Simulated real life situation on Arena software to perform analysis and make useful inferences for the business

1. Jatin Saini
MS Business Analytics
University of Cincinnati
HABANERO – ARENA SIMULATION

2. CHAPTER 01
SUMMARY
1.1 Introduction
This project is a part of the course BANA 7030 Simulation Modelling and the focus of this
project is to understand resource utilization and customer service time during peak hour rush
in a restaurant in terms of entities, resources, processes and other different modules. The
purpose of this project is to build a working simulation model of the Habanero Restaurant at
Ludlow Avenue, Cincinnati, which is famous for its Mexican/Latin American dishes using the
software Arena. The objective by building the simulation model is to understand following
things at Habanero
o Wait time in queue for the customer at the order counter
o Wait time for a customer to collect the order from pickup counter
o Utilization of staff members performing different tasks
o Delay factors to reduce customer service time
Arena software gives specific output for different statistical accumulators such as total number
of entities entered within the system, total number of entities that left the system, total time in
queue for all entities, maximum time in queue, average time in queue etc. Apart from the
regular statistical accumulators, Arena allow users to define their own statistics through record
modules. For this project, data is gathered in the following way
o By recording customer arrival rate
o Input from staff on food preparation time
o Input from staff on service times
o Recording packaging and parcel handover time
The data was gathered for two rush hours on Friday from 6 pm to 8 pm. The model was
replicated for 30 days and based on the results suggestions were made to improve the efficiency
economically with better utilization of resources.
1.2 Problem Statement
Habanero food outlet at Ludlow Avenue in Cincinnati is famous for its Mexican delicacies.
Some of the dishes that are must try in this food outlet are Burrito, Taco, Quesadillas and other
signature platters.
As per the staff in Habanero, on weekends the outlet experiences rush in the evening. Because
of the rush the waiting time for the customers in queue is considerably higher and average wait
time reaches up 20 minutes. As per the information from the staff, many times it happens that
customers leave food outlet without ordering anything as the wait time is higher on rush hours.
As customers avoid longer wait queues, this project will focus on how the existing system can
be made more efficient so that longer wait times in queues are reduced for the customer and
the utilization of resources is improved.

3. 1.3 Assumptions
The existing system at Habanero could not be modelled and simulated exactly the way it is due
to natural variabilities and unscheduled activities. There must be some assumptions to exclude
these activities. Even though the model does not delineate the exact situation at Habanero, the
statistical inferences that we get from Arena are very useful in analysing the situation. Here are
a few assumptions that were made for the model in this project —
 The shop is open 2 hours a day.
 There are no work shifts between the workers.
 There are no breaks for the workers during the time when the model is running.
 One customer is served by only one staff member at a time.
 For pickup counter, after packing the food, the counter person hands over the food to
the customer in no time and the customers are waiting near the counter for their order.
 The time of customers who don't buy anything is not counted in the overall average
customer time in the system.
 Some of the data for decision modules was taken from the Habanero authority due to
lack of sample points.
 The service time varies for different cuisines.
 Customer may/may not stay at the restaurant after his/her order is served. This is not
considered to obtain the statistics. “Customer leaves” implies that the customer
transaction is complete, and he/she received his order. He may/may not leave the
resataurant.
 There was no activity which caused any deviation from all the above assumptions.
All these assumptions hold valid for the time when the model is running.

4. CHAPTER 02
DATA COLLECTION AND DISTRIBUTION FITTING
2.1 Data Collection
To build a simulation model for Habanero, data collection is one of the important aspect.
Having clear, concise and specific data is as important as building a model. This model
considers the resources that are present in the Habanero. Following points explain the data
collection process in detail.
1. There are 2 order counter desks in habanero. On each desk, a resource is stationed in such a
way that he/she takes an order from the customer, confirms the order to the cook in the kitchen
area and does transaction with customer for the dish ordered.
2. It was observed that on Friday evening, there were 4 cooking resources. 2 cooks are
designated to prepare Mexican dishes, whereas remaining 2 cooks are designated to prepare
Italian dishes only.
3. Habanero has one cold drink machine. To get cold drinks, customers walks up to machine
and get a cold drink when their ordered dish is being prepared. This part has not been included
in the model.
With the permission of Habanero Food outlet authority and with the help of Habanero staff
following data points were collected. The following data is recorded in between 6 – 8pm on
Friday.
o Interarrival times of customers in the food outlet
o Service time for the resource at order counter
o Probability of ordering Mexican/Italian food
o Food preparation time for Mexican and Italian dishes
o Pickup counter – packaging and handover time
Data on type of dish ordered by the customers in the observed period was provided by the
resources at the ordering counter. It was found that around 75% of customers ordered Mexican
dishes, whereas 15% of customers ordered Italian dishes and around 10% of customers ordered
combination of Mexican and Italian dishes.
Once the dish is prepared, then resources at the ordering counter takes the dish from the kitchen
area (next to order counter area) and packs it or serves it in the food plate to the customer as
per customer’s request. Customer collects these dishes from dish receiving area which is
adjacent to order to counter area.
As per the inputs from the pickup counter staff member, it takes around 2 minutes in packing
or preparing serving plate for the customer.

