Integrating Supply Chain Life Cycle Decisions: Empirical model validation for a large-scale case

Abstract

Companies are under growing pressure to improve the sustainability of their supply chains, yet quantifying and optimizing life cycle impacts remains challenging in practice. Life Cycle Assessment (LCA) provides a standardized methodology for evaluating environmental impacts, but its integration with supply chain optimization has been limited in scope and scale. The Supply Chain Life Cycle Optimization (SCLCO) framework (Hülagü et al., 2025) addresses this gap by embedding LCA within a matrix-based optimization model that jointly considers environmental and economic objectives across the supply chain, for a small-scale and single-objective illustrative example. This paper presents the first large-scale, multi-objective empirical application of SCLCO, applied to a semi-finished aluminum rolled products manufacturer with 2294 customer orders and 51 alloy groups. The case study extends the generic SCLCO model (Hülagü et al., 2025) with case-specific formulations for a real-life industrial setting. It demonstrates the model’s capability to handle multifunctional processes by avoiding allocation (which the earlier mono-functional example could not evaluate) and integrates real-life company data with LCA databases within a unified data structure, tackling the heterogeneity and incompleteness issues inherent to industrial data. This matrix-based data structure proves effective for systematic data collection and cross-disciplinary communication between LCA and supply chain teams, making data gaps immediately visible. Moreover, computational experiments validate that the SCLCO model delivers actionable, data-driven sustainability insights for complex supply chains. Analysis of the Pareto-optimal solutions reveals a relationship between carbon footprint and cost that is governed by sourcing and energy decisions. Finally, sensitivity analyses confirm that the resulting supply chain configurations are robust to energy data variability and demonstrate that optimizing for different environmental indicators yields distinctly different optimal supply chain designs.