Bala Dr
Members
-
Joined
-
Last visited
-
You mean "AUTOMATION" Yes. Automating a process also can be considered as part of a Green Belt Project. However, the reason for automation must be clear. It should be justified properly by a study of the current (non-automated) process and pointing out the limitations in increasing the process capability and reasoning out how automation can eliminate those limitations.
-
If you can describe any measurement that is being made in the case of any BPO (say a call evaluation process) - that is explain us what is being measured and how it is being measured, then I can help you in explaining how the attribute Gage R&R study will help in determining the reliability of the measurement system.
-
Note: 1. the create Gage R&R study worksheet option is not meant for creating a "sample" data for doing a Gage R&R study, but to help one to list out all the required combinations of the parts and operators and trials and for suggesting a randomized run order 2. the run order can be different each time you try - because it is randomized unless you use the same random seed 3. however the standard order and the corresponding part and operator numbers would remain the same for the given number of parts and operators and trials.
-
The Parts column is for the serial number of the parts that are to be measured. the serial number is arbitrarily chosen. You can select a bunch of parts and give them their serial numbers in any order (say time of production, increasing size, decreasing size or just random) The Operators column gives the serial number (or names) of the operators who carried out the measurements. Standard Order is the systematic order which we get while listing down all the combinations of Operators and parts along with the number of trials to be done. The Run Order is a randomized order in which the measurements have to be carried out in order to eliminate any systematic error from affecting the measurements. the result of the measurements have to be collected and entered into minitab in the Measurement column. (Arranging the table according to run order and collecting the data according to run order will help ensure the correct entry) Now please repeat your question again after taking into account the above explanations of the different data required for the analysis of the Gage R&R study.
-
Dear Prabhu, it is not clear what exactly you wanted to know about the attribute R&R. What is "acceptable" obviously depends on the "cost" of the error. If you are looking at ppm levels of error then nothing less than 100% is acceptable. Even 99% would mean 10000 defects escaping detection out of a million. so, it is left for you to decide, what is "acceptable" Yeah, it is quite obvious that Inspection by Attributes is very very subjective and achieving 100% overall screen effectiveness vs standard is practically impossible. So, the best you can try is go as near to 100% as is practically possible. If it is not possible to improve it beyond say 90% and if the "error" is not tolerable (remember, the question of whether it is tolerable or not is decided from the business point of view and not from statistical point of vew) then you need to figure out alternate ways of measurement - (say implementation of automation, quantification etc...)
-
-
Prabhu, regarding your question about how Cpk is related to the "shift" in the target, may be you could not figure it out from the expression in terms of the USL and the LSL. So, you better use the following formula (which is equivalent to the expression given by me earlier. You can do the maths and find out how these two are equivalent). Cpk = Cp - |m-μ|/3σ Here you know what is Cp already. m is the mean achieved by the process and μ is the target required by specs. σ is the standard deviation observed in the process. Note: The vertical lines around m-μ denote that you must take only the absolute value of the difference. that is m-μ is m is greater and μ-m if m is smaller. I hope the above expression clears your doubt about how Cpk is related to Cp and also how it is related to the "shift" in the mean. See the same equation rewritten below. Cp - Cpk = |m-μ|/3σ Do you see the relationship clearer now?
-
I hope by now you would have understood the following. Cp is the absolute measure of variation in the process ignoring whether the target is actually achieved or not. Cpk is the measure of how close the process was aimed at the target. Now coming back to your question, why look at Cp first and then Cpk - here is the answer. it comes from the primary focus of the six-sigma methodology. The primary objective of a six-sigma practitioner is to reduce variation first. Only After reducing the variation, one must look at the location (achievement of targeted value) The assumption here is that the target can easily be achieved by "shifting" the appropriate control variable which is already known and within the control of the operator. If the variation is too large (say Cp<<1) then of course Cpk will be far too small too.... Then where do you focus??? If you are a six-sigma practitioner, you must first focus on Cp to bring it up to be atleast Cp>1 (note six-sigma requirement is Cp>2). then Cpk can be looked at by appropriately "tuning" the process towards the target. If the process is centered on "target', then Cpk=Cp or Cp-Cpk is close to zero.
-
-
Shimaa, Both the concepts of sigma level and the process capability are related. The formula for sigma level is sigma level = (Upper spec.Limit - Lower Spec.Limit)/(2 * sigma) Thus, the sigma Level = 6 means, the total specification range (USL-LSL) is equal to 12 Sigmas (Standard deviations) The Process Capability is defined through two different formulae Cp and Cpk. The Cp value tells only about variation, using the formula Cp = (USL-LSL)/(6 *Sigma) Thus we can see that Sigma level and Cp are related Sigma Level = Cp * 3 If Cp=1, then Sigma Level =3 & If Cp =2, then Sigma Level = 6 Next, Cpk value tells not only about variation, but also tells us, how close the data is to the target, using the formula Cpk = Minimum of (USL-mean)/(3*Sigma) and (mean-LSL)}/(3*Sigma) Now, we can see that Cp and Cpk will be equal, only if the mean is matching with the target. If the mean deviates from the target then Cpk will be lower than Cp Thus the difference between Cp and Cpk is an indicator for the deviation of the data from the targeted value. Hope that helps.
-
-
Sridhar, If you want to define a single metric to measure the combined improvement of the mean and the standard deviation, you must use the Process Capability measurement. Cpk is the metric which you would look at. Cp would tell you how godd or bad is your variation, and Cpk will tell you how far you are away from the target. Also, please remember, though your business goals might force you to optimize BOTH the mean and the variation at the SAME time, please note, six sigma method will tell you to minimize the variation first and then the mean. And even before you look at variation and mean, please also make sure that your process is under control and that the variation is not caused by any special causes - Clue to find it out.... is look at the outliers in a histogram or a box plot... Hope these hints help you... I realize you had posted it long back - Hopefully, you would get back and check this answer.
-
One way of defining the Six Sigma level is by translating the rejection levels into sigma level. For example saying 3.4 ppm is a six sigma (short-term) level. Even in this case, we will be talking only about a final product and not the whole process of the company. If we talk about the whole company, we need to talk about the variations and wastages at all stages... and there is no way you can combine all into one sigma level number - since not all processes may be sequential or related in a static way. So, I do not support the view of defining a single sigma level for an organization as a whole.
-
Sigma level is defined in terms of the ratio of the customer specs to the standard deviation. Thus it is a relative measure of the variation in process in comparision to the customer requirement. In that sense, it is not right to talk about the Sigma Level of the company. Even if we do, we will actually be talking about the Sigma level of the product characteristic in comparision to the customer expectation. So, in your case also you have to define the crtical customer specific characteristic of your product and then evaluate its variation in relation to the range specified by the customer to evaluation your sigma level. I agree with Vikram Khanna. It is futile to talk about the sigma level of the "company" on the whole. There are too many variables - not all are necessarily measured, nor that all are specified. It is futile and inefficient even if tried....
-
Ravikanth, The test done by Munesh is to test the hypothesis whether - Your 3 month performance is statistically significantly lower compared to your standard of 85% or not. But from your question I understood that you are trying to find out which of the three identified factors (no-support, transfer, connectiviy) is significantly responsible for the lower customer satisfaction. For this you have to look at the detailed data regarding how many customers rated these these factors as responsible for their dissatisfaction. I suppose you would have this data. Please do a frequency analysis of this data first.