Dr. Jalal Delaram | Statistical Quality Control

Statistical Quality Control

Overview

Statistical Quality Control (SQC) plays a pivotal role in ensuring the consistent quality of products and services in various industries. By applying statistical methods, SQC helps identify and monitor variations in processes, enabling organizations to maintain high-quality standards and reduce waste. The importance of SQC has grown exponentially as industries have become more complex and globalized. It provides the necessary tools to control processes, identify defects, and improve efficiency, making it essential in industries such as automotive, electronics, pharmaceuticals, and food production. The foundational principles of SQC, including control charts, process capability analysis, and design of experiments, are critical for analyzing and improving production processes.
Historically, SQC emerged in the early 20th century with the contributions of pioneers like Walter A. Shewhart, who introduced the control chart, and W. Edwards Deming, whose work transformed the post-World War II industrial landscape in Japan. Over the years, SQC has evolved to incorporate modern tools and techniques, such as Six Sigma, lean manufacturing, and real-time data analytics, to address emerging challenges. As industries face increasing demands for efficiency, sustainability, and customization, SQC remains a cornerstone of quality management. Looking forward, the integration of artificial intelligence, machine learning, and big data analytics into SQC will further enhance the ability to predict, control, and improve quality in even more sophisticated and automated environments.