This document summarizes a simulation project to optimize the process at a university campus Subway outlet. The current process leads to long wait times during lunch hours. The simulation models the current process and a proposed process with additional resources. Model 2, which adds one employee each to the order counter and billing counter, reduces average wait times and total time in the system based on the simulation results and statistical analysis. Therefore, hiring two new employees is recommended to improve customer experience and satisfaction.

1. By: Jasmine Sachdeva
M No.:M10669285
Simulation Project in Arena
Optimization of Subway Outlet at UC Campus
Contents
Objective .......................................................................................................................................2
Current Process..............................................................................................................................2
Problem and Counter Proposal.......................................................................................................2
Data Collection...............................................................................................................................2
Fitting Data....................................................................................................................................3
Model Assumptions........................................................................................................................5
Model............................................................................................................................................5
Model Results................................................................................................................................6
Model 2.........................................................................................................................................8
Output Analyzer.............................................................................................................................9
Process Analyzer..........................................................................................................................10
Conclusion:..................................................................................................................................11

2. By: Jasmine Sachdeva
M No.:M10669285
Objective
 To improve the effectiveness,productivityandsalesof Subway byminimizingwaitingtime and
maximizingthe speedof service.
Thiscan be done byunderstandinghow the customerwaittimesvariesindifferentstagesfrom
the time theyenterthe queue till the time theyreceivetheirorder.
Current Process
 CustomersenterSubway,waitinthe queue orgoto the firststage where theyselectthe size
and type of bread,meat,and cheese.Thendependingif the customerchose tobake or notbake
theirbread,the customergoesthroughthe secondstage whichisthe oven. The customerthen
movestothe next stage toadd veggies,meatandcondiments.Once the sandwichismade, the
customermovestothe billingcounter afterchoosingadditionalchipsand/or drinks.Afterthis,
the customermay or may notgo to the soda machine toget theirdrinks.
 Each stage hastwo resources exceptatthe billingcounter.
Problem andCounter Proposal
 Duringlunchhours i.e.fromabout10:00 AMto 2:00PM, there’sa longerqueue atthe order
counteras well asthe billingcounterwhichleadsto unsatisfiedcustomersand a chance of
people decidingnottogo eatat a Subwaybecause of the longwaitingtime.
 Staffingthe rightnumberof employeesatthe righttime andhavingthe rightpersoninthe right
place couldsolve the problem.
 The restaurantcan have an additional resource atthe ordercounterorbilling counter,whichcan
make a significant difference in terms of waiting times and consequently customer satisfaction
levels.
Data Collection
PermissionhadbeentakenfromSubwaytocollectthe data observe interarrival andprocessingtime.The
time intervals were manually recorded for the following processes to get a rough estimate of entire
system
 Inter-arrival time of customers coming on a day
 Processtime forchoosingbreadandcheese

3. By: Jasmine Sachdeva
M No.:M10669285
 Processtime forchoosingvegetablesandsauces
 Processtime forbilling
Fitting Data
Arena’sInputAnalyzer tool wasusedto fitthe probabilitydistributiontothe data.
a. CustomerInter-arrival times
Followingisthe schedule of customersgenerallyobservedinaday. 10 AMto 2 PMand 7 PMto
9 PMhave beenobservedasthe peakrushhours.
9 - 10
AM
10-11
AM
11AM-
12 PM
12 - 1
PM
1- 2
PM
2 - 3
PM
3 - 4
PM
4 - 5
PM
5 - 6
PM
6 - 7
PM
7 - 8
PM
8 - 9
PM
9 - 10
PM
22 42 47 48 50 9 19 11 27 9 47 36 10
b. Processtime for Choosingbread and cheese
Distribution Summary
Distribution: Beta
Expression: 0.33 * BETA(1.41, 1.64)
Square Error: 0.004607
Chi Square Test
Number of intervals = 16
Degrees of freedom = 13
Test Statistic = 17.4
Correspondingp-value = 0.196
Kolmogorov-SmirnovTest
Test Statistic = 0.0481
Correspondingp-value > 0.15
Data Summary
Number of Data Points = 350
Min Data Value = 0.5
Max Data Value = 1.2
Sample Mean = 0.829
Sample StdDev = 0.208
Histogram Summary
HistogramRange = 0.43 to 1.27
Number of Intervals = 18

4. By: Jasmine Sachdeva
M No.:M10669285
c. Toast
It was observed that the toasting time for bread is uniform between 0.33 mins (20 secs) to 0.66
minutes (40 secs), depending on the type of bread and the meat chosen.
UNIF (0.33, 0.66)
d. Processtime for Choosingvegetables,saucesand condiments
DistributionSummary
Distribution:Gamma
Expression: 0.11 + GAMM(.45, 4.23)
Square Error:0.015364
Chi Square Test
Number of intervals = 17
Degrees of freedom = 14
Test Statistic = 391
Correspondingp-value=0.496
Kolmogorov-Smirnov Test
Test Statistic = 0.113
Correspondingp-value>0.01

