Jul 2026· i-manager's Journal on Instrumentation and Control Engineering· Vol 14, pp. 47· 0 citations
TL;DR
Analysis of current trends and future prospects emphasizes that PLCs remain a fundamental component of the Fourth Industrial Revolution, playing a vital role in shaping the future of intelligent manufacturing and advanced automation systems.
Abstract
The emergence of the Fourth Industrial Revolution has transformed traditional industrial systems into highly intelligent, interconnected, and automated environments. At the core of this transformation, Programmable Logic Controllers (PLCs) continue to play a crucial role by enabling reliable and efficient control of industrial processes. The evolving role of PLCs within the framework of the Fourth Industrial Revolution highlights their contribution to smart and intelligent industrial automation. Originally designed for basic control and sequencing operations, PLCs have significantly advanced through integration with modern digital technologies such as the Internet of Things (IoT), cloud computing, and data analytics. These developments allow PLCs to support real-time monitoring, data-driven decision-making, and seamless communication between machines in smart factory settings. PLCs enhance productivity, operational efficiency, flexibility, and system reliability in industrial environments. At the same time, key challenges such as cyber security risks, high implementation costs, and the demand for skilled technical expertise remain important considerations. Analysis of current trends and future prospects emphasizes that PLCs remain a fundamental component of the Fourth Industrial Revolution, playing a vital role in shaping the future of intelligent manufacturing and advanced automation systems. Furthermore, the continued evolution of PLC technology is expected to strengthen the integration of smart systems and promote sustainable industrial development, highlighting the growing importance of PLCs in achieving efficient, innovative, and future-ready industrial solutions.
Industry 4.0 has transformed traditional manufacturing into smart, data-driven, and highly connected production environments by integrating the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Machine Learning (ML), Cloud and Edge Computing, Cyber-Physical Systems (CPS), and Big Data Analytics. Smart Manufacturing Analytics (SMA) continuously collects and analyzes real-time data from sensors, machines, robots, programmable logic controllers (PLCs), and enterprise systems to enable intelligent decision-making. Unlike conventional manufacturing, SMA supports descriptive, diagnostic, predictive, and prescriptive analytics for applications such as predictive maintenance, fault diagnosis, quality inspection, production forecasting, energy optimization, and adaptive process control. Emerging technologies including digital twins, intelligent robotics, and Explainable AI (XAI) further enhance manufacturing resilience, transparency, and automation. Despite significant advancements, challenges such as interoperability, real-time data integration, network scalability, cybersecurity, device reliability, and decision-making under uncertainty remain. A multi-tier smart manufacturing framework combining IIoT, machine learning, cloud-edge computing, and optimization algorithms enables real-time asset monitoring, anomaly detection, predictive maintenance, resource allocation, and production optimization. Overall, Smart Manufacturing Analytics improves productivity, equipment health, product quality, energy efficiency, and operational resilience while reducing downtime and manufacturing costs, providing a strong foundation for next-generation intelligent and sustainable manufacturing ecosystems.
Alan Turing, Donald Davies· International Journal of Int...· 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
The digital transformation of industrial systems under the Industry 4.0 paradigm has introduced cyber-physical systems (CPS) as a core enabler of vertical integration and data-driven production environments. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has accelerated this transformation, fostering the development of the Industrial Internet of Things (IIoT) and creating smart industries characterized by real-time monitoring, automation, and human–robot collaboration (HRC). While these advancements establish a digital ecosystem capable of optimizing production through intelligent data analysis and decision-making, their practical implementation remains constrained by unresolved challenges and gaps in validation. With the emergence of Industry 5.0, the focus shifts toward human-centric, sustainable, and resilient industrial ecosystems, where AI-driven cognitive computing further enhances interaction between humans and machines. This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges. By categorizing existing literature into key application areas, the study highlights both the opportunities and risks inherent in current approaches, bridging the conceptual design of smart industries with their real-world realizations. The findings underscore the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Asmarani Ahmad Puzi, Ahmad Anwar Zainuddin, Muhammad Afham Anuar et al.· International Journal of Inn...· 0 citations
The rapid advancement of Industry 4.0 has accelerated the adoption of intelligent automation technologies that enhance productivity, flexibility, quality, and operational resilience in manufacturing. Cyber-Physical Production Systems (CPPS) have emerged as a key enabler of smart manufacturing by integrating physical production equipment with Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud and edge computing, digital twins, and autonomous control. Unlike traditional centralized automation, CPPS enables decentralized communication, real-time data exchange, intelligent decision-making, and adaptive production control. Autonomous factory automation allows machines, robots, and sensors to monitor operating conditions, predict equipment failures, and perform corrective actions with minimal human intervention. AI techniques such as machine learning, deep learning, reinforcement learning, and predictive analytics optimize production scheduling, resource allocation, predictive maintenance, and operational efficiency. Meanwhile, IIoT sensors, edge computing, and cloud platforms provide continuous monitoring, low-latency processing, and enterprise-level data analytics.This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization. The framework enables manufacturing systems to dynamically respond to equipment failures, production disturbances, and changing customer demands while maintaining operational stability and product quality. Mathematical models for production optimization and resource allocation demonstrate improvements in throughput, equipment utilization, energy efficiency, and production time. Overall, the proposed framework establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories, supporting next-generation smart manufacturing through the seamless integration of AI, IIoT, and cyber-physical engineering.
Michael Rabin, Amir Pnueli· International Journal of Int...· 0 citations
The paper provides a holistic insight into 5G-powered industrial automation through an extensive literature review, comparative analysis, and well-calculated approach to future research by offering strategic solutions to the research issue.
Waraporn Srisuk, Laura Anuwat· International Journal of Mod...· 0 citations
Industry 4.0 is a broad framework that includes many partially overlapping concepts such as smart manufacturing, cloud computing, industrial Internet of Things (IIoT), and smart factory. It relies on technologies such as Internet of Things (IoT), Cyber-Physical Systems (CPS), and Cloud Computing to enhance and streamline industrial processes. This paper focuses on the programming of an IoT-based laboratory-scale, automated, and smart factory using SIEMENS programmable logic controllers (PLCs), along with human machine interfaces (HMIs). The integration of IoT technologies is to achieve three main tasks. Firstly, remote update of the storage, retrieval, and processing of products in the factory by implementing a Radio Frequency Identification (RFID) based tracking system using Node-RED dashboard. Secondly, remote real-time monitoring of the factory by implementing a camera-based surveillance system. Finally, remote assessment of the temperature, humidity, pressure, and air quality of the factory by integrating an environmental sensor. A timed test was conducted to determine the time duration required for one workpiece (product) to complete the entire processing operation. The duration for the entire processing operation of the smart factory from retrieval to final storage is 1 minute and 37 seconds.
Abeer Imdoukh, Rawan Abosedo, Salah Al Swaileh et al.· 2026 6th International Confe...· 0 citations
