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Benchmark Six Sigma Expert View by Venugopal R People who are not trained or exposed to the principles of Control charts often find it difficult to understand the significance of the Control limits and their interpretation. A good understanding of control charts starts with understanding data types. Then one has to understand some probability theory. Then the principle of Normal distribution. Good clarity on Special and Common causes. Then preferably an insight into Central Limit Theorem. Such a foundation prepares a person to have a good grasp of the underlying principles of Control Charts, their different types and application of each type of chart and so on. Even with all this understanding, usually the control charts are used for the observing any points falling outside the control limits, though there are 8 rules defined to observe statistical instability. There still remains the confusion in the minds of some as to how the Control limits differ from the specification limits and some are not comfortable with out including the specification limits also on the control charts. The run charts are much simpler and their understanding and interpretation do not require the extent of subject knowledge as above. Run charts do not have ‘Control limits’ much to the relief of those who had discomfort with control limits of control charts. Those who use Minitab to create run charts would have seen the chart has p values pertaining to the types of instability viz. Mixtures, Clusters, Oscillations and Trends. I am not explaining these terms here, since I am sure many respondents will do a good job there. However, if we go through the rules to detect instability as per the control chart, we can see that not only the four terms that are used for run charts are well covered by those rules, but additional ones as well. One may choose to use Run charts or Control charts depending upon the situation and the ease of comprehension by stakeholders involved. In many instances, some of the instability observations will be quite evident on a run chart and one may proceed by taking decisions for improvement.

In DMADV, focus is on new product/service design, unlike for existing product/service in DMAIC, during the last phase of DMADV, verification of design is performed and whether the design is capable of meeting needs of the customer is validated. Numerous pilot runs will be required to validate and verify the design outcomes. Major aspect of this phase to check whether all metrics which are designed are performing as expected. Conformance to Specification. Some of the common used tools in verify phase includes Control charts, control plans, Flagging, Poka Yoke, check sheets, SOP’s and work instructions. Software Application Design: In a new design viewpoint, Verification is whether Software Application developed in right way & Validation is whether Right Software Application is being produced In simple terms, verification is checking whether the application works perfectly without any errors/bugs and validation is checking whether the application is meeting the requirement and expectation Verification Validation Application and design review, code walk through, code inspection Black Box and White box testing It is static testing It is dynamic testing Performed first Usually performed post verification Verification done without software execution Validation done with software execution Automotive Manufacturing: Reference to a gearbox manufacturing, as per the new design in DMADV process, in actual manufacturing high level steps include preforming, annealing, machining, producing teeth, shaving, grinding and inspection. Here verification is, comparing the gearbox to design requirement of material, dimension, tolerance etc., that is all specs are verified Whereas, in validation, post inspection assembling gearbox and doing a dry run, test it to check whether it runs as expected. Verification Validation Done during development, review and inspection, production and scaleup Usually done before scaleup and after the actual production Random inspection can be done for verification Stringent checks are done during validation Validation can be done directly by skipping verification in some scenarios, especially when we are not able to measure component outcomes or when cost of verification is very high. Medical Devices: Verification usually done on the design: design input, process and the output. It is done by test, inspections and analysis. Validation is checking whether the intended need of the medical device is met Source: U.S. Food and Drug Administration (FDA)

Dear Ransingh A very good question and the link shared by VK will help you visualize how CLT works. I want to highlight a common misconception about Central Limit Theorem. It is probably one of the most misunderstood concepts in Lean Six Sigma. Most of the people assume that if they have a large sample size (read greater than 30), then the data set follows normal distribution. This is far from truth. Irrespective of the sample size, the sample will always follow the distribution of the original data set. So if the original data set is Not Normal, then the sample (be it size 1 or 2 or 10 or 30 or 100 or however big) will also be Not Normal. Then where does CLT apply? CLT applies on the distribution of the sample means or sample sums i.e. if i pick up multiple samples from the Not Normal data set, calculate either the sum or the mean of all the samples and plot them on a histogram, then it will follow a Normal distribution. For e.g. consider a roll of a single dice. Possible values are 1,2,3,4,5,6 each having the same probability. A common misconception would be if i roll the dice multiple times (say 6000) times, I will get a normal distribution. This is not true. Roll of a dice follows a Uniform distribution and hence if you roll it 6000 times, it is likely that 1 through 6 will occur 1000 times each. However, what happens if 2 dice are rolled and sum of each roll is noted. The possible values are 2,3,4,5,6,7,8,9,10,11 and 12. Here however the probability is not the same. Prob. of getting 2 = 1/36 (only 1 combination will give 2) Prob. of getting 3 = 2/36 (2 combinations will give us 3) Prob. of getting 4 = 3/36 (3 combinations will give us 4) and so on...... 7 has the maximum probability (6/36) of occurrence while 2 and 12 have the least (1/36). Now, if I roll the 2 dice for 6000 times and plot the sums of each roll on a histogram, the plot will start resembling a normal distribution because of the variation in the probabilities of each number. Here if you notice closely, 1. The original distribution is Not Normal 2. Taking 2 data points from the original data set will give me a sample (equivalent to rolling of 2 dice). Then for each sample, the sum is being calculated and plotted 3. CLT is being applied on the sum and not on the individual data points The same is evident in the animation link shared by VK. So let's be aware of the misuse of this theorem and apply it correctly. P.S. there are multiple online sources where you can also find the mathematical proof of the the Central Limit Theorem.