E-Commons Café Final Report

Louisiana State University

E-Commons Café Final Report

Michelle Blount Shreya Mehta Laura Levert Casey Robelot

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Contents Overview ....................................................................................................................................................... 2

Objectives ..................................................................................................................................................... 2

Problem Description ..................................................................................................................................... 2

Conceptual Model ......................................................................................................................................... 3

Alternatives ................................................................................................................................................... 3

Evaluation ..................................................................................................................................................... 3

Data Collection and Analysis ......................................................................................................................... 3

Problems, Limitations, or Biases ................................................................................................................... 7

Models .......................................................................................................................................................... 7

Base Scenario ............................................................................................................................................ 8

Alternative 1 ............................................................................................................................................ 10

Alternative 2 ............................................................................................................................................ 10

Alternative 3 ............................................................................................................................................ 13

Process Logic ............................................................................................................................................... 16

Verification and Validation ......................................................................................................................... 17

Project Plan ................................................................................................................................................. 17

Output Analysis, Results, and Conclusions ................................................................................................. 18

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Overview The E-Commons Café is a coffee shop that is located in the LSU bookstore and is a popular hub for students and faculty during breakfast and lunch hours. During these times, there are usually only three employees, one of which is a cashier, who rings up the customers, while the other two are baristas, who make the coffee orders. There is also one register currently in use at the cafe. This causes problems during peak hours of business such as long queues, decrease in mobility around the store area, and balking. These problems lead to less business and therefore less profit.

Objectives Decrease queue lengths in order to decrease the number of balking customers. This will allow E-

Commons café to move more customers in and out of their store in order to gain more revenue.

Decrease average flowtime to improve on several lean objectives such as minimizing number of balking customers.

Problem Description For this project, we will analyze the process that customers go through when arriving at E-commons Café. The scope of this project is limited to the hours of 7:30 am – 9:00 am and 11:30 am – 1:00 pm. We will record observations during peak times. From these observations, we will model the following:

Average flowtime

Average number of people in queue

Number of servers

Layout of the store

Time differences for the different types of beverages and food

The following are excluded from this project:

Measuring inventory such as how much food, drinks, and materials they have in stock or any

stock outages

Defects

Company expenditures

Reneging customers

Equipment failure

The following are assumptions that have been made:

Random arrivals and service times

Two different types of customers

o Convenience store customers

o Coffee customers

Specialty coffee customers

Plain coffee customers

Finite number of servers

Infinite calling population

Server schedules unless a shift change occurs

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Conceptual Model Figure 10 is the simulation for the current system in use at the E-Commons Cafe. There are two types of customers: coffee shop customers and convenience store customers. Convenience store customers first choose items they want from the store and then get in queue for the register. Coffee customers get in queue immediately upon entering the store. Both customers enter the same queue line if they do not balk. If they balk, they are disposed from the system. There is only one register and two baristas working at one time. Convenience store customers are rung up by cashier and are then disposed from the system. Coffee customers are rung up by the same cashier and then enter another queue to wait for their coffee order. Once order is received, they are disposed from the system. The logic in the model can be seen below in Base Scenario.

Alternatives Currently at the coffee shop there is only one register and two baristas. There are not enough employees to accommodate the appropriate amount of work during peak breakfast and lunch hours. During these times, the store is busy with a lot of customers, forming a long single line. Some customers also leave without buying anything because of the long wait (balking). Since the customers leave the store, there is a decrease in the profit made by the coffee shop. To improve the current queue in the coffee shop, we have proposed three alternatives that will make the customer time in queue shorter. To reduce the amount of time customers spend in queue and balking, it is important to add more registers and baristas to decrease flowtime. For Alternative 1, we added one more barista and another register, with the idea that both coffee and convenience store customers will be taken care of on a first-come first-served basis. For Alternative 2, we added two specialty coffee baristas and one plain coffee barista, for a total of three specialty coffee baristas and one plain coffee barista. We also added another register, but for this model one register will be used for specialty coffee customers only and the other will be used for convenience store and plain coffee customers. For Alternative 3, we added one more barista and another register. One register will be for convenience store customers only, while the second register will be for plain and specialty coffee customers only. By analyzing the current model and the alternatives created, we hope to eliminate some of the process time spent at the register and time spent making coffee by the employees. The result of our analysis is discussed later in the paper.

