Nova Southeastern University H. Wayne Huizenga School of Bus.docx

Nova Southeastern University
H. Wayne Huizenga School
of Business & Entrepreneurship
Assignment for Course:
QNT5040-Business Modeling
Submitted to:
Dr. Phillip
Submitted by:
Nicole Mateus
Robert Edson
Abdellah Sabere
Date of Submission: November 3, 2013
Title of Assignment: ZZ Airlines Simulation Case Study
CERTIFICATION OF AUTHORSHIP: I certify that I am the
author of this paper and that any assistance I received in its
preparation is fully acknowledged and disclosed in the paper. I
have also cited any sources from which I used data, ideas or
words, either quoted directly or paraphrased. I also certify that
this paper was prepared by me specifically for this course.
Student's Signature: NM, RE, AS___________
*****************************************************
************
Instructor's Grade on Assignment:
Instructor's Comments:

TITLE OF RUBRIC: Case Analysis (Page 1 of 2)
Course: QNT 5040
LEARNING OUTCOME/S: (see syllabus)
Date: 11-03-13
PURPOSE: To facilitate effective decision making under
uncertain conditions by quantifying risk.
Name of Student: NM, RE, AS
VALIDITY: Best practices in Monte Carlo simulation.
Name of Faculty: Dr. Phillip
COMPANION DOCUMENTS: Assignment and format
instructions, Case
Earning maximum points in each box in ‘PROFICIENT’ column
and / or
points in columns to the right of ‘PROFICIENT’ meets
standard.
<<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . more quality >>>>>>>>>>
Performance Criteria
Basic
Developing
Proficient

Accomplished
Exemplary
Score
Identify the problem
Does not
identify the problem, or does not identify the right problem.
(0 pts)
Identifies symptoms
(5 pts)
Identifies some elements of the problem.
(10 pts)
Substantially
identifies the problem.
(12 pt)
Effectively and succinctly
identifies the problem.

(15 pts)
Describes assumptions and methods
Does not describe assumptions and methods used
(0 pts)
Does not precisely describe the
assumptions and methods used
(3 pts)
Somewhat describes assumptions and methods used
(7 pts)
Substantially
describes assumptions and methods used
(8 pts)
Effectively describes assumptions and methods used
(10 pts)
Calculate statistics using a spreadsheet
Does not calculate appropriate statistics using a spreadsheet
and/or
does not provide evidence of calculations
(0 pt)
Calculates appropriate statistics using a spreadsheet (most

answers are not
correct)
(13 pts)
Calculates appropriate statistics using a spreadsheet (not all
answers are correct)
(21 pts)
Calculates appropriate statistics using a spreadsheet (most
answers are correct)
(25 pts)
Effectively
calculates statistics using a spreadsheet (almost all answers are
correct)
(30 pts)
Explain
implications of
output of statistical analysis
Does not explain
implications of

(0 pt)
Partially
explains
implications of
(3pts)
Somewhat explains
implications of
(7 pts)
Substantially
explains
implications of
(8 pts)
Effectively explains
implications of
(10 pts)
TITLE OF RUBRIC: Case Analysis, cont. (Page 2 of 2)
Course: QNT 5040
LEARNING OUTCOME/S: (see syllabus)
Date: 11-03-13
PURPOSE: To facilitate effective decision making under
uncertain conditions by quantifying risk.
Name of Student: NM, RE, AS
VALIDITY: Best practices in Monte Carlo simulation.
Name of Faculty: Dr. Phillip
COMPANION DOCUMENTS: Assignment and format

instructions, Case
Earning maximum points in each box in ‘PROFICIENT’ column
and / or
points in columns to the right of ‘PROFICIENT’ meets
standard.
<<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . more quality >>>>>>>>>>
Performance Criteria
Basic
Developing
Proficient
Accomplished
Exemplary
Score
Generates solutions based on analysis and context
Does not
generate appropriate solutions based on analysis and context.
(0 pt)

