Mayank Gupta

Interesting examples quoted by Anupam and Kirpa Shanker. Anuj has highlighted very good points to address Abilene Paradox. The winner for this answer is Anupam Goswami for quoting an example and also suggesting multiple methods for addressing it. P.S. Sometimes people confuse it with groupthink. However both are different. Read more about groupthink at the following link https://www.benchmarksixsigma.com/forum/topic/35899-avoiding-groupthink/

Hello Ruchika Please refer to the below link https://www.benchmarksixsigma.com/lean-six-sigma-case-studies/

Many participants tried to answer this most basic and fundamental question. However, except for one answer all other answers were either incorrect or failed in plagiarism. There are no winners for this question. I have put a detailed explanation and the correct answer below. Kindly review. Let's first look at a Bell Curve (or a Normal Distribution). In this case, Mean = 21 and standard deviation = 1. Bell curve has a lot of useful properties, however, I am going to focus on the property that talks about the spread of data. - If you move 1 standard deviation away from the mean on both the sides, then 68.27% of points will get covered - If you move 2 standard deviation away from the mean on both the sides, then 95.45% of points will get covered - If you move 3 standard deviation away from the mean on both the sides, then 99.73% of points will get covered Many practitioners feel that above graph depicts a Six Sigma performance. This is probably one of the most common misconceptions in the industry. The above graph only depicts one property of a Bell Curve. Infact, we can extend this property to 4 / 5/ 6 standard deviations away from the mean on both the sides. Just for example, below is how it looks for 4 standard deviations away from mean on both the sides. To visualize a Six Sigma performance, we will need to also know the specification limit. Let's fix the Upper Specification Limit at 24. Sigma level is the number of standard deviations that can fit between the mean and the specification limit. With the current details, this process is operating at 3 Sigma. Below is how you will visualize this process. Now let's visualize a Six Sigma process. Specification Limit cannot change, hence it will remain 24. We will have to improve the process by reducing the standard deviation. Let's assume that we reduce the standard deviation from 1 to 0.5 Now 6 standard deviations can fit between mean and specification limit. Below is how you visualize Six Sigma performance. Even in the improved process, the properties of normal distribution still hold good. Just for ex. - If you move 3 standard deviation away from the mean on both the sides, then 99.73% of points will get covered To conclude, If you move 3 standard deviation away from the mean on both the sides, then 99.73% of points will get covered - this is a property of bell curve and it holds good for all normal distributions For checking the Sigma Level, one needs to know the specification limit. And Sigma Level is the number of standard deviations between mean and specification limit. Hence not all bell curves are performing at Six Sigma Level (it depends on the position of Mean and Specification Limit).

Both the published answers are correct and hence both have been selected as the winners! Congratulations to both!

There are no correct answers. Below is the answer which I was looking for - 1.5 Sigma shift is used for Discrete data and not for Continuous data. For Continuous data, we need the mean and standard deviation to calculate Sigma Level. For Discrete data, we first calculate the DPMO or DPU or Yield and then we convert them to Sigma Level. Short term capability is typically for a rational sub-group and for continuous data we can independently determine the mean and standard deviation for sub-groups. Hence we don't need the approximation. For Discrete data, the # of defects or defectives could be zero for a rational sub-group and in such cases Sigma Level cannot be determined. Hence, we have to use the 1.5 Sigma shift approx.

It is a little sad to know that none of the respondents even remotely tried to answer the question The question specifically asked about the applicability of nelson rules for a six sigma process. Well, the correct answer is that if a process is operating at six sigma level, then only the first rule is sufficient. I leave the reasoning part up to the readers

Interesting answers to a seemingly simple question. The best answer has been provided by Himanshu Sharma.

Very interesting question and all the responses are detailed. The best answer has been provided by Gulshan.

Himanshu Sharma has given a very unique explanation of the 1.5 Sigma Shift and hence his answer has been selected as the winner. My 2 cents 1. Another reason for Sigma Shift is the fact that we typically do not control all factors of a process. We could keep a handful in control however the others might go out of control and the gradual shift 2. Concept of Sigma Level makes perfect intuitive sense, however do not go by the number 1.5. It was what Motorola observed. It is very likely that you might observe a completely different number. So go on, observe your own data and identify your number

Excellent answer from Rahul Arora. His answer explains the contradictions along with examples and hence his answer has been selected as the winning answer.

Rahul Arora has provided the winning answer to this question. He has explained the concept, its comparison with p-value along with how it is measured in a concise and effective manner.

This was comparatively an easy one to answer. There are two answers which stand out - Saurabh Dhaked and Rahul Arora - for the example quoted and the clarity of explanation respectively. Hence both the answers have been selected as the best answers. Well done!

While all published answers address the question, There is one answer that also adds the details for the daily stand up meeting. Hence Dr. Babita's answer has been selected as the winner.

It was a difficult one to choose. All the answers are correct and explain the various uses of a heatmap. Dimple Tiwari's answer has been selected as the winner as she has provided the most varied use cases of a heatmap. Well done!

Excellent answers from all respondents. If was a tough one to choose from. However, given the examples, structure of the answer and a clear comparison between bowtie and FMEA - Mohamed Asif's answer has been selected as the winner. All answers are a must read to get a good understanding of the tool!

Some of the responses are for Tree Diagram. The question was on Treemap and not on Tree Diagram Dimple's answer has been selected as the best answer. Congratulations! Rahul's answer is also a must read.

While all published answers are correct and with relevant examples, Gulshan's answer also highlights the benefits of identifying the relations in project management. Hence his answer has been selected as the winning answer.

There are 2 winners for this question - Dimple Tiwari and Priyanka Bose. Congratulations to both! In case you are an Excel fan and want to create a Tornado chart in Excel, do check out the answer from Gulshan

Amongst the published answers M Vijayakumar Elangovan's answer has been selected as the best answer as he has captured the essence of the three financial metrics in terms of its components.

Very detailed answers to this question. The best answer has bee provided by Anjali Nair. Answers from Ashish Kumar Sharma, Dimple Tiwari, M V Ramana are also a must read!

All published answers are correct. Many answers have provided detailed step by step methods of creating a Waterfall chart in various applications - power point, excel, google sheets etc. One part of question was to identify areas where it can be used in business excellence. There are two answers that have tried to answer that part of the question - Prashanth Datta and Rahul Arora. Between the two, Rahul's answer has been selected as winner. One must read responses from - Prashanth Datta, Anjali Nair, Chandra Sekhar Achyutuni. Great answers from them as well.

Interesting answers from all participants. Structured experiments are always better than trial and error. Situations where Trial and Error could potentially be used are a. Cost is a big constraint and hence structured experimentation is not possible b. Complexity is high and all factors cannot be controlled There are 2 solutions which hinted towards these concepts - Ashish Kumar Sharma and Godwin Thomas. Hence they are the joint winners for this question

Both appear to be same. Purely going by the name, Correction of Error seems to be focused more on Corrective Action. Whereas in CAPA, we first take the Corrective Action then also focus on preventive action so that the error does not re-occur. Important thing - post corrective action, always think about preventive actions.

Simulations are covered in the BME (Business Modelling Expert) competency of Master Black Belt program.

Hello Senthilnathan What is it that you are referring to by the term COE?