Evaluation For this project we will evaluate and improve how the E-Commons Café handles large crowds of customers during peak times of the day. We will focus on maximizing registers and employees to improve on several lean objectives such as decreasing customer flowtime and minimizing balking, which would improve customer satisfaction. We will also address the mobility of customers through the café. Through careful evaluations and calculations, we will be able to accurately model the process and make necessary revisions.

Data Collection and Analysis From the existing system, we will collect inter-arrival times of customers, the average flowtime, number of coffee customers versus number of convenience store customers, number of balking customers, customer time in queue, service rate of cashiers and baristas, and amount of time convenience store customers take to choose items. We collected arrival times by observing customers walking in the store,

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and recording the time they entered. We then subtracted the difference between the times to get the inter-arrival time. For flowtime, we started the stopwatch from the time the customer entered the queue to the time the customer received their order or finished checking out. For number of coffee vs. convenience store customers and amount of balking, we made a tally of the number of balking customers, and of how many people ordered coffee versus how many people bought items from the convenience store. We noted the number of customers who ordered plain coffee drinks versus those who ordered specialty drinks. This data will be used analyzed and used in our alternatives. We measured customer time in queue by recording the time from the stopwatch how long it took from the time the customer entered the queue to the time the cashier completed the transaction. We measured the service rate of one server by recording the time it took for the cashier to ring up the customers. The amount of time it took convenience store customers to choose items they wanted to purchase was found by recording the time they entered the store till the time the cashier completed the transaction. We found there to be more coffee customers than convenience store customers. Twenty-two out of the thirty (73.33%) customers that completed the process were coffee customers while eight were convenience store customers. To break it down further, three of those coffee customers (10%) ordered plain coffee which we found to have a lower wait time when waiting to receive their order than those who ordered specialty drinks. We also found that on average, there were fifteen people in line when a customer balked. From the data we collected, we noticed that on average, people were arriving every 45 seconds. Convenience store customers would take time to pick out an item, which was on average 73.57 seconds. They would then proceed to the line that coffee customers joined when they entered. This line was the queue for the cash register and the average time spent in this line was 33.37 seconds. Those who ordered a cup of coffee would then wait for the barista to make their order. Plain coffee customers would wait on average 24 seconds and specialty coffee customers would wait on average 94.67 seconds.

Once we collected the data, we put the data into text files. With these text files, we were able to upload them into Input Analyzer. Once uploaded, we chose the best fit for the data and used these distributions in various blocks in our models. If the best-fit distribution was an obscure one, we went with the next best-fit distribution, which was determined using corresponding square errors found in the “Fit All Summary”. For all of our Input Analyzer graphs, we used a significance level of 0.05 to determine the best fit. Figure 1 shows that the convenience store hold time data was fit with a normal distribution. This result was used in our model to represent the time it takes for a convenience store customer to pick an item before joining the queue at the checkout line. The inter-arrival times for the customers was exponentially distributed as shown in Figure 2. This data was used for the create block in order to create entities at a rate similar to the real rate of the E-Commons Café. The original data that was distributed for the interarrival times was creating several problems in our models, especially when we were trying to validate our base scenario. After collecting more data on interarrival times, we put the new data into Input Analyzer and found the distribution to be the same but the mean was higher. We used this new expression in our create block which solved all of the problems from earlier. Figure 3 shows the processing time of the cashier, which is exponentially distributed. This rate is used to process all of the entities since they all must checkout before leaving the system. Figure 4 and 5 show barista service times for plain coffee customers and specialty coffee customers, respectively. Plain coffee customer service times were distributed exponentially while specialty coffee customer service times were distributed normally. These rates were used in separate blocks in order to allow different types of customers to have different processing times in our model.

