Aruna Rao
HPM 850
STATISTICAL PROCESS CONTROL CHARTS AND ANALYSIS
The data I am analyzing refers to the number of patients admitted into a hospital on a weekly basis and out of the total number of patients admitted some were missing wristbands. 1. Control Chart that is most appropriate for this data set: * I used the u-chart to analyze the data because it is discrete data with unequal sample size. 2. The U-chart below shows that during weeks 2-3 there was a sharp spike in missing wrist bands, and again midweek between weeks 4-5 there is a sharp spike in missing wrist bands. In fact two of the incidents occurred outside the ±3 standard deviations. These are also known as non-conformity points. * If the processes were stable, 99.73% of all data will fall within ±3 standard deviations of the mean. * Traditional research normally uses large data sets, however with SPC smaller samples can be used to test that a single sample value is different from the other variations in the series of data points. 3. Control Chart Constructed Below using Quality Improvement Plug-in into Excel. * The first chart shows the two values in the sample that fall outside the upper control limit. These points to a problem with regards to the missing wristband. The data points were calculated as follows: i. Uj = countjnj where uj are number of defects per sample size nj ii. u= j=1m(count)jm
Where u is the centerline iii. UCL = u+3unj
Where UCL is the Upper Control Limit iv. LCL = MAX 0, u-3unj
Where LCL is the Lower Control Limit

The second chart shows the process improvement that was administered and the resulting improvement in each sample values. 4. What does the U-Control Chart tell us? * It monitors process variation due to fluctuations in the missing wrist band per week. It is also useful in determining how many items are not conforming or “defective” per patient day. (In this case Defective = Missing Wrist Bands)