FREMONT, CA: More data and better analytical applications are going hand in hand as per industries' demands with the advent of the Industrial Internet of Things (IIoT) and other thriving technologies. Driven by high-powered analytics, including Machine Learning (ML) and Artificial Intelligence (AI) machine are promising excellent operational benefits. The sector still faces daunting challenges despite the investments in data capture and analytics, attempting to achieve continuous improvement. The outcome on the shop floor, is often characterized by data overload and inaction.
If the processes are in control or not, or how massive amounts of data can be sorted to find root causes are questions that plant operations staff need to answer confidently. Answers to these improve Key Performance Indicator (KPI) of current manufacturing operations. This will also enhance the development of more robust designs for future products. In a research paper, Data-analytics Gives Manufacturing Plants Insights for Continuous Improvement, a proven strategy for continuous improvement in manufacturing can provide insights into process variability. Correlations with product quality measuring, real-time statistical process control, and hierarchically structured data for regulatory compliance is also provided. Powerful machine learning algorithms handle extensive and disparate data and are prevalent.
IoT increases the volume and variety of data accessible to process manufacturers. These analytics algorithms then provide the capability to improve and optimize manufacturing operations continuously. Data analytics, including ML algorithms, can deliver breakthrough advancements by making the data exploration phase efficient and real-time monitoring and prediction easy.
Review of uni-variate control charts reveals underlying method variability. Operations teams can deploy real-time SPC if coupled down with event synchronized drill downs to the time domain. These can lead to an enhanced understanding of process variability, predictions of process failures, product correlations, and insights to improve future products with reduced commercialization times and more robustness in full-scale production. When a company employs a data-driven perspective, it means it makes strategic judgments based on data analysis and interpretation. Data analytics, including ML algorithms, can deliver breakthrough advancements by making the data exploration phase efficient and real-time monitoring and prediction easy.