An analytical framework for decision criteria validation in complex supply chains

Multi-Criteria Decision Making (MCDM) in supply chain management often applies rigorous methods for weighting and aggregation yet devotes little attention to the structural validity of the decision criteria that precede them. Even when organisations do not proceed to full MCDM model application, criteria are still elicited during problem structuring and used to justify initiative selection. This paper introduces a topological validation framework that addresses this asymmetry by representing criteria as a high-dimensional Decision Criteria Configuration (DCC). Using tools from Topological Data Analysis (TDA), we translate foundational MCDM axioms into measurable invariants: completeness through connectivity, non-redundancy through structural impact analysis, and logical consistency through cycle detection. Two industrial experiments demonstrate the framework's utility. In a supply chain strategy-setting workshop, TDA diagnosed the criteria set underpinning initiative selection as a "conceptual monolith," revealing significant redundancies and systemic feedback loops overlooked by conventional facilitation. In a subsequent inventory classification exercise, the audit resolved expert deadlock by reducing 32 proposed criteria to a minimal, non-redundant core of six operationally essential levers, providing an objective and defensible basis for moving forward. By transforming criteria sets into auditable decision architectures, this approach ensures that MCDM models and the initiatives they justify rest on a validated foundation before weighting or ranking alternatives. For managers, it functions as a pre-hoc "structural audit," reducing redundancy, exposing hidden interdependencies, and directing resources toward criteria that genuinely drive strategic and operational outcomes.