Queuing System Of Chipotle Mexican Grill

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Queuing System of Chipotle Mexican Grill
Project Domain
The service process selected for this project is Chipotle Mexican Grill (CMG) food ordering system. CMG is a Mexican cuisine fast food chain that offers a healthy focused menu in its 1,230 operated restaurants over the Unites States, Canada, and the UK. It is considered as one of the most popular and successful Mexican restaurants in the United States. CMG is a firm with a strong business model, impressive brand image, and a focused growth strategies, which it has been able to successfully and consistently grow year after year.
Chipotle does serve a focused menu of soft burritos, tacos, burrito bowls, and salad. It also provides a variety of adds-on, such as guacamole and tortilla chips
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My project aims to simulate Chipotle queue system. As it was mentioned before, Chipotle’s customers can place their orders through different ways. However, this project will be focusing on the regular placing order through the channel of the waiting line at the restaurant, to simulate its queue line and serving line.
The overall purpose of this simulation test is to find out the time a customer has to wait before he get served, and the time from the moment he places his order until he finishes the payment process; in order to find the average waiting and serving time in chipotle system; and seek to improve this system by reducing the service time.
Literature Review In fast-food industry, the term “fast” usually refers to quick-service. When customers coming to a fast-food restaurant, they do not expect to wait for a long time to get the food service. The waiting time for customers to get the food service becomes one of the key performance characteristics in a fast-food restaurant. Wait time is affected by the design of the waiting line system. A waiting line (or
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Other assumptions/limitations of the study:
• The queue discipline will be measured as first-come, first-served (FCFS)
• The pattern of arrivals is assumed to be following a Poisson probability distribution, as each arrival is independent of others in the system and arrives in a random fashion.
• The service time for this process follows an exponential probability distribution.
• The arrivals wait in a single queue before moving onto to the next station, when it becomes idle for service.
• The model will be built in a scenario that a customer may place up to two orders at one time.
• To simplify the model, the model used in this case assumes that customers are patient; they do not balk or renege.

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