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RANSINGH SATYAJIT RAY

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About RANSINGH SATYAJIT RAY

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  • Name
    RANSINGH SATYAJIT RAY
  • Company
    AXALTA COATING SYSTEMS
  • Designation
    DATA ANALYST

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  1. The Process FMEA is used to not only minimise the the risk associated with a process but also define them whenever possible. It helps in identifying the known or potential failure modes and provide follow-up and corrective action before the first production run.The Process FMEA is related to Lean Six Sigma DMAIC in the following ways to reduce variation in the process and generation of waste. 1. Reduces product development time and costs. 2. Help reduces the redundancies in the process. 3. Help identify the significant characteristics 4. It helps in identifying the sequence of tasks that come into play in a process. 5. Identifying errors and facilitate. 6. Helps in defining the corrective action. 7. Helps in knowing the magnitude of failures and their effect.
  2. While managing the quality driven processes and parameters, there is always a question that arise of whether variables affecting the product or service meet the requirement. For this several parameters like Cp, Cpk (When data is fairly normal) and Ppk indices when the data is non normal (after suitable transformation for stable data without out of control data points), Z Scores, DPMO, etc are used. Many of the indices (Cp, Cpk and Ppk) used require a good amount of data (in terms of number of data points per sample) for providing valid result on the other hand with respect to many organisation faced with short production runs for being responsive to customer needs and specialising the product for future demand require a Capability measuring parameter to meet their quality requirements. The Z Score comes handy delivering status of process performance in both the cases. Z Score| LSL= (Individual value (Or Mean) -LSL)/ Process Standard Deviation Z Score| USL= (USL-Individual value (Or Mean))/ Process Standard Deviation By comparing the Z Score with the critical value (at a given alpha level) we check the status of performance.
  3. Root causes are the causes beyond which we cant go. Root cause would be terminated only when we get change in system, process, technology, training, policy. Hence in that case Root cause would refer to cause in those cases like a. technology is missing, ' b. policy doesn't say anything at present, c. training process has not been initiated for something, d. changes/tweak in system are made. Few examples where causes refer to root causes: 1. (changes/tweak in system are made) We could consider causes as root causes when we are performing controlled experiments where we have experimental group and control group with only experimental group being treated with factor of interest to observe its impact. If any changes seen in the experimental group then it could be directly inferred that causes of the changes is the root cause that is the change in the factor of interest. Some of the fields it is used is chemical processing, pharmacology,aeronautics.
  4. Is there any method to calculate OEE (Overall Equipment Effectiveness) based on the data from MTTR (Mean time to repair), MTTF (Mean time to Failure) , MTBF (Mean Time between Failure) in equipment used in non manufacturing utility industries.
  5. Thanks VK, One question does all Equipment Life data should in generally follow Weibull distribution or it is confined to specific product. Any example of its application?
  6. Is reliability and maintainability linked with Six Sigma. Is there any significant application of Weibull distribution.
  7. In the central limit theorem (CLT) establishes that, in some situations, when independent random variables are added, their properly normalised sum tends toward a normal distribution even if the original variables themselves are not normally distributed. The Question is how could this be proven as it looks very intimidating at times. Why is 30 considered the minimum sample size in some forms of statistical analysis? Is there any rationale for this.
  8. Given the skewness and Kurtosis we could predict the shape of a probability distribution. Skewness: The Lack of Symmetry in the probability distribution is called Skewness, A distribution is positive skewed when it has a long tail to the right (Right tail are + skewed) and a distribution is negative skewed if it has a long tail towards left. Further it is also interesting to know that when we check the data points using the Box plot if the mean of the dataset is greater that the median then its negative skewed and when the mean is less than median then its positive skewed. Kurtosis: The sharpness in the probability distribution is referred to as Kurtosis. Flatter curves are PlatyKurtic (-ve Kurtosis) and Sharper curves are (+ve Kurtosis)
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