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Defects Per Million Opportunities (DPMO) is a very powerful metric in understanding the performance of the process. However, following are the pitfalls while using DPMO 1. Calculation of DPMO makes sense only if we have Discrete (Attribute) data. It is difficult to imagine the number of opportunities for a Continuous (Variable) data. E.g. if we are monitoring temperature with an USL of 30. Then what is an opportunity? Defect is easy to tell (temp. going above 30) but determining the opportunity is difficult. Should be each second / minute etc. It is for this reason that for Continuous Data we first calculate the Sigma Level which is then converted to DPMO 2. Even for Discrete Data, DPMO is a metric that could portray a false picture about the process performance. Let's take an example. Number of Units made = 1000 Opportunities for error (OFE) = 10 Total # of Defects = 124 Total # of Defectives = 36 (i.e. all these 124 defects were found in 36 units only). Now, one could calculate the following metrics Defects Per Unit (DPU) = 124/1000 = 0.124 Defective % = 36/1000*100= 3.6% Defects Per Million Opportunities (DPMO) = 124 / (1000*10)*1000000 = 12400 Converting all these numbers to Sigma Level DPU = 0.124; Z (long term) = 1.19 Defective % = 3.6%; Z (long term) = 1.80 DPMO = 12400; Z (long term) = 2.24 It is evident from the above example that for the same process and same numbers, the DPMO provides the best Sigma Level which might be misleading. This is the primary reason that vendor always wants to calculate quality in terms of DPMO while the client always insists on either DPU or Defective %. 3. For DPMO calculation, all defects have same importance. This sometimes becomes a challenge in service industries where some of the defects are considered more critical than others 4. DPMO does not give any indication on the number of units which have defects. It is quite likely that most of the defects could be found in only a handful of units while on the other hand it could also mean that same kind of defect could happen in multiple units. E.g. in my example 124 defects happened only in 36 units. However, these 124 could also happen in 124 units (1 defect in each of the 124 units).

While Amlan, Vastupal and Natwarlal have given good answers, the winner this time is Natwarlal as he has picked the most important elements and explained them in the most lucid manner.

Let’s see an example for DPMO calculation for cinder blocks evaluated on length, breadth and height. Item/Criteria Length Breadth Height Defective # of defects cinder block #1 correct incorrect correct yes 1 cinder block #2 correct incorrect incorrect yes 2 cinder block #3 incorrect correct correct yes 1 cinder block #4 correct correct correct no 0 cinder block #5 correct correct correct no 0 Opportunities/Unit 3 Total Units 5 Total Opportunities 15 Total Defects 4 DPO 0.266667 DPMO 266,667 Area to right 0.27 Area to left 0.73 Sigma level (with 1.5 sigma shift) 2.12 The flaws in using DPMO as metric are obvious, and listed below 1. DPMO/Sigma Level are metrics which can theoretically be used to compare unlike products and processes 2. Complexity of defects can’t be represented with DPMO; not all defects are equal sometimes 3. Defect density is not captured by DPMO; i.e. a needle in haystack OR box of needles in haystack 4. Back calculating DPMO from sigma level, if defects doesn’t follow a normal distribution then sigma level will be overestimated 5. DPMO and PPM are not the same, except if # of opportunities for a defect/unit = 1. These are used interchangeable very often 6. To make a jump from 2 to 3 sigma, DPMO has to be reduced by 241,731 while from 5 to 6 sigma is mere 230 (all with 1.5 sigma shifts). This shows that DPMO is sensitive to tails of distribution which is not always a nice thing. How? a Burr distribution with c=4.873717 and k=6.157568 perfectly resembles a standard normal distribution with mean = 0, sigma = 1, skewness = 0 and kurtosis = 3 but are very difference from DPMO standpoint. i.e. our realization of the ‘true’ distribution of a process will never coincide perfectly with the truth 7. Chasing zero defects in accordance with DPMO, a good process can be made better but not perfect. 8. Over relying on DPMO may give inappropriate approximations of Cpk

Six Sigma: A approach to process improvement which is defect oriented is very popular in many industries like: general Electric, Texas Instrument, Kodak and much more. The main objective to reduce output variability to increase customer satisfaction or we can say that this approach tries to keep specification limit more than six standard deviation in both direction. it means it wants lower defect level or below than 3.4 defects per million opportunities or 3.4 DPMO. Now the question comes when Six Sigma is not called Six Sigma and answer for this is that, when it is used as the Six Sigma Metric and there are various pitfalls of using as a Metric, which are given below; 1. We use a term very often called opportunities to calculate DPMO, even DPMO full form is Defects per million opportunities and if the customer gives a weightage to the opportunities as per their importance , it will be very poor phenomenon for customer satisfaction because their are chances that metric can better and on the other hand customer satisfaction will be worse. for example we are improving one type of defect at the expense of any important one like someone is trying to eliminating 15 unimportant defect and while doing this he is leaving 5 important defects resulting overall improvement of 10 defects , leaving behind a poor customer satisfaction. 2. Every process has its own limitations and while calculating DPMO , it ignores process limitations and it consider only the gap between its existing performance and zero defects, so it fails to consider redesigning of the process. 3. You can play a game very easily with this unless until it is complemented by someone other. for example we are having two different group of experts and we have given them a job to identify the opportunities for the defect and we see that there will be huge difference in their list.