Abstract

Abstract
Cognitive Digital Twin (CDT) has been taking considerable attention in several recent studies. CDT is considered as a promising evolution of Digital Twin bringing new smart and cognitive capabilities. Therefore, it is important to understand how companies can exploit this new technology and create new data-driven business models. Considering that context this article aims to identify Smart PSS business model based on Cognitive Digital Twin platforms. To reach this goal a literature review was conducted. As a principal contribution this study brings a set of new business models to offer Smart PSS based on cognitive digital twins. Moreover, the study presents several real cases of companies that are currently using the cognitive capabilities supplied by edge companies of the digital twin technologies. © 2022, IFIP International Federation for Information Processing.

Abstract
Recent studies have highlighted the potential of supply chain visibility (SCV) to improve companies' performance, support the decision-making process and increase the resilience and sustainability of supply chains (SC). However, the definition and the approaches to manage visibility are not consensual, especially when it comes to its assessment. This paper studies the influence that some characteristics of the information shared among SC partners have on SCV and provides a model that allows to assess the level of visibility. Focus group research was used to discuss the visibility assessment model with academic and practical experts and case studies were conducted to evaluate the implementation of the model in real contexts. The results support the applicability of the model in complex SCs and show that the visibility indices proposed can help managers to identify the SC partners where improvement actions are needed. This study contributes to literature and practice by means of a model to assess the visibility throughout the SC by considering different types and characteristics of the information shared.

Abstract
The integration of AI in supply chains offers opportunities to enhance efficiency, sustainability, and decision-making. However, effective implementation requires attention to both technical and socio-technical aspects. This study examines AI maturity in the pulp and paper sector using the SC-STAI profiling tool, assessing AI integration across technical, social, human, and organizational domains. Based on nine case studies from Brazil and Portugal, the research identifies key areas for improvement and highlights uneven AI adoption. Findings show that performance and resilience are most impacted, while job role adoption remains the lowest. The study emphasizes the importance of Socio-Technical AI Maturity Models in guiding responsible AI adoption and improving socio-technical alignment in supply chains, contributing to a better understanding of AI readiness in traditional industries and demonstrating the SC-STAI tool's applicability for strategic AI planning.

Abstract

Abstract
Introduction/Objective: the objective of this article is to analyze the current academic literature on smart cities in Brazil with evidence of the application of Digital Twin or Digital Shadow technology. Method: Integrative Literature Review was used as the research instrument, analyzing in the articles: a) objective; b) research method; c) study subject (location); d) application of Digital Twin or Digital Shadow; e) Results and conclusions. Results: portfolio with 25 articles on the topic and qualitative analysis regarding objective, method, study location, Digital Twin technology, Digital Shadow, and results. Studies with elements of Digital Shadow are perceived timidly in two cases of smart cities in Brazil. Conclusions: smart city technologies should be centered on the interests of users to not lose their humanity. It is worth adding that people’s needs change and, therefore, smart technologies should have a forward-looking vision to anticipate the needs of future generations. Digital Twin technology is a model that can contribute in this sense, monitoring and providing readings of future scenarios for smart cities. © 2024, Programa de Pos-Graduacao em Gestao da Informacao, Universidade Federal do Parana. All rights reserved.