Shrivastava Shalvi project_report

Starbucks Simulation

Starbucks Centre

Steger Student Life Centre,

University of Cincinnati

Submitted by:

Shalvi Shrivastava

M10839782

University of Cincinnati

Table of Contents

1. Introduction and Problem Statement 3

2. Data and Model Distributions 5

3. Simulation Model in Arena 8

4. Results And Interpretations 14

5. Improving the System 16

6. Conclusion and References 18

7. Appendix 20

1. Introduction and Problem Statement 1.1 Introduction

Starbucks, based at Steger Student Life Centre, University of Cincinnati, is one of the most popular spots, specialising in beverages such as Coffee and Tea. Every day, nearly millions are served around the world.

We have used Simulation of Starbucks here to assist in evaluating operations within the coffee shop in an effort to evaluate its performance in this highly competitive industry. Data was collected from Starbucks Centre at Steger Student Life Centre after the permission of the then present shift lead named Kayla Parr. The primary aim is to ensure continued financial returns and productivity. A simulation model was built that used customer order database to ensure continued financial returns and optimal productivity. It used customer database to allow queueing methods, order taking and other service procedures to be analysed.

1.2 Description of Problem

Starbucks is one of the busiest place on campus. University of Cincinnati has classes throughout the

day and as well in the evening. This makes the coffee centre more popular. The basic flow of activity

inside the shop is shown below.

Steps involved in the process of acquiring service:

1. Customer arrives at Starbucks.

2. Customer joins the queue of order and billing.

3. Customer waits if the server is busy.

4. Customer reaches server and places the order.

5. Customer waits in the waiting area to get served.

6. Customer gets served and exits.

The above cycle continues for next customer in the queue at any server.

Standing in queues is an inconvenience for customers everywhere. Taking the example of campus

Starbucks, with classes in vicinity, waiting in line is a loss for both, students as well as professors,

who are in rush to classes.

The parameters obtained from the above procedures could be used to analyse ways to potentially

increase efficiency and decrease queue length.

1.3 Assumptions

The coffee Shop works for 12 hours a day.

There are two counters, one for Ordering and Billing having one server and Waiting counter

which has two servers.

The system is memoryless, in that each arrival is independent of the previous arrival, and

that the variables are identically distributed.

The servers are working throughout without any change in shifts.

2. Data and Model Distributions

2.1 Data Collection:

In order to collect the data needed to create simulations and analyse wait times, trips to Starbuck

shops located in Steger Student Life Centre were taken. An equal amount of time of 6 hours was

spent in taking data. Tables were set up in each location in order to collect data regarding the wait

times of customers while in line and while waiting for an ordered drink. In order to get accurate

information, tables were chosen that were in view of the entrance of the shop, the cashier’s

counter, and the waiting area.

Entities are as following:

Customers

Servers

Waiting area

In order to record the times spent in the queues within the store, a timer was used. The timer was

pressed once a customer entered the store, once a customer ordered a drink, and once a customer

received a drink. This was helpful in ensuring that the data recorded using the timers was not mixed

up between different customers who ordered different. All of the information being recorded was

sent to a spreadsheet. We have to be careful as snacks and beverages have different average

preparation times.

2.2 Variables:

Input:

Inter arrival Time of Customer

Time taken at Order and billing counter

Serving time

Response:

Waiting time – Time for which customer waits

Average Wait time for order

Maximum Wait time for order

Average Wait time for order

Maximum Wait time for order

Average Queue – Number of average people in a queue for giving orders

Maximum queue – Number of maximum people in queue for giving orders

Average wait time for delivery

Maximum wait time for delivery

Average total time Spent by Customer

Maximum Total Time spent by customer

Utilization – It is estimated by dividing the amount of time than the server is busy during a

simulation by the amount of time covered by the simulation.

2.3 Distribution:

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

the best fit for the data given. With the fit all option these are the distributions that were observed.

Inter Arrival Time:

Similarly the Input Analyser was fitted with all the other raw input data of Counter and Billing,

Beverage Service and Snacks Service distribution.

Counter and Billing Distribution:

Beverage Service time Distribution:

Snacks Service time Distribution:

3. Simulation Model in Arena

3.1 Modelling the System:

We will be using Create, Process, Decision and assign module for creating the model in Arena.

1. Arrival of Customer

2. Counter and Billing.

3. Order placing: Beverages and Snacks.

4. Service line wait

5. Customer Exit

We will go through the model step by step.