Generates solutions (does not justify conclusions).
(7 pts)
Partially: *generates and justifies solutions based on analysis
and context; and *justifies conclusions.
(15 pts)
Substantially: *generates and justifies solutions based on
analysis and context; and *justifies conclusions.
(17 pts)
Effectively: *generates and justifies solutions based on analysis
and context; and *justifies conclusions.
(20 pts)
Uses prescribed format (including cover sheet and grading
rubric) and writing style (language, grammar, punctuation, and
spelling)
Does not use prescribed format and writing style
(0 pt)
May use prescribed format OR writing style (only one)
(3 pts)
Generally uses prescribed format and writing style

(7 pts)
Substantially uses prescribed format and writing style
(8 pts)
Effectively
uses prescribed format and writing style
(10 pts)
Uses APA format
(APA Style Manual 6.0)
Does not provide references.
(0 pt)
Does not apply APA style to references.
(1pts)
Partially applies APA style to references.

(3 pts)
Substantially applies APA style to references.
(4 pts)
Effectively applies APA style to all references.
Optimal quality and quantity of citations.
(5 pts)
OVERALL GRADE (100 total possible points):
%
Comments:
_____________________________________________________
_________________________________
12
Management Report for ZZ Airlines
Executive Summary
ZZ Airlines is trying to determine whether or not to proceed
with their advertising campaign to increase phone inquiries. If
they decide to proceed, they want to determine if hiring a
second agent to help with its toll-free reservation system is
recommended. A sample of data was taken based on similar
campaigns in the past.
After completing several types of analysis using @RISK, it can
be concluded that adding an additional agent to help the toll-
free system is a must but proceeding with the advertising
campaign is determinant on the owner’s willingness to exceed
his low wait-time expectations.
Background
ZZ airlines recently opened its doors as a commuter service.

They have seen business grow rapidly and thus identified the
need to improve their customer service system in order to meet
the demands of their growing customer base. Initially ZZ
airlines started a toll-free reservation system that operated
between 12:00 A.M. and 6:00 A.M. with only one agent on duty.
In addition to the toll-free system, they are planning to roll out
an advertising campaign. With this campaign and growing
customer base, management is expecting higher than normal call
volumes, and needs to prepare so they are able to meet the
owners demand for no more than a three or four minute
customer hold time. High-quality customer service is crucial to
the success of any business and it is important for ZZ airlines to
make the right adjustments in order to keep their customers
satisfied.
Problem
The main problem we are facing is how we ensure our
recommendation to hire a second agent is justified from an
operational cost. Our dilemma is to maintain customer
satisfaction while ensuring an efficient agent utilization with
the least amount of agent idle time. One of the criteria set-forth
by management for customer satisfaction is to maintain the wait
time between 3 and 4 minutes.
Analysis
Our analysis started by looking at the original call distribution
and identifying the key performance indicators that would yield
a stable condition for the queue. The main key performance
indicators we measured and analyzed are indicated in the
following order:
1. Average wait time
2. Maximum wait time
3. % Agent idle time
4. % Agent utilization
Before we proceeded with our analysis and comparison of the
above key indicators, we looked at the mean service time for
each case scenario and compared it to the mean inter-arrival

time of the calls. If the mean service time was less than the
mean inter-arrival time we simply concluded that the
probability that the queue is stable would be higher. This means
that the agents were able to take calls and service customers
gracefully without impacting customer satisfaction and resume
to take the next call, which did not sit in the queue for more
than three minutes. A simple glance at the data shows the mean
service time of a sample of incoming calls from the original call
distribution was slightly higher than the mean service times.
This led us to the assumption that the queue was at higher
capacity and that the one agent servicing customers prior to the
advertising campaign did not have too much idle time available
between calls.
Data analysis prior to the Advertising campaign
Using the sample data of current incoming calls from our
homework, with one agent servicing customers, the average wait
time was fairly high at 46.88 minutes. The agent total idle time
for the entire shift was 2.55 minutes which represented 0.71%
idle time. This data showcased that the queue in this case
scenario was far more stretched and quite unstable since
customers had to wait for longer than the desired 3-4 minutes
that management set out as threshold for customer satisfaction.
Table 1. Stats for 1 Agent
Total wait time
4688.484
Average wait time
46.88484
Max wait time
91.50561
Total idle time
2.552547
Agent Idle time %
0.71%

