A Non-Compensatory Framework Integrating LCA and QFD for Robust Manufacturing Sustainability Decisions Under Uncertainty: An OCC Paper Machine Case Study

Manufacturing decarbonization and sustainability improvement require decision-support methods that can prioritise actions across multiple, often conflicting dimensions, including product quality, process stability, resource efficiency, and environmental performance. In industrial practice, such decisions are further complicated by stochastic variability and the presence of dominant drivers, which limit the usefulness of conventional linear, weighted-sum scoring approaches. This paper proposes a non-compensatory decision framework with explicit stochastic uncertainty propagation that integrates quality function deployment (QFD) with life cycle assessment (LCA) to support robust, value-driven prioritisation of manufacturing improvement actions under uncertainty. The approach combines QFD-style influence factor modelling with LCA-based environmental indicators and employs a nonlinear, non-compensatory aggregation scheme to reduce sensitivity to arbitrary weighting and to better capture dominant and tail-risk effects. Uncertainty is propagated using Monte Carlo simulation, and the stability of prioritisation outcomes is analysed using sensitivity measures. The framework is demonstrated on an industrial old corrugated container (OCC) paper machine line using operational data from plant information systems, including quality, process control, laboratory, and maintenance databases. Results show that the proposed integration yields more stable and interpretable prioritisation of improvement actions than conventional compensatory scoring methods, particularly under variable operating conditions. The proposed approach enables practical, data-driven sustainability decision-making in complex manufacturing processes under variable operating conditions and alternative process configurations.