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  • ALL TIME GB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Purvi Gupta

    100

    Del 2019

     

    Bhawana Sethi

    100

    Del 2015

     

    Adyan Prabhakaran

    100

    Hyd 2014

     

    Thirumoorthi.M

    99

    Chn 2019

     

    Sneha Vivek More

    99

    Mum 2019

     

    Sumita Maiti

    99

    Kol 2017

     

    Vidula Valavalkar

    99

    Hyd 2014

     

    Vishal Tillu

    99

    Mum 2014

     

    Yashwanth J G

    99

    Bng 2013

     

    Jyothi Kanuri

    99

    Hyd 2013

     

    Vrajesh Parekh

    99

    Mum 2013

     

    Gnanasekaran D

    99

    Chn 2012

     

    Benoy Ramachandran

    99

    Chn 2012

     

     

    Muthu Naveen S

    99

    Mum 2012

     

    Ketan Trivedi

    99

    Mum 2012

     

    Piyush Mangal

    99

    Del 2011

     

    Sourav Thakur

    99

    Del 2011

     

    Tushar Chaudhari

    99

    Mum 2011

     

    Komal Bansal

    99

    Mum 2011

     

    Parag Suresh Kamble

    99

    Mum 2011

     

    Ritik Gupta

    99

    Pun 2011

     

    Amit Kumar Makkar

    99

    Del 2010

     

    Shaifali Singh

    99

    Del 2010

     

    Clarence Wong

    99

    Hyd 2010

     

    Devendra Singh Baghel

    99

    Hyd 2010

     

    Varun Hemrajani

    99

    Pun 2010

     
         

    Here is the complete list of all time Lean Six Sigma Green Belt Top Scorers

  • ALL TIME BB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Kunal Obhrai

    98

    Del 2019

     

    Mahesh P K

    98

    Bng 2017

     

    Balaji M

    97

    Bng 2017

     

    Rohit Arora

    97

    Bng 2017

     

    Amit Kumar Makkar

    97

    Del 2015

     

    Kanishk Jain

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    Bng 2014

     

    Akshay Khatri

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    Del 2013

     

    Rahul Kumar

    97

    Mum 2013

     

    Sairam Balakrishnan

    97

    Hyd 2011

     

    Ashish Sharma

    96

    Pun 2019

     

    Sunil M. Bhat

    96

    Bng 2017

     

    Rohan Chavali

    96

    Del 2017

     

    Apoorve Arya

    96

    Mum 2014

     

    Sandeep P.R. 

    96

    Chn 2013

     

    Awojide Martins Olabisi

    95

    Mum 2020

     

    Zeshan Abubacker

    95

    Bng 2019

     

    Kumar Kaushal

    95

    Del 2019

     

    Vishal Kanojia

    95

    Hyd 2019

     

    Swati Malhotra

    95

    Mum 2019

     

    Nithin Sandhyala

    95

    Bng 2017

     

    Abhishek Arora

    95

    Del 2017

     

    Satishkumar Jain

    95

    Mum 2017

     

    Atirakshit Bhatt

    95

    Mum 2017

     

    Narendra Anil Murdeshwar

    95

    Pun 2017

     

    Rupinder Kaur Narang

    95

    Del 2016

     

    S Sujay Kumar

    95

    Mum 2016

     

    Kuljinder Kaur

    95

    Del 2015

     

    Vetrivendhan K P

    95

    Bng 2014

     

    Sunil Bissa

    95

    Chn 2013

     

    Mayank Gupta

    95

    Pun 2011

     