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Figure 1: Convenience Store Hold Times

Figure 2: Interarrival Times

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Figure 3: Cashier Server Process Times

Figure 4: Plain Coffee Service Times

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Figure 5: Specialty Coffee Service Times

Problems, Limitations, or Biases Some problems we encountered included varying time differences depending on the type of drink order for coffee customers, and how many items customers, shopping in the convenience store, purchased. It was difficult to keep track of these various problems at times when customer arrivals increased. Some biases we encountered were the amount of coffee vs. convenience store customers. Coffee customers were much more prevalent than convenience store customers; therefore, most of the data collected was for coffee customers. Some limitations included seating areas within the coffee shop, which made observing various processes difficult, such as arrival times of customers and recording how long it took convenience store customers to get in line. Another limitation included remembering which customer belonged to which time recorded. Not being able to interview employees or customers is another limitation because we cannot gather exact time data for how long it takes to make certain types of drinks, how often the register malfunctions or breaks down, and whether customers are satisfied with the service.

Models Below are various descriptions of each model implemented in Arena. The logic behind each model is explained, along with verification of the alternatives and process logic. For each model, the arrival rate is EXPO (45) seconds, Entities per Arrival is 1 + POIS(0.5), maximum arrivals are infinite, the logic in the decision block labeled “Balking” is as follows: Type is 2-way by Condition, If: Expression, Value: NQ(Cash Register.Queue) < 15. Other expressions that remain the same amongst the alternatives are various processing times. The distribution of plain coffee customers, specialty coffee customers, and convenience store customers found in the assign block is DISC(0.1, 1, .733, 2 ,1 ,3), respectively. Time for plain coffee customers to receive their order is 11.5 + EXPO(12.5), and time for specialty coffee customers to receive their order is a normal distribution with the mean equal to 94.7 and the standard deviation equal to 14.8. Time for convenience store customers to pick an item is NORM(73.6,30.6)

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seconds. Time for cashiers to checkout customers is 12.5 + EXPO(20.9). For our verification and validation, we ran each simulation 20 times with each replication running for 3 hours, which is the total time, covered in our scope. Each replication was run with a significance level of 0.05, which is the standard value used in Arena.

Base Scenario The steps followed for this model were described above under Conceptual Model. The model logic is as follows: Description:

Customers Arrive (Create Block)

o Includes inter-arrival time of customers and entities per arrival

o Time Between Arrivals is EXPO(45) seconds

o Entities per Arrival is 1 + POIS(0.5)

Balking (Decide Block)

o Will determine how many customers leave the system before getting in line

o 2 Way by Condition

o If statement

If: Expression

Value: NQ(Cash Register.Queue) < 15

Number Lost to Balking (Record Block)

o Records the amount of customers that balk in the system

o Type: Count

Goodbye (Dispose Block)

o Disposes balking customers

Assign 1 (Assign Block)

o Assigns distribution of specialty coffee customers, plain coffee customers, and

convenience store customers

o Assignment: Attribute, Product, DISC(0.1,1,.733,2,1,3)

Coffee Shop or Convenience Store (Decide Block)

o Determines the type of customer (convenience store vs. coffee)

o 2-Way by Condition based on If statement

If: Attribute

Named: Product

Is: <=

Value: 2

Look for Convenience Store Items (Delay Block)

o Delays processing of convenience store customers (calculates average time for customer

to select product to purchase)

o Delay Time: NORM(73.6,30.6) Seconds

Cash Register (Process Block)

o Seize Delay Release

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o Holds customers in queue and while cashier rings up customers

Delay Type: Expression

Units: Seconds

Allocation: Value Added

Expression: 12.5 + EXPO(20.9)

Decide 3 (Decide Block)

o If customer only purchased convenience store item, they are disposed from system

(Goodbye 2)

o If customer ordered coffee, they are sent to either Plain Coffee or Specialty Coffee (both

Process Blocks)

o Type: N-Way by Condition

o Conditions:

