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The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition.

The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis.

Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later.

The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.

The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0.

The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.

This paper aims to present a systematic review of the development process of Industry 4.0 in the textile and apparel sector, as well as to show some concepts, examples found in the literature on the application of the principles and technologies involved like the internet of things (IoT), cloud computing, Big Data, autonomous robots, three-dimensional (3D) printing, augmented reality, virtual prototyping, horizontal and vertical system integration and cybersecurity.

The methodology adopted in this study was a systematic literature review aided by the use of SciMAT, a scientific mapping software. Documents were collected from the Web of Science and Scopus database from 2011 to 2020 using the words “Textile” and “Industry 4.0” that result in 865 documents and 115 were analyzed.

The literature review showed that the textile industry in the international context is at an incipient stage of the implementation of Industry 4.0. The main aspects of Industry 4.0 that were identified in the textile industry initially focus on the implementation of technologies aimed at computerization and automation of processes, whose main focuses are increasing productivity and reducing costs. Projects for the implementation of augmented reality and 3D printing and simulation technologies in the textile industry, clothing and apparel area are still embryonic, normally implemented through tools and software oriented toward the creation and development of new models of processes, products and commerce.

The search in the databases was carried out on October 17, 2020. Therefore, for future study, other combinations of search terms and time update are suggested, in addition to including more databases besides Scopus and Web of Science.

This literature review served as the basis for the development of a questionnaire that was applied to 72 people in an industry in the clothing sector, located in the state of Santa Catarina, southern Brazil.

The benefits of industry 4.0 are perceived in people with its implementation, such as a reduction in energy consumption of around 15%, an increase of up to 25% in work efficiency, in addition to more assertive decision-making, improvement of processes and balance between life and work.

Machine learning, artificial intelligence, smart fabrics, IoT, supply chain management, environmental protection, Big Data, autonomation and cyber physics were the strongest terms found, consolidating as a prominent field for current and future studies. From emerging and/or still unexplored areas of Industry 4.0 in the textile sector, there is real-time communication, computer applications, carbon, fibers, health care and sustainable development. Some strategic actions that are taking place in some countries are summarized and in Brazil the adoption rate is 29% for this sector, revealing itself as a needy area and suitable for the development of studies that address the subject.

Industry 4.0 envisions a future of networked production where interconnected machines and business processes running in the cloud will communicate with one another to optimize production and enable more efficient and sustainable individualized/mass manufacturing. However, the openness and process transparency of networked production in hyperconnected manufacturing enterprises pose severe cyber-security threats and information security challenges that need to be dealt with. The paper aims to discuss these issues.

This paper presents a distributed trust model and middleware for collaborative and decentralized access control to guarantee data transparency, integrity, authenticity and authorization of dataflow-oriented Industry 4.0 processes.

The results of a performance study indicate that private blockchains are capable of securing IoT-enabled dataflow-oriented networked production processes across the trust boundaries of the Industry 4.0 manufacturing enterprise.

This paper contributes a decentralized identity and relationship management for users, sensors, actuators, gateways and cloud services to support processes that cross the trust boundaries of the manufacturing enterprise, while offering protection against malicious adversaries gaining unauthorized access to systems, services and information.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.