Jump to content
Click here to know about ONLINE Lean Six Sigma Certiifcation ×
  •  

    Banner for Forum (1).png

  • Use this link to inquire about Lean Practitioner and Lean Guide (or other programs) - https://lnkd.in/gXp4usE


    THE OPEN QUESTION on the forum is Design ScorecardThis is part of our TWO QUESTIONS PER WEEK initiative. One question is launched on Tuesday and the other on Friday at 5 PM IST. Best answers are recognized well on this most active Lean Six Sigma forum. (Benchmark Six Sigma Forum ranks first on Google Search based on popularity) 

     

    The most recently answered question and the best response(s) can be seen here - Control Charts with Transformed DataAll questions can be seen here. The entire Dictionary of Business Excellence terms is here.

     

  •  

     

    1. High value Workshops - Our Corporate Workshops offered across the World can be seen here and the Open Enrollment Workshops (can be signed up from anywhere in the world) can be seen here
    2. If you are going through our online practice project, you can view details and updates here
    3. Questions? - For Industry specific questions or for general questions w.r.t Lean Six Sigma (or the broader domain of Business Excellence), please use the forums below. 

     

     

Forums

  1. Discussion Center

    1. We ask and you answer! The best answer wins!

      Questions need to be answered in a limited time and use a specific format. The answers are showcased with the definition of the term in the World's Best Business Excellence Dictionary. 

      5.1k
      posts
    2. You ask application questions here. The best question wins!

      Share an interview question or a real life application question here. Get answers. Rate the best answer. 

      32
      posts
    3. Debate Halls

      Debates on process improvement thoughts, articles, links, news, in addition to views on what scholars & authors say.

       

      debate catapult medieval2 launching.png

      259
      posts
    4. Fun and Trivia

      Jokes, Fun ideas, Idle thoughts - All come here

       

      Fun.png

      140
      posts
    5. Benchmark Six Sigma Cartoon Strip

      Cartoon strip co-created by the community. Submit your ideas based on characters. If approved, the strip shall be published with your name.

      20
      posts
  2. Learning Business Excellence and Lean Six Sigma

    1. 530
      posts
    2. 1.5k
      posts
    3. 1.9k
      posts
    4. Bench and Mark Cartoons

      Bench is on the bench. Mark is on the mark. With a series of cartoons, see how Bench and Mark behave differently in approach towards solving management problems. Learn why Mark is successful when Bench is not. New toon appears every Wednesday. Discussions are open against each cartoon. 

       

       

      Bench and Mark Dual.PNG

       

       

      65
      posts
    5. Debate : For or Against

      Debate area for Excellence Ambassadors 

      201
      posts
  3. Six Sigma Practice Project for Benchmark Participants

    1. Six Sigma Practice Project - all updates

      The story of Six Sigma Practice Projects as it is progressing. Check back here for the latus status. Click on the title to see latest status. 

      8
      posts
    2. Enrolling for Practice Project

      Describes process for enrolling in practice project

      3
      posts
    3. Did not get email from Academy?

      What to do if you do not get an email from Academy after filling registration form within Progressive Group? 

      1
      post
    4. Working with Phases of Practice Project

      Guidance on how to address issues found while working with phases of practice project. 

      1
      post
    5. Registering and participating in webinars

      Guidelines for participating in webinars and accessing the recording later

      1
      post
  4. Know about Benchmark Six Sigma

    1. 2.2k
      posts
  5. Discussions related to Training

    1. 152
      posts
    2. 181
      posts
    3. Master Black Belt and its competencies

      The Business Excellence MBB is globally recognized and carries several competencies within it. You may go for 1 or 2 competencies at a time instead of trying MBB at one go.  

      20
      posts
    4. Refresh your basics on data analysis

      This free basic maths refresher subgroup has been created for Six Sigma enthusiasts. Please feel free to reply with topic specific application examples or queries.

      55
      posts
  6. Benchmark Six Sigma Participants Support Forum

    1. Post Workshops Guidelines

      Includes discussions, success stories, advice, support, tips, and ideas.

