Why Do We Have Out of Specifications (OOS) and Out of Trend (OOS) Batches


While developing a product, we are bound by the USP/EP/JP monographs for product’s critical quality attributes (CQAs) or by the ICH guidelines and we have seen regular OOT/OOS in commercial batches. It’s fine that, every generic company have developed an expertise in investigating and providing corrective & preventive action (CAPA) for all OOT and OOS, but question that remained in our heart and mind is that,

Why can’t we stop them from occurring? 

Answers lies in following inherent issues at each level of product life cycle,

We assume customer’s specification and process control limits are same thing during the product development.

Let’s assume that USP monograph gives a acceptable assay range of a drug product between 97% to 102%. The product development team immediately start working on the process to meet this specifications. The focus is entirely on developing a process to give a drug product within this range. But we forget that even a 6sigma process has a failure rate of 3.4ppm. Therefore in absence of statistical knowledge, we consider customer’s specification as the target for the product development.

The right approach would be to calculate the required process control limits so that a given proportion of the batches (say 95% or 99%) should be in between customer’s specifications.

Here, I would like to draw an analogy where the customer’s specification like the width of a garage and the process control limits is like the width of the car. The width of the car should be much less than the width of the garage to avoid any scratches. Hence the target process control limits should be narrower for the product development.

For detail see earlier blog on car parking and 6sigma“.

Inadequate statistical knowledge leads to wrong target range  for a given quality parameters during Product development.

Take the above example once again, customer’s specification limit for the assay is 97% to 102% (= garage width) now, the question is, what should be the width of the process (= car’s width) that we need to target during the product development to reduce number of failures during commercialization? But one thing is clear at this point, we can’t take customer’s specification as a target for the product development.

Calculating the target range for the development team

In order to simplify it, I will take the formula for Cp


Where, Cp = process capability, σ = standard deviation of the process, USL & LSL are the upper and lower specification of the customer. The number 1.33 is least desired Cp for a capable process = 3.9 sigma process.

Calculating for σ


Calculating the σ for the above process


Centre of the specification = 99.5 hence the target range of the assay for the product development team is given by

Specification mean ± 3σ

  = 99.5±3×σ = 99.5±1.89 = 97.61 to 101.39

Hence, product development team has to target an assay range of 97.61 to 101.39 instead of targeting the customers specifications.

There is other side of the coin, whatever range we take as a target for development, there is a assumption that 100% of the population would be in between that interval. This is not true because, even a 6 sigma process has a failure rate of 3.4 ppm. So the point I want to make here is that we should also provide a expected failure rate corresponding to the interval that we have chosen to work with.  


For further discussion on this topic, keep vising for the forth coming article on Confidence, prediction and Tolerance intervals

Not Giving Due Respect to the Quality by Design Principle and PAT tools

Companies not having in-house QbD capability can have an excuse but even the companies with QbD capability witness failures during scale-up even though they claim to have used QbD principle. They often think that QbD and DoE are the same thing. For the readers I want to highlight that DoE just a small portion of QbD. There is a sequence of events that constitute QbD and DoE is just on of those events.

I have seen that people will start DoE directly on the process, scientist used to come to me that these are the critical process parameter (CPPs) and ask for DoE plan. These CPPs are selected mostly based on the chemistry knowledge like, moles, temperature, concentration, reaction time etc. Now thing is that, these variables will seldom vary in the plant because warehouse won’t issue you less or more quantity of the raw material and solvents, temperature won’t deviate that much. What we miss is the process related variables like heating and cooling gradient, hold up time of the reaction mass at a particular temperature, work-up time in plant (usually much higher than lab workup time, type of agitator, exothermicity,  waiting time for the analysis and other unit operations. We don’t understand the importance of these at the lab level, but these monsters raises their head during commercialization.

Therefore a proper guidelines is required for conducting a successful QbD studies in the lab (see the forth coming article on DoE). In general if we want a successful QbD then we need to make a dummy batch manufacturing record of the process in the lab and then perform the risk analysis to the whole process for identifying CPPs and CMAs. Brief QbD process is described below


Improper Control Strategy in the Developmental Report

Once the product is developed in the lab, there are some critical process parameters (CPPs) that can affect the CQAs. These CPPs are seldom deliberated in detail by the cross functional team to mitigate the risk by providing adequate manual and engineering control. This is because we are in a hurry to file ANDA/DMF and other reasons. Once the failures become the chronic issue, we take actions. Because of this CPPs vary in the plant resulting n OOS.

Monitoring of CQAs instead of CPPs during commercialization.

