A Simulation Model of Starbucks Cafe using Arena Software

SIMULATION MODELING PROJECT
Starbucks Coffee Centre
Steger Student Life Center
University of Cincinnati, Ohio
Submitted by
Sagar Vinaykumar Tupkar
MS-Business Analytics’16
University of Cincinnati

CHAPTER 01
INTRODUCTION AND PROBLEM STATEMENT
1.1 Introduction
This project is a part of the course BANA 6035-Simulation Modeling where the main focus lies on
the simulation software called ‘Arena’ owned by Rockwell Software. The aim of this project is to
prepare a working simulation model of the Starbucks Centre at Steger Student Life Centre,
University of Cincinnati using the software ‘Arena’. The model in Arena gives a precise output of
the statistical accumulators like total number of entities served, sum of the queue times for all
the entities, maximum time in queue, sum of the total times in system, maximum total time in
system observed etc. where customers would be the Entities for this model. This model uses the
facts and parameters that are available from the layout of the shop, management system,
options of order and sequence followed, resources available etc. and puts them in Arena to
prepare the skeleton of the model. For this model, the input data was the inter-arrival times of
the customers and the service times at each counter which was collected for the rush hours at
Starbucks to run the model. The model was run for 30 days to analyze the results and based on
the statistical conclusions, suggestions were made to economically improve the efficiency and
working of Starbucks Shop.
1.2 Problem Statement
The Joseph A. Steger Student Life Center (SSLC), named after UC's 24th president, is one of the
most unique buildings on MainStreet due to its 800+ foot length and 40 foot width. SSLC, in the
heart of MainStreet, houses offices and meeting spaces for student groups and organizations.
‘Starbucks’ is one of the favorite and busiest places at SSLC. With the academic classes,
Recreational Centre, Engineering Research Centre in the vicinity, Starbucks has rush almost all
the time. Also, with the arrival of autumn at this time of the year, coffee seems like an irresistible
beverage for most of the students. According to the authentic information received from the
Starbucks authority, the autumn season experiences almost 1000 customers at the shop
sometimes. While the shop has rush almost all the time, there is comparatively more rush for
evening hours from 4 pm to 7 pm. Also, during the rush hours the average waiting time of the
customer reaches upto 8 minutes sometimes. The system has to be more efficient as students
have less window of time to shop between two classes and the shop loses potential customers
due to this problem. The simulation project done uses the model parameters to probe into the
Starbucks system and analyze how the existing system can be ameliorated to decrease the
average time spent by a customer at Starbucks.

1.3 Assumptions
The existing system at Starbucks could not be modeled and simulated exactly the way it is due to
natural variabilities and unscheduled activities. There has to be some assumptions in order to
exclude these activities. Even though the model does not delineate the exact situation at
Starbucks, the statistical inferences that we get from Arena are very useful in analyzing the
situation. Here are a few assumptions that were made for the model in this project –
1. The shop is open 13 hours a day.
2. There are no work shifts between the workers.
3. There are no breaks for the workers during the time when the model is running.
4. Every counter has a single server.
5. The time of customers who don’t buy anything is not counted in the overall average
customer time in the system.
6. Some of the data for decision modules was taken from the Starbucks authority due to lack
of sample points.
7. The service time varies for different workers.
8. Customer may/may not stay at the Store after his/her order is served. This is not taken
into account to obtain the statistics. ‘Customer leaves the store’ implies that the customer
transaction is complete and he/she received his order. He may/may not leave the store.
9. There was no activity which caused any deviation from all the above assumptions.
All these assumptions holds valid for the time when the model is running.

CHAPTER 02
DATA COLLECTION AND DISTRIBUTION FITTING
2.1 Data Collection
The model prepared in this project considers the resources present at the Starbucks Centre like
Cash Counter, Food items server, Hot beverages server, Cold Beverages server etc. to keep track
of the sequence of order deliverance. To input the times for each resource, data was collected
for inter-arrival times of customers and service times at each of the resource in the shop with the
permission of the Starbucks authority. The data was collected for an hour from 5pm to 6 pm
which is the rush hour at Starbucks for a week. Also, the type of order for each customer was
also noted and it was found that, over the course, nearly 2% of the customers leave without
buying anything as the desired product is not available in the store. For the customer who place
order, only 20% get a food item like snacks, cakes, cookies etc. and out of those, 95% of the
customers opt for beverages; rest 5% just have something to eat with no drink. Now, of all the
customers who have beverages, 80% go for a hot beverage (the data was collected during the
autumn season) and rest for cold beverages.
2.2 Data Fitting to Distributions
The Arena model requires the data as an input to run and analyze the situation. For convenience,
Arena need the data in the form of a distribution which best fits the raw data. The discrete values
are taken as the numbers that are randomly chosen from these distributions. So, the raw data of
the inter-arrival times and service times for each resource needed to be fitted to a distribution
which then would be used in Arena. Hence, the raw data was saved in a text file which was then
used in a software called ‘Input Analyzer’ to know the best distribution fit for that raw data.
Input Analyzer loads histogram of the raw data in the input text file and does the fitting to that
histogram. Here is the fitted histogram and the distribution summary of the fitted data for the
inter-arrival times of the customers –

Similarly, the raw data was fitted and distributions were obtained using Input Analyzer for all the
resources namely cash counter, food item server, hot beverage server and cold beverage server.

