A Data-Driven and Cognitive Analytics Framework for Sustainable Supply Chain Transformation in Industry 6.0

Abstract

Este estudio propone un marco analítico cognitivo y basado en datos para transformar las cadenas de suministro hacia el paradigma de la Industria 6.0. El trabajo conceptualiza la cadena de suministro como un sistema cognitivo capaz de aprender, anticipar y adaptarse mediante aprendizaje federado y simulaciones con datos sintéticos. Se introduce el Coeficiente de Adaptabilidad (Ac), que integra velocidad de aprendizaje, capacidad anticipatoria y exposición tecnológica para medir la madurez cognitiva de la red. Los resultados de simulación muestran que las cadenas adaptativas reducen costes de disrupción, disminuyen la intensidad de emisiones y mejoran la resiliencia frente a entornos inciertos. El estudio concluye que la adaptabilidad cognitiva constituye una capacidad estratégica que vincula eficiencia operativa, sostenibilidad ambiental y coordinación distribuida, ofreciendo implicaciones relevantes para la gestión y la gobernanza industrial.

The transition from data-driven to cognitively adaptive supply chains represent a critical step toward Industry 6.0, where learning, coordination, and sustainability must be addressed jointly. Existing supply chain analytics approaches remain limited in capturing adaptive and systemic behaviors under uncertainty, particularly in resource- and energy-intensive industrial contexts. This study proposes a Cognitive and Data-Driven Framework for Supply Chains based on federated learning and synthetic data simulations grounded in aggregated industrial benchmarks. The framework introduces the Adaptivity Coefficient (Ac), a composite metric integrating learning velocity, anticipatory responsiveness, and technological exposure to quantify cognitive readiness at the network level. Results from simulation experiments show that cognitively adaptive supply chains achieve significant performance improvements compared to conventional predictive approaches, enhancing cost efficiency, resilience, and environmental performance. The study concludes by outlining managerial and policy implications for embedding cognitive learning into sustainable supply chain governance.