Development of a dynamic modular life cycle assessment framework integrated with enterprise resource planning for sustainable manufacturing pathways generation

Sustainable industrial manufacturing requires decision-support tools that integrate environmental, economic, and technical considerations within real production systems. However, conventional life cycle assessment (LCA) approaches are often static, evaluate only a limited number of predefined alternatives, and lack integration with enterprise resource planning (ERP) data, restricting their usefulness for operational decision-making. This study develops an ERP-driven modular LCA framework incorporating prospective life cycle modelling, uncertainty analysis, and multi-criteria decision analysis to automatically generate and evaluate technically feasible manufacturing pathways over time. To illustrate this framework, a proof-of-concept study on a specific furniture component that compared 61 material–process pathways and three decision criteria (environmental impacts, production cost, and mechanical performance). Magnesium alloy ZM21 was found to be the strongest candidate, with human health and ecosystem impacts reduced by up to 80%, production costs lowered by more than 60%, and mechanical performance elevated by roughly 50% relative to the aluminium casting baseline. These advantages were sustained across 10,000 Monte Carlo simulations and future prospectives of electricity decarbonization scenarios, demonstrating that the accumulative benefits observed were due to decreased material demand or fewer synthetic routes, rather than solely background assumptions. The results also highlight how lightweight material substitution with low energy manufacturing processes can simultaneously optimize environmental and economic performance. Even though the developed framework has been tested by a single product only, it proposes an approach that can be applied in diverse products and industrial sectors for introducing sustainability assessment to ERP supported manufacturing decision making.