Bridging the implementation gap in MCABC inventory management: from a taxonomy to practical archetypes

Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single-criterion analyses. While multi-criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience-sustainability-benefit trade-offs in inventory, their adoption remains limited due to fragmented methodologies, lack of standardization, and unclear design guidance. This study addresses this gap by developing a taxonomy of eight dimensions and 25 characteristics to structure the MCABC analysis design space. Using a dataset of 108 configurations, cluster analysis identifies six recurring archetypes that serve as implementation-ready templates for inventory analysis. Archetype 2: artificial intelligence (AI)+-driven cluster minimalist enables rapid, cost-focused inventory structuring with minimal data and no expert input. In contrast, Archetype 1: AI+-driven complex ranker uses expert-weighted multi-criteria analysis to support holistic and sustainability oriented inventory strategies. The taxonomy and archetypes provide a unified framework for researchers to theorize inventory design trade-offs and for practitioners to apply scalable blueprints for mature inventory analyses.