Hot Beverage Server
Cold Beverage Server

CHAPTER 03
ARENA MODEL
3.1 Modeling the System
The Starbucks store system was bifurcated into pieces to prepare the model in Arena. Various
modules e.g. Create, Process, Record, Assign, Decision etc. are used in Arena to simulate the real
world scenario. The procedure that any customer follows at Starbucks is divided into certain
steps to give a flow in the Arena model. The steps can be treated as –
1. Customer enters the store
2. Customer waits in queue to place the order
3. Customer places the order
4. Customer waits in queue for food item
5. Customer gets the food item
6. Customer waits in queue for hot/cold beverage
7. Customer gets hot/cold beverage
8. Customer leaves the system
There are certain decision modules which decide the path of the customer in the Model. Also,
the model consists assign and record modules to calculate the average customer time in the
system and the number of customers who bought food items and hot, cold beverages. The
important parameters in the Arena model are the Resources and Queues. Here is an overview of
the model parameters –
Arena Starbucks Type / Action
1. Entities Customers Part
2. Resource 1 Cash Counter Person Seize Delay Release
5. Resource 2 Food Item Server Seize Delay Release
3. Resource 3 Hot Beverage Server Seize Delay Release
4. Resource 4 Cold Beverage Server Seize Delay Release
6. Queue 1 Cash Counter FIFO
9. Queue 2 Food Item Service FIFO
7. Queue 3 Hot Beverage Service FIFO
8. Queue 4 Cold Beverage Service FIFO

Here is a snapshot of the Arena Model that was prepared –
To explain the model parameters stepwise, we will go through each step mentioned earlier
looking closely into the modules and logic used to prepare the model.
1. Customer enters the store –
The customers enter the store by a create module named ‘Customers Arrive here’ in Arena
whose dialog box is shown below. The expression here is the one that we got from the input
analyzer.

An Assign module named ‘Arrival Time Recorded here’ is used after the Create module to
note the arrival time of the customers. An Attribute named ‘Arrival Time’ with a value TNOW
is assigned to each customer. TNOW is an inbuilt function in Arena which records the specific
time whenever any entity passes through it. Here is the dialog box for Assign module –
2. Customer waits in queue to place the order –
The customers join the queue if there is any, to place their order. This queue is shown in a
different white box inside the model. During rush hours, there is a long queue so mostly
people have to wait in queue to place order.
3. Customer places the order –
After completing the wait in queue, the customers reach the cash counter and places the
order. We add a process module with action as ‘Seize Delay Release’ and the expression for
the service time of this process is the one we got from Input Analyzer. Here is the dialog box
of the Process module for Cash counter.

After this, a decision module is placed called ‘Decision for product availability’ which
decided whether the product is available or not. If the product is not available, the
customer leaves the system else he continues further in the process. The probability for
this is input in the module as 98% for availability. Here is the dialog box –
Another decision module is placed for food item named ‘Decision for Food items’ which
decides whether the customer wants any food item. The probability for this is 20%
customers here opt to buy food items and they go through the food item server, rest go
directly to the beverage counter. Here is the dialog box –
4. Customer waits in queue for food item
The customers who opted for Food items wait in queue of the food items customers before
they are served the food items.

5. Customer gets the food item
A process module named ‘Food items served here’ is added to the model with action as ‘Seize
Delay Release’ and the expression for the service time of this process is the one we got from
Input Analyzer. Here is the dialog box of the Process module for Food item.
Another decision module is placed for beverages named ‘Decision for Beverages’ which
decides whether the customer wants any beverage. The probability for this is 95% customers
here opt to buy beverage and they go through the beverage server, rest leave the system.
Here is the dialog box –

Another decision module is placed for beverages named ‘Decision for Hot or Cold Beverages’
which decides whether the customer wants hot or cold beverage. The probability for this is
80% customers here opt to buy hot beverage and they go through the hot beverage server,
rest will go through the cold beverage server. Here is the dialog box –
6. Customer waits in queue for hot/cold beverage
The customers wait in the respective queues of the beverage before they get the beverage.
7. Customer gets hot/cold beverage
After completing the wait in queue, the customers gets their respective beverages. We add a
process module with action as ‘Seize Delay Release’ and the expressions for the service time
of this process are the ones we got from Input Analyzer. Here are the dialog boxes of the
Process modules for Beverage Servers –

