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Business Modeling & Simulation Project

Simulation for KFC Order Counter at Rajiv Gandhi
International Airport, Hyderabad
...
INTRODUCTION
KFC Corporation, based in Louisville, Kentucky, is the world’s most popular chicken restaurant chain, special...
Figure 1 Schematic representation of the KFC order counter at Rajiv Gandhi International Airport, Hyderabad

Thus the majo...
Variable Definition
Input variables




Inter - Arrival Time of Customer – Time between two successive customer arrival...
SIMULATION RESULTS
Expression (Sec)

12 am to 8 am

8 am to 4 pm

4pm to 12 am

Inter – Arrival time

-0.001+476*β(1.08,0....
CONCLUSION
From the above table we can see that shift from 8 AM to 4 PM is the most busy shift with longest average waitin...
ANNEXURE 1: Real Time data collected from KFC, Rajiv Gandhi International Airport, Hyderabad
Morning

Afternoon

Evening

...
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Simulation for kfc order counter at rajiv gandhi international airport, hyderabad

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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

Veröffentlicht in: Business, Technologie
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Simulation for kfc order counter at rajiv gandhi international airport, hyderabad

  1. 1. Business Modeling & Simulation Project Simulation for KFC Order Counter at Rajiv Gandhi International Airport, Hyderabad Submitted to, DR. Surajit Ghosh Dastidar Submitted By Arkadeep Meta 12A1HP033 Pankaj Gaurav 12A1HP035 Ritesh Sahoo 10/4/2013 12A3HP003
  2. 2. INTRODUCTION 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 substantially. PROBLEM DESCRIPTION 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: 1. 2. 3. 4. 5. 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. 1 Source: L. Cook, “Simulation Applications at KFC, “Softletter, 7,1 (Spring 1991, published by Pritsker Corporation, Indianapolis, Indiana.
  3. 3. 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. METHODOLOGY 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. Day Day 1 Day 2 Day 3 Time of taking sample 7 AM - 8 AM 1 PM - 2PM 8 PM – 9 PM Table 1 Sample Description Shift time 12 AM- 8 AM 8 AM – 4 PM 4 PM – 12 AM
  4. 4. Variable Definition Input variables    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 Response variables            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 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 Delivery: Delay Figure 2 ARENA Model Simulation Runs 3 different simulations have been run 8 hour each for 3 shifts, which accounts for 24 hours of operation in a day.
  5. 5. SIMULATION RESULTS Expression (Sec) 12 am to 8 am 8 am to 4 pm 4pm to 12 am Inter – Arrival time -0.001+476*β(1.08,0.683) 12+Gamma(163,0.669) 28+Expo(136) Order Taking Time Normal (102,14.7) 83.5 +46*β(1.21,0.95) 91.5+39*β(0.886,0.717) Delivery Time Normal (249,106) Normal (258,113) Tri (52,101,520) 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 0.2 Min 5 Min 1.56 Min Max Wait time for Order 2.82 Min 13.2 Min 7.8 Min Avg. Queue 0.035 2.464 0.625 Max Queue 2 8 5 Avg. Wait time for Delivery 4.2 Min 4.3 Min 3.85 Min Max Wait time for Delivery 7.6 Min 10 Min 8.34 Min Avg. Total time spent by a Customer 6.1 Min 11.2 Min 7.27 Min Max Total time Spent by a Customer 10.5 Min 22.13 Min 15.6 Min Server Utilization 34.24 % 88.03 % 74.10 % Idle Time (Server) 5.26 Hrs 57.5 Min 2.07 Hrs Table 3 Output of ARENA Simulation
  6. 6. CONCLUSION 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. RECOMMENDATION 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.
  7. 7. ANNEXURE 1: Real Time data collected from KFC, Rajiv Gandhi International Airport, Hyderabad Morning Afternoon Evening Morning Afternoon Evening Morning Afternoon Evening Inter Arrival Time (secs) Inter Arrival Time (secs) Inter Arrival Time (secs) Order Time (secs) Order Time (secs) Order Time (secs) Delivery Time (secs) Delivery Time (secs) Delivery Time (secs) 0 12 160 111 98 104 90 110 60 220 89 66 94 102 118 232 225 216 126 135 168 101 122 129 248 239 158 250 35 216 98 111 92 294 346 174 283 69 71 122 103 107 356 109 171 439 15 158 64 94 124 60 234 181 418 20 28 118 99 129 420 265 520 406 40 35 93 84 94 230 279 162 224 67 97 114 129 103 215 210 52 268 75 55 108 102 118 220 355 192 476 86 522 97 118 99 410 155 470 368 200 63 108 91 117 212 555 518 108 335 107 127 420 350 134 74 113 109 471 135 179 286 125 121 112 222 203 40 99 98 121 60 346 29 116 106 153 166 420 41 121 111 222 230 55 694 110 128 264 35 185 100 130 293 25 208 129 248 35 69 105 264 15 109 218 40 118 189 150 180 270 169 214 215 404

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