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Harish Jose

Lean

Process Validation, Part 2

The problem of induction

Published: Tuesday, July 11, 2017 - 11:02

In today’s column, I will be looking at process validation and the problem of induction. Yesterday, I looked at process validation through another philosophical angle by using the lesson of the Ship of Theseus.

The U.S. Food and Drug Administration (FDA) defines process validation as “the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product.”

My emphases on the FDA’s definition are the two words “capability” and “consistency.” One of the misconceptions about process validation is that once the process is validated, then it achieves almost an immaculate status. One of the horror stories I have heard from my friends in the medical devices field is about a manufacturer that stopped inspecting its product since the process was validated.

The problem with validation is the problem of induction. Induction is a process in philosophy—a means to obtain knowledge by looking for patterns from observations and coming to a conclusion. For example, if the swans that I have seen so far are white, I conclude that all swans are white. This is a famous example to show the problem of induction because black swans do exist. However, the data I collected showed that all of the swans in my sample were white. My process for collecting and evaluating the data appears capable and the output consistent.

Black-Swan

The misconception that the manufacturer held in the example above was the assumption that the process was going to remain the same, and thus the output also would remain the same. This is the assumption that the future and present are going to resemble the past. In philosophy this type of thinking is considered an assumption of “uniformity of nature.” This problem of induction was first thoroughly questioned and looked at by the great Scottish philosopher David Hume (1711–1776). He was an empiricist who believed that knowledge should be based on one’s sense-based experience.

One way of looking at process validation is to view the validation as a means to develop a process where it is optimized such that it can withstand the variations of the inputs. Validation is strictly based on the inputs at the time of validation. The six inputs—man, machine, method, materials, inspection process, and environment—can all suffer variation as time goes on. These variations reveal the problem of induction: The results are not going to stay the same. There is no uniformity of nature. The uniformities observed in the past are not going to hold for the present and future as well.

In general, when we are doing induction, we should try to meet five conditions:
1. Use a large sample size that is statistically valid
2. Make observations under different and extreme circumstances
3. Ensure that none of the observations/data points contradict
4. Try to make predictions based on your model
5. Look for ways to test your model to fail

The use of statistics is considered as a must for process validation. The use of a statistically valid sample size ensures that we make meaningful inferences from the data. The use of different and extreme circumstances is the gist of operational qualification. Operational qualification is the second qualification phase of process validation. Above all, we should understand how the model works. This helps us to predict how the process works and thus any contradicting data point must be evaluated. This helps us to listen to the process when it is talking. We should keep looking for ways to see where it fails in order to understand the boundary conditions. Ultimately, the more you try to make your model fail, the better and more refined it becomes.

The FDA’s guidance on process validation and the Global Harmonized Task Force (GHTF) guidance on process validation both try to address the problem of induction through “continued process verification” and “maintaining a state of validation.” We should continue monitoring the process to ensure that it remains in a state of validation. Anytime any of the inputs are changed, or if the outputs show a trend of decline, we should evaluate the possibility of revalidation as a remedy for the problem of induction. This brings into mind the quote “Trust but verify.” It is said that Ronald Reagan got this quote from Suzanne Massie, a Russian writer. The original quote is “Doveryai, no proveryai.”

I will finish off with a story from the great Indian epic, the Mahabharata, which points to the lack of uniformity in nature.

“Once a beggar asked for some help from Yudhishthir, the eldest of the Pandavas. Yudhishthir told him to come on the next day. The beggar went away. At the time of this conversation, Yudhishthir’s younger brother Bhima was present. He took one big drum and started walking toward the city, beating the drum furiously. Yudhishthir was surprised.

“He asked the reason for this. Bhima told him:

“‘I want to declare that our revered Yudhishthir has won the battle against time (Kaala). You told that beggar to come the next day. How do you know that you will be there tomorrow? How do you know that beggar would still be alive tomorrow? Even if you both are alive, you might not be in a position to give anything. Or, the beggar might not even need anything tomorrow. How did you know that you both can even meet tomorrow? You are the first person in this world who has won the battle against time. I want to tell the people of Indraprastha about this.’

“Yudhishthir got the message behind this talk and called that beggar right away to give the necessary help.”

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About The Author

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Harish Jose

Harish Jose has more than seven years experience in the medical device field. He is a graduate of the University of Missouri-Rolla (U.S.), where he obtained a master’s degree in manufacturing engineering and published two articles. Harish is an ASQ member with multiple ASQ certifications, including Quality Engineer, Six Sigma Black Belt, and Reliability Engineer. He is a subject matter expert in lean, data science, database programming, and industrial experiments. Harish publishes frequently on his blog harishnotebook. He can be reached on LinkedIn.