1. Karan Chaudhry
ISEN 424-901 Final Report
Spring 2012
Poor Yorick’s Service Time Improvement
INTRODUCTION
The purpose of this project is to reduce customer wait times at Poor Yorick’s Coffee Shop. The
following paragraphs entail the type of analysis that we did, the shop’s simulation model, some
recommendations to improve efficiency at your coffee shop and their benefits.
PROBLEM STATEMENT
Poor Yorick’s Coffee House, located on the first floor of Evans Library, is famous for its wide
selection of fruits, muffins, croissants, sandwiches, gourmet coffee or even specialty drinks that
it offers to thousands of students, faculty and staff every day. However, apart from its popularity,
its big name has notoriously been linked to big waiting lines, thus leaving customers dissatisfied
in the speed of service and making them leave the shop without a purchase. Consequently, the
shop is not only loosing money but most importantly loosing goodwill of the customers.
In an attempt to fix this, we collected data at the shop over the month of March. We then built a
computer model for the shop to analyze the data gathered during the study. And then we tested
alternative shop layouts and possible solutions to cut back wait times.
DATA COLLECTION AND SUMMARY
Over the month of March, we made several visits at the shop and recorded data during its busiest
time i.e. between 10:30 AM and 3:30 PM.
We split up into two teams of two members each. The first team was responsible for recording
processing times of the customers while the second team was responsible for recording the
operational times of the baristas. The first team positioned itself across from the two registers
and recorded the time that the customers entered the line, the time that they spent to order and
pay at the cashier and the time that they spent waiting for their order to be processed. Once they
received their order, the customers were considered to be out of the system. Moreover, on at least
three different days, we began collecting data when the shop had no customers in line and
finished when the shop again had no customers in line. The data that we collected in regards to
the aforementioned is summarized as follows.
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3. 2:57:45 3:01:45 3:02:20
3:03:00 3:04:10 3:04:45
3:03:20 3:04:55 3:05:05
3:05 3:06:30 3:06:51
3:08:00 3:10:25 3:11:10
3:09:55 3:12:50 3:13:10
3:14:25 3:15:40 3:16:00
3:16:40 3:17:45 3:18:45
3:19:50 3:20:10 3:21:10
3:21:15 3:21:35 3:21:45
3:22:00 3:22:30 3:22:45
3:23:25 3:24:25 3:25:15
3:25:55 3:26:50 3:27:05
3:29:05 3:29:25 3:29:55
3:30:05 3:30:55 3:31:05
3:31:10 3:32:15 3:33:25
Table 1: Data for processing times of the customers at Poor Yorick’s
Figure 1: Distribution for processing times of the customers at Poor Yorick’s
As a major part of our analysis of the data in Table 1, we used the @Risk application, an
extension in Microsoft Excel, to obtain the statistical distribution for processing times of the
customers at the shop as shown in Figure 1. We found that triangular distribution best fit our data
from Table 1.
On the other hand, the second team recorded the time that it took the barista to make a drink, and
the time it took the barista to deliver a drink to the customer after the drink was made. The data
that we collected in regards to the aforementioned is summarized as follows.
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4. Time Start Time End
machine
1
machine
2 Blender
Total time
(in sec)
1 3:05:00 3:07:09 X 129.00
2 3:05:30 3:06:58 X 88
3 3:07:00 3:08:55 X 115
4 3:08:00 3:10:27 X 147
5 3:09:40 3:10:50 X 70
6 3:10:00 3:12:45 X 165
7 3:11:00 3:15:30 X 270
8 3:13:20 3:16:20 X 63
9 3:15:45 3:16:55 X 70
10 3:16:02 3:18:20 X 138
11 3:17:25 3:19:20 X 115
12 3:17:38 3:21:53 X 255
13 3:20:50 3:24:25 X 215
14 3:21:37 3:23:32 X 115
15 3:22:15 3:26:58 X 283
16 3:22:20 3:28:21 X 361
17 3:27:23 3:30:48 X 205
18 3:28:01 3:34:28 X 387
19 3:32:04 3:33:45 X 101
20 3:32:23 3:34:28 X 125
21 3:34:42 3:35:45 X 63
22 3:35:02 3:36:15 X 73
23 3:38:30 3:40:20 X 110
24 3:39:30 3:40:38 X 68
25 3:41:58 3:43:37 X 99
26 3:44:21 3:45:36 X 75
27 3:44:30 3:45:03 X 33
28 3:46:40 3:48:20 X 100
Avg.
