Life Sciences
2 min read

Statistical tools are crucial for process validation

Uncontrolled variation in manufacturing processes may generate non-conformities and poor product quality. Consequently, manufacturers shall adequately monitor the variation to control the process performance and ultimately the product quality. In order to address these requirements, the use of statistics is highly recommended and will result in a robust process design and validation.


Through our relevant experience with statistical methods and analysis, our qualified team will provide you with the proper tools for all the different stages of the Process Validation. We will support you along the Product Life Cycle of process validation, including Design Of Experiments (DOE), sampling plans, capability study, and Statistical Process Control (SPC).


Let the data talk to you

FDA affirms that ‘a successful validation program depends upon information and knowledge from product and process development. This knowledge and understanding are the basis for establishing an approach to control the manufacturing process that results in products with the desired quality attributes’, (FDA,2011).

In other words, FDA expects to answer the pivotal questions:


  • Do manufacturers understand the sources of variation in their processes
  • Do manufacturers have tools to detect the presence and degree of that variations?
  • Can manufacturers adequately monitor the variation to control the process performance and ultimately the product quality?

To answer those questions, the implementation of statistical discipline and tools in process validation are key.

Brief & non-exhaustive recommendation for statistical tools

Stage 1: Process Design


  • Utilize design of experiments (DOE) to define the critical process parameters
  • Utilize regression analysis and correlation to make relationship between key process factors

Stage 2: Process Qualification


  • Use statistical power for sampling plan
  • Use statistical process control charts for stability study
  • Use normal probability plot for normality study
  • Use process capability report for process capability study
  • Use ANOVA test, two-sample t-test and other hypothesis tests to make batch comparison

Stage 3: Continued Process Verification and Capability


  • Use tolerance Interval Plot to know the probability of population meeting tolerance
  • Use Outlier Plot to identify special causes
  • Use Statistical Process Control (SPC) to collect and analysis process and product CTQ’s


In order to make these tools efficient, a specific attention should be paid for the following aspects


  • The nature of data being collected; attributive versus variable
  • How the data are being collected (a Gage R&R study is required for the measurement equipment)
  • How many samples to take

How can we work together?

Our consultants will closely work with you to understand your business challenges at its core. They will analyse and interpret the data and involve you in every step to develop a customized solution together.

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