Abstract
Digital Twin (DT) is recognized as a key enabling technology of Industry 4.0 and 5.0 and can be used in collaborative networks formed to fulfillment of complex tasks of the manufacturing industry. In the last years, the variety and complexity of DTs have been significantly increasing with new technologies and smarter solutions. The current definition of DT, such as cognitive, hybrid, and others, embraces a wide range of solutions with different aspects. In this sense, this article discusses DT definitions and presents a five-dimensional analytical framework to classify the different proposals. Finally, to better understand the proposal, we analyzed 12 articles using the analytical framework. We argue this research may help researchers and practitioners to better understand digital twins and compare different solutions.

Abstract
Industry 5.0 complements the Industry 4.0 approach by enabling the transition of industry digitization to a sustainable, human-centered and resilient paradigm. This paper delves into the exploration of enabling technologies that facilitate both Industry 4.0 and Industry 5.0 in the context of supporting supply chain (SC) logistics. The paper defines the principles of Logistics 5.0, which focuses on smart logistics systems for customized distribution, transportation, inventory management and warehousing by emphasizing interconnectivity, digitization, and optimization across SC operations. The traditional logistics framework requires innovative solutions grounded in emerging Industry 5.0 technologies capable of capturing and processing extensive datasets to empower decision-making based on information and knowledge. A comprehensive research has enabled to critically analyze enabling Industry 5.0 technologies by assessing their application status through real-case scenarios within SC Logistics 5.0. Furthermore, the paper identifies research gaps in the reviewed technologies by outlining promising areas for each Industry 4.0 technology. This guidance aims to direct future studies toward the practical application of technologies in supporting Logistics 5.0.

Abstract
For decades, the collaborative networks community has studied supply chains, focusing on trust, visibility, collaboration, and innovation, with emergent technologies being a key area of research. The rise of digital technologies has led to extensive studies on supply chain digital transformation. With the surge of AI-based technologies, there is an increasing body of research on AI's human and social impact on Supply Chain Management (SCM). However, while Socio-Technical Systems (STS) thinking has been applied to digital transformations, it has not yet addressed AI-induced changes in supply chains. This paper synthesises recent research on AI integration in SCM and the use of STS thinking in AI systems design. We propose a mapping approach for profiling AI-induced supply chain transformations for strategic design. We also present the Supply Chain Socio-Technical AI (SC-STAI) profiling tool in practice, demonstrating how it maps supply chain participants' current and desired states regarding AI integration.