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Benchmark Six Sigma Expert View by Venugopal R One of the important tasks that most of us would have to encounter while working on improvement projects is to establish controls for sustaining our gains. In this context, it is not only important to identify the cause-effect relationship relevant to our problem, but also, prove and implement sustenance measures. Once a cause and effect relationship is established and we have proven the relationship between two variables, we would certainly like to express the association in a best possible manner. To examine whether an established cause-effect relationship should necessarily exhibit strong correlation, let’s look at some examples and think about this question. Correlations that remain valid within a range: Let’s take an example of a compression moulded component. It was proven that the cause for the poor hardness of the moulded component was due to low temperature setting. Once the temperature setting was increased, other parameters being maintained, the required hardness was attained. Both the dependent and independent variables are continuous in nature. In this case if a study is taken up by measuring the hardness levels against various temperature settings, we can certainly expect to see a positive correlation. However, this correlation may not continue beyond a certain range of temperature value. The correlation between the cause and the effect is valid within a certain range of the cause variable and would have an optimal value. Discrete causal variable: Let’s take an example of vehicle fuel mileage. Based on studies, it was established that the type of spark plug used was an important cause for the mileage of the vehicle. In this case we have 3 different types of spark plugs to choose from, thus making the causal variable a discrete one. In a strict sense, we may not be able to establish a co-relation between the proven cause and effect, since we do not have a sets of variable data sets to derive the correlation. However, those interested in deeper research may identify a variable factor within the spark plug that causes the difference and try to establish a correlation to the effect. Discrete variables for both cause and effect: Let us take another example where a login account is not opening and the cause is identified as usage of wrong passcode. Once the right passcode is used, the login works. The variables involved in the effect and cause are both discrete. Is there a way to establish a ‘correlation’? Continuous causal variable and discrete effect: Let us consider a case where the input (causal) variable is continuous and the output (effect) variable is discrete. Consider a drop test for a packed Hardware equipment, where the input variable is the drop height and the output variable is “whether the equipment is damaged or not”. It may not be possible to derive a correlation directly. However, if we can perform multiple tests for each drop height, then the proportion of products getting damaged for different drop heights, within a certain range could show a correlation. Considering the destructive nature of such tests, it may practically be expensive. To sum up, a proven cause-effect relationship establishes an association between the two variables, dependent and independent. However, correlation could be one of the tools to depict this association, but may not be the best applicable tool in all situations. Other tools such as tests of hypothesis, ANOVA, logistic regression etc. may be more appropriate depending on the types of data.

Benchmark Six Sigma Expert View by Venugopal R Hypothesis testing is no doubt a very powerful method for objectively deciding whether we have enough reason to believe that two populations are different. Once we understand the concept of hypothesis testing, one can discover that it has potential to be applied in almost all the phases on DMAIC. However, if we need to look at some of the key reasons why the tool is not patronized to the extent it could be, I would put down the below points, though these may not be exhaustive. 1. A green belt professional can gain adequate proficiency and confidence in the use of TOH only by repeated practice and deep thinking. The few examples used in a GB training are meant to illustrate the tool and its application, but many more examples need to be tried out. 2. From the various examples that are done, the participant needs to relate situations in his / her work area where the type of data used can be comparable. For instance, an example from a manufacturing situation can be compared to one in a services industry as far as the data is concerned. It could be “number of units produced vs number transactions served”. 3. The non-availability of statistical software like Minitab, Sigma Magic or equivalent has been seen as a deterrent. Most participants get trained using a trial version and later they are not equipped with the software. 4. Many a time, the leader (& sponsor) is anxious to implement improvement actions and do not spend adequate time and effort to have baseline data. Once improvement is done, even if they want to compare with the ‘before’ situation, they are constrained due to lack of baseline data. 5. Participants are sometimes unsure of the choice of the tests as applicable to their projects. Hence, they tend to avoid using this tool, in fear of using a wrong test. 6. The sponsors and other senior management leaders may not have the knowledge to appreciate the usage of Test of Hypothesis, which could discourage the GB to try it out, unless strongly supported by a good Blackbelt / Master Black belt. 7. The ability to interpret the results in a “Business language” rather than a “Statistical language” is another important skill for a Project leader to impress the benefits derived by using TOH, and other tools. 8. There may be some instances where the volume of data available could be very large, or the delta is large, to show very obvious differences between populations, which could render the usage of hypothesis testing as redundant. 9. There could be some who would not have gained an acceptance nor belief to the usage of the method and continue to be comfortable with ‘gut feeling’ decisions. There would be many other reasons as well, which I expect other ambassadors to narrate. On the whole, usage of TOH will be improved with more mentor-ship, exercises, making the software available, and getting the senior leadership exposed to appreciate the use and power of such tools.