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
Simulation for kfc order counter at rajiv gandhi international airport, hyderabad
Business Modeling & Simulation Project
Simulation for KFC Order Counter at Rajiv Gandhi
International Airport, Hyderabad
DR. Surajit Ghosh Dastidar
Arkadeep Meta 12A1HP033
Pankaj Gaurav 12A1HP035
KFC Corporation, based in Louisville, Kentucky, is the world’s most popular chicken restaurant chain, specializing in
Original Recipe ®, Extra Crispy TM, and Colonel’s CrispyStrips® chicken with home style sides and five new freshly made
sandwiches. Every day, nearly eight million customers are served around the world. KFC’s menu everywhere includes
Original Recipe® chicken—made with the same great taste Colonel Harland Sanders created more than a half-century
ago. Customers around the globe also enjoy more than 300 other products—from a Chunky Chicken Pot Pie in the
United States to a salmon sandwich in Japan.
KFC continues reaching out to customers with home delivery in more than 300 restaurants in the United States and
several other countries. And in quite a few U.S. cities, KFC is teaming up with other restaurants, Taco Bell and Pizza Hut,
selling nearly fifty years ago; Colonel Sanders invented what is now called “home meal replacement” – selling complete
meals to harried, time-strapped families. He called it, “Sunday Dinner, Seven Days a Week.”
The Kentucky Fried Chicken (KFC) Operations Group was formed which had used simulation models to assist in
evaluating operations within its restaurant system in an effort to remain a key player in a highly competitive industry.
The primary aim was to ensure continued financial returns and optimal productivity. A simulation model was built that
used customer order database to allow equipment configurations, queuing methods for producing menu items, order
taking and other service and packaging procedures to be analyzed. The results1 provided the optimal equipment
configurations, minimum labor requirements, and alternate packing techniques required to increase restaurant volume
The Situation dealt with in this project is with respect to the server utilization constraints and waiting time of various
servers involved in the system at KFC order counter at Rajiv Gandhi International Airport, Hyderabad.
The schematic representation of the KFC order counter at Rajiv Gandhi International Airport, Hyderabad is shown below:
Steps Involved in the process of acquiring service:
Customer arrives at KFC order counter at Rajiv Gandhi International Airport, Hyderabad.
Customer joins service queue at any one of the two servers.
Customer reaches server and places his/her order.
Customer waits in the waiting area to get served.
Customer gets served once service is finished and leaves the server.
The above cycle continues for next customer in the queue at any server.
Source: L. Cook, “Simulation Applications at KFC, “Softletter, 7,1 (Spring 1991, published by Pritsker Corporation, Indianapolis, Indiana.
Figure 1 Schematic representation of the KFC order counter at Rajiv Gandhi International Airport, Hyderabad
Thus the major entities of the system can be described as follows:
Customers: People who come to avail the services of KFC.
Staff at server: Employees who are involved in taking orders, billing and serving the customers
Waiting area: Area where a customer waits until him/her being served for asked services at KFC.
OBJECTIVE OF THE SIMULATION
To determine whether existing system is efficient or there is a scope of reducing the waiting time and idle time.
Data Collection: Data has been collected through observation of customer arrival, Ordering and Delivery at the outlet
which includes observations for 1 hour in 3 different days at 3 different shifts.
Time of taking sample
7 AM - 8 AM
1 PM - 2PM
8 PM – 9 PM
Table 1 Sample Description
12 AM- 8 AM
8 AM – 4 PM
4 PM – 12 AM
Inter - Arrival Time of Customer – Time between two successive customer arrivals.
Ordering Time – Time taken by counter to take an order from the customer.
Delivery Time – Time taken to deliver the food items to customer
Waiting Time – Time for which customers wait
Idle Time - Time for which servers are idle.
Average wait time for order
Maximum wait time for order
Average queue – Number of average people in a queue for giving orders.
Maximum queue – Number of maximum people in a queue for giving orders.
Average wait time for delivery
Maximum wait time for delivery
Average total time spent by customer
Maximum total time spent by customer
Sever utilization – It is estimated by dividing the amount of time that the server is busy during a simulation by
the amount of time covered by the simulation
Using input analyzer in ARENA, the data collected has been analyzed to obtain the best fit distribution for each working
hour shift. The ARENA model is depicted below.
Following actions have been considered for the below ARENA model:
Order Taking: Seize, Delay, Release
Figure 2 ARENA Model
3 different simulations have been run 8 hour each for 3 shifts, which accounts for 24 hours of operation in a day.
12 am to 8 am
8 am to 4 pm
4pm to 12 am
Inter – Arrival time
Order Taking Time
TABLE 2 Output of Input Analyzer in ARENA
12 am to 8 am
8 am to 4 pm
4pm to 12 am
Avg. Wait time for Order
Max Wait time for Order
Avg. Wait time for
Max Wait time for
Avg. Total time spent by
Max Total time Spent by
Idle Time (Server)
Table 3 Output of ARENA Simulation
From the above table we can see that shift from 8 AM to 4 PM is the most busy shift with longest average waiting time
of 5 mins and longest queue of 8, whereas the shift from 12 AM to 8 PM is least busy shift.
The observation about the server utilization constraints and waiting time of 3 servers at 3 different shifts depict the
problem of less server utilization at shift from 12 AM to 8 PM and Max Wait time for Order at shift from 8 AM to 4 PM.
The possible tactics to deal with these problems as proposed by us are as follows:
Adding servers in the shift from 8 AM to 4 PM
Introducing “Happy Hours” deal in the shift from 12 AM to 8 PM. Happy hours deal may include combo offers or
discounted food items.
Tradeoffs – Increase in the number of servers would be cost intensive and may require recruiting more staff.
Introduction of happy hours may increase arrival rate but there would be cost implications of this deal.