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.
The Project is done as a final project for the course BANA 7030-Simulation Modelling where the focus is in understanding the basics of simulation modelling using Rockwell Automation’s “Arena”.
The goal of the project is to study working of the Shell gas station and food mart at 3337 Clifton Ave, using Arena simulation and increase the resource utilization of the resource or the pumps.
The Shell Petrol gas station is a facility that sells fuel and engine lubricants for motor vehicles. Also, along with gas station there is also a Food Mart which is a located in the same premise as the gas station, which is basically a convenience store.
The model uses the layout, operation and resource allocation of the gas station and the food mart etc in Arena to simulate the real-life scenarios.
The Project is done as a final project for the course BANA 7030-Simulation Modelling where the focus is in understanding the basics of simulation modelling using Rockwell Automation’s “Arena”.
The goal of the project is to study working of the Shell gas station and food mart at 3337 Clifton Ave, using Arena simulation and increase the resource utilization of the resource or the pumps.
The Shell Petrol gas station is a facility that sells fuel and engine lubricants for motor vehicles. Also, along with gas station there is also a Food Mart which is a located in the same premise as the gas station, which is basically a convenience store.
The model uses the layout, operation and resource allocation of the gas station and the food mart etc in Arena to simulate the real-life scenarios.
Simulation Modeling on Campus Starbucks Coffee CenterNiharika Senecha
Simulation Modeling of Campus Starbucks Coffee Center was done using Arena simulation software in order to reduce the long waiting time and increase the utilization of resources. The results were analyzed and a suggestion (a new and improved simulation model) was also made to improve the system.
The Burger King Fast Food joint at Tangeman University Center is one of the main joints that UC students frequent to grab a quick bite. The store runs from 7 am to 7 pm on weekdays and for reduced hours on weekends. Majority of the business/ influx of students for the joint is observed on weekdays with the peak
hours being 11 am to 3 pm.
The project helped identify bottlenecks observed in the system during peak hours and suggested an alternate resource restructuring with the same man hours. A reduction of 53% in customer wait time was observed in the new solution.
Arena® was chosen as the software to simulate the Burger King setup and identify areas of improvement.
An attempt at finding an optimized working model using Arena for a barber shop ameliorating the customer wait time, thus attracting more customers with minimum cost
The project is done as final project for the course BANA 7030 where the focus lies on the simulation software called ‘Arena’ developed by Rockwell Software. The main purpose of the project is to prepare a working simulation model of the UDF store on Clifton Ave using the software ‘Arena’. For this model the input will be the inter-arrival time of the customers and service times at each of the counters during rush hours. The model in Arena will give a precise output of the statistical accumulators like total number of entities served, average wait time in the queue, maximum waiting time in queue, average total time in system, maximum total time in system, resource allocation and utilization levels, and efficiency of the processes. Our aim will be to study the statistical accumulators, identify inefficiencies and suggest changes in the model to improve the efficiency. In the scope of the project the customers will be the entities. The model uses the layout of the store, management systems, options of purchase, sequence followed, resources available in Arena simulate real life scenarios. The model was run for 16 hours for a busy day and 10 replications are conducted to validate the result. Certain changes in the model are also introduced and their impact on the performance parameters are also studied to arrive at the optimal solution.
Simulation of SM Paints production facility using ARENA simulation software. Making improvements using OptQuest software, and data analysis of current state simulation, to suggest recommendations for achieving desired level of productivity.
Arena simulation for Superette gas station, Vidor, Texas to evaluate the effectiveness of operating the gas station for 24 hours instead of 16 hours and find the optimum number of gas pumps to attain maximum revenue. Data collection, simulation, and analysis led to the conclusion that operating the gas station for 24 hours with 6 gas pumps ultimately having an impact on the maximum profit of $25 per day (16 hours) to $55 per day (24 hours) which was adopted by the gas station.
