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Rupinder N

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Rupinder N last won the day on August 18

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About Rupinder N

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    Rupinder Narang
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    Principal Consultant
  1. Coefficient of variance can be used to compare the spread of two different populations, with values varying over different ranges. Being a unitless value it finds application in several areas as highlighted in several answers. The biggest drawback is that it cannot be used when the mean of a sample is zero. The three best answers are for Kavitha, Rajesh and Mohan. The chosen best answer is Kavitha's which outlines several examples in addition to the pros and cons.
  2. Specification Limits

    The twist to this question definitely made it very interesting. It was great going through all the responses and a lot of creativity shows through in the answers. The best answers are those of Anirudh Kund, Rajesh Chakrabarty, Venugopal R and Mohan PB. It was rather tough to choose the best answer to this question as everyone has brought out one or the other unique way to act in this situation. The best answer amongst these 4 is that of Anirudh as it mentions many of the pertinent questions that can be asked in such a situation. The rest of the answers are worth giving a thorough read, too.
  3. ARMI / RACI

    ARMI provides information on the roles of the key stakeholders and how they may evolve during the course of a project. Whereas, RACI provides role assignment at the level of each task, hence, providing more information and clarity to execute a job well. The three best answers are that of Sumanta Das, Sandhya Kamath and R Rajesh. Sumanta's answer is the chosen best answer because of the sheer detail and how it has been broken out into palatable pieces.
  4. Rolled Throughput Yield

    RTY is the percent good in a process with sub-steps. Hence, when RTY is 100%, it is an indication of "effectiveness" and not "efficiency". This effectively means that even though a process may attain higher values of RTY, it may not necessarily be efficient. Keeping the differentiation between effectiveness and efficiency in mind, the three best answers are those of Mohan PB, Venugopal R and Ronaaq. Mohan's answer is the chosen best answer owing to the apt and crisp verbiage, and the multiple examples provided.
  5. This was a tricky question and the answer is that exact reversal of the same statement is not possible. Hence, one situation's null hypothesis, in the same form, cannot be alternate hypothesis in another situation. Keeping this in consideration, Ronaaq's answer has been chosen as the best answer - owing to the fact that this is the only answer that says so. Close second is Venugopal's answer in which he has indicated so with the help of an example. A big applause to everyone who attempted this question as it undoubtedly was a tough one.
  6. Process Maturity

    This is a question that fetched us a lot of similar answers, especially about the definitions of maturity levels. The similarity in answers made it specifically difficult to choose the three best ones. Raghavendra Rao answered all parts of the question asked and also articulated the answer in his own words, though it could have been structured better to further improve the quality of the response. Venugopal R has mentioned how assessing process maturity and not rolling back is the way to go. Not verbose at all, very well structured. Rajesh Chakrabarty has clearly outlined how a process evolves and matures and also shared a tabular summary of maturity levels. This is the chosen best answer to the process maturity question.
  7. Continuous data

    This question again challenged us to think beyond what we always stress upon - collecting continuous data wherever possible.Kudos to everyone who made an attempt. Great examples provided by Venugopal, Mohan PB, Kavitha and R Rajesh. The chosen best answer is that of Kavitha's as she provides a direct comparison of examples where we have continuous data but still choose to collect discrete data.
  8. Statistical Significance

    It is heartening to see that the complexity of the topic did not deter so many of us from taking a stab at the answer. That shows the true spirit of excellence! There were two parts to this question, and that was the tricky part. Not all attempts answered both parts - the meaning of statistically significant and practical application. Needless to say, articulation in your own words is a key, too. The answer which was complete in all respects was by Mohan PB. Rajesh Gadgil and Venugopal R made a very good attempt too. Cheers to everyone who contributed.
  9. VOC, Voice of customer

    We have the winning answer for this question. Thank you all for taking out the time to contribute to the World's Biggest Dictionary, during the festive season. The best answer is given by Venugopal R of Club 46. This answer clearly explains, with the help of examples, where over emphasis on VOC could cause more harm to the business than benefits. There were other answers that were close, but examples and articulation in your own words is always going to get some brownie points. Cheers to excellence!
  10. Hypothesis Testing