5. By: Jasmine Sachdeva
M No.:M10669285
Model Assumptions
• The two resourceswhotake the orderandprepare the sandwich are equallyefficient andhave the
same service time.
• The time takento use the soda machine has not beenaddedin the model,since it doesn’taddto
the queue time. The model has been simulated only till the billing counter.
Model
My arena model has 7 modules as given below:
1. Arrival Module:The customerarrivesatthe restaurantandjoinsxxaqueueatone of the counters
based on the length of the queue
2. Seize ‘Sub Resource’: The customer goes to one of the resource who is idle and orders his sub.
The same resource prepares the sub for a particular customer.
3. Delay Moduleto choosebread, meat and cheese: The customerchoose the type of bread,meat
and the cheese and the Resource prepares the sub before toasting it.
4. DecisionModule forToast/NoToast decision: The customercan chooseto toastor not toasthis
bread in oven.
5. Delay Module for Toasting bread in Oven: Bread is toasted in the oven. It takes 20 to 40 secs,
depending on the type of bread, cheese and meat chosen
6. Delay Module to prepare the sub: The resource prepares the sub by adding vegetables,
condiments and sauces.
7. Release ‘Sub Resource’: Once the sub is prepared, the resource is released to be seized by the
next customer (or from the queue, if any).
8. Process Modulefor BillingCounter:Whenthe subis ready,customersfrombothcountersmove
to a single billing counter.
Once the customerfinalizesthe order,he/she canchoose to get a glass of soda/waterif it’spart
of the order. If the customer decides to get a glass soda/water along with his order, he goes to
the soda machine and gets his glass filled. (This part is not included in the model)

6. By: Jasmine Sachdeva
M No.:M10669285
Following is the outlay of the Arena model.
Model Results
The model wasinitiallyrunfor50 replicationsandthe numberof replicationsrequiredforaprecisionof
9% was calculated.
The model wasfinallyrunfor52 replicationsand the resultsobtainedare asshownbelow.
A.

7. By: Jasmine Sachdeva
M No.:M10669285
B.
C.
D.
The waitingtime of customersinthe queue are 5.07 minsfor the billingqueue and2.58 for the Order
queue.
Due to extreme rushinpeakhours, eventhe average total time insystemis 11 minuteswhichis quite
high.Therefore,itis proposed toincrease the resources.Thisisimplementedinthe secondmodel.

8. By: Jasmine Sachdeva
M No.:M10669285
Model 2
In thismodel,The SubResource andBillingResourcehasbeenincreasedbyone unit.
Thismodel isrun fora 52 replicationsandthe resultsare as givenbelow.
A.
B.
C.
D.
To check the authenticityof these results,itisimportanttoanalyze themstatistically.Thiscanbe done
by usingthe OUTPUT ANALYSERand PROCESS ANALYZER.

9. By: Jasmine Sachdeva
M No.:M10669285
Output Analyzer
The Statisticsto be checkedare: Total Time spentby the Customer inthe System, Average WaitTime
in the BillingQueue and Average Wait Time in the Order Queue.
All of these are outputstatisticsi.e.theirresultsgetstoredin.DATfilesspecifiedbyus.The Output
Analyzercanbe employedtoperformT-Testsonthe samplestodetermine the hypothesis:
H0: the meansof the two samplesof the statistic are same
Ha: the meansof the two samplesof the statistic are not the same.
We can use the filesfromboththe modelstocompare the Means of these Statistics.
We rejectHo forall the three Responses,as we can’tsay that there isa statisticallysignificantdifference
inthe means.

10. By: Jasmine Sachdeva
M No.:M10669285
ProcessAnalyzer
Followingare the resultsobtainedfromthe ProcessAnalyzer:

11. By: Jasmine Sachdeva
M No.:M10669285
From thischart and the resultof the PAN we can say that, increasingbothresourcesbyone unitwould
be a betterapproach.
Conclusion:
Aftergoingthroughall the results,chartsand graphswe can clearlysee that reducingbothresourcesby
a unitwouldgreatlydecrease the waittime of customers,thereby decreasingthe total time of
customers insystem.
Therefore,we cansaythat, using the Secondmodel,i.e.byhiringanadditionalemployee we can
improve the customerexperience bydecreasingthe waittime andhence furtherimprove the reputation
of subway.
So the final conclusioncomesouttobe that Model 2 isa validandbetterapproach. Hence two new
employeescanbe hiredby the restaurant.
References:
1. SimulationwithArena
2. Data collectedfromSubway,UC