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Suresh Jayaram

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Suresh Jayaram last won the day on February 19 2019

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About Suresh Jayaram

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  1. Six Sigma is all about reducing variation, reducing defects, and improving customer satisfaction. All businesses are made of processes - hiring process, ordering process, inventory management process, etc. All processes have variation - it is not possible to have a single process with no variation in it. There is bound to be some variation - however small. The key question is - is this variation bigger than what your customers and/or business stakeholders can tolerate? So, identify the key processes, identify the amount of variation you currently see in these processes, indentify t
  2. Hi Shalini, Don't worry - these are advanced topics that are covered in MBB training. Most of the questions here refer to Multiple Linear Regression which is not covered in BB training - only Simple Linear Regression is covered. You should be able to answer 1 and 4 based on BB training. Best Regards, SJ
  3. Dear Shalini, This concept is very similar to Acceptance Sampling. Please refer to the following article: http://www.itl.nist.gov/div898/handbook/pmc/section2/pmc2.htm Best Regards, SJ.
  4. Dear Kiran, Ideal state refers to perfection. For example, if the current Work in Process (WIP) = 1000, the ideal WIP = 0. However, we cannot get to the ideal state within a short period of time. So, people plan for an interim future state usually six months or one year from now. This is called the future state. For this example, maybe the future state could show that we want to reduce WIP from 1000 to 500. After the end of one year, the future state becomes the new current state and we plan for another future state at that point with the goal of getting to the ideal state in the long run.
  5. Dear Kiran/Shalini, Both of you are right. If both are known, then we usually work with the defects and if we drive the defects down the zero, then defective also goes down to zero. However, if we are working with only defectives, some people assume defects = defectives. As Kiran points out, technically, they are different. Please note that there are some practitioners who believe that we should only be working with defectives and not defects because we may artificially claim a good process sigma level if we inflate the number of possible defects in a product/service. Best Regards, SJ
  6. Hi Kiran, If you look at the confidence interval of Cp/Cpk (process capability index), you will find that the confidence interval is pretty wide when the sample size is small. For example, the confidence interval is around +/- 0.4 when the sample size is 30. If you calculate the process capability index as 1.0, it could be as low as 0.6 or as high as 1.4. If you use a smaller number of samples as recommended by the Sequential Test Method, say 15, then the confidence interval would be a lot higher. This would mean that the error in analysis could be a lot higher. A process that is shown to be
  7. Dear Kiran, We need to differentiate between continuous and discrete variables. Let's first look at the discrete case. For example, if we are talking about tossing a coin. The probability of getting a head is 0.5 and the probability of getting a tail is 0.5. The value 0.5 can be referred to as the probability mass function. For continuous variables, the probability mass function is referred to as the probability density function. However, the value of the probability density function does not equal the probability of getting a value in the continuous case. In fact, the probability of exactl
  8. Hi, I am not sure I could comment on the temporary vs. permanent solution with this exercise. You can only state whether there is or is not a statistical difference between the two proportions. For example, proportion of licenses with reconciliation is statistically similar to the proportion of licenses without reconciliation. Statistically, there is a difference between license policy of 60 days vs. 90 days. You will have to determine if the change you detected is practically important and how you can establish a good control plan so that the process will work over the long term. Best Re
  9. Dear Adhiraj, You can calculate incremental costs and claim these as benefits for your project. Cost A: Cost that would impact your organization assuming you did not do this project. Cost B: Cost that would impact your organization assuming you did this project. The difference between A and B would be your project benefits. For example - If you did not do the project, the revenue would increase by 2% and cost by 1% If you did the project, the revenue would increase by 5% and cost by 3%. Cost A: 1% increase in margin Cost B: 2% increase
  10. Dear Satheesh, Regression model is nothing but a relationship between your input(s) and your output. It is primarily used when your input(s) and output are continuous. Typically, we build a linear model between the input(s) and output. If you have one input and one output, we use simple regression of the form Y = m*X + c. Where, X is your input and Y is your output. For your question, if I understand it correctly, you have three inputs, X1, X2, X3, in which case, we would use multiple regression where the model would be: Y = m1*X1 + m2*X2 + m3*X3 + c Once you build a regression model and
  11. Dear Shanka, If you look at the right hand top corner of this website, you will find a search feature. If you search for Lean Six Sigma as the keywords, you will find several articles on this topic. Best Regards, SJ
  12. Dear Shiva, Just wanted to understand why you indicate that a 2-proportion test is not relevant here. Could you not do a 2-proportion test with the first case (90-day policy) and determine how many licenses would be freed (events) vs. total number of licenses (trials). Similarly, you can calculate for the second case (60-day policy) how many licenses would be freed (events) vs. total number of licenses (trials). Using this information, you should be able to use the 2-proportion test and get an appropriate value of P based on the discrete distribution (in this case a Fisher's test). As an a
  13. Dear Anish, There are several approaches to solve any problem. You will have to pick the most appropriate one depending on your situation. Box-Cox transformation is a group of transformations that help with transformation of the data from Non-normal to Normal. This transformation includes, among others, taking the reciprocal of the data set, or taking the square of the data set, or taking the logarithm of the data etc. in order to make it Normal. Box-Cox tries different combinations and then picks the transformation that best makes the data Normal. There is NO guarantee that if you apply the
  14. Dear LR, It is okay to take action when points go beyond the control limits (especially when there is a special cause which causes the points to fall out of the control limits). In order to compute process capability, we compare the process performance to specification limits. The control limits are an indication of process performance. When we talk about specification limits, we implicitly use LSL or USL or both. However, when we calculate DPMO or Sigma level using this approach, we assume any parts that fall outside LSL/USL as defects - which causes some problems as you have correctly ide
  15. Dear SK, These are two different tools within Six Sigma with totally different objectives – even though they do have some similarities. QFD (Quality Function Deployment) is a tool that is used to translate Voice of the Customer (VOC) into design requirements. It basically translates what the customers desire into specific measurable metrics with well defined targets. QFD is one of the primary tools in Design for Six Sigma (DFSS) projects. FDM (Function Deployment Matrix) is a tool that is also called the Cause and Effect Matrix (C&E Matrix) or the XY matrix. This tool can be used to
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