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by Dr Willem Beeker
At Cambridge Design Partnership we are always looking for new ways to improve our clients return on investment in innovation. So how do you increase your project’s chances of success?
As a physicist I am interested in understanding why things happen. Game theory is a study of strategic decision making; games dictated by strategy and not luck require the invaluable skill of interpreting the rules and their boundaries. Insight into how your actions influence a positive outcome will pave the way to success. Sometimes the best playing strategy is fairly obvious, as in cases where the number of different realizations is fairly small. However, for some games the complexity of the interplay between rules and the sheer number of options can present quite a challenge. This is certainly the case in innovation!
In some ways, designing a new product is rather like learning to play a new game. You start by examining the rules and boundaries. To lead your chosen market you need to beat well-established players at their own game. This means you need detailed insight into their product space, and you need to learn that rather quickly. But key to successful innovation means not paying too much attention to how the others play the game. You are aiming to find a novel and better way to make use of the physical and technical bandwidth available.
One powerful card we can play to accomplish this is through a structured approach of numerical modelling of the physics and mathematics involved when a new product delivers value to consumers. To create a representative model in a short timescale one must capture only the most relevant aspects of a product or technical feature. This summarizes it to its smallest form factor, e.g. an equation, graph, spreadsheet or image. In other words, by constructing a model you establish the essential game rules. It is often the case that, through constructing such a model, new insights are achieved; either opportunities for improvement become apparent or certain features turn out to be irrelevant to the performance and hence the design can be simplified.
When operating at the edge of knowledge and at the technological frontier, ready-made formulations are often not available. This presents an exciting situation, as you start to discover the hidden rules of play in unknown territory. The key challenge in establishing a representative model is to make the right simplifications and to be able to justify the outcome, ideally through lab experiments. However, quick evaluation of the model using a range of parameters is usually much more cost and time effective compared to running that variation in lab experiments. Therefore, modelling has proven to be a very effective tool in raising the game quickly.
For example we were recently asked to innovate in a mature consumer market so we started by researching stakeholders unmet needs. We discovered that the fundamental performance of the product left consumers wanting more, so how could we significantly improve a long standing product category? We constructed a mathematical model from fundamental physical principles. Looking at the equations we could understand and predict how the design variables would affect performance, allowing us to focus our creative engineers on making a step change to key elements of the product. While the results are still confidential, they contradicted the accepted industry wisdom, and using mathematical modelling opened the door to a disruptive new product launch.
At Cambridge Design Partnership we have a team of experienced scientists that can construct a wide variety of algebraic bespoke models and evaluating them numerically often forms a core part of our evidence based design process.
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