5. 2.2 Data Fitting to Distributions
In Arena we can input the data using input analyser to analyse the input data. Input analyser
has inbuilt function of different distributions and it helps the user to fit a distribution that
matches with the input dataset. To input data set into the Input Analyzer of Arena we perform
following steps
o Store the observations in the form of (.dst) file
o Select Input Analyzer from Arena
o Open the (.dst) file in input Analyzer
o Choose different distributions provided in the input analyser
o Fit the dataset with the most appropriate distribution
Input Analyzer loads histogram of the raw data in the input text file and fits the dataset to a
specific distribution. Given below is the fitted histogram and the distribution summary of the
fitted data for the inter-arrival times of the customers.
Customer Inter-Arrival Time

6. Mexican Dishes Prep Time
Italian Dishes Prep Time

7. CHAPTER 03
ARENA MODEL
3.1 Modelling the System
The Habanero Restaurant system was bifurcated into pieces to prepare the model in Arena.
Various modules e.g. Create, Process, Assign, Seize-Delay-Release etc. are used in Arena to
simulate the real-world scenario. The procedure that any customer follows at Habanero is
divided into steps to give a flow in the Arena model. The steps can be treated as –
1. Customer enters the store
2. Customer waits in queue to place order at order counter
3. Customer places the order
4. The order is prepared by chefs as per the requirement (Mexican/Italian/Combo dishes)
5. The dish, after preparation, is packed.
6. Customer goes to pickup counter
7. Customer gets the dishes
8. Customer leaves the system
There are certain decision modules which decide the path of the order in the model. Also, the
model consists of assign and seize-delay-release modules to calculate average customer time
in the system and the number of customers who bought food items. The Important parameters
in the Arena model are the Resources and Queues. Here is an overview of model parameters –
S.no. Arena Habanero Restaurant Type/Action
1. Entities Customers Part
2. Resource 1 Mexican Chef Seize Delay Release
3. Resource 2 Italian Chef Seize Delay Release
4. Resource 3 Cash Counter Person Seize Delay Release
5. Queue 1 Cash Counter FIFO
6. Queue 2 Mexican Dish Preparation FIFO
7. Queue 3 Italian Dish Preparation FIFO
8. Queue 4 Combo Dish Preparation FIFO
9. Queue 5 Pickup Counter FIFO
Here is a snapshot of the Arena Model that was prepared –

8. To explain the model parameters, we will go through each step mentioned earlier looking
closely into the modules and logic used to prepare the model.
1. Customer enters the restaurant –
The customers enter the restaurant by a create module named ‘Incoming Customers’
An assign module named ‘Assign Customer Picture’ is used after the create module to show
animation in the model. Here is the snapshot of the dialog box –
2. Customer waits in queue to place the order –
The Customer joins the queue if there is any, to place their order. This queue is shown just
above the ‘order counter’ module. During rush hours, there is a long queue so mostly people
must wait in queue to place order.
3. Customer places the order –
There are 2 order counters but they have a single queue. Customer in the queue can go to
either order counter wherever the order counter resource is free. Once the customer goes to
the order counter, he/ she seizes the order counter resource and orders the dish. Order

9. counter proceeds with the payment transaction with the customer and finally order counter
resource passes this order to the cook in the kitchen. Type of ordered dish is either Italian,
Mexican or combination of both.
In the following dialog box, we can see the Seize-Delay-Release Process dialog box and
the resource used is the resource at order counter. The expression for the order counter
resource is obtained from the input analyser.
3. Decide
After this, a decision module is placed called ‘Decision for product availability’ which
decides whether the order is for Mexican dish or Italian dish or a combination of both. As
per the collected data, 75% of the orders are of Mexican dishes, 15% of Italian dishes and
the rest 10% for Combos.

