Design Engineering
Showcase 2020

Circular Economy has become the face of a movement towards a more sustainable future. It is the front runner in the race to solve the issues of environmental impact, economic volatility and resource scarcity created by our traditional ‘take-make-dispose’ approach to consumer goods (Ellen MacArthur Foundation, 2013). When designing circular systems, it is important to consider the barriers to success of that system: Social, Regulatory, Economic, Environmental and User Experience. For a sustainable circular system, we need to maximise the success in each of these objectives/barriers. Each decision within the design process will carry trade-offs within each of these objectives. When designing a circular system, we often consider these objectives independently from one another, but to successfully design a circular system they must be considered together.

Numerical Optimisation was introduced to aid decision-making as part of the pre-implementation design process or the 'Deliver Phase'. It is suitable due to its ability to explore the effects and trade-offs of decisions. Multi-Objective optimisation is most suitable for a design process as it provides a set (Pareto Frontier) of optimal solutions from which the designer can chose and, hence, does not remove decision-making from the designer.

Every year, 250,000 tents are left at festivals in the UK. This equates to 1000 tonnes of landfill waste annually. A rental service was designed, that allows users to pick up a tent at a festival, pitch, use it, and then return it when they leave, avoiding the issue of lugging a tent to and from a festival. When it came to the deliver stage of the design process, the following questions still needed answers: ‘How often should the tent be used before disposal’, ‘What should the rental price be for the tent?’ and ‘What quality of cleaning is suitable?’. Each of these decisions had trade-offs within three design objectives: Economic, Environmental and User Experience. By using the optimisation technique, an optimal solution can be chosen that fits within a brand.

For example, imagine your mission statement is 'Save our World by improving our favourite events.' Then, you can choose a solution focusing on minimising environmental impact and maximising user experience. In this example, a solution can be chosen that reduces environmental impact by 85%, maintains 80% of the user experience of a brand new tent, and makes an annual revenue of £650,000. For this, the optimisation process gives us a solution suggesting the tent should be used seven times before disposal, the tent should be professionally disinfected between uses and the rental service should cost £50.

Imagine you are working for a different company attempting to implement the same system. Their mission statement is 'Introduce circular solutions to improve financial sustainability.' Therefore, your choice of solution will be focusing on economic impact. A solution chosen for the company made an annual revenue of £784,000, maintained 91% of the user experience of a new tent, and reduced environmental impact by 40%. For this solution, the tent should only be used twice, shouldn't be cleaned between uses and, again, should be rented at £50 for a festival weekend.

The beauty of this technique is that, although these solutions are focusing on different objectives, it is easy to find solutions for both. In under five minutes the solution space can be explored to understand the trade-offs and an optimal solution can be found for your design.