From Data to Impact: Automating Product Carbon Footprint Assessment through Integrated AI-ERP-LCA Systems

The growing demand for transparent and auditable environmental reporting, driven by regulatory frameworks like the Corporate Sustainability Reporting Directive (CSRD) and mounting market pressure poses a critical challenge for manufacturers: how to perform rigorous Life Cycle Assessments (LCA) efficiently, accurately, and at scale. Traditional LCA methods remain largely manual, disconnected from day-to-day operations, and unfit for organizations managing extensive product portfolios. This paper presents an innovative framework that transforms LCA into a dynamic, automated, and scalable process by integrating four key pillars: internal ERP data streams, scientific databases (e.g., Ecoinvent), ISO standards (14040, 14067), and Product Category Rules (PCRs). At the core of this system lies an artificial intelligence engine that orchestrates data processing, harmonization, and interpretation. The engine leverages AI for: (1) Intelligent data mapping, using NLP to translate unstructured ERP product data into standardized LCA inputs; (2) Data quality assurance, detecting anomalies and inconsistencies in real-time production data; (3) Predictive analytics, forecasting the environmental footprint of new product designs, enabling proactive ecodesign strategies. Implemented in the plastic packaging sector, this AI-enhanced model enables cradle-to-gate Product Carbon Footprint (PCF) assessments with minimal manual intervention, aligning with both ISO and GHG Protocol standards. Results demonstrate how automation not only accelerates analysis but also improves data reliability and transparency, empowering organizations to integrate LCA into operational and strategic workflows. The proposed solution marks a paradigm shift: from static, expert-driven assessments to a self adaptive system capable of supporting autonomous sustainability reporting and real-time decision-making. This approach paves the way toward embedding cognitive environmental intelligence into the digital backbone of manufacturing enterprises.