10. 4. Dish Preparation
A. For Mexican Dish
The order for Mexican dish joins the queue for food preparation to seize-delay-release a
Mexican cook (out of the 2 available) who works on preparing dish as per the order queue and
this is shown in the dialog box below. In this dialog box, the resource is seized delayed and
released once he/she finishes preparing the order. The expression is Mexican Prep time which
is obtained through the Input Analyzer and this expression is defined in Expression.

11. B. For Italian Dish
Italian cook starts working on preparing Italian dishes as per the order queue. This is shown in
the dialog box below. In this dialog box, the resource is seized, delayed and released once
he/she finishes preparing the dish for an order. The expression is Italian Prep time which is
obtained through the Input Analyzer and this expression is defined in Expression box.
Italian dishes require additional 5 minutes of baking time per dish for its preparation and this
is shown in the delay module dialog box below.
C. Combination of Italian and Mexican dishes in a Single Order
In 10% of the cases, as mentioned before, orders consist of both Mexican and Italian dishes. In
such cases, Mexican part of the order goes in the queue for Mexican chef and Italian part of
the order goes in the queue for Italian chef and these dishes after preparation are combined to
form a single order. This is shown in Arena using Separate and Batch modules which are
displayed below –

12. In the above image, it is shown that the combo order has been separated into 2 different orders
(by making a duplicate copy) and then the orders are prepared normally as any other Mexican
or Italian order. After both parts have been prepared, they are joined together using Batch
module according to the serial numbers of each order.
5. Pickup Order Counter
After the orders are prepared, it takes 2 minutes on an average for each order to be packed or
served in a plate by the resource at pickup counter and then handing it over to the customer.
The queue here is of the order prepared, and customers receive their orders as and when their
order gets prepared.

13. In the above dialogue box, we are seizing one cashier for 2 minutes per order for packaging
order and handing it over.
6. Exit
Customers exit the system after receiving their respective order and leave the system. After
leaving our system, they may choose to eat their food in the restaurant or they may leave the
restaurant.
This has been shown using Dispose module.
Apart from these steps, another important feature of this model are the Resources and the
Queues as mentioned earlier.
 Resources:
As explained before, there are 6 resources in the model. Each resource has its own service time
which was fitted by the distributions. Here is a table of the resources:
Arena has a display of all the resources in the resource module where all the information about
the resources is displayed. Here is a snapshot of the resource information –

14.  Queues –
There are 7 queues in the model where only 2 are for the customers, rest are for order
preparation.
Below is the snapshot of the queues
3.2 Simulating the Model
The Arena Software has an option of Run window where we must mention the Run information
like replication length, number of replications etc. The model prepared here was run for 2 hours
and 30 replications were made to consider variabilities. Here is a snapshot of the Run dialog
box –

15. CHAPTER 04
RESULTS AND INTERPRETATIONS
4.1 Results
The Arena Software produces a detailed and structured result window which allows the user to
view results by Entity, Queue, Resource and anything that is specified in the model. For
Habanero model, which was run for a replication length of 2 hours, the average Number Out
value is 40, for 30 replications.
4.1.1 By Entity –
The most important attribute attached with the entity is ‘time’. Arena gives a detailed output
with Average value, Minimum, Maximum, half width etc. for various times that are observed
by the entity during its stay in the system. In Habanero model, the main output is the Total time
in the system, the wait time and service time. It also gives the number of entities in and out of
the system. Here is the output from Arena –

16. The average waiting time for customer is 17.17 minutes and the average service time for the
customer is 14.76 minutes. This makes the average total time spent by a customer in the system
to be 31.57 minutes. We want to reduce this to increase the efficiency of the Habanero system.
4.1.2 By Queue -
Arena gives the Waiting time and Number of entities waiting for each queue in the model. We
will observe the results for the maximum waiting time and this is where we need to bring some
changes to reduce the waiting time of that queue. Here is the output for the queue –

17. The waiting time is maximum, 11.19 minutes for Mexican part preparation in the combo
counter queue followed by the queue for Mexican food preparation. We want to reduce this
quantity to increase the efficiency of the Habanero system.
4.1.3 By Resource –
Arena gives a myriad of output for Resource Usage but the most important here is the
Scheduled Utilization of the resources. This gives the utilization of all the resources in the
Model. Here is the output –

18. It can be observed that the Mexican cook is used up for a maximum time while the utilization
of other two types of resources is less comparatively.
4.2 Interpretations —
It is observed that the Mexican cook resource has —
a. High utilization
b. High Waiting time in queues
The efficiency of the Habanero Restaurant would increase when the average total time spent
by a customer would decrease. According to the above interpretations, it can be concluded that
some improvements in the above two resources are needed to reduce the average time in the
system.