Further analysis of the data using the @RISK tool, we generated
1 simulation of 1,000 iterations for the case scenario of one
agent prior to the advertising campaign. According to Palisade
Corporation, at each iteration formulated, @RISK draws a new
set of random numbers for the @RISK distribution functions
which in this case are exponential distribution for current
incoming calls with a mean of 3.356 and triangle distribution
for service times with a mean of 3.2292. The results given on
the output reports helped us validate some of our assumptions
and gave us a clearer picture on the agent utilization.
Graph 1. Average Wait Time for 1 Agent Graph 2. Max
Wait Time for 1 Agent
As shown above (graph 1), using our critical value at 95%, we
see the average wait time for our simulation to fall between
13.8 and 67.6 minutes. These numbers fall within 2 standard
deviations of our mean of 39.1 minutes. Our data is slightly
skewed to the right for both graphs. The max wait time falls
between 34.7 and 127.9 minutes within 2 standard deviations of
the mean 80.21 minutes. This clearly shows 1 agent is not
enough to support the great customer satisfaction ZZ Airlines is
trying to achieve. The longer in the morning it goes, the longer
the wait time will be as more customers start to call. Agent
utilization will be high but at the expense of longer wait times
for the customers which is not what they want.

Adding a second agent prior to the advertising campaign yielded
greater results that were aligned with improving customer
satisfaction. In fact, the maximum wait time was reduced by
98% to 2.66 minutes instead of 91.50 minutes for 1 agent. The
average wait time was null while the % agent idle time was
increased to 38.85%. This would allow agents to ensure proper
entering of the data and documentation of any cases they
logged. Since the agent idle time is higher, we would even
recommend for agents to create knowledge base articles that
customers can leverage online to deflect some of the simple
incoming calls.
Table 2. Stats for 2 Agents
Total wait time
16
Average wait time
0
Max wait time
2.6651
Total idle time
280
Agent 1 Idle time %
36.674%
Agent 2 Idle time %
41.029%
Total idle time %
38.85%
Graph 3. Average Wait Time for 2 Agents Graph 4. Max
Wait Time for 2 Agents
Graph 3 above clearly shows the better service that is received
for the customers in regards to wait time. For our simulation of
1,000 iterations, the average wait time is 1.15 minutes which is
a short reasonable time to wait and falls way ahead of the 3

minute mark they were aiming for. The max wait time would be
25.5 minutes which falls outside of 2 standard deviations from
the mean of 7.75 so there is a very small probability that will
happen.
Graph 5. Total Idle Time % for 2 Agents Graph 6. Idle
Time % for 1 Agent
Looking further at the agent % utilization in this case scenario
validated our thought process that having two agents prior to
the advertising campaign is highly recommended. Based on the
mean, the % agent utilization was at more than 96% with one
agent versus 62% with both agents. This gives each agent some
time to breathe and take a moment to relax before taking the
next caller. The graph for both agents is skewed to the left
while the graph for 1 agent is skewed to the right. We believe
the 34% difference in agent utilization greatly outweighs the
shorter wait time the customer would be receiving.
Data Analysis After the advertising campaign
Looking further at the data after prior advertising campaign
shows similar trends. As table 4 shows, the average wait time
increased from 28.76 to 46.37 minutes after the campaign was
done. This illustrates that the advertising has contributed to an
increase in the long wait time. The total idle time has doubled
after the campaign. Since the wait time has surpassed the 4
minute max, customer satisfaction has indeed been eroded and
this alone constitutes a reason to adjust the staffing number to
two agents instead of one. The data below shows the
comparison between one agent before and after the advertising
campaign:
Table 3. Stats Before Campaign 1 Agent Table 4.
Stats After Campaign 1 Agent
Total wait time
2876.38
Total wait time

