When I was working for a multinational corporation as a
supplier quality engineering manager, I
had seen many cases where procurement are
struggling in getting consistent supply from some key component supplier even
though they meet the goal of 1.33 for key parameters. The reason given by the supplier was they
have poor yield rate of less than 90%. In my previous article, we have learnt
that process capability index number actually correspond to potential reject
rate percentage. If the process
capability index is more than 1.00, there should be less than 0.27% reject rate
or more than 99.73% good parts. So by right if supplier reported their
process capability index PK as 1.33 this means they have about 99.99% yield
rate. So where are the gaps?
Since the reject rate is estimated from a sample,
therefore we will not have an exact match; however it should be close such as
less than 0.1% reject rate. There are a
few reasons why the reported process capability index does not match or even
come close to the projected reject rate :-
- Specification which is too wide. The specification derived does not reflect with actual customer requirement, specification tolerance could be too loose. When specification tolerance is too wide it would be very easy to achieve process capability index of 1.33
- Inaccurate quality metrics data. In order to obtain the data to generate a process capability index, we measured the selected quality metrics and the measurement process contributed too much process variation. Inaccurate measurement data will lead to inaccurate process capability index.(Refer to my article dated 28 Sep 2017 on the importance of good measurement process http://www.360qualitymanagement.com/2017/09/importance-of-performing-gage.html)
- Bias Sampling. Sample selected to calculate process capability index is NOT random sample and does not represent the actual population. Almost all suppliers I have worked with had cherry picked parts during new product (NP) trial run stage which could meet the process capability index goal of 1.33. Later in actual production they have high reject rate which could be >10% and have trouble in meeting the delivery schedule
- The sample during NP stage violated the following assumptions for process capability to give a meaningful reject rate. The data is NOT normally distributed. The data is NOT from a stable process which is free from special cause.
Among the 4 reasons given on why there is a mismatch
between Ppk value and reject rate, the
most common reason are related to cherry pick measurement data and the data is
not normally distributed as a result of cherry pick. Therefore validation must done on the process
capability index report provided by process engineering or suppliers:-
- Measurement data gage repeatability and reproducibility - Ensure the measurement data collected is accurate and within the requirement of GR&R goal < 10% to 30%. Request all the raw GR&R measurement data from supplier/process and check the data using statistic software
- Plot a histogram or distribution chart on the measurement data given for at least 30 samples and check the distribution. If you get the distribution pattern other than figure 1, then most probably screened data had been used. This type of data usually does NOT represent the population distribution, therefore process capability index value generated is does not give an accurate projection on the reject rate.
In order to get a meaningful process capability index
value which reflect the actual quality of the process, we must ensure that the sample
must represent the actual population.
Remember that we will never know the true population performance and we
are relying on sample to make a correct inference on the population. This is also the case with process capability
index.
I have recorded a full course on process capability analysis and share with my student for free @ Udemy, You can click on the below image or this link to enroll in the course
This is for limited time only.
