## 1 Introduction
Other studies, such as those by [34] have applied the AHP to compare LARG strategies in specific industrial sectors (e.g., cement manufacturing) revealing how context-specific criteria can influence priority setting. Likewise, [35] proposed a hybrid AHP-based decision-making model for LARG supplier selection, integrating tools like the house of quality and the Taguchi loss function to reinforce supplier evaluation frameworks. The ambiguity and subjectivity of SC evaluation has brought to the adoption of alternative MCDM approaches, such as fuzzy logic, which can better accommodate linguistic assessments and uncertainty. In line with this shift, a growing number of studies have employed fuzzy logic to evaluate LARG supply chain performance. [PERSON]. [36] introduced a SCOR-based fuzzy MCDM framework to assess SC performance under LARG principles. Their model, applied to the automotive sector, demonstrated how a fuzzy evaluation could better capture the dynamic interplay between lean, agility, resilient and green criteria, especially when data uncertainty or subjectivity is present. Similarly, [PERSON]. [37; 38] have proposed a hybrid decision-making model combining fuzzy logic with MCDM tool for supplier selection. By integrating lean, agile, resilience and green factors into a fuzzy evaluation structure, the study showcasedthe method's flexibility in supporting complex procurement decisions under uncertainty. Ref. [39] focused on assessing interoperability in LARG chains through a fuzzy-based model. Their contribution emphasized the role of fuzzy tools in evaluating collaborative capacity and structural integration across the different LARG paradigms. Ref. [40] explored the implementation of LARG strategies in line with Industry 4.0 principles, using fuzzy logic to assess operational improvements. Recently, ref. [29] proposed a fuzzy hybrid approach to identify the most relevant LARG criteria for supplier selection and evaluate the relationships between them in a decision-making process. From these contributions it can be argued that the application of AHP in the context of LARG supply chains remains largely confined to initial conceptual models or narrowly focused sector-specific studies. On the other hand, while existing studies based on fuzzy logic consistently support its suitability for LARG-oriented evaluations, they often lack a unified structure that simultaneously integrates all four LARG dimensions across the entire supply chain, encompassing procurement, production, distribution, and reverse logistics. Furthermore, many of these contributions are conceptual in nature or focus on isolated decision-making problems, such as supplier selection or process interoperability, failing to offer a comprehensive and operational framework. In recent years, the evolution of digital technologies has significantly reshaped the way SCs are designed, managed, and evaluated. Technological tools have enhanced the capabilities of SC managers, enabling real-time monitoring, predictive insights, and faster responses to disruptions. These advancements are particularly relevant within the LARG paradigm, as they provide valuable support for achieving lean operations, improving agility, fostering resilience, and ensuring environmental sustainability [41]. The integration of digital solutions also facilitates the implementation of decision-support systems based on MCDM techniques, allowing companies to move from theoretical frameworks to practical, user-friendly tools. In this context, the digitalization of evaluation methods, through platforms such as ExcelTM or customized applications, emerges as a key enabler of effective decision-making in real-world business environments. Against this backdrop, there is a growing consensus that SC performance evaluation must evolve to accommodate the complexity, dynamism, and multidimensional nature of modern systems. This calls for integrated, flexible, and data-informed frameworks that can guide organizations toward more balanced and robust decision-making processes.