I like to call ourselves “knowledgeable sinners”. This because we know that a CQA is affected by the CPPs even then we continue to monitor the CQA instead of CPPs. This is because, if CPPs is under control, then CQA will have to be under control. For example, we know that if reaction temperature shoots, it will lead to impurities, even then we continue to monitor the impurities level using control charts but not the temperature itself. We can ask ourselves what we can achieve by monitoring the impurities after the batch is complete? Answer is we achieve nothing but a failed batch, investigation, loss of raw material/energy/manpower/production time, to summarize we can only do a postmortem of a failed batch and nothing else.

Instead of impurity, if we have monitored the temperature which was critical, we could have taken an corrective action then and there itself. Knowing that this batch is going to fail, we could have terminated the batch thereby saving loss of manpower/energy/production time etc. (imagine a single OOS investigation required at least 5-6 people working for a week, which is equal to 30 man days.


Role of QA is mistaken for Policing and auditing rather than in continuous improvement.

The QA department in all organization is frequently busy with audit preparation! Their main role has got restricted to documentation and keep the facility ready for audits (mostly in the pharmaceutical field). What I feel is that, within the QA there has to be a statistical process control (SPC) group, whose main function is to monitor the processes and suggest the areas of improvements.  This function should have sound knowledge of engineering and SPC so that they can foresee the OOT and OOS by monitoring CPPs on the control charts. So, role of QA is not only policing but also assisting other departments in improving quality. I understand that at present SPC knowledge is very limited among QA and other department, which we need to improve.

Lack of empowerment to the operators for reporting deviation occurred

You all will agree, the best process owner of any product is the shop-floor peoples or the operators but, we seldom give importance to their contribution. The pressure on them is to deliver a given number of batches per month to meet the sales target. Due to this production target, they often don’t report deviations in CPPs because they know if they do it, it will lead to investigation by QA and the batch will be only cleared once the investigation is over. In my opinion, QA should empower operators to report deviations, the punishment should not be there for the batch failure but for not asking for the help. It is fine to miss the target by one or two batch but the knowledge gained from those batches with deviation would improve the process.

Lack of basic statistical knowledge across the technical team (R&D, Production, QA, QC)

I am saying that everyone should become an statistical expert, but at least we can train our people on basic 7QC tools! that is not a rocket science. This will help everyone to monitor and understand the process, shop-floor people can themselves use these tools (or QA  can empower them after training and certification) to plot histogram, control charts etc.. pertaining to the process and can compile the report for QA.

What are Seven QC Tools & How to Remember them?

Other reasons for OOT/OOS are as follows which are self explanatory
  1. Frequent vendor change (quality comes for a price). Someone has to bear the cost of poor quality.
    1. Not linking vendors in your continuous improvement journey. The variation in his raw material can create a havoc in your process.
  2. Focusing on delivery at the cost of preventive maintenance of the hardware’s

 Related Topics

Proposal for Six Sigma Way of Investigating OOT & OOS in Pharmaceutical Products-1

Proposal for Six Sigma Way of Investigating OOT & OOS in Pharmaceutical Products-2



Understanding 6sigma: Example-3 — Problem at a Soap Manufacturing Plant


Commodities products like soaps, detergents, potato chips etc. faces lot of cost pressure. Manufacturer has to ensure right quantity of the product in each pack to ensure his margins (by avoiding packing more quantity) and avoids legal issues from consumer forum (in case if less quantity is found in the pack).

Let’s take this example

A company is in the business of making soaps with a specification of 50-55 Gms/cake. Anything less than 50 Gms may invite litigation from consumer forum and anything beyond 55 Gms would hit their bottom line. They started the manufacturing and found huge variation in the mean weight of the cakes week after week (see figure-1, January-February period). They were taking one batch per week and producing 250000 soap cakes per batch. From each batch they draw a random samples of 100 soaps for weight analysis. Average weight of 100 samples drawn per batch for the month of Jan-Feb is given below.


In order to evaluate the performance of the process, a control chart is plotted with VOP & VOC (see below). Presently it represents the case-I scenario, Figure-6 where VOP is beyond VOC.


They started continuous improvement program to reduce the variability in the process using DMAIC process. They were able to reduce the variability to some extent but still majority of the soap cakes were out of specifications (March-April period, Figure-3). They continued their endeavor and reduced the variability further and for the first time the control limits of the process was within the specification limits (May-June period, Figure-3). At this point their failure rates were reduced as 95% of the soaps would be meeting the specifications.

Continuous Process Improvement
Continuous Process Improvement

We can further reduce the variability to reach the 6 sigma level where the failure rates would be 3.4ppm. But now, we need do a cost benefit analysis as improvement beyond a limit would involve investment. If 5% failure rate is acceptable to the management then we would stop here.


It is not always desirable to achieve 6 sigma level, a 3 sigma process is good enough. But there are cases where human life is involved like passenger aircraft, automobile brakes and airbags, medical devices etc. and in these cases it worth going to 6 sigma and beyond to ensure the safety of the human life.