Process simulation study of order processing at Starbucks, University of Cinc...Piyush Verma
In this project, the attempt has been made to simulate the process of ordering coffee at a Starbucks inside the University of Cincinnati campus. The components of the system include customers who will place orders, cash counter queue where customer wait for their turn, cashier and food and beverage servers as resources processing the orders and servicing the customers. These are the components which mainly decide for how long a customer must stay in the Starbucks. Apart from these, 2 more components like the processes of (a) adding sugar/flavors to coffee (which is basically when a customer adds sugar/flavor in the coffee according to individual’s taste in a nearby self-service counter) and (b) spending time in the sitting area are also added in the system, as they also prolong a customer’s stay inside Starbucks.
This project analyses the current scenario- fans arriving at the Nippert Stadium through various lanes. The current scenario has been modeled using Arena and a better case scenario has been developed using the same software.
Simulated an outlet of Chick-fil-A Express located at the University of Cincinnati campus to identify bottlenecks and propose changes in the system to reduce the average time spent by a customer.
Arena Simulation Software was used to build the model and Process Analyzer was used to compare the base model and three alternate models. The recommended model reduces the average total customer time by 51%
Simulation with Arena (Dental Clinic project)Kimseng Sok
This is a short slide presentation of my assignment in course of System thinking and modeling. I used Arena Simulation software as tool to discover and make improvements in dental clinic service.
Simulation for kfc order counter at rajiv gandhi international airport, hyder...Pankaj Gaurav
Objective of the business modelling and simulation project was to determine whether existing system is efficient or there is a scope of reducing the waiting time & idle time at KFC Order Counter at Rajiv Gandhi International Airport, Hyderabad
Conceptual Model/ Object Flow Diagram (OFD)
Simulation Implementation
Calculate Number of Model Replications
The objects design
The logic design
Statistic by individual object Results Comparison
Simulation Modeling on Campus Starbucks Coffee CenterNiharika Senecha
Simulation Modeling of Campus Starbucks Coffee Center was done using Arena simulation software in order to reduce the long waiting time and increase the utilization of resources. The results were analyzed and a suggestion (a new and improved simulation model) was also made to improve the system.
The Burger King Fast Food joint at Tangeman University Center is one of the main joints that UC students frequent to grab a quick bite. The store runs from 7 am to 7 pm on weekdays and for reduced hours on weekends. Majority of the business/ influx of students for the joint is observed on weekdays with the peak
hours being 11 am to 3 pm.
The project helped identify bottlenecks observed in the system during peak hours and suggested an alternate resource restructuring with the same man hours. A reduction of 53% in customer wait time was observed in the new solution.
Arena® was chosen as the software to simulate the Burger King setup and identify areas of improvement.
An attempt at finding an optimized working model using Arena for a barber shop ameliorating the customer wait time, thus attracting more customers with minimum cost
The project is done as final project for the course BANA 7030 where the focus lies on the simulation software called ‘Arena’ developed by Rockwell Software. The main purpose of the project is to prepare a working simulation model of the UDF store on Clifton Ave using the software ‘Arena’. For this model the input will be the inter-arrival time of the customers and service times at each of the counters during rush hours. The model in Arena will give a precise output of the statistical accumulators like total number of entities served, average wait time in the queue, maximum waiting time in queue, average total time in system, maximum total time in system, resource allocation and utilization levels, and efficiency of the processes. Our aim will be to study the statistical accumulators, identify inefficiencies and suggest changes in the model to improve the efficiency. In the scope of the project the customers will be the entities. The model uses the layout of the store, management systems, options of purchase, sequence followed, resources available in Arena simulate real life scenarios. The model was run for 16 hours for a busy day and 10 replications are conducted to validate the result. Certain changes in the model are also introduced and their impact on the performance parameters are also studied to arrive at the optimal solution.
Simulation of SM Paints production facility using ARENA simulation software. Making improvements using OptQuest software, and data analysis of current state simulation, to suggest recommendations for achieving desired level of productivity.