1. Arrival of Customer:

It is represented by the Create Module in Basic Process which is called ‘Arrival of Customers’.

The expression is the distribution obtained from ‘Input Analyser’ from Inter Arrival time.

2. Order and Billing Counter

The Queue for the counter and billing is given by the following process:

3. Ordering Preference? Snacks or Beverage?

Next we had observed that the Orders placed by the customers were divided in either beverages or

snacks. They have a different time distribution as snacks take lesser time to be serviced than the

beverages. It was observed that the orders were divided in 78:22 ratio, where 78% represented the

orders for beverages and 22% were for snacks.

Hence we placed a 2-way decision module after the billing and counter queue.

4. Beverage Counter

Approximately 78% of customers prefer to purchase beverages from Starbucks. Hence an ‘Assign’

module is placed after the decision module with the distribution obtained from ‘Input Analyser’ for

Beverage Preference distribution. The attribute here is assigned ‘Prep_Time’ which will converge

into Service Module after it.

5. Snacks and Cookies Counter

Snacks are preferred by approximately 22% of incoming customers. The time taken for reheating the

already prepared snacks is lower than preparing the beverage. The distribution is as obtained from

the ‘Input Analyser’. The attribute here is also assigned ‘Prep_Time’ which will converge into Service

Module after it.

6. Servicing Queue

The Servicing Module, refers to the total time a customer will wait for his order to be ready, this will

be the addition of the ‘prep_time’ obtained from the previous assign modules ‘Beverages’ and

‘Snacks’.

7. Customer Exits

The final one was the dispose module as shown. It represents exiting of Customer after collecting his

order.

8. Run Setup and Replications

With 100 replications I have run my model for 12 hours (720 minutes ), with my base time as

minutes.

4. Results and Interpretations

4.1 Output Result:

We get a detailed report after we run the model as described above. It has a pre-defined Number

Out. This gives us the Number Out i.e. those who have successfully left the system. Here I have run

the model for 12 Hours, which gives me the Number Out as 453, for 100 replications.

The detailed report produced by Arena is given below. We can view the following categories:

Entity

Queue

Resource

1. Entity

Under Entity we have Time. It gives us output regarding Average Value, Minimum, and

Maximum. The Output is as follows:

The average waiting time for a customer is 6.42 min and the average service time for customer is

4.46 min. We have to reduce the overall system time.

1. Queue:

We obtain the Waiting Time and Number of Entities waiting from the queue report.

We see that the waiting time is .63 min for Order and Billing Counter while 5.7 min for Servicing

Queue.

2. Resource:

Under resource, we look at the Scheduled Utilization of the Resources.

This gives the utilization of all the resources. The output is as follows:

It can be seen that ‘Resource 2’ for Service Queue is used maximum while the other resource for

Order and Billing Queue is less.

5. Improving the System

5.1 Suggestions:

To reduce the Average Total Time, we increase the capacity of

Order and Billing Counter

Servicing Counter

This would reduce the waiting time of queues.

Changes:

The Resource Capacity of Order and Billing Counter was increased to 2.

The Resource Capacity of Servicing Counter was increased to 3.

5.2 Comparison of Results:

Rockwell Software provides us with a complimentary tool with Arena called Process Analyser. We

can use it to compare different scenarios when we increment the resource at each counter. We used

it for two scenarios:

1. Increasing Order and Billing Resource Scenario: Here we increment Resource_1 at Billing and

Order Counter from ‘1’ to ‘2’.

2. Increasing Serving Resource Scenario: Here we increment Resource_2 at Serving Counter

from ‘2’ to ‘3’.

It is seen that the Customer Total Time in system is reduced from 10.895 min to 9.865 min and Time

average of number of people in system (WIP) from 7 to 6 in Scenario 1 (Increasing Order and Billing

Resource Scenario). This is further reduced in Scenario 2 (Increasing Serving Resource Scenario) with

Customer Total Time in System to be 5.143 min and Time Average of number of people in system to

be 3.

Here, the best scenario is marked in red. Both the charts depict that the improved model is

the best when we increment the Serving Resource from ‘2’ to ‘3’.

5.3 Alternate Scenario

We observe from the Process Analyser Output that the best case scenario would be if we

increment server at the Serving Counter.

Hence once we increase the resource in our current model with 100 replications, we observe

the following result.

We see the Serving Queue Average Waiting Time decreases from 5.79 min to 0.036 min. This

is an incredible reduction in time, making it convenient for customers. There is also

reduction in the Average Number of Customers waiting in queue, from 4 (approx. value 3.7)

to 0.

