Frameworks and tOols for evaluating LOgistics processes and enhancing Green, Resilient, Agile and Lean performance (OLOGRAL)

The vulnerability of global supply chains (SCs) has become evident in recent years, due to events such as the COVID-19 pandemic, geopolitical tensions, and increasing resource scarcity, which have highlighted the limitations of traditional models based exclusively on efficiency and cost reduction. Modern approaches must take into account a combination of activities and strategies that integrate efficiency, resilience, flexibility, and sustainability. In this context, the lean, agile, resilient, green (LARG), and sustainable (LARGS) paradigms provide a comprehensive framework to support companies and stakeholders. The measurement of key performance indicators (KPIs) is a fundamental management tool that provides essential support for improving the SC. This doctoral thesis aims to develop frameworks and quantitative tools to evaluate logistics processes and improve SC performance according to LARGS paradigms. The research addresses two main questions: (i) how LARGS paradigms can be integrated into a unified decision-making framework and (ii) what interdependencies, barriers and enabling conditions influence their implementation. To answer these questions, the study follows a progressive methodological approach that integrates conceptual development with practical validation, combining multi-criteria decision-making (MCDM) approaches such as analytic hierarchy process (AHP), fuzzy logic, DEMATEL, analytic network process (ANP), and interpretive structural modeling (ISM)-MICMAC. First, the study introduces the LARG framework for mapping and prioritizing KPIs in procurement, production, distribution and reverse logistics. Next, the LARG-AHP model is developed to provide a hierarchical structure and quantitative assessment of supply chain performance. A fuzzy logic-based tool is proposed to manage uncertainty and linguistic judgments, transforming qualitative evaluations into quantitative results and enabling flexible assessment across all SC phases. The research introduces the sustainable dimension, extending the framework to LARGS paradigm, through a hybrid Fuzzy Delphi-DEMATEL-ANP model that captures the interrelationships between criteria and supports sustainable-oriented decisions. This latter dimension is further analyzed in a specific context (i.e., perishable products) using the ISM-MIMAC methodology. The models are validated through several case studies and numerical examples to demonstrate their applicability in real-world contexts.