Validate an Improvement in Key Quality Characteristic

Let’s say we put in a lot effort to reduce all the variations in process input,  the next question would be how do we know if we have really improve the key quality characteristics.

To begin with, we must be able identify what is the key quality characteristic to the consumer and if the key quality characteristic is measurable.  We cannot manage what we cannot measure (Deming, E.W).  Do not jump into improving process yet before collecting the current performance of the key characteristic.  Once the current performance is known such as yield rate of certain quality attributes or process capability of the quality variables,  then improvement effort can kick start.  This is follow by data collection again to gather data on quality performance index after improvement. 

In relation to our above question,  we will need to compare before and after improvement plan data to validate if there is real improvement.  This mean we will need to check  if there is any real shift either in variation or mean of the process.

Shift in process center and variation

Shift in process center 

We have to test our hypothesis that process output quality had improved using statistical hypothesis testing check if null hypothesis, Ho or alternate hypothesis, Ha is valid.  Ho usually state that there is no change in status quo or there is NO change in process output quality and Ha state there is a change in process output quality.  It is also known as comparative statistics method.  In this technique we can compare the following :-

1. Variable data process center such as mean/median before and after improvement
2. Variable data variation before and after improvement - ANOVA
3. Attribute data mean before and after improvement.

Although statistical computer software had made this technique become simple with a few press of button to get the analyzed results,  unfortunately this technique is not widely used or it is not deployed correctly.  It could be due to :

1. Sample size is not sufficient to detect if there is a shift in the process
2. Sample does NOT represent actual population
3. Data measurement process is not validated or corrected
4. Do not use the correct test
5. Do not know how to interpret the results
6. Do not check whether the data is normally distributed
7. Do not understand the concept of confidence interval which is use to estimate the population attribute in process center and variation.

Statistical comparative methods is a very important technique in decision making such as before making a huge investment to change process.  It is the technique to check whether there is a real improvement being made and couple with statistical process control it can also determine if the improvement is sustainable.  This is especially critical in high volume mass production environment where it is not possible to measure every single output and yet we have to ensure every single piece in whole population is consistently good quality.

You can learn more about SPC and process capability through my courses,  Click on the image below to link you to the course.