4637.746726
Average wait time
28.7638
Average wait time
46.37746726
Max wait time
63.75539
Max wait time
80.45607433
Total idle time
2.552547
Total idle time
5.457470206
Agent Idle time %
0.71%
Agent Idle time %
1.52%
Graph 7. Average Wait Time Graph 8.
Average Wait Time
After Campaign for 1 Agent After Campaign
for 2 Agents
Looking at our simulation of 1,000 iterations, the average wait
time with 2 agents brings down the mean from 56.35 to 1.77
minutes. The 95% critical value for 2 agents has average wait
times between .72 and 3.62 minutes both falling inside 2
standard deviations from the mean. Those numbers show that 2
agents even after the campaign, can still produce high volume
of calls and still maintain the 3 to 4 minutes of wait time for the
customers.
Graph 9. Max Wait Time Graph 10. Max

Wait Time
After Campaign for 1 Agent After Campaign for 2
Agents
Adding two agents in our staffing model and running the
@RISK simulation provided a great deal of insight on the
improvements of the KPI’s. In fact, the maximum wait time
customers would have to endure with 2 agents would be 28
minutes and that’s falling way outside 2 standard deviations
from the mean. Based on the mean of 9.4 minutes with 2 agents,
it still falls past the 4 minute max the airline wants a customer
to wait but these numbers are worst case scenarios which have
some possibility of happening but the best case scenarios still
outweigh the worst case scenarios.
Graph 11. Agent Idle % for Graph 12. Agent Idle
% for
1 Agent after Campaign 2 Agents after Campaign
According to graph 12, our total idle time for both agents after
the campaign had a mean idle % of 27.5%. That’s roughly
almost 75% agent utilization which still looks like a high
percentage for the increasing amount of calls coming in. This
illustrates great improvement in the quality of service the agents
are providing since they are not rushing to pick up calls that
have been waiting on hold for more than 3 minutes. This
staffing model will reduce from agent burn out and low morale
compared to the one agent model where he/she is being utilized
almost 98% of the time. It is also a fault tolerant system in case
one of the agents gets stuck on a call longer than normal or has
to use the restroom or get a sip of water.
Based on tables 5 and 6, it shows both agents have similar idle
times meaning they more or less spend the same amount of time
on calls. We can only recommend that the agents have some
degree of experience with airline customer service to be able to
expedite the calls quicker.

Table 5. Summary Statistics for Table 6.
Summary Statistics for
Agent 1 Idle Time % after Campaign
Agent 2 Idle Time % after Campaign
Statistics
Percentile
Statistics
Percentile
Minimum
8.276%
5%
14.037%
Minimum
7.662%
5%
14.917%
Maximum
54.843%
10%
16.928%
Maximum

55.102%
10%
17.732%
Mean
27.388%
15%
18.779%
Mean
27.702%
15%
19.319%
Std Dev
8.152%
20%
20.371%
Std Dev
8.031%
20%
20.835%
Variance
0.006645135
25%
21.531%
Variance
0.006450188
25%
22.098%
Skewness
0.197034985
30%

22.458%
Skewness
0.251267417
30%
23.185%
Kurtosis
2.865965601
35%
24.047%
Kurtosis
2.927817806
35%
24.191%
Median
27.325%
40%
25.019%
Median
27.291%
40%
25.377%
Mode
29.752%
45%
26.242%
Mode
27.325%
45%

26.433%
Left X
14.037%
50%
27.325%
Left X
14.917%
50%
27.291%
Left P
5%
55%
28.392%
Left P
5%
55%
28.373%
Right X
41.106%
60%
29.504%
Right X
41.366%
60%
29.292%
Right P
95%
65%
30.459%

