## The Idea in Brief Statistical Process Control (SPC) is a method for managing and improving processes through statistical analysis. It is based on the principle that all processes show variation, but not all variation is the same. By distinguishing between _common causes_ (inherent to the process) and _special causes_ (signals of unusual change), SPC helps organisations detect problems early, maintain quality, and reduce waste. It is central to modern quality management systems and widely used in both manufacturing and service industries. --- ## Key Concepts ### 1. Variation in Processes - **Common cause variation**: Natural, expected fluctuations due to the process itself. - **Special cause variation**: Irregular, unexpected changes that usually indicate a specific problem. - Effective quality control depends on recognising which type of variation is present. ### 2. Control Charts - Introduced by Walter Shewhart in the 1920s. - A control chart plots process data over time with an average line and upper/lower control limits. - Points outside the limits, or unusual patterns within them, indicate a potential special cause. - Types include: - _X-bar and R charts_ (for averages and ranges of samples) - _p-charts_ (for proportions of defective items) - _c-charts_ (for counts of defects) ### 3. The Shewhart Cycle - Also known as **Plan–Do–Check–Act (PDCA)**. - A continuous improvement cycle that complements SPC by applying learning from data to refine processes. --- ## Implications In an era of automation and digital manufacturing, SPC remains vital. Modern systems integrate SPC with real-time data collection, machine learning, and Industry 4.0 platforms. By combining traditional statistical methods with advanced analytics, organisations can maintain tighter control and adapt more quickly to changes.