Aug 2026· Veredas do Direito· 0 citations· 29 references
Abstract
Industrial diversification, localization and sustainability have become central themes of Saudi Vision 2030 and industrial transformation initiatives. Within this setting, smart manufacturing is not just an issue of technological enhancement but a business model for running plants in which machines, people, materials and decision making connect with trustworthy data. This review explores how IIoT architectural design contributes to productivity, quality and sustainability of Saudi plants. Instead of traditional reviews of technology in which sensors, edge computing, cloud platform, artificial intelligence and digital twin are considered separately, this review treats all of these as components of one path from data signals to factory performance. The structured narrative review technique was used for literature and policy analysis between 2020 and 2025. The findings show that productivity comes from real-time data visualization, overall equipment effectiveness tracking, identification of bottlenecks, scheduling adaptation and predictive maintenance. Quality gains come from traceability, machine vision, statistical process control, process capability analysis and fast root-cause analysis. Sustainability is realized by means of operational measurement of energy, water, waste and carbon footprint indicators and their linking to processes of continuous improvement. At the same time, a set of barriers is found such as legacy equipment, cybersecurity risks, weak data governance, fragmented vendors, workforce skill gaps and different levels of digital maturity of small and medium-sized factories. Finally, this paper suggests phased roadmap for implementation of IIoT architecture in Saudi Arabia starting with readiness assessment and pilot projects. The key value of this paper is in the developed review framework for linking IIoT architecture with productivity, quality and sustainability of Saudi Arabia plants.
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
Smart Manufacturing Supply Chain Management (SM-SCM) is one of the most important parts of Industry 4.0. It uses smart digital technologies to make manufacturing networks more sustainable, resilient, and efficient. This review examines the fundamental concepts, data-driven supply chain functions, automation technologies, and core digital innovations that support modern manufacturing systems. It discusses the role of data in procurement, planning, production, inventory management, transportation, and customer service for enhancing real-time visibility and decision-making. The examination delves deeper into the topic of automation by way of smart factories, robots, cyber-physical systems (CPS), the Industrial Internet of Things (IIoT), PLCs, SCADA, and digital twin technologies. Analysis also takes a look at the ways in which important supporting technologies like blockchain, AI, ML, cloud, and the Internet of Things have impacted predictive analytics, safe data sharing, and smart manufacturing. The paper also highlights practical application scenarios in automotive, aerospace, electronic equipment, manufacturing equipment, and energy and process industries, demonstrating how these technologies improve productivity, flexibility, quality, and resource utilization. Finally, the review identifies current challenges and future opportunities for developing sustainable, resilient, and autonomous manufacturing supply chains, providing valuable insights for researchers, industry practitioners, and policymakers working toward next-generation smart manufacturing ecosystems.
Dr. Prathviraj Singh Rathore· International Journal of Nex...· 0 citations
This research contributes to the advancement of smart manufacturing by delivering a holistic roadmap for effective and scalable Industry 4.0 implementation, supporting cross-sector benchmarking, interdisciplinary collaboration, and strategic innovation to accelerate the transition of manufacturing enterprises toward digital maturity, operational excellence, and sustainable competitiveness.
Accurate production has been facilitated as a pillar in the contemporary industrialization, prompted by the growing desire of quality products, decreasing production expenses, minimal wastes, and accelerated time-to-market. The technology behind Internet of Things (IoT) has converged with Big Data analytics which has drastically changed conventional manufacturing paradigms that have permitted real time monitoring, intelligent decision-making and predictive control of manufacturing processes. IoT makes it easy to pervasively sense and interconnect machines, tools, products and human operators, creating large volumes of heterogeneous data from the manufacturing lifecycle. The computation and analytical resources needed to store, process and extract actionable information out of this data are available through the use of big data technologies. The following paper demonstrates an in-depth study of the methods of integrating IoT and Big Data to achieve precision manufacturing. It examines system architecture, data acquisition, architecture, analytics, and decision-support models that promote accuracy and efficiency in the processes and quality in the products. An extensive literature review identifies areas of recent innovations and research lag in the field of smart manufacturing, Industrial IoT (IIoT) and data-driven manufacturing. The suggested methodology gives a description of an end-to-end system that includes the deployment of sensors, data ingestion, data preprocessing, analytics, and feedback control. The experimental findings and discussion illustrate how the Big Data analytics based on IoTs enhance predictive maintenance, quality assurance and optimization processes. The paper also ends with a note on the major challenges, research directions and the use of new technologies like artificial intelligence and digital twins in enhancing precision manufacturing.
Liam Walker, Grace Young· International Journal of Mod...· 0 citations
In recent years increasing concern about climate change, resource depletion, environmental degradation, industrial competitiveness etc. has made sustainability one of the most important priorities in engineering, technology and management. Technology has evolved from Industry 4.0 to Industry 5.0, changing the way engineering and management is done: from traditional to intelligent and sustainable systems, with the support of many new technology solutions, such as Artificial Intelligence (AI), Internet of Things (IoT), Industry 5.0, blockchain, digital twins, cloud computing and big data analytics. Such technologies enable the efficient use of resources, lower carbon footprint, increase manufacturing productivity and help in taking decisions with environmental responsibility. At the same time, sustainable organizational growth has been put at the heart of management concepts like Environmental, Social and Governance (ESG) and the principles of the green supply chain management, lean manufacturing and circular economy. In this research paper, the recent advances of engineering, technology and management are discussed to understand emerging trends in sustainable innovation. It also discusses industrial applications, implementation issues, future research directions and strategic advice for organizations striving for sustainable. The study suggests that the effective implementation of digital technologies in the context of sustainable engineering practices and responsible management strategies will be crucial for realizing the United Nations Sustainable Development Goals (SDGs) and sustainable industrial resilience, at least for the future.
R. Kumar, R.A.Arul Raja, Enock Immanuel et al.· Journal of Intelligent Decis...· 0 citations
In the era of Industry 4.0, the Internet of Things (IoT) has emerged as a transformative technology with the potential to enhance organizational performance in the manufacturing sector significantly. The primary aim of this paper is to review previous studies on how the manufacturing industry can improve its organizational performance by implementing IoT Smart Decision-Making Processes. Despite the numerous benefits associated with IoT, including real-time monitoring, predictive maintenance, and process automation, many firms face significant challenges such as high implementation costs, limited technical expertise, cybersecurity risks, and uncertain returns on investment. The paper addresses critical questions regarding which IoT Smart Decision-Making processes are instrumental in enhancing organizational performance, how these processes drive improvements in manufacturing outcomes, and what essential elements constitute an effective IoT Smart Decision-Making process in the manufacturing industry. By bridging the gap between the theoretical potential of IoT and its practical application in developing economies such as Malaysia, this paper proposes an IoT smart decision-making process for industry practitioners, informs policy formulation, and contributes to the academic discourse on smart manufacturing and Industry 4.0.
Siti Nur Nabilah Isnin, Nurul Farhaini Razali, N. A. A. Aziz et al.· International journal of res...· 0 citations
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