Right P
95%
65%
30.586%
Diff X
27.069%
70%
31.403%
Diff X
26.450%
70%
31.684%
Diff P
90%
75%
32.475%
Diff P
90%
75%
32.851%
#Errors
0
80%
34.267%
#Errors
0
80%
34.517%
Filter Min

Off
85%
35.595%
Filter Min
Off
85%
36.275%
Filter Max
Off
90%
37.839%
Filter Max
Off
90%
38.357%
#Filtered
0
95%
41.106%
#Filtered
0
95%
41.366%
Conclusion and Recommendation
When reviewing the data outputs above, it is apparent that ZZ
Airlines is better off adopting a two agent customer service
model instead of staying with their current one agent model. In

order to lower customer wait times and meet the expectation set
forth by the owner of having no more than a three to four
minutes hold time, this should be put into action right away.
Not only will it improve customer experience but it will also
improve the agents overall quality of work life. It is no secret
that customer service plays a key role in the sustainability and
profitablity of a company. In order to ensure positive customer
service it is also important to have happy and motivated
employees. Overworked and unhappy employees are not able to
provide the top-notch service that is required. It is our
recommendation that having two agents in place will strengthen
the business and keep customers satisfied. Regarding whether or
not they proceed with the campaign, if the owners of the airline
feel they do not want to risk customers waiting no more than 4
minutes maximum, then they should not proceed with the
campaign because based on our simulation with 1,000 iterations,
there may be instances where 2 agents might get extended calls
and wont be able to control the wait time below their requested
mark. The agent idle % might also pass 50% in certain scenarios
so that number might be too high for some owners. Definitley
they have to have 2 agents even if they don’t want to proceed
with the campaign. If they feel the small probabilities of both
agents getting tied up in extended calls are worth the reward of
higher call volumes with low waiting times, then doing both
campaign and getting a second agent is the way to go. An added
recommendation would be to screen the customer as soon as
they call so the agent can know what the customer is calling
about before they are passed on to them. That information
would save some time and the customer will not feel like that
time was considered waiting time.

Format of a Management Report for Case Analysis
Executive Summary
This section should appear on a separate page at the beginning
of the report. It should be
limited to a maximum of 200 words and give a very brief
summary of the background, the
problem, the method of analysis and the recommendations.
Please note that this summary
must have all four of these elements.
Background
In this section, the context of the problem and the current
situation is described from the case.
Only the essential details should be covered and this section
should NOT be a synopsis of the
case. This section should be limited to a maximum of 250
words.
Problem
A succinct statement of the problem/ dilemma/ issue should be
stated here. Be careful to
identify the real problem and not the symptoms of the problem.
Analysis
This is the most important section of the report. A clear, step-

by-step description of how the
data in the case was analyzed should be given. Technical terms
should be kept to a minimum as
the focus is on producing a document that can be understood by
management. Details of
calculations and the technical details of the analysis should be
appended to the report (usually
in the form of Excel spreadsheets). Summary tables and graphs
may be used within this section
to illustrate the important results of the analysis. It is important
to cite any references in the
text to support your analysis.
Conclusions and Recommendations
In this section, the recommended solution to the problem or
resolution of the dilemma should
be presented. The reason for the recommendation should be
justified and the implications of
the solution articulated. Be sure that your recommendations are
related to the stated problem
and avoid going off at tangents.
Bibliography

A list of the books, articles, websites, software etc. consulted or
used in understanding the
situation, writing the report and generating the solution should
be presented. These references
should be in APA format. (A great deal of time can be saved by
using EndNote for this purpose.
This software is available free to students from the NSU library
website. It can be installed as an
MS Word add-in and the “cite while you write” function used to
create in-text references and
the list of references in the correct format.)
Appendices
The detailed workings and calculations used should be
presented here and referred to at the
appropriate place in the text so that readers (i.e. managers) who
require details can determine
where they are located. These will usually take the form of MS
Excel spreadsheets or other
outputs from any software used. The most convenient way to do
this is to imbed these files into
the report as this will enable you to submit a single file for the
assignment/ case analysis. [In MS
Word this can be achieved by selecting the Insert tab (second
from the left), then Object

