Robotic Process Automation (RPA) has been a transformative technology that allows companies to automate plastic-based tasks that are repetitive and basis rule-based in heterogeneous information systems without having to change the underlying infrastructure. Since organizations are under growing pressure to enhance both operational efficiency, accuracy, and scalability, RPA provides a cost-effective solution to the digital transformation process since it simulates human interactions with programs. The following paper is a detailed analysis of the concept of RPA within the current enterprise processes in terms of its architecture, deployment patterns, and quantifiable business outcomes. The paper is meticulously conducting a scientific review of literature to determine existing trends, advantages, challenges, and gaps in the present research on RPA. It suggests using the structured methodology that encompasses the process discovery, bot design, orchestration, and governance mechanisms according to the enterprise standards. Measures of performance like reduction in execution time, minimization in error rate, cost savings, and return on investment are measured to check effectiveness. The convergence of RPA and artificial intelligence and machine learning is also discussed and results in intelligent automation that has the potential to process semi-structured and unstructured information. At the end of the paper, the authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
Kenji Sato, Aiko Yamamoto· 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