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Everything posted by nilesh.ghm

  1. The bullwhip effect is defined as an extreme change in the supply position upstream(near the start of the process) that is generate by a small change in depend downstream(near the customer) in the supply chain. The bullwhip effect The below is an example taken from a case study involving children’s diaper sales of a large conglomerate 1. Data on purchase patterns suggested that there is no clear pattern in purchasing behavior. Parents of very small children, have very different diaper buying patterns and this changes in fairly small increments owing to fact
  2. Given the current competitive environment, mark is well aligned to scale ahead !!!
  3. "Adam Smith was wrong!! Adam Smith was wrong!! The best result will come, when the individual does what's good for himself, AND the group", a phrase by Russel Crowe portraying John Nash in the lovely movie A Beautiful Mind. And those very words describe everything that there is about the Nash Equilibrium. At the outset, Game theory tries to look at how individuals (or a collection of individuals) make choices that will, in turn affect other's choices. Nash Equilibrium makes reference to a condition in which each individual makes an optimized outcome, based on the expected decisions
  4. The median is used as an indicator of central tendency when there is high skewness or asymmetry in the underlying data. Comparison to the mean, which is dynamic, the median is completely static Typical examples of datasets where the median is preferred are include salaries, real estate prices, etc. Having the above, it is observed that the median is quite easily jumped upon as a preferred method of evaluation, especially when data is found to be not-normal, considering that it is more robust and makes one use the distantly related concepts of non-parametric tests. I would like
  5. I would prefer in the below order 1. Resolution Resolution represents the measurement system’s capability to detect and indicate small changes in the characteristic measured. Resolution is also known as discrimination. E.g. a tape measure with gradations in cm cannot be distinguish between measurements lesser than 1 cm, like 2mm say. If an instrument is not able to measure the required attribute in the first place, there is no point in proceeding 2. Bias Bias can be defined as the difference between the mean or the expected results (say of a s
  6. The Will Roger's phenomenon, is a phenomenon which revolves around increasing "averages". It was made popular owing to the comedian Will Roger: When people of one geographical state left the state and moved to another geographical state, this had an effect of raising the average intelligence levels in either of these states. In the medical field(s), particularly in cancer research, such a phenomena is used in a way that any new clinical investigation includes more accurate cancer staging data than previous data; this results in a spurious, apparent increase in survival rates by sta
  7. Although Mark's approach seems to fit perfectly with a well seasoned Six Sigma professional's way of thinking, it also sounds a bit like having a hammer and everything appearing to be a high protruding nail. While I may not completely disagree with Mark, I would say that a few times, we really need to evaluate the impact of Mark's approach. Sometimes, mature process, once thoroughly evaluated, can be allowed to "let go"
  8. All three parameters revolve and compare around the “Interest Rate”, which is a way of expressing the risk free expected gains. Note the below example: Assume that we have a risk free interest rate of 15%, and we seek to have Rs. 100 at the end of the year. If we seek to have Rs. 100 at the end of the year (owing to say, the project being completed), and if we assume the risk free rate above 15%, we would have to invest (100/1.15) = Rs. 86.95 (at the start of the year) The difference of the values 100 – 86.95 = 13. 05 is the NPV (Net Present Value)
  9. I would choose the second one, mainly owing to the statistical power I desire. Statistical power ie. the probability of rejecting an incorrect null hypothesis is usually understood as (1-beta), depends on the alternative hypothesis being true !! Ways to increase such statistical power could depend on: 1. The direction of the hypothesis: uni-directional hypothesis would be more stronger as it would concentrated only on one side of the curve 2. The Alpha level I choose: I may get more statistical power if I choose a "relaxed" alpha (eg. 0.1, which may be suitable for some experim
  10. I guess the biggest concern would be data privacy. However, there are a few below which may be relevant: 1. Design Details of the system 2. Improper testing 3. Malware / Virus Attack
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