A team from Singapore University of Technology and Design has developed a faster, AI-driven method to conduct life cycle assessments (LCA), significantly reducing the time and complexity involved in measuring a product’s environmental impact.
Researchers from Singapore University of Technology and Design (SUTD) have developed a new AI-driven model to shorten the time taken to measure the impact of a product on the environment.
The findings, published in the journal Proceedings of the Design Society, mark a significant step toward making environmental impact assessments an integral part of product design, potentially transforming how industries approach sustainability.
Background
Choices made during product design can heavily influence the environmental impact of the final product. From material selection to manufacturing methods, early decisions leave lasting impressions on ecosystems and supply chains.
However, life cycle assessment (LCA), the primary tool to gauge these impacts, is often inaccessible due to its complexity, cost and time requirements.
LCA provides an extensive overview of a product’s environmental footprint — from raw material extraction, through its usage, to eventual disposal.
Despite its merits, a traditional LCA is a resource-intensive process demanding months of data collection and analysis by experts, making it unrealistic for many small and medium-sized enterprises or even larger companies operating on fast product development cycles.
“Product designers face many challenges: difficulty in assessing the impact of different materials because of a lack of reliable data, limited leverage with supply chains to obtain information, and incomplete understanding of energy consumption. Without clear guidelines, they often end up making choices in the dark,” Arlindo Silva, a SUTD associate professor, said in a news release.
The Team’s Method
In a move to overcome these hurdles, Silva and his team have introduced a Streamlined Life Cycle Assessment (SLCA) method.
This breakthrough approach leverages artificial intelligence, 3D modeling and existing databases to simplify the LCA process while ensuring credible results.
Instead of starting from scratch, SLCA uses AI and secondary databases to pinpoint the most impactful components of a product’s life cycle. These key elements are then modeled in 3D to extract vital data such as weight and volume. The AI system further supports by correlating these characteristics with typical manufacturing procedures and selecting suitable data from repositories like Ecoinvent.
The outcome is an LCA that is not only faster but also requires significantly fewer data inputs.
“SLCA builds on prior knowledge to understand what matters most, instead of demanding every last detail. It uses 3D modeling to derive basic part characteristics and AI to match them with the most likely processes and materials,” Silva added.
Validating their method through a case study of a small electronic hearing aid, the team observed remarkable efficiency. The conventional LCA process for the device took three months and required 86 different data inputs. In stark contrast, the SLCA completed the task in just one week with only 26 data inputs, reducing the input requirements by nearly 70% and the time by over 90%. The SLCA results demonstrated an average accuracy of 90% compared to the full assessment.
“We ensured that the full LCA served as our ‘ground truth’. What we found is that a huge saving in time spent leads to only a minimal deviation in results — beyond a certain point, more effort does not translate into much greater accuracy,” added Silva.
With the advent of SLCA, designers can now swiftly test various concepts, identifying environmentally taxing materials or processes early in the design stage. This capability is especially beneficial for industries with rapidly evolving products, such as consumer electronics and wearables.
“Our approach is especially suited for early-stage design, where uncertainty is high. It enables teams to spot hotspots without waiting for every specification to be finalized, avoiding surprises later when a full LCA shows the impact is higher than intended,” Silva added.
Next Steps
The research team seeks to enhance their methodology by testing it across diverse product types and refining its user-friendliness. They envision a future where AI continues to advance in this domain, blending automation with transparency and embedding sustainability into the design process from the very beginning.
“Right now, LCA is extremely difficult to integrate at the design stage — it is usually done when it is too late to do something about it,” added Silva. “We hope this work contributes to embedding sustainability into design from the very start, where it can make the biggest difference.”