SlideShare a Scribd company logo
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
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
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
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
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)
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.
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.
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.
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.
By: Jasmine Sachdeva
M No.:M10669285
ProcessAnalyzer
Followingare the resultsobtainedfromthe ProcessAnalyzer:
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

More Related Content

What's hot

Simulation Modeling on Campus Starbucks Coffee Center
Simulation Modeling on Campus Starbucks Coffee CenterSimulation Modeling on Campus Starbucks Coffee Center
Simulation Modeling on Campus Starbucks Coffee Center
Niharika Senecha
 
Burger King Simulation
Burger King SimulationBurger King Simulation
Burger King Simulation
Rohit Jain
 
Arena Simulation of Chipotle Restaurant
Arena Simulation of Chipotle RestaurantArena Simulation of Chipotle Restaurant
Arena Simulation of Chipotle Restaurant
Rohit Bhaya
 
Barber Shop Simulation
Barber Shop SimulationBarber Shop Simulation
Barber Shop Simulation
Ravish Kalra
 
Habanero Restaurant - Simulation Project
Habanero Restaurant - Simulation ProjectHabanero Restaurant - Simulation Project
Habanero Restaurant - Simulation Project
Jatin Saini
 
Final Report-Poor Yorick's Coffee House
Final Report-Poor Yorick's Coffee HouseFinal Report-Poor Yorick's Coffee House
Final Report-Poor Yorick's Coffee Housekaranchaudhry123
 
Deone pranilfinalreport
Deone pranilfinalreportDeone pranilfinalreport
Deone pranilfinalreport
Pranil Deone
 
Arena Model for Coffe Shop
Arena Model for Coffe ShopArena Model for Coffe Shop
Arena Model for Coffe Shop
Ebru Özmüş
 
Simulation of food serving system of EWU canteen using Arena software
Simulation of food serving system of EWU canteen using Arena softwareSimulation of food serving system of EWU canteen using Arena software
Simulation of food serving system of EWU canteen using Arena software
East West University
 
Simulation with ARENA - SM Paints
Simulation with ARENA - SM PaintsSimulation with ARENA - SM Paints
Simulation with ARENA - SM Paints
hrishik26
 
Super 8 gas station model - Arena Simulation
Super 8 gas station model - Arena SimulationSuper 8 gas station model - Arena Simulation
Super 8 gas station model - Arena Simulation
Arunkumar Jagadeesan
 
Process simulation study of order processing at Starbucks, University of Cinc...
Process simulation study of order processing at Starbucks, University of Cinc...Process simulation study of order processing at Starbucks, University of Cinc...
Process simulation study of order processing at Starbucks, University of Cinc...
Piyush Verma
 
Stadium Queue Simulation
Stadium Queue SimulationStadium Queue Simulation
Stadium Queue Simulation
Aditya Singh
 
Chick-fil-A Express outlet Simulation
Chick-fil-A Express outlet SimulationChick-fil-A Express outlet Simulation
Chick-fil-A Express outlet Simulation
Maitrik Sanghavi
 
Simulation with Arena (Dental Clinic project)
Simulation with Arena (Dental Clinic project)Simulation with Arena (Dental Clinic project)
Simulation with Arena (Dental Clinic project)
Kimseng Sok
 
Computer Simulation Final Project
Computer Simulation Final ProjectComputer Simulation Final Project
Computer Simulation Final ProjectPKalico
 
Kroger Store Simulation Using Arena
Kroger Store Simulation Using ArenaKroger Store Simulation Using Arena
Kroger Store Simulation Using Arena
Dhivya Rajprasad
 
Simulation for kfc order counter at rajiv gandhi international airport, hyder...
Simulation for kfc order counter at rajiv gandhi international airport, hyder...Simulation for kfc order counter at rajiv gandhi international airport, hyder...
Simulation for kfc order counter at rajiv gandhi international airport, hyder...
Pankaj Gaurav
 
Simulation study of Gas Station
Simulation study of Gas StationSimulation study of Gas Station
Simulation study of Gas Station
Akul Mahajan
 
Bakery Production Using Arena Simulation
Bakery Production Using Arena SimulationBakery Production Using Arena Simulation
Bakery Production Using Arena Simulation
Socheat Veng
 