After the customers are served, they pass through a record module where the number of
customers for each type are counted. Then they pass through another record module where
the total time of the customer in the system is noted. Here are the dialog boxes for each of
the modules –

8. Customer leaves the system
A Dispose module is placed at the end of the model where all the entities depart from. The
entities have to go into the dispose module when their role in the model is over. We call this
module as ‘Customers Exit here’. Here is the dialog box for it –
The entities that were created at the beginning of the model were disposed off in the above
module. Apart from these steps, another important feature of this model are the Resources
and the Queues as mentioned earlier.
1. Resources
As given in the table earlier, there are 4 resources in the model. Each resource has its own
service time which was fitted by the distributions. Here is a table of the resources –
1. Resource 1 Cash Counter Person Seize Delay Release
2. Resource 2 Food Item Server Seize Delay Release
3. Resource 3 Hot Beverage Server Seize Delay Release
4. Resource 4 Cold Beverage Server Seize Delay Release
Arena has a display of all the resources in the resource module where all the information
about the resources is displayed. Here is a snapshot of the resource information –

2. Queues –
There are 3 queues in the model. We will analyze the waiting time in each queue after we run
the model. Here is a snapshot of the queue information from Arena –

3.2 Simulating the Model
The Arena Software has on option of Run window where we have to mention the Run
information like replication length, number of replications etc. The model prepared here was
run for 3 hours and 30 replications were made to consider variabilities. Here is a snapshot of
the Run dialog box –
The entities were given an animation of persons so the entities could actually be seen when
the model is in run mode.

CHAPTER 04
RESULTS AND INTERPRETATION
4.1 Results
The Arena Software produces a detailed and structured result window which allows the user
to view results by Entity, Queue, Resource and anything that is specifies in the model. The
category overview has a pre-defined KPI as the Number out. This gives the number of entities
which successfully left the system. For the Starbucks model, which was run for a replication
length of 3 hours, the Number Out value was 196, for 30 replications.
4.1.1 By Entity –
The most important attribute attached with the entity is ‘time’. Arena gives a detailed output
with Average value, Minimum, Maximum, Half width etc. for the various times that are
observed by the entity during its stay in the system. In Starbucks model, the main output is
the Total time in system, the wait time and the service time. It also gives the number of
entities in and out of the system. Here is the output from Arena –

The average waiting time for a customer is 3.31 minute and the average service time for the
customer is 1.53 minutes. This makes the average total time spent by a customer in the
system to be 4.83 minutes. We want to reduce this quantity in order to increase the efficiency
of the Starbucks system.

4.1.2 By Queue –
Arena gives the Waiting time and Number of entities waiting for each queue in the model.
We will observe the results for the maximum waiting time and this is where we need to bring
some changes to reduce the waiting time of that particular queue. Here is the output for
queue –
It can be seen that the waiting time is maximum, 2.82 minutes for the Cash counter queue
followed by the queue for Hot Beverages. We want to reduce this quantity in order to
increase the efficiency of the Starbucks system.

4.1.3 By Resource –
Arena gives a myriad of outputs for Resource Usage but the most important here is the
Scheduled Utilization of the Resources. This gives the utilization of all the resources in the
Model. Here is the output –
It can be observed that the Cash Counter and Hot Beverage Resource is used up for a
maximum time while the utilization of other two resources is very less comparatively.
4.1.4. By User Specified
Arena specially gives an output for any other user specified attribute. In this model, we
specified the Average Customer Time and it is recorded to be 4.856 minutes on an average.
Also, we recorded the count of customers of different types. Here is the output –

4.2 Interpretations –
It is observed that the Cash counter resource and the Hot Beverage resource have –
a. High utilization
b. High Waiting time in queues
Also, the customers spend more than twice the time waiting in the queue as compared to the
time when they are being served.
The efficiency of the Starbucks store would increase when the average total time spent by a
customer would decrease. According to the above interpretations, it can be concluded that
some improvements in the above two resources is needed in order to reduce the average
time in the system.