    Hypothesis Testing is among the most powerful tools used in Business Excellence. It takes away the decisions based on gut feeling or experience or common sense e.g. Site A has better performance than Site B, we should hire more experienced employees as their accuracy is higher, it takes lesser time if we use System A vs System B, older customers are less likely to use self-help as compared to other age groups, are we meeting the cut-off defective %age or not, based on the proportion defectives we see. Hypothesis testing allows to collect valid sample sizes and make decisions for population - it keeps the gut feeling and statements such as "in our experience" out of the picture. You have statistical proof of whatever you "feel" or "think" is right. What must be kept in mind is that it is an OFAT testing technique - only one factor under consideration can be varied while all other Xs must be maintained constant. Hypothesis Testing can be used in any and every phase of the DMAIC cycle. Define - Usually all "1" tests or tests where we compare a population to an external standard are used in this phase e.g. 1 proportion test (if I have x out of y defects, am I meeting the client quality target of 95%?), 1 Sample Z, 1 Sample T, 1 Sample Sign etc. (Has the cost of living gone up as compared to the mean or median cost 10 years ago?). It helps us decide "do we even have a problem". Measure - One can look at data and the eye can catch a "trend". But can we really say that the performance has dipped, is the difference in performance statistically significant. Hypothesis testing can give you the answer. Analyze - this hardly needs any explanation as everyone has using hypothesis testing extensively in this phase to compare two populations or multiple populations e.g. do the five swimming schools create the same proportion of champions out of all enrolled in them, is the lead time for a process on machine A better than machine B, does Raw Material X give better quality than Raw Material Y, does Training Methodology 1 give better results as compared to Methodology 2, 3 and 4, does Vendor A have fewer billing discrepancies than Vendor B etc. Improve - tests involving two populations are generally used. E.g. comparing Y pre and post solution implementation (we implemented a solution to improve the yield of a machine). Is the post-solution yield higher than pre-solution yield, is the TAT post solution better than the TAT before implementing the solution, are more customers buying our product than before etc. Control - We get different CTQ numbers every month post we made an initial improvement. Can we really say that we have improved as compared to before? For 5 months after improve, if we saw a lower number for the metric, was that really different than other months. Can we say that we are consistent? We can use Hypothesis testing again. Business Excellence is nothing but an iterative process to drive excellence throughout the business. As Hypothesis Testing helps us validate or invalidate what we suspect every step of the way in the DMAIC cycle, it is a "must use" tool for the armor.
  11. Root Cause Analysis

    Necessary - X is a must for Y to occur. Y cannot occur unless X is present. Sufficient - X is enough to cause Y. However, Z may also cause Y. Scenario 1 - Cause is necessary but not sufficient. X occured at some time for Y to have occured but alongside other factors. In this case, other causes that could have caused Y when combined X have to be found. E.g. there was a case of cars catching fire if hit from behind when the right indicator was on. Having the right indicator on was necessary but not sufficient for a car to catch fire. It had to be combined with the other factor of being hit from behind in order for it to catch fire. Hence, we are looking for critical combinations of other causes with this X. 2. Cause is sufficient but not necessary - means that X on it's own can cause Y. But this is not the only cause leading to Y. It is required in this case to make sure that other causes are also found out, else the problem may remain unresolved even when X is fixed. E.g. not having enough water in a day can cause headaches. But so can not eating on time. Even if you keep having water, but not having food could.still trigger the headaches. 3. Neither sufficient nor necessary - Even if X happens, Y will not occur. In this case this cannot.be deemed as a root cause. Solving for this X will be futile. Other causes ought to be explored in order for the problem to be solved. E.g. an executive assistant not having an app for calling a cab for her boss is neither a reason sufficient to not get a cab, nor is it necessary. A cab can still be called via a phone call, by asking someone else to order, or booked through a website, by hailing from the street. 4. Both sufficient and necessary - must be solved for as whenever X occurs Y will occur. If this is not solved, you have not resolved the problem
  12. Six Sigma & Release Management

    Dear Abhishek, As a Change Analyst, the tools mentioned above by VK may be helpful in your role, too. As a System Administrator, you may be responsible for the installation, support and upkeep of servers or systems. You may work on projects that involve fixing reasons for frequent breakdowns in a certain area. You may work on projects which involve reducing security policy breaches, time taken to resolve any issues/problems, reduction in escalation for resolution etc.
  13. Sample Size Determination

    Dear Krishna, I see that you completed your BB post you asked this question. The BB training would have answered this question for you. However, for everyone's benefit, in order to determine sample size, the following are the pre-requisites: 1) What is the hypothesis being tested (what do you want to ascertain with this sampled data) 2) Level of Significance and Power of the Test In the example that you have provided, what are you trying to test using this data is not clear. If you could provide more clarity, I would be able to guide further.
  14. Preference to Students with Six Sigma Skills

    Dear Forum Member, At the risk of over simplification, companies prefer candidates with Six Sigma knowledge because it tells the companies that these candidates will be able to put a structure to solving any business problem they are faced with. Anyone who has learnt the methodology will look at opportunities to address areas which could lead to business benefits for any organization they work for. Good luck!
  15. Dear Rajiv, I am sure by this time you would have completed this project and many more. Gurshit's direction was good. In this kind of problem, you could start your analysis with time taken to make a call, time taken to make a good call (successful interview) by observation (or time in motion as it is called) and then working out the number of FTE required. If you find that you have too many surveyors, you will need to do an analysis around where the time in the day is being spent. Other data, I believe, such as these lines being used for other purposes could be easily available. This data can be analyzed and reasons eliminated or addressed.