Introduction to Statistical Process Control (SPC)

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:

Participants will get an overview on the statistical basis for SPC, i.e. the distribution of data. This will be illustrated in detail by the commonly expected normal (Gaussian) distribution. Such common cause variation is inherent to the process and is typically stable over time. The other type of variation is called special cause variation, or systematic bias. SPC aims to distinguish between these two types of variation so that appropriate actions can be taken. By using SPC tools, problems can be detected early, variations in product quality reduced, and continual improvements achieved.

Participants will learn about the so-called “magnificent seven” SPC tools histograms, check sheets, pareto charts, cause-and-effect diagrams, defect concentration diagrams, scatter diagrams, and control charts. For the latter, a general introduction will be provided, with more detailed information on various types of control charts in a separate Webinar.

Areas Covered in the Session:

• Error types (common cause variation / random error and special cause variation /systematic bias)
• “Normal” distribution of data (process and analytics) and the respective statistical parameters, capability indices
• SPC tools: Histograms, check sheets, pareto charts, cause-and-effect diagrams, defect concentration diagrams, scatter diagrams 
• Introduction to control charts

Who Will Benefit:

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