Abstract
Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Online collaboration growing significantly in the development of open-source hardware and software has led to a surge of research interest. However, no comprehensive bibliometric review has investigated the management of digital communities in these ecosystems. In this study, academic contributions to the field of online community management in open-source hardware and software were mapped, highlighting influential research streams and trends. A bibliometric review was conducted based on a keyword search analysis of research databases (IEEExplore, Scopus, ScienceDirect, Web of Science), with a sample comprising an overall 399 papers. The study identifies the most impactful articles in the field, maps the diverse streams of research on online collaboration and community management, visualizes focus areas and trends, and pinpoints areas for further investigation. These findings will support future research within this rapidly evolving domain. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision-making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical-scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision-making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company's experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy-AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman's rank correlation coefficient (rs) and Kendall's coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

Abstract
Information and Communication Technologies (ICT) emerge as a strategic tool, indispensable for the survival of any organization. However, excessive use of ICT can lead to drawbacks such as equipment dependency, rapid obsolescence, and rising maintenance costs. Inefficient use of ICT can result in higher costs for organizations, which may cause them to lose important competitive advantages. In this context, outsourcing printing may be an alternative for institutions to reduce their costs and become more competitive. The aim of this paper is to evaluate scenarios about the financial viability of printing outsourcing in a Federal Higher Education Institution (Instituicoo Federal de Ensino Superior - IFES). To this end, an exploratory research was conducted, with a quantitative approach, using computational modeling, and using the System Dynamics methodology, with simulation and evaluation of three different scenarios: current scenario, likely scenario, unlikely scenario. The results obtained indicate that the unlikely scenario (100% of leased printers) resulted in lower printing costs over the period of the experiment, totaling R$ 637,828.00; the probable scenario (50% of leased printers) totaled R$ 1,000,480.00; while the current scenario (20% of leased printers) resulted in costs of R$ 1,603,060. With the simulations, it was found that printer leasing (outsourcing printing) is the best financial alternative for IFES, which served as a reference for the study, resulting in lower printing costs compared to printer acquisition. The use of systems modeling to evaluate scenarios has been used to support managers in the decision making involving ICT; however, there were no studies in the literature focused on the financial viability simulation of printer outsourcing. Thus, this work sought to bridge this gap by developing and validating a simulation model that can be applied in different scenarios and types of organizations, and work as a reference for future studies.