(second last block), click “Create from File”, browse for the
correct file, click “Display as Icon”
and then press OK.]
Mike Bendixen
June 27, 2009
[email protected]Created By Version6.0.1Required
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@RiskSample of current service times (time to process inquiries
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Assume Exponential distribution 2.612.5Sample of current
incoming calls (time between calls in minutes) Assume
Exponential distribution @RiskSample of data taken on
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calls in minutes) Assume Exponential distribution
1.912.2Mean3.35601268442.93709821892.712.9Stdev1.4643839
8321.46959491574.511.92.812.73.711.5Sample of current
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Distribution1.312.4Mean3.2292From Triangle
Distribution2.711.7Max7.6877From Triangle
Distribution4.712.43.814.32.513.34.513.21.915.12.916.72.615.9
2.513.93.512.23.712.13.812.43.221.02.221.73.127.82.921.23.52
5.23.821.03.223.13.221.85.621.83.623.31.124.84.424.62.422.62.
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2.444.33.043.73.841.21.244.92.644.15.742.03.542.42.842.55.94
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252.32.451.43.852.52.955.72.451.82.752.23.251.61.952.12.851.
24.651.24.452.82.765.82.962.41.360.72.162.85.063.13.465.53.1
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RROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERROR:#N
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K1210ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ER
ME?11ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ER

ME?100ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?E
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serviceStart serviceService timeEnd serviceWait timeAgent 1
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?ERROR:#NAME?ERROR:#NAME?18ERROR:#NAME?ERRO
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ERROR:#NAME?25ERROR:#NAME?ERROR:#NAME?ERROR:
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1 Agent advertising campaignPerformance of One Agent after
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arrival timeCall start serviceService timeCall end serviceWait
timeAgent idle
time000000001ERROR:#NAME?ERROR:#NAME?ERROR:#NA
R:#NAME?2ERROR:#NAME?ERROR:#NAME?ERROR:#NAM
:#NAME?3ERROR:#NAME?ERROR:#NAME?ERROR:#NAME
NAME?4ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?E

AME?5ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ER
ME?Total wait timeERROR:#NAME?From column
Average wait timeERROR:#NAME?From column
Max wait timeERROR:#NAME?From column
Total idle timeERROR:#NAME?From column
H9ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERROR
Agent Idle time %ERROR:#NAME?From cell
K1210ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ER

ME?100ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?E
AME?
2 Agents advertising campaignTwo Agents Performance after
prior Advertising CampaignsAgent #1Agent #2Custo-merTime
between arrivalsArrival timeServer #Start serviceService
timeEnd serviceStart serviceService timeEnd serviceWait
timeAgent 1 idle timeAgent 2 idle
time00.0000000000000001ERROR:#NAME?ERROR:#NAME?1
NAME?ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?2E
E?ERROR:#NAME?3ERROR:#NAME?ERROR:#NAME?ERRO
NAME?ERROR:#NAME?ERROR:#NAME?4ERROR:#NAME?E

E?5ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERRO
NAME?ERROR:#NAME?6ERROR:#NAME?ERROR:#NAME?E
OR:#NAME?ERROR:#NAME?ERROR:#NAME?7ERROR:#NA
NAME?Total wait timeERROR:#NAME?From column
NAME?ERROR:#NAME?Average wait
timeERROR:#NAME?From column
NAME?ERROR:#NAME?Max wait timeERROR:#NAME?From
column
K10ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERRO
NAME?ERROR:#NAME?Total idle timeERROR:#NAME?From
columns L &
L12ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERRO

NAME?ERROR:#NAME?Total idle time
%ERROR:#NAME?From cell
P1514ERROR:#NAME?ERROR:#NAME?ERROR:#NAME?ERR

