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It also gives the number of entities in and out of the system. Here is the output from Arena — The average waiting time for a customer is 3.
This makes the average total time spent by a customer in the system to be 4. We want to reduce this quantity in order to increase the efficiency of the Starbucks system.
We will observe the results for the maximum waiting time and this is where we need to bring some changes to reduce the waiting time of that particular queue.
Here is the output for queue — It can be seen that the waiting time is maximum, 2. This gives the utilization of all the resources in the Model.
Here is the output — It can be observed that the Cash Counter and Hot Beverage Resource is used up for a maximum time while the utilization of other two resources is very less comparatively.
By User Specified Arena specially gives an output for any other user specified attribute. In this model, we specified the Average Customer Time and it is recorded to be 4.
Also, we recorded the count of customers of different types. Here is the output — High utilization b. High Waiting time in queues Also, the customers spend more than twice the time waiting in the queue as compared to the time when they are being served.
The efficiency of the Starbucks store would increase when the average total time spent by a customer would decrease.
According to the above interpretations, it can be concluded that some improvements in the above two resources is needed in order to reduce the average time in the system.
Hence to reduce the average total time in the system, we can increase the capacity of the Cash Counter and Hot Beverage Resource to 2.
To do this economically, the resource for Cold Beverage should be cross-trained to serve Hot Beverage as well. This would help in balancing the utilizations of the resources and reduce the waiting time in the longer queues.
As far as the changes in the system flow is concerned, there is one change that can increase the efficiency of the system.
Instead of having another resource for food items, Starbucks can have two Cash Counter and both of these resources can serve food items to the customers there itself.
The model was renovated with the above suggestions and here is a glimpse of the new model — Changes — 1.
The process module and hence the queue for Food items was removed 2. The Resource capacity for Cash counter was increased from 1 to 2 3.
The service time for Cash counter was increased to accommodate the service of food items Now, the model has only 3 resources and the resource capacity of Cash counter is now 2.
Comparing the results — We discussed the Arena results for the original model earlier and by interpreting the results we came up with a few suggestions and implemented those to obtain an improved version of the model.
Now, we will statistically compare the results of the changes that were incorporated in the model.
Rockwell Software also provides a complimentary tool with Arena called Process Analyzer. Using Process Analyzer, we can compare the results of different models or different scenarios of the same model statistically and plot the graphs accordingly.
The Process Analyzer was used to compare 3 scenarios — 1. Original Model with capacity 1 for each resource. Food Item resource present.
Cash 2 Model with capacity 2 for Cash counter resource. Food Item resource absent. Here is a snapshot of the Process Analyzer scenarios — It can be seen that the average customer time is reduced significantly 2.
The time is even further reduced for Scenario 3 1. After comparing the responses of Average Customer Time for all the above scenarios, graphs were plotted for the response variation across these scenarios.
Here, we get a visual perception about the response and the best scenario is marked in red. Both the charts depict that the improved model with capacity 2 for both the resources is the best scenario.
In this section, we compare the results of PAN statistically to prove that Scenario 3. Below is a table showing the output data from PAN which gives information about the maximum, minimum, average value of the response.
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