      • No posts here yet
    2. Career & Project Discussions

      Jobs available, jobs wanted, online project support

      4
      posts
    3. 4
      posts
  7. Coronavirus Discussions

    1. Coronavirus forecasts

      Forecasts to see if the trend is likely to change and to review how we are performing against Coronavirus

      40
      posts
  • Most Solved

  • Recently Browsing

  • Recent Achievements

  • Member Statistics

    • Total Members
      54,708
    • Most Online
      990

    Newest Member
    Dom
    Joined
  • Forum Statistics

    • Total Topics
      3.2k
    • Total Posts
      16.1k
  • Gallery Statistics

    • Images
      2.5k
    • Comments
      43
    • Albums
      79

  • 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

    97

    Bng 2014

     

    Akshay Khatri

    97

    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

    • Vijay Tomar has provided the best answer to this question by elaborating the IMR chart and also providing an example of working with Control Chart for transformed data.
    • Q 571. What is a Design Scorecard? Elaborate how it can be used in DMADV project? Is there any use case for it in a DMAIC project?   Note for website visitors - This platform hosts two questions per week, one on Tuesday and the other on Friday.  All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/.  To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until the following Tuesday or Friday evening at 5 PM Indian Standard Time, depending on the launch day.  Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved.  If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting.  All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using ChatGPT to find forum answers. This is a risky approach as ChatGPT is error prone as our questions are application questions (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/
    • The assumption of normality in IMR (Individuals and Moving Range) control charts is indeed a common practice. However, it is true that in the real world, data often deviates from a perfect normal distribution. While transformed data can be used to address non-normality to some extent, its usefulness in checking process stability depends on various factors, including the nature of the transformation applied and the specific characteristics of the data.   Transforming data involves applying mathematical operations to the original dataset to achieve a more desirable distribution or meet certain assumptions. Some common transformations include logarithmic, square root, and Box-Cox transformations. These transformations can help make the data more symmetrical and reduce skewness or variability.   To illustrate the usefulness of transformed data for checking process stability, let's consider an example with 20 data points. Suppose we have collected data on the time it takes for a process to complete a certain task in min. Here are the analysis original data points:   AHT1     25          30          32          31          29          27          30          28          33              26          24          28          34          40          45          42          50          38              35          30   First, lets perform the normality test and create an IMR control chart using the original data. Control chart was plotted using these data points and analyze the process stability based on the control limits and the presence of any out-of-control points             Next, let's apply Logarithmic transformation to the data, perform the normality test and create a new control chart. The transformed data points are as follows:       LOG AHT1          1.40       1.48       1.51       1.49       1.46       1.43       1.48              1.45       1.52       1.41       1.38       1.45       1.53       1.60       1.65              1.62       1.70       1.58       1.54       1.48               Interpretaion:   By analyzing both control charts, we can assess the process stability based on the control limits and patterns in the data points. If the data points fall within the control limits, show no significant patterns, and exhibit randomness, the process is considered stable.   In this example, both the raw and transformed data control charts show data points with same pattern. Therefore, we can conclude that there is no change in the process stability, even though the raw data was more skewed than the transformed data. The transformation allowed us to assess the process stability accurately. In this examples , though the moving range suggests process stability, one needs to understand the reason for shift in pattern along with the one data point that failed at point 17 in the individual chart.   The point to remember is that  the appropriateness of the transformation and the resulting interpretation depends on the specific context and understanding of the underlying process. It's always recommended to consult with subject matter experts and consider additional analyses if needed.   In order to utilize an I-MR Chart, there is no mandatory requirement for  the data to be always normal distributed, however   extremely skewed data can cause some unwanted outcome including high false-alarm rates. If data appears skewed, we can investigate to see whether that is an indication of an out of control process or as per expectations for this  type of process, and if expected, we can look for transformation of the data.
