The manufacturing sector stands at a turning point. As the pressure mounts to reduce environmental impacts and meet the demands of the circular economy, Life Cycle Assessment (LCA) has become a critical tool. However, traditional LCA approaches—while powerful—are often time-consuming, static, and limited by the availability and granularity of data. This is where machine learning (ML) comes in, offering a transformative leap forward in how we assess and improve sustainability in manufacturing.
Within the scope of the PSS-Pass project, which supports European industry in transitioning toward circular and service-oriented business models, machine learning is not just a futuristic concept—it’s an emerging enabler. As we explore digital innovations like the Digital Product Service System Passport (DPSSP), integrating machine learning into LCA is a natural evolution.
Machine learning can automate and accelerate the LCA process by rapidly analyzing vast amounts of data from across the product and service lifecycle. Rather than relying solely on manually collected datasets, ML models can extract meaningful patterns from real-time sensor data, digital twins, and enterprise systems. This enables more dynamic and adaptive assessments, where sustainability impacts can be evaluated continuously rather than retrospectively.
Imagine a manufacturing firm that uses machine learning to predict the carbon footprint of a product configuration before it ever reaches the factory floor. Or a service provider that can simulate the environmental effects of different usage or maintenance scenarios in seconds. These are no longer distant ideas—they’re fast becoming feasible through the combined use of LCA, ML, and digital infrastructure like the DPSSP.
Another key advantage of ML-enhanced LCA is its ability to deal with complexity. Modern Product-Service Systems often involve multiple stakeholders, value loops, and feedback channels. This complexity can make traditional LCA unwieldy or even unfeasible. Machine learning can manage these multi-dimensional systems, helping to identify hotspots, optimize decisions, and suggest interventions that balance economic and environmental goals.
For the PSS-Pass project, the implications are significant. As we support the implementation of circular PSS models, having access to intelligent, data-driven environmental insights will empower manufacturers and service providers to make better, faster, and more sustainable choices. It also enables us to embed environmental considerations directly into the digital tools we’re building—such as the DPSSP—ensuring that sustainability becomes an integral part of design, operation, and decision-making processes.
Of course, the adoption of machine learning in LCA is not without challenges. Ensuring data quality, maintaining transparency in model predictions, and aligning with LCA standards are all important considerations. But with collaborative efforts—like those fostered within PSS-Pass—we can address these challenges and pave the way for more robust, scalable, and actionable assessments.
As the manufacturing industry continues its digital transformation, the integration of machine learning into LCA will be a key step in aligning competitiveness with climate responsibility. And for those embracing the shift toward Product-Service Systems, it will become not just a technical upgrade—but a strategic necessity.
