OK, it is not my target to discuss DOE step by step in this post, my key objective today is just to talk about the next step after we got the optimum solution from DOE.

Picture below shows some of the chart that use for DOE analysis; no further discussion about the regression model and charts. Well, now we got the standard optimum solution from DOE, what next then?

Usually, as an engineer, we will just pick up the solution developed from the DOE and carry out trial run with a predefine batch size, we collect the data from small pilot run and compare against the process performance based on previous solution.

Two possible solutions could be draw from the pilot run, either the result showing significant improvement or no significant improvement compare against the previous process. Certainly, we how to have a result that showing significant improvement, on the other hand, we may need to revisit the DOE, to fine tune the process parameter in case the result is not following our wish.

Don’t forget DOE is carry out in a well controlled condition, data collected from DOE, or the regression model form from the DOE can only represent a short term process capability, there are many other influencing factor that exist in the production was not factor in, therefore, it is quite common to see that solution from a DOE model do not really fit to the actual production condition.

Actually, we can counter check or predict the long term process performance with the regression model developed with a set of random data generated with consideration of predefine standard deviation.

For example, I m trying to generate a set of random data with introduction of 5% variation (recommended by the process engineer) and plug it into the regression model developed from the DOE.

Here is the respond distribution with 500 random data of each factor with 5% process variation. Now, I m able to estimate the process capability before the pilot run, performance necessary corrective action before pilot run, therefore lower down the risk of wasting unnecessary resource.

OK, that's all for today...

Picture below shows some of the chart that use for DOE analysis; no further discussion about the regression model and charts. Well, now we got the standard optimum solution from DOE, what next then?

Usually, as an engineer, we will just pick up the solution developed from the DOE and carry out trial run with a predefine batch size, we collect the data from small pilot run and compare against the process performance based on previous solution.

Two possible solutions could be draw from the pilot run, either the result showing significant improvement or no significant improvement compare against the previous process. Certainly, we how to have a result that showing significant improvement, on the other hand, we may need to revisit the DOE, to fine tune the process parameter in case the result is not following our wish.

Don’t forget DOE is carry out in a well controlled condition, data collected from DOE, or the regression model form from the DOE can only represent a short term process capability, there are many other influencing factor that exist in the production was not factor in, therefore, it is quite common to see that solution from a DOE model do not really fit to the actual production condition.

Actually, we can counter check or predict the long term process performance with the regression model developed with a set of random data generated with consideration of predefine standard deviation.

For example, I m trying to generate a set of random data with introduction of 5% variation (recommended by the process engineer) and plug it into the regression model developed from the DOE.

Here is the respond distribution with 500 random data of each factor with 5% process variation. Now, I m able to estimate the process capability before the pilot run, performance necessary corrective action before pilot run, therefore lower down the risk of wasting unnecessary resource.

OK, that's all for today...