International Science and Technology Journal

In This paper proposes a new model for analytical big data in Internet of Things (IoT) environments, utilizing Digital Twin (DT) techniques to enhance manufacturing monitoring. This suggests a fresh approach for analyzing large amounts of data in IoT settings, by incorporating Digital Twin methods to improve monitoring in manufacturing. The new model was created to tackle issues with data management and analytics in manufacturing settings, where processing real-time data is essential, through the integration of digital twin methodologies. The goal is to enhance operational efficiency, predictive maintenance, and decision-making processes in manufacturing monitoring environments by leveraging big data analytics techniques within IoT settings. This research will analyze the model's efficiency in a manufacturing setting through and Monitoring, seeking to add to the expanding knowledge base in IoT and big data analytics. The model will combine advanced analytics, machine learning, and digital twin technology to allow real-time monitoring, predictive maintenance, and process optimization in manufacturing environments. The suggestion will use prior research and top industry methods to create a plan for putting the model into action, concentrating on improving resource distribution, accuracy, predictability, and innovation. The proposed design for manufacturing and monitoring in IoT environment utilizes a new Analytical Big Data Model with DT techniques to reduce redundancy and specify technology interactions. The proposal will draw on existing research and industry best practices to develop a framework for the implementation of the model, with a focus on optimizing resource allocation, enhancing accuracy and predictability. The new model of new Analytical Big Data Model in IoT environment Using DT Technics: A new Design for Manufacturing and Monitoring................ Keywords...............Big data in IoT, Big data analysis, Manufacturing Monitoring, DT.