What's hot (20)

Simulation Modeling on Campus Starbucks Coffee Center
Simulation Modeling on Campus Starbucks Coffee CenterSimulation Modeling on Campus Starbucks Coffee Center
Simulation Modeling on Campus Starbucks Coffee Center
 
Burger King Simulation
Burger King SimulationBurger King Simulation
Burger King Simulation
 
Arena Simulation of Chipotle Restaurant
Arena Simulation of Chipotle RestaurantArena Simulation of Chipotle Restaurant
Arena Simulation of Chipotle Restaurant
 
Barber Shop Simulation
Barber Shop SimulationBarber Shop Simulation
Barber Shop Simulation
 
Habanero Restaurant - Simulation Project
Habanero Restaurant - Simulation ProjectHabanero Restaurant - Simulation Project
Habanero Restaurant - Simulation Project
 
Final Report-Poor Yorick's Coffee House
Final Report-Poor Yorick's Coffee HouseFinal Report-Poor Yorick's Coffee House
Final Report-Poor Yorick's Coffee House
 
Deone pranilfinalreport
Deone pranilfinalreportDeone pranilfinalreport
Deone pranilfinalreport
 
Arena Model for Coffe Shop
Arena Model for Coffe ShopArena Model for Coffe Shop
Arena Model for Coffe Shop
 
Simulation of food serving system of EWU canteen using Arena software
Simulation of food serving system of EWU canteen using Arena softwareSimulation of food serving system of EWU canteen using Arena software
Simulation of food serving system of EWU canteen using Arena software
 
Simulation with ARENA - SM Paints
Simulation with ARENA - SM PaintsSimulation with ARENA - SM Paints
Simulation with ARENA - SM Paints
 
Super 8 gas station model - Arena Simulation
Super 8 gas station model - Arena SimulationSuper 8 gas station model - Arena Simulation
Super 8 gas station model - Arena Simulation
 
Process simulation study of order processing at Starbucks, University of Cinc...
Process simulation study of order processing at Starbucks, University of Cinc...Process simulation study of order processing at Starbucks, University of Cinc...
Process simulation study of order processing at Starbucks, University of Cinc...
 
Stadium Queue Simulation
Stadium Queue SimulationStadium Queue Simulation
Stadium Queue Simulation
 
Chick-fil-A Express outlet Simulation
Chick-fil-A Express outlet SimulationChick-fil-A Express outlet Simulation
Chick-fil-A Express outlet Simulation
 
Simulation with Arena (Dental Clinic project)
Simulation with Arena (Dental Clinic project)Simulation with Arena (Dental Clinic project)
Simulation with Arena (Dental Clinic project)
 
Computer Simulation Final Project
Computer Simulation Final ProjectComputer Simulation Final Project
Computer Simulation Final Project
 
Kroger Store Simulation Using Arena
Kroger Store Simulation Using ArenaKroger Store Simulation Using Arena
Kroger Store Simulation Using Arena
 
Simulation for kfc order counter at rajiv gandhi international airport, hyder...
Simulation for kfc order counter at rajiv gandhi international airport, hyder...Simulation for kfc order counter at rajiv gandhi international airport, hyder...
Simulation for kfc order counter at rajiv gandhi international airport, hyder...
 
Simulation study of Gas Station
Simulation study of Gas StationSimulation study of Gas Station
Simulation study of Gas Station
 
Bakery Production Using Arena Simulation
Bakery Production Using Arena SimulationBakery Production Using Arena Simulation
Bakery Production Using Arena Simulation
 

Similar to Simulation Project Report

Soterias Medical Final Report
Soterias Medical Final ReportSoterias Medical Final Report
Soterias Medical Final ReportJack Jung
 
Simulation for Bank Teller System
Simulation for Bank Teller SystemSimulation for Bank Teller System
Simulation for Bank Teller System
Ahmed Al Salih
 
Mohamed Ahmed Afifi (16-2179) Master Thesis
Mohamed Ahmed Afifi (16-2179) Master ThesisMohamed Ahmed Afifi (16-2179) Master Thesis
Mohamed Ahmed Afifi (16-2179) Master ThesisMohamed Ahmed Afifi
 