    Here is the complete list of all time Lean Six Sigma Black Belt Top Scorers

  • Posts

    • This seemingly simple question has proved to be very challenging.   The statement - "Sigma Level for a process cannot be calculated if there are zero defects" - is only true for Discrete Data. This statement does not hold good for Continuous Data. For Continuous Data, Sigma Level can be calculated (without knowing the actual defect count) and it indicates the probability of getting defects (DPMO). For Discrete Data, we first calculate DPMO / DPU / Yield% (with the actual defect count) and then convert them to Sigma Level. Hence, if defects are 0, there is no Sigma Level for Discrete Data.   While there are a few answers which seemed to be moving in the right direction, however no answer is completely correct. Hence, there are no winners for this question.
    • Q 473. Many organizations claim that lot of effort is being put on lean improvements, however the numbers don't reflect the same. How could Lean Accounting practices help organizations track the lean efforts and their benefits? Elaborate its benefits over traditional accounting practices using an example.      Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday.  When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting.  The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
    • Six Sigma is a data driven approach which aims at reducing the variation in a process. As per the Measure of Central Tendency, mean and SD are the 2 important parameters that helps to characterize a process.             Mean helps to define how far the process is performing from the specification (target), whereas Standard Deviation helps to define the Spread or Variation in the proces. In general, we assume that the process data is normally distributed.            From the Normal distribution curve, the distance between the Process mean and the Specification mean can be measured by counting the number of Standard deviations between them, which is denoted by Z (Sigma level). A 6-Sigma process has the specification limits which are 6 standard deviations from the specification mean (as shown below).   In a process, any value that lies between the Specification mean and the Specification Limits (USL & LSL), is called a defect.               Sigma level can be calculated for both the continuous data and Discrete data available from the process. Continuous Data – anything that can be measured. Eg – Weight, Length, Diameter, Temperature etc. Discrete Data – anything that can be counted or classified. Eg – number of products, number of samples, number of pallets, Classification of Good products & defective products etc.   Sigma Calculation for Continuous Data:   To calculate the Sigma Level for Continuous data, we need to calculate the CpK.                                                           CpK = Min [(USL-Xbar)/3σ, (Xbar-LSL)/3σ] Process Sigma = 3 * CpK   Example: Specification Mean = 50 mm Upper Specification Limit = 50.1 mm Lower Specification Limit – 49.8 mm Standard Deviation = 0.02 mm   CpK = Min [(50.1-50)/(3*0.02), (50-49.8)/(3*0.02)] CpK = Min [1.67,3.33] CpK = 1.67   Process Sigma Level = 3*1.67 = 5   Conclusion: If the number of defects are Zero means, all the measured values are falling within the specification limits. But that doesn’t mean that the Process mean is equal to Specification mean.   The normal curve which we draw for the continuous data is an asymptote – which never touches the X-axis and the area of the curve can never be 100%. Hence it is not possible to calculate the Sigma level for zero defects , in other words, it is practically not a feasible situation to get zero defects, even in a stable and consistent process.   Sigma Level Calculation for Discrete Data: In order to calculate the Sigma Level, the following steps can be followed: 1)      Define the number of Opportunities (O) – which is the number of lowest defects noticeable by the customer 2)      Define the number of Defects (D) - which is the number of values that deviate the specification limits in actual 3)      Calculate Yield = [(D)/((U)*(O))] 4)      Look up for the Sigma Level from the Yield to Sigma Conversion Table.     Example: # Opportunities = 10 # Defects = 5 # Units = 4   DPO = 5/(10*4) = 0.125 DPMO = DPO *1000000 = 0.125*1000000 = 125000   From the chart, we can find the Sigma level = 2.65   We can also calculate the sigma level in excel using the formula below: Sigma Level = -NORMSINV(DPO)+1.5 Sigma level = -NORMSINV(0.125)+1.5 = 2.65   Note : This formula accommodates the 1.5 Sigma shift that happens in any stable process over a longer term.   In this case, lets assume that, number of defects as Zero. # Opportunities = 10 # Defects = 0 # Units = 4   DPO = 0/(4*10) = 0 DPMO = DPO * 1000000 = 0   Conclusion: This usually happens when we collect samples over a shorter period of time or sometime when the samples are biased. This will give a false indication that the process is very good. In fact, this is only possible because we did not have a sufficient sample size. Hence Sigma level cannot be calculated for a process with Zero defects or in other words, zero defect process doesn’t exist on a longer time period and we need to collect sufficient sample size.
    • Yes. It is true .Sigma level for a process can not be calculated if there are ZERO defect.    For Discrete data  & Continuous data    Suppose example of 100 units ,There are ZERO defectives. The Yield will be 100 %. Sigma level overall will be Infinity    Hence Sigma level for a process can not be calculated     
    • The Six Sigma level represents zero defects as 3.4 DPMO. Sigma level 6 and greater is considered generally an excellent process. But true zero defect is not possible. So I agree that Sigma level for a process can’t be calculated if there are zero defects. 1.    Let’s take an example by using Continuous Data: Suppose, we have height data of African country citizens and based on sampling the average height is 6.7 Ft with standard deviation 0.153 and we are interested in Upper limit only and say USL is 8. What is the Sigma Level and whether is it feasible? X Bar – 6.7 s – 0.153 USL – 8 We are Skipping Area 2 as there is no LSL limit Determine Area 2: N/A As highlighted in the circle, Sigma Level couldn’t be calculated as UCL is very far away from the mean means close to zero defects.   99.9996% Yield @ 6 Sigma Level. Hence, 100% pragmatically not possible so as Sigma level also can’t determine.   2.    Let’s take an example by using Discrete Data: Suppose we are HRC (Hot rolled coil) steel manufacturing company and our target to meet or exceed customer expectations. Steel market in India is booming and customer have lots of choices. So management decided to stringent the target which is zero defect. Before processing, they want to check Sigma level at zero defects and also current Sigma Level. No of Unit (N) – 500 Coils Total Defects (D) – 57 Defect opportunities per unit (O) – 3 DPMO = (D*1000000)/(N*O)             = 38000 Sigma Level from Conversion Table – 3.27 If suppose defect is zero than calculate Sigma Level:   Hence proved that if defect is zero, Sigma Level couldn’t be calculated. So Company will look to increase Sigma Level from 3.27 to 4 at the initial stage.    
    • Yes, it can be calculated we can easily calculate it by CPk*3= Sigma level 
    • Sigma level represents number of defects per million opportunities. If there are no defects, then there is no way we can calculate the sigma level for a process.
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