Attribute, Product, ==1

Attribute, Product, ==2

Goodbye 2 (Dispose Block)

o Disposes convenience store customers after cashier rings them up

Plain Coffee (Process Block)


o Holds customers in queue until they receive their order


Units: Seconds



Specialty Coffee (Process Block)


o Holds customer in queue until they receive their order

Delay Type: Normal

Units: Seconds


Value (mean): 94.7

Std. Dev: 14.8


o Disposes of coffee customers after they receive their order (both plain and specialty

coffee)

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Alternative 1 From the original model in Figure 10, we changed the number of cashiers from one to two and the number of baristas from two to three under capacity in resources. Customers choose the register based on a first-come first-served basis. Otherwise, this alternative follows the same logic as stated in the Base Scenario.

Alternative 2 In this model, customers arrive and then decide whether or not to balk. If a customer decides to balk they are disposed from the system. If not, they continue through the system to the assign block, where the type of customer is assigned based on a distribution. The types of customer for this model include specialty coffee customers, plain coffee customers, and convenience store customers. After the assign block is another decide block, which separates customers into coffee or convenience store customers. Coffee customers precede to another decide block where they are separated into plain

C us tomers Arr iv e True

Fa ls e

Balk ing

Goodby e

True

Fa ls e

Convenience Store

Coffee shop or

C as h Regis ter

Items

Convenience Store

Look for

Goodby e 3

As s ign 1

Decide 3

Els e

Produc t==1

Produc t==2

Goodby e 2

Plain C offee

Spec ia lty C offee

Balk ingN umber Los t to

0

0

0

0

0

0

0

0

0

0

0

Figure 10: Base Scenario. This is a simulation model of the system currently implemented at the E-Commons Café.

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coffee or specialty coffee customers. Convenience store customers proceed to a delay block, where they pick the item they wish to purchase. After the delay these customers will continue on to the checkout process block along with plain coffee customers. Once processed, if the customer was a convenience store customer, they exit the system. If the customer is a plain coffee customer, they proceed to a process block where they wait for their order. Once their order is received, they are disposed from the system. Specialty coffee customers proceed to their own process block, where they wait in queue until the cashier rings them up. They then proceed to the next process block where they wait to receive their order. Once their order is received, they are disposed from the system. Description:








o If statement

If: Expression




o Type: Count

Balk (Dispose Block)






Coffee or Convenient (Decide Block)



If: Attribute

Named: Product

Is: <=

Value: 2

Convenient Store Customer Picking Item (Delay Block)




Specialty Drink or Plain Coffee (Decide Block) o Decides if customer ordered plain coffee or specialty coffee by If statement

If: Attribute

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Named: CustomerType

Is: ==

Value: 2

Checkout Convenient Store Line

o Holds customers in queue until cashier rings up customer



Units: Seconds



Plain Coffee or Convenience (Decide Block)

o Decides if customer is a convenience store customer or plain coffee customer

o 2-Way by Condition determined by If statement

If: Attribute

Named: CustomerType

Is: ==

Value: 3

Plain Coffee Wait Time (Process Block)

o Holds customers in queue until they receive their coffee order



Units: Seconds



Leave (Dispose Block)

o Disposes of convenience store customers and plain coffee customers (once their order is

received)

Checkout Coffee Line (Process Block)

o Holds customers in queue until cashier rings up customer



Units: Seconds



Wait for then Receive Order (Process Block)

o Holds customers in queue until they receive coffee order


Delay Type: Normal

Units: Seconds


Value (mean): 94.7

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Std Dev: 14.8

o Leave 2 (Dispose Bock)

Disposes of specialty drink customers

Figure 11: Alternative 2. In this simulation, customers are still broken down into convenience store and coffee customers, but coffee customers are separated further based on the type of drink ordered. Customers are divided into two separate

queues: specialty coffee drink customers and convenience store/plain coffee customers.

