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The model in Arena gives a precise output of the statistical accumulators like total number of entities served, sum of the queue times for all the entities, maximum time in queue, sum of the total times in system, maximum total time in system observed etc.
This model uses the facts and parameters that are available from the layout of the shop, management system, options of order and sequence followed, resources available etc.
For this model, the input data was the inter-arrival times of the customers and the service times at each counter which was collected for the rush hours at Starbucks to run the model.
The model was run for 30 days to analyze the results and based on the statistical conclusions, suggestions were made to economically improve the efficiency and working of Starbucks Shop.
SSLC, in the heart of MainStreet, houses offices and meeting spaces for student groups and organizations. With the academic classes, Recreational Centre, Engineering Research Centre in the vicinity, Starbucks has rush almost all the time.
Also, with the arrival of autumn at this time of the year, coffee seems like an irresistible beverage for most of the students. According to the authentic information received from the Starbucks authority, the autumn season experiences almost customers at the shop sometimes.
While the shop has rush almost all the time, there is comparatively more rush for evening hours from 4 pm to 7 pm.
Also, during the rush hours the average waiting time of the customer reaches upto 8 minutes sometimes. The system has to be more efficient as students have less window of time to shop between two classes and the shop loses potential customers due to this problem.
The simulation project done uses the model parameters to probe into the Starbucks system and analyze how the existing system can be ameliorated to decrease the average time spent by a customer at Starbucks.
There has to be some assumptions in order to exclude these activities. Even though the model does not delineate the exact situation at Starbucks, the statistical inferences that we get from Arena are very useful in analyzing the situation.
Here are a few assumptions that were made for the model in this project — 1. The shop is open 13 hours a day. There are no work shifts between the workers.
There are no breaks for the workers during the time when the model is running. Every counter has a single server. Some of the data for decision modules was taken from the Starbucks authority due to lack of sample points.
The service time varies for different workers. 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.
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