weeks the new system faces the reality: customers are not happy with the new channel, suppliers find it more costly to comply with the new flow, and frontline employees find the new system more tedious. What went wrong? How much did this innovation effort cost?

Innovation practices are generating a great deal of buy-in and enthusiasm in companies; however, when using the methodologies and frameworks, a vital part of the process is often left out of the plan: experimentation.

Innovation is a process, not novel ideas.?

First of all, innovation refers to substantive changes in the company's business model that can result in discrete increases in profitability. Such innovation can occur in products, operating models, distribution channels, supply chains, customer relationships, among others.

Unlike continuous improvement, that optimises value incrementally over a business chain, innovation involves substantive changes to the business model that can result in discrete increases in profitability and market position.

Innovation processes work in environments of uncertainty, risk and ambiguity. Therefore, it would be unwise to leave more elements to chance, as this would only make the outcome of change even less predictable; we require an orderly and consistent process.

This is where experimentation forms a key role within the innovation process. It allows gaps of unknown information to be narrowed, and offers a structured mechanism for learning about the problem to maximise the chances of success.

Let's go back to where it all began: experimentation

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Whenever we review the great feats that have forged our scientific knowledge, we encounter experiments' iterative use. In these stories the persevering path of experiments has a romantic and, in some cases, heroic meaning.

Modern experimentation, as part of the innovation process within organisations, has the same essential cycle as the scientific method: making assumptions or hypotheses, designing tests to validate or discard the assumptions, collecting and analysing the results, and defining a next step or iteration. If conducted in a structured way, experiments move us closer to a better understanding of the problem and thus the solution.

Whether in the manufacture of a product or the provision of a service, incorporating iterative stages of experimentation, gradually building on increased knowledge gained and validated hypotheses, allows evidence-based decisions to be made.

There are at least four reasons why experimentation brings great value to any business' innovation process:

  • Generates learning. Captures realistic and objective information about the business problem, avoiding the bias of desk-based arguments.
  • Anticipates failed projects. Identifies errors in early stages of development to avoid project failure or irreversible changes.
  • Reduces implementation cost. Controlled and detected errors in small-scale scenarios allow the identification of adjustments that would be very costly if tested with the change running at full scope.
  • Enables better products and services. During the solution development process, feedback from customers, suppliers, users and stakeholders allows insightful views to be detected and improvements to be incorporated at early stages of implementation.

And why does the process of experimentation often cost so much?

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The science of business management has grown on a deep-rooted premise of task control and a refusal to make mistakes. Experimental processes by definition have a high failure rate, which tends to generate a sense of anxiety in the organisation, as opposed to the modern values of efficiency and predictability. As professionals, it is very challenging for us to feel that we do not have control of the results and to accept that an important part of the knowledge of the business problem is still unknown to us.

That is why our organisations, even if they are very efficient and neat for the daily business activities called exploitation activities, often find it difficult to cope comfortably with substantive changes in the environment by carrying out activities called exploration activities.

In large part, facilitating the right environment in organisations to make the most of experimentation has to do with a cultural change so that the "rules of the game" promote curiosity and admitting mistakes as part of the learning process.

Signs of misuse or lack of experimentation

  • In the context of innovation projects, some typical symptoms include:
  • Testing phases are seen as time lost and should take as little time as possible.
  • Project direction changes as a result of senior management's point of view and not as a result of the customer voice evidence
  • Project development decisions are made without having completed the entire testing phase.
  • New systems and software are procured without testing over real processes
  • Considerable budget variations are detected in the final stages of the project.

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Recommendations for effective experimentation

If we go back to the initial example in this article, a possible summarised approach to reduce the possibility of failure would start with listing all the assumptions involved in launching a new distribution channel (accepting that there is information we do not know with certainty), then identify the critical assumptions, design a set of experiments to validate the assumptions, field test with customers, suppliers and users, evaluate the results and adjust until a robust design of the solution is achieved.

Managing the anxiety to quickly start implementing as a result of a great new idea is not a trivial change of mindset.

Some recommendations for effective experimentation are:

  1. Lose the fear of making mistakes. The use of prototypes, proofs of concept and pilot tests are made to gain insight into the business challenge. Invalidating an assumption is as valuable as validating it. Think of this stage as an investment.
  2. Keep it simple. By definition, experiments are small-scale and low-cost tests. Whenever possible, favour tests that take the idea to real environments, where customer or user behaviours are reviewed. A field test is more valuable than a survey.
  3. Value feedback. Customers’ and stakeholders’ voice is crucial. Eliminate biases and prejudices. Remember that it is a learning phase. Our knowledge as business experts is typically an obstacle. Avoid jumping to conclusions on incomplete evidence; if a result is unclear, it is better to go to the source.
  4. Follow a structure. Do not lose sight of the fact that testing is part of a larger process. Even if testing requires low investment, the time to shape learning and move to the next stage will be crucial to the business opportunity. Follow a plan and be diligent in following through. ?
  5. Prioritise. In each iteration there are experiments and assumptions that offer more value than others validating the essential hypotheses of the new proposal. Distinguish those that require more attention and move forward with sufficient analysis to make a decision. ?

In times of volatile environments and uncertainty, organisations face the need to deal with the intrinsic anxiety of classical management. In order to achieve successful innovation processes, the first step is to value the activities that generate learning, this will bring us closer to innovation decisions with a higher probability of success.

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