Study and Analysis of Tube Failure in Water Tube boiler
Study and Analysis of Tube Failure in Water Tube boilerStudy and Analysis of Tube Failure in Water Tube boiler
Study and Analysis of Tube Failure in Water Tube boiler
ArunMalanthara
 
Donhauser - 2012 - Jump Variation From High-Frequency Asset Returns
Donhauser - 2012 - Jump Variation From High-Frequency Asset ReturnsDonhauser - 2012 - Jump Variation From High-Frequency Asset Returns
Donhauser - 2012 - Jump Variation From High-Frequency Asset ReturnsBrian Donhauser
 
Gagan nir s103217540 capston_project_presentation
Gagan nir s103217540 capston_project_presentationGagan nir s103217540 capston_project_presentation
Gagan nir s103217540 capston_project_presentation
Gagan Nir
 
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...Kanika Anand
 
Supply chain design and operation
Supply chain design and operationSupply chain design and operation
Supply chain design and operation
AngelainBay
 
LPG Booking System [ bookmylpg.com ] Report
LPG Booking System [ bookmylpg.com ] ReportLPG Booking System [ bookmylpg.com ] Report
LPG Booking System [ bookmylpg.com ] Report
Nandu B Rajan
 
Gas condensation process
Gas condensation processGas condensation process
Gas condensation process
Sundararajan Thangavelu
 
Thesis_Eddie_Zisser_final_submission
Thesis_Eddie_Zisser_final_submissionThesis_Eddie_Zisser_final_submission
Thesis_Eddie_Zisser_final_submissionEddie Zisser
 
Bakery Production Using Arena Simulation
Bakery Production Using Arena SimulationBakery Production Using Arena Simulation
Bakery Production Using Arena Simulation
Socheat Veng
 
Venturini - X ray imaging for cheese
Venturini - X ray imaging for cheeseVenturini - X ray imaging for cheese
Venturini - X ray imaging for cheese
Lorenzo Venturini
 
Ee380 labmanual
Ee380 labmanualEe380 labmanual
Ee380 labmanual
gopinathbl71
 
W240 pf700 vc sugar centrifuge lab1
W240   pf700 vc sugar centrifuge lab1W240   pf700 vc sugar centrifuge lab1
W240 pf700 vc sugar centrifuge lab1
confidencial
 

Similar to Simulation Project Report (20)

Soterias Medical Final Report
Soterias Medical Final ReportSoterias Medical Final Report
Soterias Medical Final Report
 
Simulation for Bank Teller System
Simulation for Bank Teller SystemSimulation for Bank Teller System
Simulation for Bank Teller System
 
Mohamed Ahmed Afifi (16-2179) Master Thesis
Mohamed Ahmed Afifi (16-2179) Master ThesisMohamed Ahmed Afifi (16-2179) Master Thesis
Mohamed Ahmed Afifi (16-2179) Master Thesis
 
Study and Analysis of Tube Failure in Water Tube boiler
Study and Analysis of Tube Failure in Water Tube boilerStudy and Analysis of Tube Failure in Water Tube boiler
Study and Analysis of Tube Failure in Water Tube boiler
 
Donhauser - 2012 - Jump Variation From High-Frequency Asset Returns
Donhauser - 2012 - Jump Variation From High-Frequency Asset ReturnsDonhauser - 2012 - Jump Variation From High-Frequency Asset Returns
Donhauser - 2012 - Jump Variation From High-Frequency Asset Returns
 
Gagan nir s103217540 capston_project_presentation
Gagan nir s103217540 capston_project_presentationGagan nir s103217540 capston_project_presentation
Gagan nir s103217540 capston_project_presentation
 
thesis
thesisthesis
thesis
 
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...
An_expected_improvement_criterion_for_the_global_optimization_of_a_noisy_comp...
 