19. CHAPTER 05
IMPROVING THE SYSTEM
5.1 Suggestions -
As mentioned in the interpretations, the Mexican Chef has the maximum utilization and longest
queues on an average. Our aim is to reduce the wait time in queue without economically
changing the model.
Suggestion to achieve the above objective is to cross train the resources. In the base model,
Cooks specialize in preparing only one type of dish i.e. either Mexican or Italian. We want to
cross train the resource in such a way that a cook can prepare both Italian and Mexican dishes.
To achieve the above cross training scenario
o We add a resource name Cross Trained Chef who can cook both Mexican as well as
Italian dishes
o We prepare a Set of names Mexican and Italian
o Mexican set will have Mexican Cook and Cross Trained Chef in such a way that
Mexican cook will be first preference and Cross Trained Chef will be second preference
o Italian set will have Italian Cook and Cross Trained Chef in such a way that Italian cook
will be first preference and Cross Trained Chef will be second preference
Now in Mexican dish preparation, Italian dish preparation, Combo dish preparation we use
specific set rather than individual resources.

20. Using Process Analyzer [PAN]
To get the best-case scenario where we have minimum customer wait time, we create different
situations in process analyser tool, which is an Arena extension tool that helps us find best case
scenario out of multiple situations for given responses.
Below is the PAN view of the scenarios and the response variables:
5.2. Comparing the results —
Process Analyzer was used to compare 5 different scenarios
1. Base – where we have 2 specialised chefs for each Italian and Mexican dish
2. With 1 Cross Trained and 1 Italian Chef – where we keep the 2 Mexican chefs but
replace one Italian chef with one cross trained chef
3. With 2 cross trained and none Italian chefs – where we replace both Italian chefs with
cross trained chefs but keep both the Mexican chefs
4. With 2 cross trained and 1 each Italian and Mexican chef – where we keep 1 specialised
chefs for each Italian and Mexican dish and keep 2 cross trained chefs
5. With 3 cross trained and 1 Mexican Chef – where we replace both Italian chefs with
cross trained chefs and replace one Mexican chef with cross trained chef
From the analysis, we see that average Customer Total Time in the system is minimum (19.86
minutes) when we have 3 cross-trained chefs and 1 Mexican chef. We say that with this
scenario in place, our resources would have the best possible outcome.

21. CHAPTER 6
CONCLUSION
The Habanero Restaurant at Ludlow Avenue, Cincinnati was modelled in Arena Simulation
Software and the results about the relevant parameters were generated. A deep analysis was
done on the output results of Arena and it was observed that the average customer time in the
system during rush hours was large enough for Habanero to lose potential customers and
downgrade the business. By probing into the flow of food order, it was observed that the
waiting time in the queue for preparation of Mexican dishes was significantly large. So, taking
into consideration the resources available at Habanero restaurant, a suggestion was made to
cross train its chefs for both Mexican and Italian cuisines. To increase the efficiency
economically, it was suggested that Habanero should not increase the staff but replace the staff
with cross trained chefs or train the current staff in both cuisines. Keeping 3 cross trained chefs
and 1 Mexican chef gives us the best-case scenario. Hence, with certain changes in the hiring
and training of chefs at Habanero, they can reduce the customers lost due to longer wait time.
Again, there can be ample suggestions and modifications in the model to optimize the output
both economically and commercially; but we have discussed only one of them.
REFERENCES –
1. Content –
Simulation with Arena 6/e- W. David Kelton – University of Cincinnati, Randall P.
Sadowski, Nancy B. Zupick, Rockwell Automation
2. Data –
Habaero Restaurant, Ludlow Avenue, Cincinnati, Ohio
3. Image –
https://www.linkedin.com/company/habanero-mexican-grill