INCORPORATE STATISTICAL METHODS FOR REAL-TIME DATA ANALYSIS, ANOMALY DETECTION, AND PREDICTIVE ANALYTICS IN QUALITY CONTROL PROCESSES.

Abstract

Ensuring quality control is crucial in modern companies to keep a competitive edge and satisfy customers. These days, no quality control procedure is complete without using some kind of statistical tool for analyzing data in real-time, finding outliers, or making predictions. Improving decision-making, minimizing errors, and optimizing production processes through the integration of statistical approaches inside quality control frameworks is the focus of this research. The approach involves tracking, identifying, and forecasting quality deviations using analyzing time series, machine learning algorithms, and statistical process control (SPC) charts. When real-time sensor data is combined with historical data, abnormalities may be detected quickly, allowing for prompt steps to be taken to fix them. With the use of predictive analytics models, businesses may take preventative measures to avoid quality deviations and save money on rework and product recalls. These models may foresee potential problems with the process or errors in the future, even before they manifest. Case studies and simulations demonstrate the usefulness and efficacy of statistical methods for quality control in various industries. These methods are crucial for achieving product perfection and operational efficiency.