A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

Nowadays, different Industry 4.0 technologies have been implemented into diverse industries. However, these implementations are not standardized across similar industries and countries. Consequently, companies are actively looking for assessment instruments—maturity models, roadmaps, frameworks, readiness assessments—to assist in their digital transformation, to determine their Industry 4.0 level, and to identify technologies and strategies that should be implemented in specific areas, thus developing a feasible implementation plan. A review is conducted following the PRISMA (Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses) methodology to analyze the different research works on assessment instruments focused on Industry 4.0. A total of 538 articles, book chapters, conference proceedings, editorial material, reviews, and reports written in the English language were retrieved. Of these, 132 research papers were examined using a mixed analysis format to generate bibliometrics, and 36 articles were then deeply studied for a complete meta-analysis. The findings and insights of this meta-analysis led to a compilation and summary of dimensions, outcomes, enablers, and key components typically involved in Industry 4.0, which are comprehensively integrated to present the best practices for assessing Industry 4.0 and deploying a digital transformation that can impact productivity, flexibility, sustainability, quality, costs, and time.

This paper proposes a maturity model (MM) to become a smart organization considering Lean as a key enabler to drive I4.0 adoption. A systematic literature review on I4.0 and Lean concepts plus I4.0 adoption models was conducted through the PRISMA method based on articles from Scopus and Web of Science databases, and records from official websites (e.g., consulting firms) published between 2011 and 2022. Identifying the Lean and I4.0 relationship and comparing the models’ relevant characteristics allowed the development of the MM proposal. Although previous research refers to Lean and I4.0 collaboration, the opportunity to design a reference model for adopting both approaches was identified since their interaction enhances value creation. The comprehensive model supports structuring the types of Lean principles/methods/tools and I4.0 technologies and their action to link them and define which of them need to be implemented according to the maturity level chronologically. Additionally, the proposed MM provides an adoption roadmap that starts eliminating non-added activities in the initial stages for process improvement to integrate I4.0 enabling technologies later. The model makes it possible for practitioners to generate implementation and development processes oriented toward I4.0 adoption based on maturity levels in which Lean has the starting point at the first ones. Hence, it defines the enabling technologies to be incorporated and linked throughout the value chain, enhancing a Lean culture. This model will help organizations to become “smart” by allowing them to transition toward the best technology investment and continuously add value to their processes, people, and products. Moreover, the results will motivate researchers to study further the application of models for I4.0 adoption in which Lean is integrated to fill the gap with the I4.0 embrace caused by quickly changing industrial environments and the uncertainty and unknowledge of guidelines associated with incorporating new technologies.

Lean Thinking has been one of the preferred approaches to improve healthcare processes in developed countries; however, it has been barely used in Latin America. This study presents a Lean implementation in a Mexican public academic medical center. The goal was to reduce the time required to discharge patients from the Internal Medicine Department. This non-experimental intervention study measured, analyzed, and improved the process flow through a time study, value-added/non-value-added analysis, and the four-step quick changeover approach. Once changes were implemented, inferential statistics were used to compare results. Lean implementation allowed reducing the time to discharge patients from 6 to 3 h by eliminating 57% of non-value-added activities and 70% of errors found in discharge orders. This represents a 2% annual capacity increase and a 6.423 h bed made available without investment. Other findings include barriers regarding untrained staff about process improvement and departmental barriers and a successful quick changeover implementation. This is one of the handful of implementations of Lean Healthcare in Latin America. Furthermore, this is one of the very first studies that showcase the implementation of the quick changeover approach in healthcare. Further research should focus on the long-term impact and how the specific environment encourages/discourages future implementations.