CHAPTER 05
IMPROVING THE SYSTEM
5.1 Suggestions –
As mentioned in the interpretations, the Cash Counter and Hot Beverage Resource have the
maximum utilization and longest queues on an average. Hence to reduce the average total time
in the system, we can increase the capacity of the Cash Counter and Hot Beverage Resource to
2. To do this economically, the resource for Cold Beverage should be cross-trained to serve Hot
Beverage as well. This would help in balancing the utilizations of the resources and reduce the
waiting time in the longer queues.
As far as the changes in the system flow is concerned, there is one change that can increase the
efficiency of the system. Instead of having another resource for food items, Starbucks can have
two Cash Counter and both of these resources can serve food items to the customers there itself.
As most of the food items available at Starbucks are readily available and doesn’t require any
preparation, e.g. snacks, cakes, cookies etc. , this is a feasible option.
The model was renovated with the above suggestions and here is a glimpse of the new model –

Changes –
1. The process module (and hence the queue) for Food items was removed
2. The Resource capacity for Cash counter was increased from 1 to 2
3. The service time for Cash counter was increased to accommodate the service of food
items
Now, the model has only 3 resources and the resource capacity of Cash counter is now 2. i.e. an
extra cash counter was introduced.
5.2. Comparing the results –
We discussed the Arena results for the original model earlier and by interpreting the results we
came up with a few suggestions and implemented those to obtain an improved version of the
model. Now, we will statistically compare the results of the changes that were incorporated in
the model. Rockwell Software also provides a complimentary tool with Arena called Process
Analyzer. Using Process Analyzer, we can compare the results of different models or different
scenarios of the same model statistically and plot the graphs accordingly.
The Process Analyzer was used to compare 3 scenarios –
1. Original Model with capacity 1 for each resource. Food Item resource present.
2. Cash 2 Model with capacity 2 for Cash counter resource. Food Item resource absent.
3. Cash and Hot 2 Model- capacity 2 for both Cash Counter and Hot Beverage Resources.
Food Item resource absent.
Here is a snapshot of the Process Analyzer scenarios –
It can be seen that the average customer time is reduced significantly (2.216 minutes from 4.856
minutes) for the improved model. The time is even further reduced for Scenario 3 (1.752 minutes)
when both the Cash counter and hot beverage resources have 2 capacity.

After comparing the responses of Average Customer Time for all the above scenarios, graphs
were plotted for the response variation across these scenarios.
Here, we get a visual perception about the response and the best scenario is marked in red. Both
the charts depict that the improved model with capacity 2 for both the resources is the best
scenario.

5.3 Insights into the Statistics
The Process Analyzer (PAN) Software has an inbuilt capability to give results by comparing
different scenarios using hypothesis testing by various extant tests for the same. In this section,
we compare the results of PAN statistically to prove that Scenario 3. is the best scenario.
Below is a table showing the output data from PAN which gives information about the maximum,
minimum, average value of the response. Also, based on the data, it gives a 95% Confidence
Interval along with the Half Width.
Scenario
no.
Scenario Min Max Low Average High 95% CI
1. Original Model 0.001021 18.6 4.369 4.856 5.342 0.4867
2. Cash 2 9.12E-05 8.412 2.126 2.216 2.306 0.09019
3. Cash and Hot 2 0.006089 5.465 1.715 1.752 1.789 0.03693
It can be observed that not only the average value, but also the Half Width for 95% Confidence
level is minimum for the third scenario. For this result to be statistically significant at a confidence
level of α=0.05, we can show that the upper limit of Scenario 3. is lower than the lower limits of
all other scenarios. So, this case proves to be the best case. It can be concluded that for scenario
3, an assertion that the average customer time in the system will lie between 1.715 and 1.789
with an average of 1.752 is true with 95% confidence.

CHAPTER 06
CONCLUSION
The aforementioned Starbucks store at Steger Student Life Center (SSLC) located in the University
of Cincinnati, Ohio was modeled in Arena Simulation Software and the results about the relevant
parameters were generated. A deep analysis was done on the output results of Arena and it was
observed that the average customer time in the system during rush hours was large enough for
Starbucks to lose potential customers and downgrade the business. By probing into the flow of
counters of the system, it was observed that the waiting time in the queue at the cash counter
was significantly large as compared to other queues. So, taking into consideration the resources
available at Starbucks, a suggestion was made where the food items would be served at the cash
counter itself and another cash counter would be added. To increase the efficiency economically,
it was suggested that the server for Cold Beverages should be cross-trained to serve Hot
Beverages as well. The new model was compared statistically for the Average Customer time in
the System using a software called Process Analyzer and it was found that the new, suggested
model has a statistically significant decrease in the Average Customer time as compared to the
original model. Hence, with certain suggested changes in the management and operations at
Starbucks, there can be an increase in their Business. Again, there can be ample suggestions on
and modifications in the model to optimize the output both economically and commercially; but
we have discussed only one of them.
REFERENCES –
1. Content –
Simulation wit Arena 6/e- W. David Kelton- University of Cincinnati, Randall P. Sadowski,
Nancy B. Zupick, Rockwell Automation
2. Data –
Starbucks Store, SSLC, University of Cincinnati, Ohio
http://www.uc.edu/mainstreet/sslc.html
3. Image –
http://7-themes.com/data_images/out/45/6923895-art-logo-starbucks-coffee.jpg

A Simulation Model of Starbucks Cafe using Arena Software

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A Simulation Model of Starbucks Cafe using Arena Software