Organisations now regularly face issues relating to the integration of innovative product design and sustainability. As a result, companies need to integrate environmental aspects into their business strategies at product design stage (Eco-design). One of the outputs from the Eco-design process is to provide as much information as possible in order to support decision making; for example, which product, which material. However, design at the early stages process suffers from lack of confirmed data. To solve this problem, uncertainty analysis provides an evaluation of the level of confidence of the results given the uncertainty in the input data. In addition, the result of uncertainty analysis can stimulate and guide the collection of additional data if the overall uncertainty is found to be significant. In reality, the collection of verified reliable data is expensive, or even impossible to obtain. Therefore, decision makers require in-depth information to identify the relevant factors that have the greatest influence on the overall uncertainty. The objective of this paper is to apply a two-steps sensitivity analysis to identify the uncertainty hotspots in a case study for Eco-design. The case study was built for the design of an electronic tablet in which the “cradle to gate” energy consumption was estimated with the consideration of uncertainty. The relevant information of the components was determined based on a tool for materials information management and their associated uncertainties represented as a range of value. With regard to sensitivity analysis, an initial screening method was carried out using the extreme values (i.e. minimum and maximum values) for each input in order to estimate its influence on the overall energy consumption. This first step gave a preliminary insight into the uncertainty hotspots and reduced the number of inputs that needed to be considered more specifically (by ignoring those that were calculated to be insignificant). Secondly, these selected uncertainty hotspots were characterized by specific probability distributions according to the observations or expert judgments. This provided a more precise representation of uncertainty in the selected hotspots. Finally, a Monte Carlo simulation was applied to explore how the energy consumption would change in response to the variation of the selected uncertain inputs. By doing so, a sensitivity index was calculated to rank the order of the contributions of the inputs to the overall uncertainty of output. The results show that the process given above identified and prioritized the most important uncertainty hotspots. To reduce the uncertainty associated with the input data, a number of actions can be taken: choosing different solutions that have more accurate data, undertaking more focused data collection. Alternatively, if the uncertainties are not deemed to be important, it may be appropriate to simply accept the risk. In conclusion, this process provides a cost-effective way to filter the large volume of information and help decision makers to make progress towards their business goals.
Keywords: Eco-design, uncertainty hotspots, sensitivity, decision making
5b. Design for sustainability