NAME?100ERROR:#NAME?ERROR:#NAME?ERROR:#NAME
ROR:#NAME?ERROR:#NAME?
Output 1@RISK Output Report for Total wait time Performed
By: Yurova, YuliyaDate: Friday, August 30, 2013 11:59:41
AMSimulation Summary InformationWorkbook NameZZ
Airlines data on incoming calls and service time
(2).xlsxNumber of Simulations1Number of
Iterations1000Number of Inputs1000Number of
Outputs24Sampling TypeLatin HypercubeSimulation Start
Time8/30/13 11:57Simulation Duration00:00:11Random #
GeneratorMersenne TwisterRandom Seed589150474Summary
Statistics for Total wait
timeStatisticsPercentileMinimum331.6848132535%1331.120099
8025Maximum8674.798639517310%1696.0034732537Mean389
0.157783073315%2049.1028696107Std
Dev1660.102852132520%2366.0572114981Variance2755941.47
96584625%2625.6950975789Skewness0.269680014130%2918.5
477442187Kurtosis2.671066502935%3141.5724414353Median3
845.740456744440%3375.289941672Mode3800.420239757645
%3609.094920222Left
X1331.120099802550%3845.7404567444Left
P5%55%4052.915175105Right
X6764.119555915760%4266.1569863436Right
P95%65%4509.7978359931Diff
X5432.999456113270%4753.6965777516Diff
P90%75%5053.2740641161#Errors080%5301.3994602641Filter

MinOff85%5603.7096315362Filter
MaxOff90%6051.6415706097#Filtered095%6764.1195559157C
hange in Output Statistic for Total wait
timeRankNameLowerUpper114 / Time between
calls3217.83660553434420.9424794017234 / Time between
calls3293.20953493144253.62189551871456 / Service
time3359.09323561064285.5296780155
Output 2@RISK Output Report for Average wait time
Performed By: Yurova, YuliyaDate: Friday, August 30, 2013
11:59:42 AMSimulation Summary InformationWorkbook
NameZZ Airlines data on incoming calls and service time
Statistics for Average wait
1Maximum86.747986395210%17.8014761448Mean39.10135717
0415%21.0632759231Std
Dev16.386438121220%23.9239005918Variance268.5153542997
25%26.8544202588Skewness0.302137485430%29.2659355117
Kurtosis2.687829013835%31.54610506Median38.45740456744
0%33.775268772Mode38.366461319245%36.2145827789Left
X13.822118944150%38.4574045674Left

P5%55%40.6569025014Right
X67.641195559260%42.8050698151Right
P95%65%45.1298681652Diff
X53.81907661570%47.5369657775Diff
P90%75%50.5327406412#Errors080%53.0139946026Filter
MaxOff90%60.5164157061#Filtered095%67.6411955592Chang
e in Output Statistic for Average wait
calls36.133178472545.63339596911456 / Service
time33.831223010442.9995902012
Output 3@RISK Output Report for Max wait time Performed
Statistics for Max wait
6Maximum171.397621640210%42.9506778049Mean80.2063114
18615%48.9070765452Std

Dev28.44826173920%54.5648748881Variance809.3035959725
%59.2035970892Skewness0.23085039230%63.3306379039Kurt
osis2.811540125335%67.8600281508Median80.051066267540
%71.6631782536Mode87.77818031745%75.8191871691Left
X34.652547031650%80.0510662675Left
P5%55%84.1513365915Right
X127.91755652960%87.651649259Right
P95%65%91.4764415845Diff
X93.265009497470%95.1622426542Diff
P90%75%100.1215848998#Errors080%104.2402401087Filter
MaxOff90%117.2681000274#Filtered095%127.917556529Chan
ge in Output Statistic for Max wait
calls70.8039775588.433898751575 / Service
time69.835367664887.2711505256617 / Time between
calls70.648384030586.71725721871256 / Service
calls71.305977524587.16269572671442 / Service
time71.638967421287.4934786937
Output 4@RISK Output Report for Total idle time Performed

