I am in a position where I am looking at complaints for different products. It has been tradition to monitor the complaints using a modified p-chart approach with a rolling baseline. The monitoring is done on monthly basis over a 15 month period with 9 months used as a baseline period and 6 months used as a signaling period. The complaints are examined as a rate: complaints/number of units sold in a month. I actually have a few questions about this method:
1. The standard textbook approach to calculating the upper control limit is done by multiplying the standard deviation by 3. What amount of error are we accepting by only using a 9 month baseline period?
2. We have had outside consultants examine our approach, and they suggest using a binomial distribution for calculating an upper control limit. Is this approach appropriate given that our data is continuous and a binomial distribution is discrete?
3. Unfortunately our method is not without limitations. Our number of units sold per month is not a good denominator since it does not tell us how many products are actually in use. It also creates false alarms when sales are volatile. Does anyone have any suggestions of a better way to monitor complaints or to adjust the denominator?
Any insight would greatly be appreciated.