Statistical Process Control (SPC) - Control Charts

Overview:

Statistical Process Control (SPC) is a method of monitoring, controlling, and improving processes through the use of statistical techniques. SPC is a contribution to maintain product quality, increase efficiency, reduce waste, and identify areas that need improvement. In pharmaceutical manufacturing, it is a regulatory requirement to establish an “An ongoing program to collect and analyze product and process data that relate to product quality must be established (§ 211.180(e)). The data should be statistically trended and reviewed by trained personnel. should verify that the quality attributes are being appropriately controlled throughout the process.” (FDA Guidance for Industry: Process Validation: General Principles and Practices (2011)).

Why you should Attend:

At the core of SPC is the use of data to understand the behavior of a process over time. As a graphical representation of how a process behaves, control charts display data over time, thus indicating the variation in a process. Participants will get information about different types of control charts and what are considerations to appropriately select them. Control charts aim to estimate the “normal” behavior, which can be used to identify unusual deviations, or special cause variation. This may indicate that the process is out of control, and corrective action may be required. 

Participants will learn how to establish control chart limits and what out-of-control rules exist, with practical precautions. 

As data always include the impact of the measurement, it is important to understand the contributions from manufacturing process and analytical procedure to derive practically meaningful conclusions.

Areas Covered in the Session:

• Control chart principles
• Selection of suitable control charts
• Statistical out-of control rules (WECO, Nelson)
• Shewhart-, Moving range-, X-bar-control charts
• CUSUM-, EWMA-control charts
• Replicate (range, standard deviation) control charts
• Important assumptions and prerequisites (practice checks)
• Consideration of contributions (process and analytics)

Who Will Benefit:

• Analysts, Lab Supervisors and Managers in Manufacturing, Quality Control, or Quality Assurance