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Maggie’s Stop and Go fast food restaurant is one of the prominent drive-by fast food restaurants in Connecticut. Over the years, Maggie’s Stop and Go has relied extensively on the use of human labor to run and determine their operations and processes. This has resulted in a decline in customer numbers primarily due to poor service, and lack of proper coordination.
The manager of this restaurant resulted in applying a Discrete Event Simulation model that would enable them to coordinate their activities in a repeated fashion with the arrival of every customer. This is bound to bolster their position as one of the best drive-bys in Connecticut.
The entities that will be applied in the model adopted by Maggie’s Stop and Go are of both permanent and temporary nature. The permanent entities include the building, the drive-by lane, the ordering machine and the collection point. These are entities that this fast food joint has spent a small fortune improving, as part of their make-over program. The temporary entities in play will include the customers, the payment methods, the customer queue, and the employees.
The employees are grouped as temporary entities due to the fact that they work on a shift basis. The employees are shuffled between shifts hence no employee is assured of a regular time slot on a daily basis. The ordering machine uses smart technology and is touch sensitive. This ensures that the customers place their orders in a more efficient manner in comparison to the previous method where they had to speak into the microphone to place their order. The collection point has been separated from the payment area to ensure all customers pay their dues before they collect their orders.
The events at Maggie’s Stop and Go have been narrowed down to a sequence of events that occur repeatedly with the arrival of each customer. In order to ensure a more reliable event queue, the microphone was replaced with the touch-sensitive ordering machine. This was to prevent situations in which the customer was either misinterpreted, or not heard clearly when placing their order.
This reduces to very low rates, the possibilities of complaints from customers that their needs have not been met by the customer service team. The simulation model will be multi-threaded due to the fact that many of the events taking place at Maggie’s Stop and Go are of concurrent nature. However, all events are designed to be sorted in a chronological order, and the same applies to the preparation of orders placed by the customers.
The event queue for the simulation model starts at the arrival of a customer and ends with their departure after collecting their order at the collection point. The first event on the event queue is the arrival of the customer at the drive-by lane where they slow down their car and approach the ordering machine. This is in the event that no other customer is using the ordering machine at the time of their arrival. In the event that another customer is using the machine, then the new customer is forced to wait in line until the first customer finishes placing their order.
The second event is the placing of the order. The ordering machine is updated automatically and provides the customer with the full menu of available food for purchase. The customer selects their desired food and states the quantity of the food either in packets or bottles or any measure that is provided. The customer then confirms their order and moves their car to the payment area. Upon confirmation of the order, an electronic receipt is created by the ordering machine as the customer simultaneously moves to the payment area.
The electronic receipt is used for the order preparation by the kitchen staff. At the payment area, Maggie’s Stop and Go receives payment for food and prints out a hard copy receipt that is handed to the customer as proof of their purchase. Payment is acceptable in terms of cash or credit card. The payment time taken ensures that the kitchen staff complete packing the customer’s order, and they place it in the collection point. The customer then collects their order and leaves the premises.
The event list for this model is as follows:
CUSTOMER ARRIVAL-CUSTOMER WAITS IN QUEUE-CUSTOMER PLACES ORDER-ORDER MACHINE PRINTS ELECTRONIC RECEIPT-CUSTOMER MOVES TO PAYMENT AREA-ORDER IS PREPARED-CUSTOMER COMPLETES PAYMENT-ORDER PLACED IN COLLECTION POINT-CUSTOMER COLLECTS ORDER-CUSTOMER DEPARTURE.
This event list is completed in chronological order, with exceptions in the event that one of the events in the list is not necessary. For example, the absence of a waiting queue would prompt the model to skip the event and hence the customer would proceed to place their order. This will also apply to the clock time.
The model developed for Maggie’s Stop and Go warrants the availability of event handlers that ensure all events proceed in the intended order. The first event handler is the guard at the gate who directs the vehicles into the drive-by lane. He ensures that all customers are served in chronological order, and that they also join the queue in the event that there is one.
The most important event handlers are the kitchen staff that ensures the order is prepared to customer satisfaction and specification, and placed in the collection point. The next event handler is the payment clerk who ensures that the payment transaction for the purchase is completed. All other events are automated and do not require event handlers to guarantee their successful completion. The manager is the overall event handler who oversees the successful completion of the sequence of events from start to finish.
This model makes use of a number of system states. These states are directly influenced by the events on the list, and they change in accordance to this list. The states that are affected by the events list include the status of the payment clerk, status of the kitchen staff, and the number of customers waiting in the queue. These states directly influence the simulation of events as their run-time is essential in the collection of statistical data that are necessary in understanding the simulation model.
The states also operate in a domino effect. A change in any of the states offsets a change in another state, such as is the case with a change in the status of the payment clerk signals a change in the number of customers waiting in the queue.
The variables in the system state can be modified in the event that change in one state does not necessarily warrant a change in another state. A good example is the change in payment clerk status. After serving the only customer at the premises, the change of payment clerk status to ‘idle’ does not warrant a change in corresponding states such as the number of customers waiting in the queue, or status of the kitchen staff. Such temporary modifications provide a clear picture of the efficiency of the simulation model. By providing data that are irregular, the simulation model can be understood from all perspectives.
The random variables in play in this simulation model include inter-customer-arrival time, the payment clerk service time, and the kitchen staff packaging time. These variables are greatly affected by the customer in question. The inter-customer arrival time affects the working conditions of the kitchen staff and the payment clerk, seeing that it increases the pressure under which they work.
Being the two most important parts of this simulation model, many customers arriving in between short periods of time translates into the need for these two components of this simulation model to operate faster than they would with fewer customers. The method of payment chosen by the customer also has great influence over the simulation model. A customer paying cash takes less time at the payment clerk as compared to a customer paying with their credit card. The order placed by the customer also determines the packaging time required by the kitchen staff. All these variables provide essential data that are useful in determining the right number of employees required to address any scenario that may arise at Maggie’s Stop and Go.
The ending condition is set based on the shifts that the employees work. Many employees work 6-hour shifts, and hence the ending condition is set to be when the clock reaches the 6 hour mark. This provides a complete loop of the simulation. The simulation model then generates a statistical report on the average time for each event in the events list between customer arrival and customer departure. The data collected over the four 6 hour loops in a day will enable the management to determine the adequate staff and measures to be taken at different time periods.
Dagpunar, J. S. Discrete Event Simulation. Simulation and Monte Carlo: With Applications in Finance and MCMC, 135-156.
Fishman, G. S. (2001). Discrete-event simulation: Modeling, programming, and analysis. New York, NY [u.a.: Springer.
Robinson, S. (2010). Conceptual modeling for discrete-event simulation.
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