Supply chain design and operation
Supply chain design and operationSupply chain design and operation
Supply chain design and operation
 
UROP MPC Report
UROP MPC ReportUROP MPC Report
UROP MPC Report
 
LPG Booking System [ bookmylpg.com ] Report
LPG Booking System [ bookmylpg.com ] ReportLPG Booking System [ bookmylpg.com ] Report
LPG Booking System [ bookmylpg.com ] Report
 
Gas condensation process
Gas condensation processGas condensation process
Gas condensation process
 
thesis
thesisthesis
thesis
 
Thesis_Eddie_Zisser_final_submission
Thesis_Eddie_Zisser_final_submissionThesis_Eddie_Zisser_final_submission
Thesis_Eddie_Zisser_final_submission
 
for printout
for printoutfor printout
for printout
 
Bakery Production Using Arena Simulation
Bakery Production Using Arena SimulationBakery Production Using Arena Simulation
Bakery Production Using Arena Simulation
 
Venturini - X ray imaging for cheese
Venturini - X ray imaging for cheeseVenturini - X ray imaging for cheese
Venturini - X ray imaging for cheese
 
Report_Final
Report_FinalReport_Final
Report_Final
 
Ee380 labmanual
Ee380 labmanualEe380 labmanual
Ee380 labmanual
 
W240 pf700 vc sugar centrifuge lab1
W240   pf700 vc sugar centrifuge lab1W240   pf700 vc sugar centrifuge lab1
W240 pf700 vc sugar centrifuge lab1
 

Recently uploaded

Company Valuation webinar series - Tuesday, 4 June 2024
Company Valuation webinar series - Tuesday, 4 June 2024Company Valuation webinar series - Tuesday, 4 June 2024
Company Valuation webinar series - Tuesday, 4 June 2024
FelixPerez547899
 
Maksym Vyshnivetskyi: PMO Quality Management (UA)
Maksym Vyshnivetskyi: PMO Quality Management (UA)Maksym Vyshnivetskyi: PMO Quality Management (UA)
Maksym Vyshnivetskyi: PMO Quality Management (UA)
Lviv Startup Club
 
Buy Verified PayPal Account | Buy Google 5 Star Reviews
Buy Verified PayPal Account | Buy Google 5 Star ReviewsBuy Verified PayPal Account | Buy Google 5 Star Reviews
Buy Verified PayPal Account | Buy Google 5 Star Reviews
usawebmarket
 
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
Boris Ziegler
 
Cree_Rey_BrandIdentityKit.PDF_PersonalBd
Cree_Rey_BrandIdentityKit.PDF_PersonalBdCree_Rey_BrandIdentityKit.PDF_PersonalBd
Cree_Rey_BrandIdentityKit.PDF_PersonalBd
creerey
 
The effects of customers service quality and online reviews on customer loyal...
The effects of customers service quality and online reviews on customer loyal...The effects of customers service quality and online reviews on customer loyal...
The effects of customers service quality and online reviews on customer loyal...
balatucanapplelovely
 
-- June 2024 is National Volunteer Month --
-- June 2024 is National Volunteer Month ---- June 2024 is National Volunteer Month --
-- June 2024 is National Volunteer Month --
NZSG
 
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
Lviv Startup Club
 
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.docBài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
daothibichhang1
 
Organizational Change Leadership Agile Tour Geneve 2024
Organizational Change Leadership Agile Tour Geneve 2024Organizational Change Leadership Agile Tour Geneve 2024
Organizational Change Leadership Agile Tour Geneve 2024
Kirill Klimov
 
Observation Lab PowerPoint Assignment for TEM 431
Observation Lab PowerPoint Assignment for TEM 431Observation Lab PowerPoint Assignment for TEM 431
Observation Lab PowerPoint Assignment for TEM 431
ecamare2
 
Sustainability: Balancing the Environment, Equity & Economy
Sustainability: Balancing the Environment, Equity & EconomySustainability: Balancing the Environment, Equity & Economy
Sustainability: Balancing the Environment, Equity & Economy
Operational Excellence Consulting
 
Premium MEAN Stack Development Solutions for Modern Businesses
Premium MEAN Stack Development Solutions for Modern BusinessesPremium MEAN Stack Development Solutions for Modern Businesses
Premium MEAN Stack Development Solutions for Modern Businesses
SynapseIndia
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
taqyed
 
Auditing study material for b.com final year students
Auditing study material for b.com final year  studentsAuditing study material for b.com final year  students
Auditing study material for b.com final year students
narasimhamurthyh4
 
Recruiting in the Digital Age: A Social Media Masterclass
Recruiting in the Digital Age: A Social Media MasterclassRecruiting in the Digital Age: A Social Media Masterclass
Recruiting in the Digital Age: A Social Media Masterclass
LuanWise
 
Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey throu...Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey throu...
dylandmeas
 
Authentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto RicoAuthentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto Rico
Corey Perlman, Social Media Speaker and Consultant
 
Authentically Social Presented by Corey Perlman
Authentically Social Presented by Corey PerlmanAuthentically Social Presented by Corey Perlman
Authentically Social Presented by Corey Perlman
Corey Perlman, Social Media Speaker and Consultant
 
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdfSearch Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Arihant Webtech Pvt. Ltd
 

Recently uploaded (20)

Company Valuation webinar series - Tuesday, 4 June 2024
Company Valuation webinar series - Tuesday, 4 June 2024Company Valuation webinar series - Tuesday, 4 June 2024
Company Valuation webinar series - Tuesday, 4 June 2024
 
Maksym Vyshnivetskyi: PMO Quality Management (UA)
Maksym Vyshnivetskyi: PMO Quality Management (UA)Maksym Vyshnivetskyi: PMO Quality Management (UA)
Maksym Vyshnivetskyi: PMO Quality Management (UA)
 
Buy Verified PayPal Account | Buy Google 5 Star Reviews
Buy Verified PayPal Account | Buy Google 5 Star ReviewsBuy Verified PayPal Account | Buy Google 5 Star Reviews
Buy Verified PayPal Account | Buy Google 5 Star Reviews
 
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
Agency Managed Advisory Board As a Solution To Career Path Defining Business ...
 
Cree_Rey_BrandIdentityKit.PDF_PersonalBd
Cree_Rey_BrandIdentityKit.PDF_PersonalBdCree_Rey_BrandIdentityKit.PDF_PersonalBd
Cree_Rey_BrandIdentityKit.PDF_PersonalBd
 
The effects of customers service quality and online reviews on customer loyal...
The effects of customers service quality and online reviews on customer loyal...The effects of customers service quality and online reviews on customer loyal...
The effects of customers service quality and online reviews on customer loyal...
 
-- June 2024 is National Volunteer Month --
-- June 2024 is National Volunteer Month ---- June 2024 is National Volunteer Month --
-- June 2024 is National Volunteer Month --
 
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
Evgen Osmak: Methods of key project parameters estimation: from the shaman-in...
 
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.docBài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
Bài tập - Tiếng anh 11 Global Success UNIT 1 - Bản HS.doc
 
Organizational Change Leadership Agile Tour Geneve 2024
Organizational Change Leadership Agile Tour Geneve 2024Organizational Change Leadership Agile Tour Geneve 2024
Organizational Change Leadership Agile Tour Geneve 2024
 
Observation Lab PowerPoint Assignment for TEM 431
Observation Lab PowerPoint Assignment for TEM 431Observation Lab PowerPoint Assignment for TEM 431
Observation Lab PowerPoint Assignment for TEM 431
 
Sustainability: Balancing the Environment, Equity & Economy
Sustainability: Balancing the Environment, Equity & EconomySustainability: Balancing the Environment, Equity & Economy
Sustainability: Balancing the Environment, Equity & Economy
 
Premium MEAN Stack Development Solutions for Modern Businesses
Premium MEAN Stack Development Solutions for Modern BusinessesPremium MEAN Stack Development Solutions for Modern Businesses
Premium MEAN Stack Development Solutions for Modern Businesses
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
 
Auditing study material for b.com final year students
Auditing study material for b.com final year  studentsAuditing study material for b.com final year  students
Auditing study material for b.com final year students
 
Recruiting in the Digital Age: A Social Media Masterclass
Recruiting in the Digital Age: A Social Media MasterclassRecruiting in the Digital Age: A Social Media Masterclass
Recruiting in the Digital Age: A Social Media Masterclass
 
Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey throu...Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey throu...
 
Authentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto RicoAuthentically Social by Corey Perlman - EO Puerto Rico
Authentically Social by Corey Perlman - EO Puerto Rico
 
Authentically Social Presented by Corey Perlman
Authentically Social Presented by Corey PerlmanAuthentically Social Presented by Corey Perlman
Authentically Social Presented by Corey Perlman
 
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdfSearch Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
 

Simulation Project Report

  • 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