Arena simulation for Superette gas station, Vidor, Texas to evaluate the effectiveness of operating the gas station for 24 hours instead of 16 hours and find the optimum number of gas pumps to attain maximum revenue. Data collection, simulation, and analysis led to the conclusion that operating the gas station for 24 hours with 6 gas pumps ultimately having an impact on the maximum profit of $25 per day (16 hours) to $55 per day (24 hours) which was adopted by the gas station.
Process simulation study of order processing at Starbucks, University of Cinc...Piyush Verma
In this project, the attempt has been made to simulate the process of ordering coffee at a Starbucks inside the University of Cincinnati campus. The components of the system include customers who will place orders, cash counter queue where customer wait for their turn, cashier and food and beverage servers as resources processing the orders and servicing the customers. These are the components which mainly decide for how long a customer must stay in the Starbucks. Apart from these, 2 more components like the processes of (a) adding sugar/flavors to coffee (which is basically when a customer adds sugar/flavor in the coffee according to individual’s taste in a nearby self-service counter) and (b) spending time in the sitting area are also added in the system, as they also prolong a customer’s stay inside Starbucks.
This project analyses the current scenario- fans arriving at the Nippert Stadium through various lanes. The current scenario has been modeled using Arena and a better case scenario has been developed using the same software.
Simulated an outlet of Chick-fil-A Express located at the University of Cincinnati campus to identify bottlenecks and propose changes in the system to reduce the average time spent by a customer.
Arena Simulation Software was used to build the model and Process Analyzer was used to compare the base model and three alternate models. The recommended model reduces the average total customer time by 51%
Simulation with Arena (Dental Clinic project)Kimseng Sok
This is a short slide presentation of my assignment in course of System thinking and modeling. I used Arena Simulation software as tool to discover and make improvements in dental clinic service.
Simulation for kfc order counter at rajiv gandhi international airport, hyder...Pankaj Gaurav
Objective of the business modelling and simulation project was to determine whether existing system is efficient or there is a scope of reducing the waiting time & idle time at KFC Order Counter at Rajiv Gandhi International Airport, Hyderabad
Conceptual Model/ Object Flow Diagram (OFD)
Simulation Implementation
Calculate Number of Model Replications
The objects design
The logic design
Statistic by individual object Results Comparison
Study and Analysis of Tube Failure in Water Tube boilerArunMalanthara
This report explain about Study and Analysis of Tube failure in water tube boiler. It tells about safe conditions to prevent accident. Different Mathematical modelling, Design, Thermal analysis, Structural analysis and Pressure analysis have been carried out to get optimum safe conditions.
Can be used by manufacturers to grab an upper hand in the competitive marketing.
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LPG Booking System [ bookmylpg.com ] ReportNandu B Rajan
BOOK LPG FROM ANYWHERE (Mini Project 2016)
During today’s busy life, no one is ready to waste the time by doing the time consuming and hassle refill booking like IVR Booking System. We are proposing a simple, interactive, hassle free, less time consuming and efficient LPG Booking System. This is beneficial for the Gas Agencies also, they get the refill booking requests and consumer details instantly. Our system is futuristic and can be updated according to the future needs easily.
Features:-
To book an LPG cylinder, you should be a authorised customer. An authorised customer can register to the website and get user id and password. After you have registered, you can log on to the LPG portal using the password and user id provided to you.
Pros:-
Consumers can book the refill by just one click, they can post queries or complaints. Needs only username and password. If they don’t have one, the valid consumers can get the username and passwords with simple registration process. The Admin can only access the database, only he can add the consumers and staff. So the system is secured. The authorized staff can see the bookings and the consumer details without any hassle. He can mark the status whether the refill delivered or not. If delivered then refill request will be automatically cleared.
Project conducted in fulfilment of the M.Eng. degree. Describes a novel computational approach to construct a 3D rendering of cheese from a limited number of X-ray projections.
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
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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