Alternative 3 In this model, customers arrive and then decide whether or not to balk. If customer decides to balk they are disposed from the system. If not, they continue through the system to the assign block, where the type of customer is assigned based on a distribution. The types of customer for this model include specialty coffee customers, plain coffee customers, and convenience store customers. After the assign block is another decide block, which separates customers into coffee or convenience store customers. If

Customers A rriveT ru e

F a l s e

Balking T ru e

F a l s e

Coffee or Convenient

Balk

LineConvenient S tore

Checkout

T ru e

F a l s e

Coffee

Specialty Drink or Plain

Item

Customer Picking

Convenient Store

Leave

LineCheckout Coffee

Receive OrderWait for then

Leave2

Assign 2

T ru e

F a l s e

Convenience

Plain Coffee or

TimeP lain Coffee Wait

BalkingNumber Lost to

0

0

0

0

0

0

0

0

0

0

0 0

0

0

0

0

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the customer is a convenience store customer, then they proceed to a delay block, where the customer decides what item they want to purchase. If the customer is a coffee customer, then they proceed to a process block, where they wait in queue until the cashier rings them up. Coffee customers then precede to another decide block ,which separates the coffee customers into either plain coffee customers or specialty coffee customers based on the distribution given in the assign block. The two types of coffee customers then proceed to two separate process blocks, which hold the customers in queue until they receive their order. Processing time varies depending upon the type of coffee ordered. Once their order is received, they are disposed form the system. Convenience store customers proceed to another process block where they wait in queue until the cashier rings them up. Once the cashier rings the convenience store customer up, they are disposed from the system. Description:








o If statement

If: Expression




o Type: Count

Goodbye (Dispose Block)






Coffee Shop or Convenience Store (Decide Block)



If: Attribute

Named: Product

Is: <=

Value: 2

Look for Convenience Store Items (Delay Block)




Checkout Convenient Store Line (Process Block)

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o Seize Delay Release o Holds convenience store customers in queue until cashier rings up customer

Delay type: Expression Units: Seconds Allocation: Value Added Expression: 12.5 + EXPO(20.9)

Goodbye 2 (Dispose Block) o Disposes of convenience store customers

Checkout Coffee Line (Process Block) o Seize Delay Release o Holds coffee customers in queue until cashier rings up customer

Delay type: Expression Units: Seconds Allocation: Value Added Expression: 12.5 + EXPO(20.9)

What Type of Coffee (Decide Block) o Decides whether customer ordered a specialty coffee or plain coffee o 2-Way by Condition based on If statement

If: Attribute

Named: Product

Is: ==

Value: 1

Plain Coffee (Process Block)


o Holds customers in queue until they receive their order


Units: Seconds



Specialty Coffee (Process Block)


o Holds customer in queue until they receive their order

Delay Type: Normal

Units: Seconds


Value (mean): 94.7

Std. Dev: 14.8


o Disposes of coffee customers after they receive their order (both plain and specialty

coffee customers)

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Figure 12: Alternative 3. In this model, customers are separated into two different checkout lines depending upon whether

they are convenience store customers or coffee customers.

Process Logic The process logic behind each alternative was to ultimately improve the number of customers

out from the base scenario. With this primary objective in mind, we developed three alternatives and selected the best one from the three simulation models implemented.

For alternative 1, we decided to change the number of cashiers from the original model from one to two and the number of baristas from two to three. We changed these numbers to see how much the number of cashiers and baristas affected the number of customers out. Based on the results from this alternative, the number of cashiers and baristas did affect the number of customers out positively, and this turned out to be the best alternative.

We based our second alternative on continuing to distinguish between coffee and convenience store customers by further breaking down the coffee customer type based on type of drink ordered. From the data collected, we inferred that specialty drinks took a considerably longer time to process than plain coffee. We started by making two separate queues; one for specialty drinks and another for convenience store customers and plain coffee customers. We took this concept a step further by having adding two more baristas for a total of four; three baristas just for specialty drinks and one for plain coffee. Based on the results from the alternative, the number of cashiers and baristas did affect the number of customers out positively, but not more than alternative 1.

