Process Capability Indices in Medical Device Manufacturing

This webinar will help device manufacturers to identify valid statistical techniques to control process capability and product characteristics. It will explain process capability indices in detail and their use.

Why Should You Attend:

Manufacturing processes for medical devices involve statistical methods. Process capability analysis examines the inherent variability in a process including the statistical distribution of the process output. When the measurement uses variables data, process variability is the “spread” of a process in statistical control. When the measurement involves attributes data, process capability is often the proportion of nonconforming units.

Process capability uses an index that relates process variability and process specifications. This webinar explains the underlying statistics of the most commonly used capability indices. In particular, the webinar explains how to use data from Statistical Process Control (SPC), particularly x-bar and R charts, to calculate these process capability indices.

With an understanding of process capability indices, the topic moves to medical device applications. The webinar illustrates applications of process capability indices in both FDA QSR and ISO 13485 systems using both examples and FDA Warning Letters to illustrate the issues.

Areas Covered in the Webinar:

• The difference between variables data and attributes data
• The concept of process variability and its common metrics
• Estimating process parameters from common SPC charts such as x-bar & R
• Definitions of common capability indices such as Cp, Cpk, Pp, and Ppk
• Determining valid statistical techniques in the context of FDA QSR
• Applications to medical device manufacturing processes

Who Will Benefit:

People in the following roles can especially benefit from the knowledge in this webinar:

• Medical Device Quality Managers
• Medical Device Quality Engineers
• Process Engineers
• Medical Device Production Managers
• Medical Device Production Supervisors
• Reliability Engineers
• Validation engineers