Global sensitivity analysis reduces data collection efforts in LCA: A comparison between two additive manufacturing technologies

Accounting for the environmental impacts in the design of technologies is becoming a necessity for manufacturers. Life cycle assessment (LCA) is a well-established method to quantify the environmental impacts of products and services through a holistic perspective and is increasingly used to support the eco-design of products and technologies. However, LCA generally faces an inherent issue with data availability. Given the constraints on both time and cost for collecting inventory data to feed the LCA model, a trade-off between data cost robustness is required with an efficient data collection strategy. The objective of this study is to develop a framework to prioritize data collection efforts in LCA using uncertainty analysis. This starts with a screening life cycle inventory analysis systematically informing all input parameters with uncertainty ranges. Monte Carlo analysis is then used to propagate the uncertainty through the model. Stochastic results are then compared with an acceptable confidence level set by the decision maker. This is followed by a global sensitivity analysis using Sobol' indices to rank different input parameters based on their contribution to the variability of the results. This paves the way for an iterative process prioritizing further data collection focusing on the most sensitive parameters. A case study comparing cold spray and wire arc additive manufacturing illustrates how to operationalize the framework. Learnings from the case study highlight the importance of defining the uncertainty ranges and the convergence criterion, where more work is needed in that domain.