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

24
18
Total Waiting Times (mins)
12
6
11
10
0 9 View B
8 9 8 No. of Dispencing Pharmacists
View A No. of Packers 10 11 7

26.0 26.0
7 Dispensing Pharmacists With Screening 8 Packers With Screening 9 Packers With Screening
24.0 8 Dispensing Pharmacists With Screening 24.0 10 Packers With Screening 11 Packers With Screening
Waiting Times 20.0 10 Dispensing Pharmacists With Screening Waiting Times 20.0
9 Dispensing Pharmacists With Screening
22.0
22.0
11 Dispensing Pharmacists With Screening
18.0
18.0
16.0
14.0 16.0
14.0
12.0 12.0
10.0 10.0
8 9 10 11 7 8 9 10 11
Number of Packers Number of Dispensing Pharmacists
View A View B
Figure 5.5 Sensitivity of total waiting times to variations in number of packers and dispensing
pharmacists in the new dispensing process with screening.

relevant stakeholders through targeted ‘Kaizen’events. The SOP and validated results
were communicated to the pharmacy staff in order to reinforce their confidence in the
new processes.

5.4.2 Relaxing exponential assumptions on service times distribution
Further sensitivity analysis can be performed to assess the effect of the exponential
assumptions on the distribution of service times. This analysis was conducted to estab-
lish whether the assumption of exponentially distributed service times would result
in more conservative system design choices. Results based on the model without the
screening process are described here. A similar sensitivity analysis can be conducted
on the model with the new screening process.
Each service station i was assumed to experience a Poisson arrival process with
mean arrival rate λ i . Single-server service stations i were assumed to experience ser-
vice processes that follow any general distribution with mean service rate μ i . This is
commonly known as the M/G/1 queuing system. For service stations with (finitely)
many servers, the service processes of each server were assumed to follow general