Understanding 6sigma: Example-2 — Getting Late for the Office


Our office timings is 8:30 AM to 5:00PM. Company requires us to reach the office between 8:00 and 8:30[1] otherwise it will lead to a pay loss of 1 hour if late for two consecutive days.

Following are the arrival time of my new colleague to the office for last 35 days.


What is “Voice of Customer” (VOC) Or the Customer’s Specifications?

Here customer is the company to whom we are providing our services and in return we are getting the salary. Now customer’s requirement is that we should be in the office between 8:00 to 8:30 AM. This is called as voice of customer or customer’s specifications. It can be represented as


USL → Upper specification limits

LSL→Lower specification limits

Customers have right to demand anything in this world, but it is most important for us to recognize the current process capability or what we can deliver?

What is Voice of Process (VOP) or Process Control Limits?

Now we need to understand that the time my colleague took to reach the office is independent of the customer’s requirement. It is a different process by itself but it is desired that the output of this process (arrival time at office) or VOP should comply with the customer’s specifications (VOC).

Now he want to understand the efficiency of his current process (arrival time at office). Simply he wants to know what his routine or the current process can offer if he makes 100 trips to the office?

The statistical calculation shows that on 95 occasions out of 100, he would land in the office between 7:41 AM and 8:54 AM.[2] This is called as lower control limit (LCL) and upper control limit (UCL) of the process respectively. This is also known as voice of the process (VOP).

Overlap of the process efficiency (VOP) and the customer’s specification (VOC) is shown below in figure-3. It is clear from the chart below that the current process is incapable to meet the customer’s specification.


Now what we need to do is to analyze why his process is incapable of meeting customer’s requirement.

Where is the GAP?

It is evident from the figure-3 that the control limits of his process is way beyond the customer’s specifications. In other words there is a huge gap between customer’s expectation and his current process efficiency, it’s the time that he needs to improve his process by optimizing the following variables that can influence his arrival time to the office.

  1. When he slept last night?
  2. Did he had drinks last night?
  3. When he woke-up?
  4. When he started from the home?
  5. How was the traffic in the morning?
  6. How fast he was driving?
  7. Which route he took?

How to Reduce the GAP?

Above mentioned variables were studied and optimized using DMAIC process,this enables him to improve his process so that on 95% of the occasions he would be landing in the office between 8:02 and 8:26 AM, hence he would be complying with the customer’s specifications as shown in figure-4. It is also evident that the control limits of the process is inside the customer’s specifications. But still on 5% of the occasions he would be outside the specification limits. Hence process needs further improvements. If the process is improved to such an extent that there is only 3.4ppm failures then it is called as six sigma process which means that if he make one million trip to office, then I will be late only on 3.4 of the occasions.


Arrival time after continuous improvement



Understanding 6Sigma: Example-1 — Child’s Counter Argument


We have seen the situation from parent’s angle (see earlier blog), now consider student as the customer and parent as supplier and student (or customer) is trying to convince his father by arguing “look dad, all universities with ranking 1 to 10 are same, it is just a statistical rating that is done for attracting students, it changes every year and the universities that you are talking about are best in science, but I want to study law for which a particular university with 6th rank is the best”. As an understanding father (supplier or vendor), he finds the argument too strong to be opposed any further and agrees to his son’s specification i.e. he modifies his current process (expectation). Here Student is setting the specifications (VOC) and father is accepting it (VOP). The process of convincing the father is six-sigma which bridges the gap between them.


Let’s be little philosophical

All of us had a dream during college days that I want to be this, I want to be that. What we did is to provide ourselves with a specification about our future life or VOC. We were also aware about our current capability (VOP) but we never took pain of performing a gap analysis and as a result we couldn’t take appropriate steps to reduce the gap between our desire and our capability, ultimately landing somewhere else in our life. Our desires are still our desires only.

Can we apply six-sigma to build our career? Or at least help our children in doing so?


 Related Blog


Understanding 6Sigma: Example-1– Encouraging your Child for Admission in a Good University!


As a parent you must have had an argument with your child that he should work hard in order to ensure a seat in top 3 universities.
Your child is also good at studies and he is aware of his current capability that he can easily get admission in universities with ranking from 4 to 6 but has to work hard to compete for top 3 universities.
If we consider parent as a customer, then what customer is demanding is the admission in top 3 universities. This is called as “voice of customer” (VOC).

If you consider your child as a supplier, then with his current efforts (current process) he can guarantee admission in the universities with ranking 4-6. This is called as voice of process” (VOP).


There is a gap between what customer wants (VOC) and what your current process (VOP) can deliver. Both are independent processes but it is desired that VOP should match VOC.

Your child understood this and made a plan to fill the gap by making more efforts.

Above methodology of bridging the gap between customer’s specification (VOC) and the current process capability (VOP) is called as 6Sigma and the methodology used is DMAIC.

 Related Blog