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This is not taken into account to obtain the statistics. There was no activity which caused any deviation from all the above assumptions.
All these assumptions holds valid for the time when the model is running. To input the times for each resource, data was collected for inter-arrival times of customers and service times at each of the resource in the shop with the permission of the Starbucks authority.
The data was collected for an hour from 5pm to 6 pm which is the rush hour at Starbucks for a week. For convenience, Arena need the data in the form of a distribution which best fits the raw data.
The discrete values are taken as the numbers that are randomly chosen from these distributions. So, the raw data of the inter-arrival times and service times for each resource needed to be fitted to a distribution which then would be used in Arena.
Input Analyzer loads histogram of the raw data in the input text file and does the fitting to that histogram. Here is the fitted histogram and the distribution summary of the fitted data for the inter-arrival times of the customers — 5.
Similarly, the raw data was fitted and distributions were obtained using Input Analyzer for all the resources namely cash counter, food item server, hot beverage server and cold beverage server.
Cash Counter Food Item server 7. Various modules e. Create, Process, Record, Assign, Decision etc.
The procedure that any customer follows at Starbucks is divided into certain steps to give a flow in the Arena model. The steps can be treated as — 1.
Customer enters the store 2. Customer waits in queue to place the order 3. Customer places the order 4.
Customer waits in queue for food item 5. Customer gets the food item 6. Customer leaves the system There are certain decision modules which decide the path of the customer in the Model.
Also, the model consists assign and record modules to calculate the average customer time in the system and the number of customers who bought food items and hot, cold beverages.
The important parameters in the Arena model are the Resources and Queues. Entities Customers Part 2.
Here is a snapshot of the Arena Model that was prepared — To explain the model parameters stepwise, we will go through each step mentioned earlier looking closely into the modules and logic used to prepare the model.
The expression here is the one that we got from the input analyzer. TNOW is an inbuilt function in Arena which records the specific time whenever any entity passes through it.
Here is the dialog box for Assign module — 2. Customer waits in queue to place the order — The customers join the queue if there is any, to place their order.
This queue is shown in a different white box inside the model. During rush hours, there is a long queue so mostly people have to wait in queue to place order.
Customer places the order — After completing the wait in queue, the customers reach the cash counter and places the order. Here is the dialog box of the Process module for Cash counter.
If the product is not available, the customer leaves the system else he continues further in the process. Here is the dialog box — 4. Customer waits in queue for food item The customers who opted for Food items wait in queue of the food items customers before they are served the food items.
Here is the dialog box of the Process module for Food item. Here is the dialog box — Here is the dialog box — 6. Here are the dialog boxes of the Process modules for Beverage Servers — After the customers are served, they pass through a record module where the number of customers for each type are counted.
Then they pass through another record module where the total time of the customer in the system is noted.
Here are the dialog boxes for each of the modules — Pipera Tunari, nr. Informatiile acestei companii nu au fost autentificate si verificate de catre Bizoo.
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