Statistics for Total idle
Maximum85.546981047410%1.48343033Mean11.591288959415
%2.1313936638Std
Dev11.017569946820%2.7671768402Variance121.38684753192
5%3.5387141462Skewness1.732598900330%4.4331039933Kurt
osis7.106411316235%5.2402798406Median8.070684974340%6.
1603959102Mode2.272632414145%7.1036116166Left
X0.615659442850%8.0706849743Left
P5%55%9.065854258Right
X33.910730409560%10.6187169707Right
P95%65%12.1309138899Diff
X33.295070966770%14.0568178768Diff
P90%75%16.122844255#Errors080%19.4107007737Filter
MaxOff90%26.9172099091#Filtered095%33.9107304095Chang
e in Output Statistic for Total idle
calls9.942162220816.3069608179101 / Service
calls8.823249198614.63859575161214 / Service
calls9.685807756815.3551846657
Output 5@RISK Output Report for Agent Idle time %

Statistics for Agent Idle time
%StatisticsPercentileMinimum0.00%5%0.17%Maximum23.76%
10%0.41%Mean3.22%15%0.59%Std
Dev3.06%20%0.77%Variance0.000936626925%0.98%Skewness
1.732598900330%1.23%Kurtosis7.106411316235%1.46%Media
n2.24%40%1.71%Mode0.63%45%1.97%Left
X0.17%50%2.24%Left P5%55%2.52%Right
X9.42%60%2.95%Right P95%65%3.37%Diff
X9.25%70%3.90%Diff P90%75%4.48%#Errors080%5.39%Filter
MinOff85%6.00%Filter
MaxOff90%7.48%#Filtered095%9.42%Change in Output
Statistic for Agent Idle time %RankNameLowerUpper11 / Time
between calls2.24%5.18%24 / Time between
calls2.51%5.19%33 / Time between calls2.57%5.07%42 / Time
calls2.62%4.94%66 / Time between calls2.46%4.57%78 / Time
calls2.56%4.39%99 / Time between calls2.76%4.53%101 /
Service time2.14%3.86%1117 / Time between
calls2.45%4.07%1214 / Service time2.43%4.03%1310 / Time
calls2.69%4.27%
Output 6@RISK Output Report for Total wait time Performed

Statistics for Total wait
timeStatisticsPercentileMinimum115%47Maximum1,10010%57
Mean11615%65Std
Dev6720%70Variance4429.506114050325%76Skewness4.43033
1877230%82Kurtosis53.177823341935%87Median10140%91Mo
de8345%96Left X4750%101Left P5%55%107Right
X23460%113Right P95%65%120Diff X18770%128Diff
P90%75%140#Errors080%152Filter MinOff85%166Filter
MaxOff90%192#Filtered095%234Change in Output Statistic for
Total wait timeRankNameLowerUpper130 / Time between
arrivals104148272 / Time between arrivals97138347 / Time
between arrivals98138433 / Time between arrivals101140516 /
Time between arrivals100137625 / Time between
arrivals103139758 / Time between arrivals103138867 / Time
between arrivals98132926 / Service time103138108 / Time
between arrivals1051391123 / Service time1081421235 /
Service time1031371351 / Time between arrivals991321412 /
Service time97129
Output 7@RISK Output Report for Average wait time
Statistics for Average wait
timeStatisticsPercentileMinimum05%0Maximum1110%1Mean1
15%1Std
Dev120%1Variance0.428136645625%1Skewness4.50696980343
0%1Kurtosis54.4603915535%1Median140%1Mode145%1Left
X050%1Left P5%55%1Right X260%1Right P95%65%1Diff
X270%1Diff P90%75%1#Errors080%2Filter MinOff85%2Filter
MaxOff90%2#Filtered095%2Change in Output Statistic for

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