For the third alternative, we decided to try implementing two separate checkout lines, one for coffee customers and another for convenience store customers, and we also added a barista. We wanted to try this alternative because of the discrepancy between the number of coffee customers versus the number of convenience store customers. The logic behind this idea was to increase customers out by having two separate queues and registers to try to decrease the queue time for both types of customers. This alternative also affected the number of customers out positively, but again not more than alternative one.

Customers A rrive T ru e

F a l s e

Balking

Goodbye

Store Items

Convenience

Look for

Goodbye 3

A ssign 1

What Type of CoffeeT ru e

F a l s e

Goodbye 2

P lain Coffee

S pecialty Coffee

LineConvenient S tore

Checkout

T ru e

F a l s e

Store

Coffee or Convenience

LineCheckout Coffee

B alkingNumber Lost to

0

0

0

0

0

0

0

0

0

0

0

0

0

0

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Verification and Validation For validation of data, we compared our observed data with the output data that we collected after running out base model. As discussed earlier, we used the best-fit distributions (from Input Analyzer) in several different modeling blocks. We set the replication length to three hours, which covers the scope of our project. After running twenty replications of our base model, we collected the average flowtime and the average wait time of our entities. In order to complete a validation analysis, we also had to collect the half-width, which was given in Arena. In order to create an interval at a 95% confidence level, we took the averages, added the half-width as well as subtracted the half-width in order to get the minimum and maximum values of the interval. For the average flowtime (in minutes), we found the interval to be [6.93, 7.85]. The average wait time (in minutes) interval came out to be [5.24, 6.18]. In order to validate these values, we also needed to find the corresponding intervals using the data that we collected. Using a t-test, we found the flowtime interval and the average wait time interval to be [6.46, 8.44] and [4.22, 6.90] respectively. As you can see, the intervals from our base model are completely contained inside of the intervals from our collected data. Therefore, we can say that our base model is valid.

Project Plan Our group is going to meet multiple times a week to coordinate and plan for the project. We will also be working individually on some of the components of the project. Due dates:

Project Proposal: Friday, September 23, 2011

o Create a draft of the proposal and brainstorm some ideas.

Problem Statement and Conceptual Model: Wednesday, October 5, 2011

o All team members visited the coffee shop and convenience store to see what would be

the best time for us to collect the data.

Input Analysis: Wednesday, October 26, 2011

o Collected data and used Input Analyzer to generate the charts.

o Revise and update Problem Statement and Conceptual Model report

o Calculations, including statistics and queuing variables, will be completed once

observations have been returned.

Develop Simulation Model for Each Alternatives: Wednesday, November 9, 2011

o Update the Arena model with alternatives

o Write description of process logic

o First iteration of final model will be completed after all the calculations have been

completed.

Project Presentations: Wednesday November 30, 2011

o Make PowerPoint presentation

Output Analysis, Results, and Conclusions: Friday, December 9, 2011

o Meet often to finish the final report

o Revisions of final model will be completed by the final project due date.

As a group, we have all contributed to the parts of the project that have been completed to date. Michelle and Casey were responsible for the data collection. Shreya was responsible for organizing the data and inputting it into Input Analyzer. All four members took part in the writing of the papers.

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Michelle and Laura were responsible for creating the original simulation model. Michelle, Laura, and Casey were responsible for each coming up with a new alternative. We also made the PowerPoint presentation together and met often to finish the final paper.

Output Analysis, Results, and Conclusions We used a replication length of 20 and length strategies of 3-hour intervals. We decided to use 20 replication lengths because you need at least 20 independent data points to estimate a normal distribution curve. We used 3-hour intervals because the time intervals in which we observed and gathered our information at the E-Commons Café were during peak business hours, which were 7:30-9 am and 11:30-1 pm. For our statistical analysis, we compared each alternative to the base scenario using Output Analyzer. We compared the statistics of three different sets of out put data: flowtimes, wait time, and number out. In each of our models, we used the statistics feature under “Advanced Process” in order to create a statistic for each data set. We created text files for Arena to enter the data into after running each model. Each text file was used to create the paired t-tests (with lumped replications) that are shown below.

