Operational Qualification

Updated: Sep 28

The fun part of process validation!

Great, you finished the IQ and now want to start with the fun part? The operational qualification (OQ) is a more exciting part; we finally get to produce some parts and see the process performance.

This post talks about:

· Why perform an OQ

· What an OQ is

· How to establish a process window

· Running at worst-case settings

· What is meant by worst-case

Why perform an OQ

The operational qualification has two objectives:

1. identify critical parameters, characterize the process, and establish an operating window (also referred to as process window)

2. run the process at worst-case conditions to demonstrate these conditions result in an acceptable product; this further demonstrates that the process is robust against variation of process parameters [1]

What is an OQ

The GHTF (Global Harmonization Task Force) defines operational qualification as “establishing by objective evidence process control limits and action levels which result in product that meets all predetermined requirements.” [2]

An operational qualification is the worst-case challenge of the process at the limit of the process parameters. That means we must set our parameters to their limits and run the process. However, we do not even have process parameters at this stage, nor do we know its limits. That leads us to the next step.

How to establish a process window

We already know that the first objective is to characterize the process and establish an operating window. The keyword here is another three-letter acronym, DoE – Design of Experiment.

Design of experiment is a vast topic, but we will try to break it down into a couple of simple steps by using it on a process most of us are familiar with – a heat-sealing process of a sterile barrier system.

The first step of a DoE is to choose the input factors and our output responses. In the case of the heat-sealing process, the input factors are temperature (T), time (t), and pressure (p). In some cases, the time (t) is given as speed (v) and the pressure (p) in force (F), but for the sake of this example, we will stay with T, t, and p as our input factors. Let's assume our output response is seal strength (Fs). Based on this information, we can already say there are 23 cases we have to produce and analyze to get a better picture of the process. You might ask where the 23 cases are coming from. The 3 input parameters are the exponent, and the basis 2 is for each end of a specific parameter, i.e., temperature high/low, time high/low, and pressure high/low.

In the next step, we combine these extremes and assign "1" to high values and "0" to low values.

Table 1 Full Factorial Design for 3 Inputs

Table 1 shows the different combinations of the input factors. Each combination will be produced and measured for its seal strength (Fs) – NOTE: TBD stands for "to be determined".

The output response is then fed back into a statistical software like Minitab to analyze the factorial design. A complete and more comprehensive guide to designing an experiment can be found here.

While a heat-sealing process is manageable in terms of the number of cases to test, a more complicated process like injection molding, where the number of input factors can easily go into hundreds, it is far more difficult to characterize a process like this.

Even though Minitab helps you analyzing the design, you might still be asking, "What values do I choose for all those 0s and 1s?" Well, that is a good question. The material datasheet can be a good and valuable source of information to answer this question. Some material suppliers provide information on where their products work best.

The above process parameters are the apparent considerations when one thinks about operational qualification and worst-case testing. However, other aspects need to be considered too; examples are:

· Software parameters

· Raw material specification

· Process operating procedure

· Material handling requirements

· Process change control

· Training

· Environmental conditions

Running at worst-case settings

Assuming we found the following set of process parameters:

Temperature (T): 150,0±10,0°C

Time (t): 2,0±0,5sec

Pressure (p): 3,0±0,5bar

The process parameters set on the machine for the two worst cases (upper and lower) are shown in Table 2.

Table 2 Worst-Case Parameter Settings

Now you know what parameters you must set and run the OQ studies. To answer the question of how many samples you must produce, read our blog posts about "Cpk of 1,33 is not enough!” and “Statistical Tolerance Intervals”.

A tip related to sample size and operational qualification is that you can use reduced reliability levels (confidence level remains at least 95%) for OQ runs. These lower requirements for OQ are because these tests are performed under worst-case conditions, which makes them a stress test [1].

What is meant by worst-case

Worst-case conditions are the settings for the input factors that cause the worst-case performance of the output responses. In the previous example of a heat-sealing process, a lower limit setting might correspond to undersealing and vice versa.

Are you concerned about the increased requirements due to MDR (Medical Device Regulation; 2017/745) and already behind schedule? Contact us today, and we'll take the burden off your shoulders and help you make your supply chain compliant.


[1] Taylor, Wayne (2017). Statistical Procedures for the Medical Device Industry. Taylor Enterprises, Inc., www.variation.com

[2] http://www.imdrf.org/docs/ghtf/final/sg3/technical-docs/ghtf-sg3-n99-10-2004-qms-process-guidance-04010.pdf

[3] http://www.quality-on-site.com/get.php?fileid=139

[4] ISO 11607-2:2019 Packaging for terminally sterilized medical devices — Part 2: Validation requirements for forming, sealing and assembly processes

[5] ISO 13485:2016 Medical devices — Quality management systems — Requirements for regulatory purposes

[6] https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=820&showFR=1

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