2. CHAPTER -1
INTRODUCTION
SYNOPSIS
This project is done as a final project for the course BANA 6035- Simulation Modeling
where the focus lies on understanding the basis of Simulation concepts using a
software called “Arena”.
The purpose of the project is to prepare a working simulation of the Kroger’s store at
4777 Kenard Avenue.
The model in Arena provides an output for many statistical values like total number of
customers served in a particular interval of time, time spent in the queues in the store,
maximum service times in the counters, resource allocation and utilization levels etc.
The model uses the layout of the store, management systems, check-out counters,
options of resource allocation etc. in Arena to simulate real life scenarios.
The input data for the model consists of inter-arrival times of the customers, the
service times at each counter and the shopping times which was collected at the peak
hours of student visits at Kroger’s store.
The model was also run for about 10 days to analyze the results, changes were made
to the existing model and based on the conclusions, suggestions are made to improve
the efficiency of the store.
PROBLEM STATEMENT
The Kroger Company is an American retailer formed in Cincinnati, Ohio in 1883 and
has established itself to become the largest supermarket chain by revenue in US and
the third largest retailer in the world.
The purpose of choosing the Kroger’s store at 4777 Kenard Avenue is that it is the most
frequently visited Kroger store by the students of UC as there is a weekend (Friday,
Saturday and Sunday) shuttle that runs from the University main campus to Kroger’s
in half hour intervals to enable students to complete their weekly grocery shopping.
According to the information from the shuttle drivers and the Kroger authorities, the
maximum influx of students’/rush hour occurs between 3pm-7pm on Fridays,
Saturdays and Sundays.
During these rush hours, the waiting time for a customer peaks to almost 16 minutes
after shopping to check-out from the store thus effectively wasting the time of the
students and lose potential customers for the store as Students might choose to shop
less frequently or in nearby stores to avoid the wastage of time.
4. CHAPTER -2
DATA COLLECTION, MODEL ELEMENTS AND FITTING OF DISTRIBUTIONS
DATA COLLECTION
The model which is prepared for the scope of this project considers the resources in
the entire shopping process like Self-Check-out Counter, Manual Counter, Express
Counter, Packing Counter, Self-Check-Out Loading etc. to keep a sequence of the
process. In order to input the service times for these different resources, data was
collected at the Kroger store in the form of inter-arrival times of customers and the
service times for the resource allocated at the various points with the permission of
Kroger authority.
The data used here was collected for between 3pm and 7 pm for three days (The days
were either Friday, Saturday or Sunday).
From the interaction with the authorities and assumptions, nearly none of the
customers leave without a purchase. About 30% use self-check-out counters, 20% use
express counters and remaining 50% use the manual counters. Out of the 30% who
use self-check-out counters, nearly 30% encounter problems, which are resolved by
an employee at a counter allocated for the same.
MODEL ELEMENTS
The entire store simulation process is shown below as a flow chart which will be used
as the basis for Arena Model creation and decision for data points to be collected as
raw data.
9. CHAPTER -3
MODELING
MODELING THE PROPOSED MODEL IN ARENA
The Kroger Store was bifurcated into different pieces / processes to prepare a model
in Arena. Various modules have been used Eg: Create, Assign, Process, Decision,
Dispose. The procedure has been split into steps according to the flow diagram
mentioned above. The different steps are:
1. Customer enters the store
2. Customer proceeds to shop
3. Customer Waits in the Queue for a counter
4. Customer Bills the purchase- Self/Express/Manual
5. The items are packed in the counter
6. Customer pays for the purchase
7. Customer leaves the store
There are also some decision modules to check for the path of the customer. Assign
and Record modules are used to assign and record the average time of the customer
in the store, in the queue and the number of customers who purchased and came out
of the store in a specific time interval. The other parameters are Resources and the
Queues allocation.
Arena Store Action Performed
Entity Customer Part
Resource 1 Self- Check Out by Customer Seize Delay Release
Resource 2 Express- Check Out Person Seize Delay Release
Resource 3 Manual- Check Out Person Seize Delay Release
Resource 4 Resolving Person Seize Delay Release
Queue 1 Self- Check Out Queue FIFO
Queue 2 Express- Check Out Queue FIFO
Queue 3 Manual- Check Out Queue FIFO
Queue 4 Resolving Queue FIFO
The Arena model that was done based on the above modules is as below:
20. CHAPTER 05
ALTERNATE SCENARIOS AND IMPROVEMENTS IN THE SYSTEM
ALTERNATE SCENARIOS
SCENARIO 1- Self-Man
As mentioned in the interpretations, we find that the Self and Manual Check Out
Counters have very high utilization levels along with waiting times, so in the first
scenario, we increase the capacity of these counters to their maximum levels.
Self-Check-out – 12
Manual Check Out- 19
while keeping the other counters at the same level.
SCENARIO 2- Res-Man
We increase the Resolution counters and Manual Check Out Counters to their
maximum capacity in the second scenario to reduce the utilization and waiting times.
Manual Check Out- 19
Resolution -2
SCENARIO 3-Res-Express
We increase the Resolution counters and Express Check Out Counters to their
maximum capacity in the third scenario to reduce the utilization and waiting times
without changing the number of operational counters of Self and Manual Checkout.
Express Check Out- 4
Resolution -2
SCENARIO 4-Res-Self
We increase the Resolution counters and Self Check Out Counters to their maximum
capacity in the fourth scenario to reduce the utilization and waiting times.
Self-Check Out- 12
Resolution -2
SCENARIO 5-Man-Express
We increase the Express counters and Manual Check Out Counters to their maximum
capacity in the fifth scenario to reduce the utilization and waiting times.
Manual Check Out- 19
Express Check Out -4