Figure 13: Paired t-test comparing flowtimes of Base Scenario vs. Alternative 1

Figure 14: Paired t-test comparing wait times of Base Scenario vs. Alternative 1

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Figure 15: Paired t-test comparing number out of Base Scenario vs. Alternative 1




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Base Scenario vs.

Alternative 1 Base Scenario vs.

Alternative 2 Base Scenario vs.

Alternative 3

Flowtimes [4.17, 5.13] [3.93, 4.91] [3.16, 4.28]

Wait Times [4.4, 5.38] [3.83, 4.84] [3.37, 4.53]

Number Out [-24.6, 3.35] [-23.6, 2.53] [-25.0, 5.3]

Table 1: 95% Confidence Intervals from Output Analyzer

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From the results that were gathered from Output Analyzer, we could determine if the alternatives were better than the base scenario. Table 1 shows the confidence intervals (at a significance level of 95%) from Output Analyzer. All three alternatives were better than the base scenario in flowtimes and wait times; however, none of the alternatives were statistically different from the base scenario. This was determined based on the interval. The intervals that have positive numbers for the lower and upper bound (no zero in the interval) mean that the two models are statistically different so we can say that the alternatives were better than the base scenario. All of the number out intervals contained zero so we could not say that the alternatives were statistically different. Since we did not compare each alternative to the other alternatives, we cannot use the intervals to determine which alternative is the best.

Number Out

Number That Balked

Number Served

Flowtime Average Wait Time

Average Number in Line

Base Scenario

351 43.4 307.6 7.39 5.71 8.97

Alternative 1 362 0.15 361.85 2.74 0.82 0.8

Alternative 2 362 1.05 360.95 2.96 1.37 0.3

Alternative 3 361 1.6 359.4 3.67 1.76 0.25 Table 2: Output data from Arena

Since we could not use the confidence intervals to compare the alternatives, we looked at the output data from arena. That data is shown in Table 2. Looking at the data, it is clear that alternative 1 gives better results than the other two alternatives in terms of number of people balking, number served, flowtime, average wait time, and average number in line. Even though our t-test showed that alternative 1 was not statistically different from the base scenario, it is clear that alternative 1 served more people than the base scenario. We chose alternative 1 as the best fit to replace the base scenario. Some challenges we encountered include problems with data collection and input analysis, establishing meeting times with group members that were convenient for everyone, and various problems with our simulation models. For data collection, some of the more prevalent problems encountered was keeping track of which customer belonged to the time recorded, and also not being able to interview E-Commons Café employees limited the amount of data we could collect, such as how long it takes to make different types of coffee drinks. This was discussed in detail under Problems, Limitations, and Biases. For input analysis, it was difficult to find the best distribution for our raw data collected, resulting in further observation and data collection that needed to take place. For the simulation models, we had problems with our base scenario model results for certain processes. We had to adjust the interarrival times for customers in the create blocks, and also had to distinguish between plain coffee customers and specialty drink coffee customers for more accurate wait times for customers to receive their orders. We also had to change the expression for our balking logic, because the initial number of people balking was not accurate to the real world model when we conducted our verification and validation. We learned that data collection is trial and error, and sometimes things that seemed outside the scope of our project actually needed to be included for the lean alternatives to be accurate. In conclusion, our goal was to improve how the E-Commons Café handles large crowds at peak business hours. To meet our objectives for this project, we came up with three different alternatives. We tested our alternatives using the Arena Simulation models. We also used Input Analyzer to compute our data. Our objectives were to decrease queue lengths in order to decrease the number of balking customers and decrease average flow time. To improve the problem statement, we came up with three

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alternatives. Our first alternative was to add a cashier from one to two. Second alternative was to have two breaks down the customers into convenience store vs. coffee customers, queue then further break down the coffee customer depending on the type of drink purchased. Our third alternative was to have a separate the customers into two different queue; one for convenience store and another for coffee customers. From our simulation models, we can conclude that our first alternative is the best alternative for E-Commons Café.

E-Commons Café Final Report

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