Aug 2026· 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)· pp. 1-7· 0 citations· 26 references
Abstract
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.
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
Smart Manufacturing Systems (SMS) is the paradigm shift in the contemporary industrial manufacturing that can unite Internet of Things (IoT) technologies, automation, cyber-physical systems, and data-driven intelligence to improve efficiency, flexibility, qualities, and sustainability. Conventional manufacturing systems tend to be inhibited by fixed production lines, reduced real-time visibility, and fixed manual decision making systems. With the advent of Industry 4.0, manufacturers can now use the interconnected and intelligent systems that can operate autonomously, preventive maintenance, adaptive control and optimize the use of the resources. The current paper is a detailed analysis of smart manufacturing systems that utilize the IoT and automation technology. It examines the architectural solutions, it is allowing technology, communication protocol, data analytics, and automation solutions that all constitute smart factories. Through an extensive literature review, recent developments, issues, and research gaps in the context of IoT based manufacturing setting are pointed at. The methodology suggests an integrated smart manufacturing model that will integrate sensor networks, edge and cloud computing, industrial automation, and machine intelligence. Performance evaluation measures are addressed in detail like production efficiency, downtime, reduction, system optimization in energy and scalability of the system. The findings indicate a high improvement in operational performance, predictive accuracy, and decision-making ability when compared to the traditional manufacturing system. The paper ends with a statement of future research directions, which are autonomous manufacturing with artificial intelligence, digital twins, and secure industrial IoT ecosystems.
Aiko Yamamoto· International Journal of Mod...· 0 citations
Industrial Internet of Things (IIoT) deployments generate high-volume operational data, yet many manufacturing systems still use this data mainly for monitoring rather than closed-loop decision support. This paper presents an integrated IIoT-optimization framework for data-driven smart manufacturing. The contribution is positioned as a conceptual and simulation-based architecture rather than a fully validated industrial deployment. The framework contains four layers: physical data acquisition, analytics and processing, optimization and decision support, and presentation/application feedback. Its Adaptive Model Integrator (AMI) is implemented as a context-aware selection policy that chooses among mathematical programming, heuristic dispatching, multi-objective optimization, model predictive control, and metaheuristic solvers according to data quality, system stability, problem structure, active objectives, and available computation time. A 720-hour synthetic MATLAB simulation of a five-line, 30-machine production system with 240 virtual sensors is used to illustrate the framework. Under the stated assumptions, the AMI-enabled configuration improves simulated throughput from 852.2 to 961.0 units/hour, reduces energy consumption from 1.198 to 1.128 kWh/unit, increases quality rate from 94.46% to 95.75%, and raises OEE from 77.39% to 84.75%. These results are reported as preliminary simulation outcomes, not as evidence of field-level industrial benefit. The paper also specifies the simulation assumptions, baseline logic, AMI decision rules, limitations, and future validation requirements.
Sami Gazem Abdullah Thabet, Mohammed Baggash· 2026 6th International Confe...· 0 citations
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.
Smart manufacturing is transforming industrial production through the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), enabling intelligent decision-making, predictive maintenance, real-time monitoring, and autonomous process optimization. Conventional lean manufacturing techniques primarily rely on human expertise and periodic inspections, limiting their ability to respond dynamically to changing production environments. The convergence of IoT-enabled sensing technologies with AI-driven analytics introduces a new generation of intelligent lean operations capable of minimizing waste, improving productivity, reducing operational costs, and enhancing overall equipment effectiveness (OEE). This paper proposes an integrated AI-IoT framework for smart manufacturing that continuously acquires machine data, performs real-time analytics, predicts equipment failures, optimizes production scheduling, and supports data-driven decision-making. The proposed architecture employs interconnected sensors, edge computing, cloud analytics, machine learning models, and digital dashboards to improve operational efficiency while maintaining product quality and resource sustainability. Performance evaluation demonstrates improvements in production throughput, energy efficiency, machine utilization, defect reduction, predictive maintenance accuracy, and manufacturing flexibility compared with conventional manufacturing systems. The proposed framework provides an intelligent, scalable, and sustainable solution aligned with Industry 4.0 principles and offers practical guidance for implementing AI-enabled lean manufacturing across modern industrial enterprises.
Anand Singh, B. Mishra, Amjid Nadeem et al.· Journal of Intelligent Decis...· 0 citations
Interrelated environments are also becoming the driving force behind how intricate physical, social, and cyber-physical systems are observed and handled. The key in this development is the incorporation of Internet of Things (IoT) sensing infrastructures and smart systems that have data analytics, learning, and decision support capabilities. The review is a well-organized account of the development of recent connected environments with a specific emphasis on the interactions of IoT sensors and intelligent systems to improve monitoring and decision-making. First, the article discusses IoT sensing infrastructures, which comprise architectural foundations, sensor technologies, communication mechanisms, and deployment considerations that facilitate the acquisition of large volumes of data. It then looks at intelligent systems data processing and analytics, with the contribution of machine learning, artificial intelligence, and edge-cloud design to the conversion of raw sensor information into insights. Based on these premises, the review examines frameworks and strategies of fusion that combine heterogeneous sensor data and intelligent analytics across multiple layers of systems. The application domains, such as smart cities, healthcare, industrial automation, and environmental monitoring, are mentioned as examples of the representative areas where the concept of intelligent connected environments has practical implications and issues. The Major technical, organizational, and societal issues—scalability, data heterogeneity, security, privacy, and trust—are discussed critically. Lastly, the article also describes the emerging trends and future research directions, such as distributed intelligence, privacy-preserving learning, and autonomous decision systems. With its holistic view of connected environments, this review is expected to help researchers and practitioners in developing robust, scalable, and trustworthy next-generation monitoring and decision support in connected environments.
Jun Zhan, Wei Yu· Journal of Environmental &am...· 0 citations
