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By not being Mutual Exclusive By not being Collectively Exhaustive By not implementing the findings

Data points being mutually exclusive Adding weightage to attributes Choosing which attributes should participate in pareto

1. They can't be used to calculate variation over any period of time 2. More than one Pareto chart would be required in majority of cases were we need to trace the cause for the errors to its source. 3. Factors outside the scope are not taken into consideration while doing Pareto Analysis

1. Always use 80 - 20 rule but not other combinations 2. Treating the defects as root cause 3. Failing to focus on counter balance metrics

While plotting defects on pareto, severity of defect is ignored and only occurence of it is considered. So if a severe defect does not contribute to 80% band and falls in 20%, there might be chances of it getting unconsidered

Both B and E. strictly speaking detection is for each cause and also can be for the failure mode as the case may be. Correction is not related to the topic. It is understood that once we detect the organization would take actions to correct both product and use this information to increase Occurrence rating for that cause and increase the level of detection to reduce risk as a cycle. The organization should constantly find ways to prevent cause/failure.

D

Dear Dr. Hemant, There are two concepts that relate to your question - defects and defectives. A product or service can have several defects, but when we are looking at defectives either the entire product or service is defective or it is not. Not all defects get translated into defectives. From a human point of view, the following can be considered defects a) breathing too fast or too slow, heart rate too fast or too slow, c) having a temperature, d) Low hemoglobin count etc. Not all of these defects get translated into defectives. A defective could be a dead person! The definition of a defective may change from person to person. Not all of the defects become defectives. While some defects if present could directly translate into being defective. Defectives are something that is important to the customer, while defects are something that the doctor monitors and wants to control so that if all the defects are driven down to zero, the number of defectives will also go down. In Six Sigma, we usually work with defects rather than defectives. So, when people talk about 4 / million, what they are referring to is if there were a million opportunities for making defects, how many were actually made. As an illustration, if we have a doctor who sees 10000 patients in a year and in each patient there are 1000 possibilities for defects. Based on medication, the doctor may be able to control/minimize/eliminate some defects while others are still there. If there are 40 defects at the end of the year, then the overall defects per million opportunities are: DPMO = 40*1000000/(10000*1000) = 4 (4/million) Note that this may not be the same as what the customer would experience as they usually worry about defectives rather than defects.

Dear Shri Khatri, In case somebody sets that goal and the average TAT actually reduces from 5min to 4min with standard deviation being same as before, we can say that there has been an improvement in the average TAT. My views are..... 1. Assuming the USL remains the same (i.e. 4 min), by reducing the average TAT from 5 to 4, the mean of the process has been shifted towards the USL; which means moving closer to the VoC, which implies there has been improvement in the average TAT. 2. By reducing the average TAT of the process from the current 5 min to 4 min, we make the mean coincide with the USL. Let us see the two situations "before" and "after" to draw any conclusion. BEFORE TAT reduction: Z- value = ( 4-5)/2 = -1/2 = -0.5 From a standard normal distribution curve, for z = -0.5, OK Region = 30.85% Defect Region = 69.15% AFTER TAT reduction: Z- value = ( 4-4)/2 = 0/2 = 0.0 From a standard normal distribution curve, for z = 0.0, OK Region = 50.00% Defect Region = 50.00% Since, the OK region has almost IMPROVED by 20%, we can resonably conclude that the average TAT has improved after this process improvement initiative. (Note:I have not addressed specifically the "actual capability" ( Cpk) related points as the question was just about improvement in the "potential capability" of the process per se when shifting the mean without reducing the variation (std.dev) Please let us know your thoughts / comments. Regards, Ari

Dear All, This is an important question. Let us see if someone can come up with the right answer. VK