Time for
Hot
Drinks=>
Avg. for
Machine
1&2(sec) 132.19
Avg.
Time for
Blended
Drinks=>
Avg. for
Machine
1&3(sec) 180.29
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5. Table 2: Data for drink processing times
When collecting the data above in Table 2, the “Time Start” was taken to be the time that the
barista began making the drink while the “Time End” was taken to be the time when the barista
ended pouring the drink in the drink cup.
Time Start taking
drink to
customer
Time End taking
to drink to
customer
Machine
1
Machine
2 Blender Delay
1 11:52:50 11:53:00 10s
2 11:53:30 11:53:35 X 5s
3 11:54:50 12:00:30 X 5m 40s
4 12:00:24 12:01:20 X 56s
5 12:01:08 12:01:10 X 2s
6 12:02:03 12:05:30 X 3m 27s
7 12:05:20 12:05:44 X 24s
8 12:05:35 12:08:33 X 2m 58s
9 12:08:27 12:12:25 X 3m 58s
10 12:12:10 12:12:40 X 30s
11 12:12:28 12:13:13 X 45s
12 12:12:52 12:14:16 X 1m 24s
13 12:14:07 12:14:55 X 48s
14 12:14:48 12:14:55 X 7s
15 12:18:59 12:19:04 X
16 12:19:25 12:19:33 X 8s
17 12:21:04 12:21:16 X 12s
18 12:23:25 12:23:33 X 8s
19 12:23:38 12:23:58 X 20s
20 12:27:24 12:27:32 X 8s
21 12:30:25 12:31:32 X 1m 7s
22 12:32:51 12:32:56 X 5s
23 12:33:54 12:33:57 X 3s
24 12:36:25 12:36:33 X 8s
25 12:37:25 12:37:37 X 12s
26 12:38:49 12:39:58 X 1m 9s
27 12:39:45 12:39:50 X 5s
28 12:39:35 12:39:58 X X 23s
29 12:41:30 12:41:35 X 5s
30 2:36:22 2:36:35 X 13s
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6. 31 2:39:12 2:39:30 X 18s
32 2:41:24 2:41:40 X 16s
33 2:43:13 2:43:25 X 12s
34 2:43:30 2:43:47 X 17s
35 2:46:55 2:47:05 X 10s
36 2:49:30 2:49:41 X 11s
37 2:49:55 2:50:12 X 17s
38 2:51:55 2:52:10 X 15s
39 2:55:45 2:55:57 X 12s
40 2:57:52 2:58:01 X 9s
41 2:59:53 3:00:00 X 7s
42 3:05:30 3:05:45 X 15s
43 3:07:12 3:07:25 X 13s
44 3:09:10 3:09:19 X 9s
45 3:11:57 3:12:14 X 17s
46 3:15 3:15:50 X 8s
47 3:17:34 3:17:44 X 10s
48 3:20:15 3:20:26 X 11s
49 3:23:56 3:24:10 X 14s
50 3:26:10 3:26:16 X 6s
51 3:28:56 3:29:19 X 23s
52 3:31:55 3:32:40 X 45s
53 3:32:49 3:33:00 X 11s
54 3:34:00 3:34:20 X 20s
55 3:34:30 3:34:43 X 13s
56 3:35:35 3:36:00 X 25s
57 3:37:26 3:37:43 X 17s
% for mach. 1 0.105263
delivery for
machine 1 1.33333
% for mach. 2 0.315789
delivery for
machine 2 4.17
% for blender 0.578947
delivery for
blender 4.075
Table 3: Data for drink delivery times
We then used the data from Table 2 and Table 3 to find its respective distribution(as shown in
Figure 2) using the same procedure as explained earlier. The data in both cases was found to be
normally distributed.
