SIMULATION PROJECT Campus Starbucks Coffee Center

Niharika Senecha

CONTENTS

1 Introduction and Problem Environment 03

1.1 Introduction 03

1.2 Problem Statement 03

1.3 Assumptions 03

2 Model Fit 04

2.1 Data collections 04

2.2 Distributions 04

3 Arena Model 05

3.1 Modeling the system 05

3.2 Simulating the model 07

4 Results 09

5 Improving the System 10

5.1 Suggestion for improvement 10

5.2 Comparing the results 11

6 Conclusion 13

References 14

CHAPTER 1

INTRODUCTION AND PROBLEM ENVIRONMENT

1.1 Introduction

This project deals with the simulation of the Campus Starbucks Coffee Center using Arena

simulation software. The motivation behind this project is to model the shop in order to reduce the

long waiting time and increase the utilization of resources. While modeling the system, the

sequence of operations for different types of orders, the layout of the shop, the processing time of

each operation, etc. were considered. The model is run for one day. The results were analyzed and

a suggestion was also made to improve the system.

1.2 Problem Statement

Starbucks coffee center is one of the most crowded places on campus. According to the information

received from Starbucks, sometimes, the count of customers in a day approaches as high as 1000.

Given the popularity of the coffee center, the average waiting time during some busy days, reaches

around 10 minutes also! Considering the little amount of time students have in between their

classes, this can be a disadvantage to them as well as the Starbucks people. The simulation done in

this project is to find out and analyze how the existing system can be improved in order to lower

the average waiting time of each customer.

1.3 Assumptions

The following assumptions are made in the model:

1. The shop is open 13 hours a day.

2. There are no work shifts between the workers in the coffee center.

CHAPTER 2

MODEL FIT

2.1 Data Collection

With the permission from the Starbucks employees, I collected some random data for a day. The

inter-arrival times between customers, the average waiting time to place order, the average

waiting time to receive the order, the types of orders and their attendants, etc. were all noted

down. It was found that 70% of the orders are for beverages, 20% for snacks and 10% for cakes

and cookies.

2.2 Distributions

The raw data collected from Starbucks was saved as text file. Using Input Analyzer, we can find out

the best fit for the data given. I opened the data file, which is the raw data text file in the input

analyzer, a histogram of the data was loaded.

Then, I clicked on the Fit All option in the Fit tab on the menu, which gave me the distribution

summary for the data as shown in the figure below. The distribution for arrival is Uniform (0, 3).

CHAPTER 3

ARENA MODEL

3.1 Modeling the system

The given system is modeled in Arena Simulation Software as shown in the diagram below. There

are four main segments in the model:

1. Starbucks Entrance

2. Place order counter

3. Based on the type of order, there are three separate processes: Beverages, Cookies/Cakes and

Snacks.

4. Starbucks Exit

The customer arrivals are created in the create module, called the Starbucks Entrance, whose

dialog box is shown below.

The distributions for the basic processes for placing order, and the different types of orders like

hot or cold beverages, snacks and cookies or cakes are triangular with the values as shown

below in the diagram.

The resource allocation for all processes was done as shown below.

For the decide module, the values were taken as shown below. It was observed that 70% of the

customers at Starbucks ordered beverages, 20% ordered snacks and the rest 10% ordered Cakes

or cookies. And hence, there are separate processes for each type of order.

The last segment is of the dispose module named as Starbucks Exit as shown below.

There were two statistics defined in order to measure and compare the existing model in terms

of its utilization. The statistics defined were Average Work in Progress and the average total

time taken by customers. They were defined as shown below.

3.2 Simulating the model

The ARENA Campus Starbucks coffee center model was simulated for one day. Parameters like

Replication length and Hours per day etc. were given in Run Setup. The diagram below shows it.

The replication length was given as 780 minutes as Starbucks is open for 13 hours in a day. Some

100 replications were carried out in order to compare results with the new improved model made

later.

CHAPTER 4

RESULTS

On running the model for 100 replications, it was found that the number out for the customers was 517.

The output was as shown below.

For the user specified statistics- Avg Total time and Avg WIP, the output was as shown below.

CHAPTER 5

IMPROVING THE SYSTEM

5.1 Suggestion for improvement

In the figure shown below, it is visible that the waiting time at the place order queue is more as

compared to other processes. Therefore, in order to reduce the wait time at the place order

counter, we modify the Starbucks model and provide two counters for placing order. One for

beverages and the other for snacks or cookies/cakes.

The new model that was created can be seen in the diagram below.

Here, a decide module was put after the arrival. 70% customers go for beverages and remaining

30% go for snacks or cookies/cakes. There is one more decide module for snacks and

cookies/cakes with 66% and 34% probability, respectively. The rest of the model remains the

same except that in this model, we have two types of resources, CustomersIn1 and CustomersIn2

for Beverages and Snacks or Cakes/Cookies, respectively.

5.2 Comparing the results

The two models were run for 100 replications each and were compared using the tool, process

analyzer. The .p files of both the models were added as the scenarios in the process analyzer tool.

The Average total time and the average WIP were added as the input responses and scenarios were

run. The values found were as shown below.

The input charts for both the responses were plotted in the form of box and whiskers plot and the

best scenario was depicted in each using the “smaller the better” criteria and keeping the error

tolerance as zero. The charts are as shown below:

Both the charts clearly depict that the improved model scenario is the best scenario with less values

for both the statistics, namely, average work in progress and average total time in system.

CHAPTER 6

CONCLUSION

The given Campus Starbucks Coffee center was modeled in Arena Simulation Software and the

results were generated. After analyzing the results, it was noticed that the waiting time in place

order counter was more compared to the other stations. So a suggestion was made to increase

the number of place order counters by one. The results before and after were compared. And it

was found that the waiting time in the place order counter decreased and the utilization of the

station increased. Again, lot of suggestions can be made in this system which can lead to cost

saving also.

REFERENCES

[1] David Kelton W., Randall P. Sadowski, Nancy B. Swets. 2013. Simulation with Arena (5e

edition.), McGraw Hill.

[2] Logo - http://www.mgmgranddetroit.com/restaurants/starbucks.aspx

[3] Report Format- https://www.scribd.com/