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62 August 31, 2006 2:55 Fortifying Six Sigma with OR/MS Tools

40
35
30
Total Waiting Times (min) 25
20
15
10 91.5
5 90.0 View B
0 88.5
8 87.0 Arrival Rates (patients/hour)
9 10 85.5
View A 11
No. of Dispensing Pharmacists

44.0 44.0
85 prescriptions/hour 86 prescriptions/hour 8 Dispensing Pharmacists 9 Dispensing Pharmacists
39.0 87 prescriptions/hour 88 prescriptions/hour 39.0
89 prescriptions/hour 90 prescriptions/hour 34.0 10 Dispensing Pharmacists 11 Dispensing Pharmacists
Waiting Times 29.0 Waiting Times 29.0
91 prescriptions/hour
34.0
24.0
24.0
19.0
14.0
14.0 19.0
9.0 9.0
8 9 10 11 85 86 87 88 89 90 91
No. of Dispensing Pharmacists Mean Arrival Rates
View A View B
Figure 5.4 Sensitivity of total waiting times to variations in number dispensing pharmacists
and patients arrival rates.

sensitivity of mean total waiting times subjected to small perturbations in arrival rates
for different numbers of dispensing pharmacists. It was observed that the original
configuration with only 8 dispensing pharmacists would result in large increases in
waiting times when there were only small increases in the number of prescriptions
arriving per hour (in the range 85--91 prescriptions per hour). This provided the
management with insights into the frequently experienced phenomenon of doubling
in waiting times on some ‘bad’ days. By having an additional dispensing pharmacist,
the system would be expected to experience a more stable waiting time distribution
for different arrival rates.
In order to elicit more improvement opportunities, additional root cause analysis
for the long waiting times was performed for the present process. This was conducted
with the aid of a fishbone (or Ishikawa) diagram, enabling. The potential of stream-
lining manpower deployment in the pharmacy can be further studied. From the anal-
ysis, it was suggested that a new process, that screening, be implemented at the point
when the pharmacy receives prescriptions from patients (prior to the existing typing