    • An I-MR Chart  is a control chart which is used when data is in continuous category and is collected once at a time. It consists of two charts placed one over above, I Chart which is individual chart and MR chart is plotted for moving range which is absolute value of the difference between two consecutive points. Data following normal distribution is an assumption while drawing I-MR chart however in practical or real-world problem data doesn’t follow normal distribution all the time hence Process stability follows major role. I-MR charts are very sensitive to Normality of the data. Non-Normal data if considered as normal data can cause unexpected behaviors including false alarm rates and difficulty in identifying the special cause variation. If data is not normal, it is always advisable to do transformation using Box-Cox or Johnsons transformation to avoid the false alarm and get the right behavior of data for stability and control. A normal distribution may have the value from minus to plus infinity.  In the real-world example this doesn’t occur physically very often.  For example, Cycle time cannot be in negative numbers.   Following is the Example for drawn I-MR charts when data is considered as normal however data is not normal and respective I-MR charts using data transformation: - Cycle time in Minutes: - Sl.No Cycle Time (In Minutes) S.No Cycle Time (In Minutes) S.No Cycle Time (In Minutes) 1 3196 11 267 21 322 2 241 12 302 22 147 3 372 13 518 23 774 4 42 14 554 24 185 5 481 15 566 25 556 6 6081 16 900 26 555 7 131 17 158 27 361 8 26 18 109 28 556 9 1445 19 167 29 898 10 363 20 51 30 170 Table 1 Probability Plot of dats Using Mini-tab Normality test for data in Table 1: - Normality test is done to illustrate whether data is normal or non-normal.   I-MR Charts drawn in Minitab for Table 1 mentioned assuming data following Normal distribution: - The chart clearly illustrates that process is out of control, however out of control points are trigged due to false alarms I-MR Chart drawn in Minitab for Table 1 mentioned after transforming days using Box-Cox transformation: - The Chart clearly illustrates that process is in control, our of control data points mentioned earlier were due to false alarm in wrong assumption of data being normal.        
    • In real world, the data rarely follows a normal distribution. Data is affected by outliers and measurement errors, because of which IMR chart may not be effective in detecting changes, as these charts assume that the data is normal.   In such cases, we can use charts for non normal data or we can transform the data, depending on the scenario and the data type. Data transformation techniques, such as the Box-Cox or Johnson transformation, can help stabilize variances and make the data more symmetric, allowing for more accurate interpretations and applications of control charts.   Data can also be split into rational sub groups and then buliding control charts on each split data.  Checking for process stability using transformed data helps to identify potential problems in a process. By transforming the data, we can highlight any trends or patterns that might not be visible in the raw data. This can help us to identify potential causes of variation. Then corrective actions can be identified to improve the stability of the process and prevent any problems from re-occurring.   Example: A call center will monitor the waiting times in the queue. If the data is not normal due to variability, then the distribution will be a non normal distribution and IMR chart may not accurately detect the actual problem or the trends. If the wait time data can be transformed, then the analysis on the data will be more accurate and will help the call center to take effective decisions.  
    • If the data is not normal and is transformed, then that impacts the accuracy and validity of the control charts if in case they are to be monitored. This makes it important to handle the transformation of data in a more cautious way. There are no particular method of transformation that fits a particular scenario. Different method of transformation have different benefits. Some of the key considerations while choosing transformational techniques are, extent of non-normality prevalent in the data, how easy it is to apply the method, how much better is the interpretability of the data, how well the transformation supports the process performance and other factors. Its worthwhile to give due consideration of non-parametric method before considering the data transformation. Median charts, run charts, Weibull distributions are few methods that fit into non-parametric methods. Data smoothing is an approach that can assist in removal of noise from the data, clustering, binning and regression methods are part of noise removal approach. Data generalization is another approach of data transformation, where low level attributes are transformed to high level attributes. This is suitable in case if there are finite number of values in data that have large number of distinct values. Like in case of data set that has data for city, street, country and state, we can define hierarchy of these attributes by ordering them with respect to their relations. Order would be country, state, city and street. These are couple of customized data transformation as needed for specific scenario.
×
×
  • Create New...