From Smart to Sustainable Logistics: Embracing Industry 5.0 for Human-Centric Resilience

Résumé

Abstract

With the development of digital technologies in the form of the Internet of Things, artificial intelligence, robotics, and big data analytics, smart logistics has become one of the key pillars of contemporary supply chain change. However, the growing complexity of the global supply chains, as well as the growing environmental and social pressures, demonstrates the shortcomings of the efficiency-only models. In this work, a thorough bibliometric review of the Scopus database is performed to trace the development, intellectual organization, and thematic trends in the development of the research topic on smart logistics since 2015. Findings indicate that there was a fast pace of scientific output, especially starting in 2020, which can be explained by the effects of disruptions on the world and the increasing requirements of resilient and data-driven logistical systems. The keyword co-occurrence networks identify five of the largest thematic clusters, including the aspects of technological innovation and algorithmic optimization, sustainability, circular economy, and new Industry 5.0 paradigms. The analysis of collaboration indicates that it is highly geographically concentrated, whereby China is the main research centre, backed by regional and international associations. The results highlight the growing significance of moving towards sustainable and human-focused logistic framework which balances technological progress with environmental sustainability and value generation to the society. By providing a systematic review of the research trends, gaps in knowledge, and emerging tendencies, this bibliometric analysis contributes to the progress of knowledge among scholars, practitioners, and policymakers aiming to define the future of logistics per the principles of Industry 5.0.

Keywords: Smart logistics; Sustainable logistics; Industry 5.0; Smart supply chain; Artificial intelligence; Digital transformation; Bibliometric analysis