## 1 Introduction
[18] suggest resilience design principles such as maintaining multiple strategic options, balancing efficiency and redundancy, fostering collaboration across SC partners, enhancing visibility into inventories and demand, and improving SC velocity through streamlined processes. Key resilience practices in SCs include strategic stockpiling, lead time reduction, maintaining dedicated transit fleets, flexible sourcing, and demand-based management [15; 17; 18; 19]. The green supply chain improves the environment, economic performance, and competitiveness [20] and provides opportunities to reduce waste and consume resources effectively and efficiently [21; 22]. In this context, the goal is therefore to minimize the ecological impact and the environmental risks of resources within the supply chain [23]. Activities that can typically be adapted to a green SC range from green purchasing to mapping the sustainable value stream, as well as eliminating packaging and implementing recycling initiatives [24; 25]. For proper implementation of the LARG concept, it is essential to measure the SC capabilities and performance along these four dimensions. This can be accomplished through the development of appropriate measurement systems, allowing decision makers to effectively monitor all key performance indicators (KPIs) relevant to SCM over a given period of time, providing an overall assessment of performance according to LARG principles [26]. However, evaluating the performance of a supply chain according to the perspectives of the LARG paradigm can be complex due to the multiplicity of factors involved, which are often heterogeneous, interdependent and difficult to quantify [27]. In this context, Multi-Criteria Decision Making (MCDM) models prove to be particularly effective tools for managing the uncertainty and complexity inherent in supply chain-related strategic decision-making processes [28]. Techniques such as the Analytic Hierarchy Process (AHP), fuzzy logic, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) [29], allow complex problems to be structured in a hierarchical manner, integrating both quantitative and qualitative data and taking into account the subjective judgement of experts. In particular, the AHP distinguishes because of its ability to decompose a problem into a hierarchical structure that facilitates the comparison of criteria and alternatives through pairwise evaluations, yielding a scale of priorities useful for guiding choices [30]. When such judgements are affected by uncertainty or ambiguity, the fuzzy extension of AHP allows for a more realistic representation of decision preferences, using fuzzy numbers to model uncertainty in the comparisons made by experts [31]. Indeed, fuzzy systems allow for a more nuanced assessment of variables that are difficult to measure precisely or may fluctuate over time. These include uncertainties such as demand volatility, supply disruptions, variable lead times, resource availability, inventory fluctuations and imprecise supplier performance metrics. These approaches are thus well suited to SC performance evaluation according to LARG principles, supporting the identification of strategic priorities and critical areas for improvement. Several studies have employed MCDM approaches to structure and support decision-making in LARG environments. For instance, [32] used AHP to examine the influence of LARG practices on supply chain performance, providing a structural interpretation of the interrelations among criteria. Similarly, [33] introduced the LARG index as a benchmarking tool to assess supply chain maturity levels in terms of leanness, agility, resilience and greenness. Their work relied on AHP to determine weights and prioritize indicators, showing how MCDM methods can support strategic supply chain assessment.