Application of Data Analytics in Process Prediction, Analysis, Management & Visualization Using Microsoft Power BI - Chukwu Christian Onyemaechi (My University, 2024)
This research explores the application of data analytics, specifically Microsoft Power BI, to optimize processes within the chemical and manufacturing industries. By analyzing large volumes of production data from a PVC (polyvinyl chloride) manufacturing plant, it aimed to identify trends, anomalies, and opportunities for improvement. The study involved collecting, cleaning, and transforming production data, including process temperature, flow rate, torque, tool wear, energy consumption, and failure types. This data was then loaded into Power BI, where it was analyzed and visualized to gain insights into the manufacturing process. Through data-driven analysis, critical relationships between process variables and product quality were identified. For instance, it was observed that variations in voltage and temperature can significantly impact product quality and energy consumption. By monitoring these variables and making timely adjustments, it is possible to optimize production processes, reduce energy costs, and minimize product defects. The findings of this research demonstrate the potential of data analytics to revolutionize industrial processes. By leveraging the power of Power BI, organizations can harness the value of their data to achieve significant improvements in efficiency, productivity, and overall performance.
This research on the "Application of Data Analytics in Process Prediction, Analysis, Management & Visualization Using Microsoft Power BI" delves into the application of data analytics, specifically Microsoft Power BI, to optimize PVC manufacturing processes. By meticulously collecting, cleaning, and transforming large volumes of production data, we uncover valuable insights that can significantly enhance operational efficiency, reduce costs, and improve product quality. Our analysis focuses on key process variables such as temperature, flow rate, pressure, and energy consumption. Through the application of advanced data visualization techniques and statistical analysis, we identify critical correlations, anomalies, and trends. These insights empower manufacturers to make data-driven decisions, predict potential equipment failures, and optimize production schedules. The findings of this research demonstrate the transformative potential of data analytics in the PVC manufacturing industry. By leveraging the power of Microsoft Power BI, organizations can harness the value of their data to achieve substantial improvements in productivity, energy efficiency, and overall profitability.
Keywords: Data Analysis; Power BI; Data Visualization; Business Intelligence; Chemical Engineering; Process Control; Climate Change; Sustainability; Energy; Waste; Environmental Pollution.