Integrating MaaS and Ex-Ante LCA for Smart and Sustainable Production

The rise of distributed and data-driven manufacturing ecosystems poses a challenge in terms of data tracking and performance assessment. In such manufacturing ecosystems, Manufacturing-as-a-Service (MaaS) platforms offer a solution that shifts from centralized, product-centric production models to agile, service-oriented manufacturing networks. This paper presents a methodological framework for integrating Ex-ante Life Cycle Assessment (LCA) into MaaS architectures to support sustainability-informed decision-making during supply chain configuration and production outsourcing. The proposed approach combines three key aspects, efficiency in production by optimized scheduling, target cost, and environmental footprint, to match customers with the most suitable providers. Suppliers register on the MaaS platform by submitting detailed datasets that cover energy, water and material consumption, working hours, and contextual qualifiers such as electricity mix or water source. These data are then combined with environmental databases in an internal tool to generate predictive sustainability indicators for supplier profiling. Such a process allows for transparent trade-offs between cost, efficiency, and environmental footprint in the matchmaking process. A pillar of this methodology is the integration of Ex-ante LCA as a core service within the MaaS platform. Unlike conventional Ex-post LCA, which relies on finalized product and process data, Ex-ante LCA enables anticipatory assessments of potential environmental impacts under variable manufacturing scenarios. This methodological shift is particularly relevant in MaaS ecosystems, where decisions about outsourcing, remanufacturing, or plant reconfiguration are made before actual production takes place. The preliminary results focus on architecture requirements such as data modeling requirements, system architecture, and computational workflow necessary for implementing Ex-ante LCA in distributed manufacturing environments. Moreover, some challenges emerge about data uncertainty, interoperability, and multi-criteria optimization. The integration of sustainability metrics into service-oriented manufacturing networks can represent a relevant step toward aligning digital manufacturing innovation with environmental objectives, enabling both responsible and adaptive production systems.