This case is about Corona Data Analysis (CDA) which processes claims for health insurance. CDA has about 90 employees out of which 75% work in the operations side of the business which is also the side of the business we were hired to consult upon. We were hired by the quality manager, Gordon Yu, who was hired by the CEO, Julieanna Sugumar. Gordon Yu has asked us to analyze the defects per unit, DPU, defects per million units , DPMO, and K-sigma quality levels. He has given us important access to the processing data and the number of errors that were made in 5 different categories including name, ss#, address, claim code, and claim date fields. This information has been given to us for the past 6 months along with the actual number of claims taken during each month. Gordon Yu has also asked for some tips on reaching Six Sigma since his boss doesn’t believe it is a good idea and is a waste of money.
DPU is also known as defects per unit which is calculated very easily by taking the total number of the defects that occurred in a process and dividing it by the total number of possible defects which is basically the amount of records in this case. Therefore to calculate DPU for each month in our case we add all the defects that were made in name, address, ss#, claim code, and claim date then we divided this sum by the total number of records for each month. These calculations are really useful because it gives us an understanding of the rate of defects that we have going on. This leads us to the next point which is DPMO.
DPMO is also known as defects per million opportunities which is calculated by taking the DPU(calculated earlier) and multiplying it by 1,000,000. Calculating DPMO gives us a better understanding of the defects that we have because it takes a bigger picture of what could happen during taking health insurance claims for a year or for more departments. This of course leads to an even bigger picture of K-sigma quality levels.
K-sigma quality levels:
K-sigma quality level is basically defects per million opportunities considering a specific off-set and a desired quality level. The off-set that we have had as specified in the information given to us is a 1.5 standard deviation. We have solved for this by using an excel formula to give us a more precise number. The formula as given in excel was NORMSINV(1-DPU)+1.5 standard deviation. The results for these calculations were between 3.774 and 3.823 which is above 3.4 which is the required amount for six-sigma but significantly lower than 32 which is the required level of 5.5 sigma. This brings us to the reason CDA should pursue six-sigma.
As we were able to see in the calculations above CDA is very close to achieving six-sigma which would be a great benefit for CDA as a cost saver and customer quality service. It is very obvious...