DoE is a third 'Advanced Tool' for problem solving.
Despite all the efforts by specialists in quality and statistics, Design Of Experiments (DOE) is still not applied as widely as it could and should be, because there is a wrong notion that it is too complex. We just need to know how the system, product or process will react if one factor is changed from one level to another level.
We can divide the experimentation process in four phases: setting-up the experiment, executing the tests, analyzing the results and drawing conclusions. We need to use basic rules from DOE to avoid mistakes.
Rule number one: write down the questions you would like to see answered by the experiment. E.g. does the "red tomato" fertilizer increase my tomato harvest by at least 20% in weight?
Rule number two: don’t forget that characteristics that are not part of the study also need to fulfill requirements.
E.g. as a result of changing fertilizer if we have 20 % more tomatoes, but they should not be of bad taste or small size. So at the end of the experiment we need to measure and evaluate these characteristics
Rule number three: make sure to have a reliable measurement system. You must be aware of the importance of the variation introduced by the measurement system and have to keep it at a minimum.
Rule number four: use statistics and statistical principles upfront. If you want to detect small differences the sample sizes increase drastically. For other cases, it can be smaller.
Rule number five: beware of known enemies. E.g. a tree causes shades on some tomato plants but not on the others. We can place half of them in the sun and half of them in the shade. In DOE this is called "blocking". For every known enemy we have to develop a strategy and keep it constant for the test.
Rule number six: beware of unknown enemies. E.g. In a garden, soil composition, effect of wind, ground water levels, etc. may or may not influence the result of our test. So the experiment is set up in such a way that these factors are distributed randomly, by chance. Randomizing within each block can be done by taking three black and three red playing cards, shuffle them and at each test location within the block pick one card. If it is a black card, treat that plant with "tomato lover", if it is a red card he treat it with "the red tomato".
This is a randomization in location, in many industrial tests, randomization in time is needed. This means that the sequence of executing the tests has to be decided by chance within each block.
Rule number seven: beware of what goes on during testing. With industrial there is no end to what can go wrong during testing. In many cases the people performing the tests have not been part of the team that designed it, they have no idea what it is about or sometimes even why it is done. So keep these two golden rules in mind:
1. He who communicates is king
2. Be where it happens when it happens.
Rule number eight: analyse the results statistically to find the mean and the standard deviation of the two types of treatment. Statistically we test the null hypothesis that the means are equal versus the alternative that the difference between the means is larger than the objective. This is done with a t-test.
If the result is positive, Sam would still have to analyze all the other characteristics that need to fulfill minimum requirements.
Rule number nine: present the results graphically. Since not all people involved in the experiment are knowledgeable of statistics, graphical presentation of results is so important in communicating. Actually, in most cases the graphical output will tell the whole story. Only when there is some doubt left, the correct numbers may be needed to take a final decision.
Conclusion
There is no such thing as a "simple" experiment. No matter how simple it may look, you need to take several rules into account if you want to be able to draw correct conclusions out of your tests. Don’t forget that it is equally expensive to run a bad or a good experiment. The only difference is that the good experiment has a return on investment.
- Ref: http://www.improvementandinnovation.com
Despite all the efforts by specialists in quality and statistics, Design Of Experiments (DOE) is still not applied as widely as it could and should be, because there is a wrong notion that it is too complex. We just need to know how the system, product or process will react if one factor is changed from one level to another level.
We can divide the experimentation process in four phases: setting-up the experiment, executing the tests, analyzing the results and drawing conclusions. We need to use basic rules from DOE to avoid mistakes.
Rule number one: write down the questions you would like to see answered by the experiment. E.g. does the "red tomato" fertilizer increase my tomato harvest by at least 20% in weight?
Rule number two: don’t forget that characteristics that are not part of the study also need to fulfill requirements.
E.g. as a result of changing fertilizer if we have 20 % more tomatoes, but they should not be of bad taste or small size. So at the end of the experiment we need to measure and evaluate these characteristics
Rule number three: make sure to have a reliable measurement system. You must be aware of the importance of the variation introduced by the measurement system and have to keep it at a minimum.
Rule number four: use statistics and statistical principles upfront. If you want to detect small differences the sample sizes increase drastically. For other cases, it can be smaller.
Rule number five: beware of known enemies. E.g. a tree causes shades on some tomato plants but not on the others. We can place half of them in the sun and half of them in the shade. In DOE this is called "blocking". For every known enemy we have to develop a strategy and keep it constant for the test.
Rule number six: beware of unknown enemies. E.g. In a garden, soil composition, effect of wind, ground water levels, etc. may or may not influence the result of our test. So the experiment is set up in such a way that these factors are distributed randomly, by chance. Randomizing within each block can be done by taking three black and three red playing cards, shuffle them and at each test location within the block pick one card. If it is a black card, treat that plant with "tomato lover", if it is a red card he treat it with "the red tomato".
This is a randomization in location, in many industrial tests, randomization in time is needed. This means that the sequence of executing the tests has to be decided by chance within each block.
Rule number seven: beware of what goes on during testing. With industrial there is no end to what can go wrong during testing. In many cases the people performing the tests have not been part of the team that designed it, they have no idea what it is about or sometimes even why it is done. So keep these two golden rules in mind:
1. He who communicates is king
2. Be where it happens when it happens.
Rule number eight: analyse the results statistically to find the mean and the standard deviation of the two types of treatment. Statistically we test the null hypothesis that the means are equal versus the alternative that the difference between the means is larger than the objective. This is done with a t-test.
If the result is positive, Sam would still have to analyze all the other characteristics that need to fulfill minimum requirements.
Rule number nine: present the results graphically. Since not all people involved in the experiment are knowledgeable of statistics, graphical presentation of results is so important in communicating. Actually, in most cases the graphical output will tell the whole story. Only when there is some doubt left, the correct numbers may be needed to take a final decision.
Conclusion
There is no such thing as a "simple" experiment. No matter how simple it may look, you need to take several rules into account if you want to be able to draw correct conclusions out of your tests. Don’t forget that it is equally expensive to run a bad or a good experiment. The only difference is that the good experiment has a return on investment.
- Ref: http://www.improvementandinnovation.com
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