Topical Article

What is Lean Process Validation?

Posted on by Congenius

In the medical devices field, the meaning of the term Lean Process Validation can sometimes lack clarity, which can lead to limited understanding of what the topic involves. In this article, our Head of Quality Dr Dirk Hüber seeks to clearly define the term, unpacking what Lean Process Validation encompasses – including its connection with regulatory requirements, risk mitigation and assessment, and process validation procedure.

Defining Lean Process Validation

Let us start by sharing our definition for what Lean Process Validation is:

To make the effort in process validation that is just necessary – not more, but also not less.

And for the sake of clarity, let’s highlight what Lean Process Validation is not:

The goal, approach, and procedure have no connection with methods such as Lean Manufacturing, Six Sigma, and similar.

Regulatory Requirements & Starting Points

In the EU, the regulatory requirement for process validation is EN ISO 13485:2016, section 7.5.6.

By rephrasing the definition of process validation provided in the standard, we can emphasise the key terms for a lean approach:

Process validation is the proof that the manufacturing process is continuously able to manufacture the product within its specified quality.

Our definition above highlights the three starting points for Lean Process Validation:

  1. Specified Quality:
    Which product characteristics must be proven?
  2. Manufacturing Process:
    How does the manufacturing process influence these product characteristics?
  3. Continuously:
    When is proof required to demonstrate that a manufacturing process factor could significantly influence the product?
    How much proof must be provided? (E.g., number of validation batches and number of products to be produced)

Whilst answering these questions will provide us with the path to Lean Process Validation, the same answers are also necessary to plan Quality Control in an efficient way – as Lean Process Validation and efficient engineering of Quality Control go hand in hand. That said, this article will focus on the process validation aspects to maintain lucidity of thought.

“Specified Quality”

Which product characteristics must be proven?

The product characteristics that need to be proven are those that are relevant to the product’s function. A product characteristic is functionally relevant, if a specified function of the product may become impaired if the product characteristic or quality attribute is not within its specification.

And so, the task here is to identify the functionally relevant product characteristics. This can be done in a practical way, through adding a column named “Functionally relevant?” to the specification table. For each specification / characteristic a simple “Yes” or “No” can be entered into this column. If “Yes”, the characteristic should be proven during process validation – if it cannot be subsequently verified, refer to EN ISO 13485:2016, section 7.5.6. This task should be jointly executed by the product and quality engineers.

“Manufacturing Process”

How does the manufacturing process influence these product characteristics?

Usually, for a part, a component, or a product there are many functionally relevant product characteristics. Testing or measuring them all during process validation (or later in Quality Control) is inefficient. So how may efforts be streamlined, without jeopardising confidence that the part, component, or product is fully within its specification?

To answer this, we need to understand the manufacturing process and how it influences the functionally relevant product characteristics. In other words, do any of the product characteristics correlate with each other, and if yes, how?

Let us take an example to investigate this topic. Consider a complex injection moulded plastic part:

  • In this scenario, it is not possible to tell which drawing measures correlate with each other as there are simply too many factors that influence such correlations.
  • An empirical approach is required to discover such correlations: by varying the process parameters (the most effective here would be the injection pressure) you produce parts within the whole range of tolerances, and outside of the tolerances.
  • You can then completely measure these parts and calculate the correlation factor between all pairs of measures – making it possible to sort the measures into correlating groups.
  • For each group, a measure that can be measured easily and accurately becomes the test measure.
  • You can calculate the test tolerance for the test measure from the correlation functions, such that all measures within the group are within their drawing tolerance once the test measure is within its test tolerance.
  • Thus, from now on you only need to measure the test measures, which usually provides a significant reduction in measurement effort.

This approach will also work for other manufacturing processes, but not for all. It is important to understand the manufacturing process itself and from there to understand if and how correlations may occur. And bear in mind, there are manufacturing processes where characteristics do not correlate.


When is proof required to demonstrate that a manufacturing process factor could significantly influence the product?

For process validation, you must reproduce routine manufacturing. Consequently, for your process validation scenario you need to understand the factors in routine manufacturing that may influence product quality, such as duration, material supply, breaks, change of shifts, weather, season, etc.

The goal is to build such factors into the validation batches such that there is no need to extent the validation runs solely for the sake of incorporating such factors.

How much proof must be provided?

In other words, how many validation batches are needed, and how many products need to be produced for validation?

Traditionally, three batches are recommended, but this is a long-obsolete figure. Instead, a statistical rationale is required.

However, since medical devices are usually manufactured continuously (and not in batches), even if they are lot-controlled, the number of required validation batches is mainly governed by how the routine manufacturing is organised, and how this can be reproduced during process validation.

The size of the validation batches and total amount of products produced during validation depends on the statistics, i.e., mainly on the confidence level and the acceptable failure rate.

There is then another aspect to be considered when deciding on the number of validation runs for Lean Process Validation – acceptance criteria. As an example, let’s assume you have settled on two validation runs, and both must be successful to pass the process validation. This situation may be denoted as 2(0,1): two runs, zero fails to pass, if one run fails, all is failed.

