## 5 Conclusions  
Moving from a previous study [42] which developed a preliminary AHP-based framework for evaluating supply chains through LARG perspectives and highlighted the need to explore additional decision-making approaches alongside AHP, as well as suggesting that computerizing the tool via apps or technological solutions could enhance its applicability for companies, this study aims to create a fuzzy logic-based framework for LARG supply chain evaluation and implement the tool using ExcelTM. The developed framework allows to accurately analyze the supply chain performance along its internal (i.e., production and reverse logistics) and external (i.e., procurement and distribution) activities and along the four LARG dimensions, offering a concrete basis for continuous improvement strategies. It provides a solid foundation for identifying inefficiencies, addressing critical issues through targeted interventions, and ultimately enhancing competitiveness, sustainability, and adaptability. Additionally, the fuzzy logic framework developed in this study is particularly suited for addressing various types of uncertainty commonly encountered in supply chain decision-making. By structuring the evaluation across four operational areas (procurement, production, distribution, and reverse logistics), the model captures specific uncertainty factors tied to each domain. In procurement, it accounts for supplier lead time variability, reliability, and flexibility, which are often unpredictable. In production, the model handles variability in equipment effectiveness, process times, and defect rates, helping mitigate risks tied to capacity constraints or operational disruptions. In distribution, uncertainties in delivery accuracy, load efficiency, and fuel consumption are integrated into the analysis, supporting better logistics planning under fluctuating demand conditions. Finally, in reverse logistics, the model incorporates uncertainties linked to return flow timing, volume, and processing efficiency, which are typically hard to forecast. By translating qualitative and variable data into fuzzy linguistic terms, the model enables a more flexible and realistic evaluation under incomplete or ambiguous information, aligning with the complex and dynamic nature of real-world supply chains. However, some limitations must be acknowledged. The list of indicators considered in this study is intended to represent a flexible starting point for company-level assessments, but it does not constitute an exhaustive or universally valid framework. It must be adapted and updated according to the specific context in which the tool is applied. Similarly, although the hierarchical structure (Figure 2) illustrates the aggregation of two KPIs for simplicity, the model is fully scalable and can handle a higher number of inputs as needed. Due to the high variability among companies, in terms of industry, market and organizational structure, it is not possible to define general fuzzification scales. These scales must be customized each time the tool is applied to a new company or each time new KPIs are introduced into the evaluation framework. Based on the above, the numerical results pointed out in this study are related to this specific context and can't be considered as universally valid; but the full potential of this model lies in its adaptability. The implementation of the framework in ExcelTM offers some advantages, as the software package is user-friendly (especially for practitioners unfamiliar with advanced programming tools), accessible in almost any company and generally suitable for small to medium instances of decision-making problems [59; 60]. Nonetheless, from a technical point of view, it could present some limits in terms of computational power, scalability and integration with enterprise information systems.