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7. Figure2: Distribution for drink delivery times
Following that, we used the @Risk application to do an input analysis on the average times of
the two machines and the blender to fit to a normal distribution. Our results are tabulated as
follows.
Input analysis on drink machines (in seconds)Input analysis on drink machines (in seconds)Input analysis on drink machines (in seconds)Input analysis on drink machines (in seconds)Input analysis on drink machines (in seconds)Input analysis on drink machines (in seconds)
Machine 1Machine 1 Machine 2Machine 2 BlenderBlender
Standard
Deviation
1.33 Standard
Deviation
1.29 Standard
Deviation
1.26
Mean 0.516 Mean 4.17 Mean 4.08
Table 4: Input Analysis on drink machines
Lastly, we observed that customers arrived individually at an inter-arrival time(time gap between
each arrival) of 150.74 seconds.
SIMULATION MODEL and RESULTS
Using the results from the data collected above, we built the model of the shop using the
simulation software called ARENA. Since our data collection was from 10:00AM until 03:00
PM, we set our model to run for that 5-hour period. Also, in order to get a much more
appropriate cycle time(i.e. the time that a customer spends in the shop from the point to when he
enters the line to the point after he gets his drink), we set the model to run for a 10-day period.
As a result, we found the cycle time for the current shop layout to be approximately 9.32
minutes.
RECOMMENDATION(S)
Upon careful analyzing and then re-analyzing Poor Yorick’s current layout and taking into
account the data collected, I have three recommendations to make, and they are as follows:
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8. 1. An additional pick up station should be made over by Machine 2 and the Blender with an
appropriate sign above it. This will significantly cut the time that it takes for the barista to take
the drink to the customer since the barista would literally be just able to turn back and hand
over the drink to the customer as opposed to walking over to the other side of machine 1
which originally took an average of 4 seconds. As a result, this would reduce drink processing
times.
2. An extra Blender should be added to keep up with the growing demand of iced drinks,
especially with summer being around the corner. Adding this extra blender will enable the
barista to make several drinks at the same time as opposed to waiting for the first drink to be
made before starting on the second one which is how it was in the first case. As a result, this
would reduce processing times of iced drinks even more and thus, in turn would cut back wait
time of customers.
3. An extra cashier should be added to keep the lines smaller. Also, cashiers should be divided by
the type of customer. In other words, customers who needed hot drinks would go over to the
cashier by Machine 1, and then would pick up their drink by Machine 1. Similarly, customers
who needed cold drinks or specialties would go to either cashier 2 by Machine 2 or go to
cashier 3 by the Blender and then pick up their drinks at the common pick-up station as
discussed in 1. This would cut back wait time of customers.
I then built my simulation model for Poor Yorick’s which I called, “Efficient Poor Yorick’s
Layout” which takes into account the above recommendations, i.e. having 3 cashiers, an extra
blender and an additional pick-up station. I have tabulated the results as follows:
System Cycle Time
(in min)
Current Poor Yorickʼs Layout 9.32
Efficient Poor Yorickʼs Layout 8.14
Table 5: Comparison of the results of the current model and its alternative
From the above table, it can be seen that the alternative shop layout has reduced the cycle time
by over 1 minute, which means that the customer wait time has been reduced by over 1 minute.
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9. CONCLUSION
This project was an attempt to reduce customer wait time at Poor Yorick’s. Taking action on my
recommendations would give you more business by improving shop efficiency; in fact, all
customers stopping by would actually be making a purchase as opposed to leaving the line due to
long wait times in the initial case. Moreover, this will also improve customer satisfaction and
leave the customers with a piece of mind, thus giving them room to actually enjoy the products
that your coffee shop has to offer.
If you have any additional questions, comments or concerns about this project, please do not
hesitate to contact me. I can be reached by phone at (979) 997-8565 or by email at
kchaudhry@tamu.edu.
Sincerely,
Karan Chaudhry
Junior Industrial & Systems Engineering major, Texas A&M University
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