Your statistic may allow for a larger number of runs where the pass criterion is larger than zero, say 4(1,2) and 6(2,3). But of course, you wouldn’t want to apply these scenarios, as they require more runs and are subsequently less efficient.

Now, what happens if your efficient scenario 2(0,1) fails? You would need to include this in a validation report, analyse the causes, correct them, and repeat the process validation. Ideally, you would want to avoid this extra effort and simply continue with additional runs. The good news is that this can easily be achieved – if the validation plan allows that in the case of 2(0,1) failing, you can continue with 4(1,2), and if that fails, with 6(2,3). With such an approach process validation might still be passed, even if one or two runs fail.


There is another aspect in statistics that is often overlooked, or sometimes ignored for simplicity’s sake, and that is the distribution which a product characteristic follows. Very often product characteristics do not follow the normal distribution. However, statistical values in process validation, like cp or cpk, are often calculated using their simple formulas for normal distribution. This may lead to grossly wrong values and incorrect conclusions.

This all demonstrates that for process validation, especially if it shall be performed lean, profound knowledge in statistics is required. As such, we strongly advise you to engage a service provider in statistics should your company not have an in-house statistician – it will pay off in the long run.

Does Risk Management support Lean Process Validation?

The short answer is no.

Risk Mitigation

Nevertheless, quite often process validation can be found as a mitigation action in PFMEAs or even in Product Risk Analyses. But process validation does not mitigate any risk, and the regulations clearly say so:

  • EN ISO 13485:2016, section 7.5.6 states that all manufacturing processes must be validated, except if the manufactured product can be and is fully verified.
  • EN ISO 14971:2019 mentions process validation only as a note in section 7.2, where it says that process validation may serve as a means to prove implementation and effectiveness of mitigation actions. This statement excludes that process validation may be a mitigation in itself.

Thus, risk management must not be used to determine which processes to validate and which to not.

That said, process validation may provide data to determine occurrence of process failures in PFMEAs.

Risk Assessment

The assessment of risks and failures in a product risk analysis or an FMEA may serve as a guide to determine sample sizes in Quality Control. But sample sizes for controls in process validation should not be determined on such a basis because process validation should prove that the sample sizes determined for Quality Control are justified. And in order to do so, sample sizes in process validation have to be significantly larger than planned for Quality Control (e.g., based on a significantly higher confidence level).

The risk assessments do, however, help to determine the acceptable failure rate for your product or for certain failures. Such failure rates are needed to calculate the amount of products to be manufactured during process validation, as explained above.

How does the Process Validation Procedure support Lean Process Validation?

Finally, let’s take a look at what the procedure for qualification and validation can do to support Lean Process Validation.

Firstly, there is the procedure itself, which is the same for each piece of equipment to qualify and each process to validate. So, nothing to gain there.

However, equipment may be classified, and depending on such classes, documentation for qualification and validation may be simplified. An often-applied classification scheme for equipment is to distinguish equipment with direct and indirect impact on product quality, and to distinguish simple and complex equipment (whereby “simple” and “complex” should be well defined, e.g. passive tools versus active, powered equipment). This gives four classes, for which requirements for documentation in qualification and validation can easily be derived. It’s worth noting that this approach is implicitly risk based and is in fact the most common way to create a risk-based qualification and validation process.

Furthermore, the volume of the documents required for qualification and validation corresponds to the complexity of the equipment and process to qualify and validate.

Lean Process Validation: The pros and cons

Like every good medicine, Lean Process Validation has its benefits, but also its risks and side effects. As we wrap up, let’s shortly assess the pros and cons:

Effort level

Following the approach outlined above requires more effort to develop the validation plan. But the gain, is that more efficient and potentially more effective validation can be leveraged later in execution.

Specialist knowledge

Strong statistical knowledge is required, which can sometimes be difficult to source.

Robust specifications

Additionally, to follow the lean approach, robust product and manufacturing process specifications are fundamental.


There is a certain risk that influencing factors might be overlooked whilst following a lean method. However, this risk may be even higher when using a standard, non-lean approach to process validation.


Furthermore, there is a risk that shortcuts are taken. Since Lean Process Validation is a fine balance in how much to do, constant vigilance regarding what is done and why is essential.


Another hurdle in Lean Process Validation is that decisions need to be made, and accountability taken for such decisions. As always, the validation approach must be justified to auditors and regulatory authorities, and potentially to peers should something go wrong. To this end, having a company culture that genuinely encourages accountability is crucial. 

In summary

The effective execution of Lean Process Validation circles back to our three starting points:

  1. Specified Quality: understand the product
  2. Manufacturing Process: understand the manufacturing process
  3. Continuously: understand your statistics

The key is to understand the validation scenario and make the correct decisions. There is no ready-made recipe, so each case must be assessed individually.

“Lean” in this context means understanding the product’s characteristics, as well as the manufacturing process and its influence on the product, then based on this knowledge, using a solid statistical and risk-based approach to find the most efficient way to perform process validation. A good basis, arguably, for professional quality engineering more holistically.

Should you have a challenge relating to process validation more generally, our Quality team is ready and happy to help. Simply get in touch to start the conversation.


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