Statistical process control is nothing new to manufacturers or industry. In 1924, a statistician at Bell Laboratories, William A. Shewart, developed the control chart and the concept that a process could be in statistical control. SPC proved to be a better and more efficient way to monitor product quality without compromising safety.
Today, advanced SPC software is widely used as a quality tool throughout many industries. SPC is an effective way to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential. As a method to measure and effectively control the quality of the manufacturing process, it’s considered by many as second to none. Quality data is collected in the form of product or process measurements or readings from various machines or instrumentation. The data is collected and used to evaluate, monitor and control a process.
SPC is ideal in mass production of medical devices for the program allows continual output through the use of control charts rather than inspecting each individual lot of a device. FDA also allows product release using SPC as long as the process is well-documented and thoroughly reviewed. This eliminates interruptions in production, and also facilitates the detection of trends and defects earlier on, which further reduces rework, retooling, and material waste.
SPC methods are also predictive tools that should be a part of a manufacturers’ mature predictive maintenance program. Well-designed, it can monitor process performance and maintain control with only periodic adjustments when necessary, and to also ensure not to over adjust. Regular monitoring of a process can save unnecessary inspection and adjustments. This information allows for a proactive response rather than a reactive response when it may be too late or costly.
Control charts, clear and uncomplicated graphs of process information, are the key to an effective SPC program. Using control charts aid quality control analysts to monitor processes and identify any variation in the performance. Data from a stable, controlled process will only display common cause variation, i.e., only that variation which is inherent to the process. Control charts are based on aggregate past data that can be measured and predicted to determine how a process will vary (within limits of common cause variation) in the future. An unstable process will display a special cause variation, i.e., a non-random variation in the data. Quite simply, control charts are robust tools for understanding process variability and to analyze process performance.
Finally, statistical process control is instrumental where precision matters in the production of components. Statistical analysis allows quality control analysts and engineers alike to analyze data, and to control and monitor production and procedures during validation and verification.
Implementing a well-designed and comprehensive SPC system will improve your product quality. An SPC system benefits manufacturers and companies by providing historical benchmarks to relate to daily and long-term process performance of products, which will in turn, greatly aid the release and delivery of a safe and effective medical device to the marketplace.