Statistical Process Control - A key tool for process understanding in the process validation life cycle

Statistical Process Control - A key tool for process understanding in the process validation life cycle

Heidelberg, Germany

Seminar Nr. 15093


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Tel.: 06221 / 84 44 0 E-Mail:


Dr Ingolf Stückrath, Sanofi-Aventis Frankfurt, Germany
Dr Sven Wedemeyer, Merck KGaA, Germany
Klemens Wendl, Baxter Healthcare Ltd., Great Britain


The new process Validation life cycle is now split up into 3 stages:

1. Process design
2. Process Qualification
3. Continued Process Verification

The new “catchword” is process understanding. Trends should be evaluated in the Stage 3.

One element to show process understanding and to monitor trends can be Statistical Process Control.

On the one hand the seminar will explain the theory of control charts e.g. how to calculate and read them. On the other hand the seminar will explore how to practically apply Control Charts, e.g. implementing control charts in production or QC and setting up a good review process. This balance of class room sessions and exercises supports a hands-on approach to manage and use Control Charts in different environments, like validation and process improvement.

Examples and case studies from the experience of the speakers will give evidence of the success and possibilities the use of Control Charts adds to your enterprise. Additionally, there is a view on the software for SPC and its GMP relevance.


With the FDA Guidance on Process Validation of January 2011 the FDA gives a new interpretation of validation. Not more than 3 validation batches are the evidence that a process is valid. The FDA now expects a validation life cycle with continued process verification throughout the commercial phase. Also the EMA stated in a Question and Answer paper, that they focus on continuous validation too. Both authorities mention that a process is in statistical control and capable. One element to show this is Statistical Process Control (SPC) as mentioned by the FDA.

Also in the ICH Q9 document “Quality Risk Management” control charts and process capability are mentioned as statistical possibilities within risk assessments.


This course is directed to staff who is involved in process understanding and optimization (e.g. process owners, validation managers, etc.) in R&D, production and quality control. It also addresses quality assurance staff.

Note: The number of participants is limited.


Six Sigma Definitions
A short introduction to Six Sigma
Six Sigma Terms

Objectives of Statistical Process Control
Create visibility of process performance
Increase process knowledge
Show process stability
Prove process capability
Support the continuous improvement process

Some mandatory Basic Statistics
Mean Value, Median, Range
Standard Deviation
Normal Distribution
Histogram and Time Series Plot

The two Types of Variability
Common cause variability
Special cause variability

Control Charts
Types of control charts
Design a control chart
Setting up control charts in Minitab®
Control limits and specification limits
Why is 3s taken as limit?
Changing control limits

Reading Control Charts to improve the Process
Statistical rules
Identifying patterns
Performance windows
General rules

Deploying and managing SPC - Connecting SPC to Continuous Improvement
Deployment Top-Down versus Bottom-Up
Root cause analysis
Paper based versus electronic control charts
Management system / cycle

Reasons to implement Control Charts
Link to quality control
Link to quality assurance
Benefits from SPC

Measurement System Analysis and SPC
Using control charts to do a MSA
Accuracy of data
Triangle of Variability

SPC as tool for Continued Process Verification
Continued Process Verification: Requirements
Case Study Sanofi-Aventis

Process Capability – What is the risk of failure of my process?
Cp, Cpk versus Pp, Ppk
Long term versus short term capability
Process robustness

Case Study Control Charts and Trending of
Microbiological Data
Computerized systems as basis
General use of control charts for microbiological data (Environmental monitoring, personnel monitoring, water monitoring, product bioburden)
Distribution of microbiological data
Minimum number of data to establish control limits
Specify „trending rules“ for microbiological data
Frequency of Trending
General approach on encountering a negative trend
Case study


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