Publication Details
Abstract
Logistics 5.0 is a new paradigm that combines eco-friendly development, organizational readiness for transformation, and human–machine collaboration to accommodate the rapid transformation of logistics systems provoked by digitalization, sustainability requirements, and human-centered development beyond the automation-oriented Logistics 4.0. Although advanced technologies and green logistics practices are regarded as hot areas in the existing studies on supply chain decarbonization, relying on unstructured operationalization and decision-support mechanisms makes it infeasible to simultaneously consider economic, environmental, and human factors. Existing studies are more isolated and often focus on technological readiness or sustainability and less on empirical models that combine human aspects into strategizing and prioritizing implementation. The overall goal of this study is to construct and implement an analytical framework that is oriented towards decision support to rank Logistics 5.0 elements according to strategic criteria and thus assists in the establishment of an optimal and systematic transformation roadmap. Results suggest that low-complexity, sustainability-oriented, human-centered measures with short payback time represent the most viable opportunities for Logistics 5.0 adoption, and that digital infrastructure serves as a vital enabler as long as it is grounded in supportive organizational processes. By systemically integrating green logistics, digital infrastructure, and organizational and human-resource perspectives into a single multi-criteria decision framework, the study presents an important contribution to theory with a socio-technical framework for green supply chain management. The findings deliver theoretical and practical contributions by a reforging of Logistics 5.0 as a ‘holistic socio-technical system’ concept whilst providing managers and policymakers an empirically-based structured roadmap for resilient, sustainable and human-centered logistics transformation, indicating avenues for future empirical and longitudinal research.