6. Conclusion and References

6.1 Conclusion

It has been observed from the Simulation modelled in Arena Software that the waiting time

at the Serving Queue was large. Once we take that counter into consideration, we get the

best scenario i.e. when we increment the resources at Service Counter we get better result

where Customers have lower system time. Changing the amounts of resources can result in

changes within the queue times due to resources being employed in different areas. By

testing multiple variations of resources, an optimal model for efficiency and profitability for

Starbucks was hence found.

6.2: References

1. Simulation with Arena (W.David Kelton, Randall P. Sadowski, Nancy B. Zupick, Rockwell

Automation)

2. Data – Starbucks (University Of Cincinnati)

3. Image : https://www.uc.edu/mainstreet/sslc.html

:%20https:/www.uc.edu/mainstreet/sslc.html

Appendix

Category Overview19:04:54 December 4, 2016

Starbucks Model

Time Units:Replications: 100 Minutes

Values Across All Replications

Key Performance Indicators

AverageSystemNumber Out 453

Model Filename: Page of1 5C:\Users\Shalvi\Desktop\Simulation\Shrivastava_Shalvi_Project2


Starbucks Model



Entity

Time

VA Time MaximumAverage

MinimumAverageHalf WidthAverage

MinimumValue

MaximumValue

4.4663Entity 1 0.02 4.2452 4.6758 0.8458 6.9708

NVA Time MaximumAverage


MinimumValue

MaximumValue

0.00Entity 1 0.00 0.00 0.00 0.00 0.00

Wait Time MaximumAverage


MinimumValue

MaximumValue

6.4284Entity 1 0.76 1.5219 25.8201 0.00 44.2353

Transfer Time MaximumAverage


MinimumValue

MaximumValue

0.00Entity 1 0.00 0.00 0.00 0.00 0.00

Other Time MaximumAverage


MinimumValue

MaximumValue

0.00Entity 1 0.00 0.00 0.00 0.00 0.00

Total Time MaximumAverage


MinimumValue

MaximumValue

10.8947Entity 1 0.78 5.7671 30.4334 0.9395 49.6569

Other

Number In MaximumAverage


Entity 1 1.15 461.69 478.00 448.00

Number Out MaximumAverage


Entity 1 1.30 453.20 470.00 437.00

WIP MaximumAverage


MinimumValue

MaximumValue

6.9508Entity 1 0.49 3.6005 19.2671 0.00 31.0000



Starbucks Model



Queue

Time

Waiting Time MaximumAverage


MinimumValue

MaximumValue

0.6393Order and Billing Counter.Queue

0.04 0.3760 1.4167 0.00 9.0468

5.7980Servicing Queue.Queue 0.76 1.0793 25.3153 0.00 43.8955

Other

Number Waiting MaximumAverage


MinimumValue

MaximumValue

0.4107Order and Billing Counter.Queue

0.02 0.2363 0.9194 0.00 6.0000

3.7093Servicing Queue.Queue 0.48 0.6745 16.0505 0.00 28.0000



Starbucks Model



Resource

Usage

Instantaneous Utilization MaximumAverage


MinimumValue

MaximumValue

0.9022Resource 1 0.00 0.8690 0.9322 0.00 1.0000

0.9643Resource 2 0.00 0.8838 0.9971 0.00 1.0000

Number Busy MaximumAverage


MinimumValue

MaximumValue

0.9022Resource 1 0.00 0.8690 0.9322 0.00 1.0000

1.9286Resource 2 0.01 1.7677 1.9942 0.00 2.0000

Number Scheduled MaximumAverage


MinimumValue

MaximumValue

1.0000Resource 1 0.00 1.0000 1.0000 1.0000 1.0000

2.0000Resource 2 0.00 2.0000 2.0000 2.0000 2.0000

Scheduled Utilization MaximumAverage


Resource 1 0.00 0.9022 0.9322 0.8690

Resource 2 0.00 0.9643 0.9971 0.8838

0.900

0.910

0.920

0.930

0.940

0.950

0.960

0.970

Resource 1Resource 2



Starbucks Model



Resource

Usage

Total Number Seized MaximumAverage


Resource 1 1.14 461.28 477.00 447.00

Resource 2 1.30 455.18 472.00 439.00

455.000

456.000

457.000

458.000

459.000

460.000

461.000

462.000

Resource 1Resource 2


Data & Analytics

Shrivastava Shalvi project_report