Supply chain : CASE 1?
Describe the scenario you propose to analyze. Clarify your creativity in this example.
Develop a model in Arena that reflects your scenario. There are no limits on how you choose to model the system. The model must have at least one of each of the following components (you may have more of each component): o Arrivals
o Decision node or nodes
o Process
o Assign
o A schedule
o Optional1 components are not required, but are worth 0.5 points each A variable
A Record module
Explain metrics used to evaluate the system. These could be the default metrics from Arena, or new metrics. You should discuss at least 4 metrics.
You may (but are NOT required to) develop a creative metric. This can be a combination of current metrics from Arena, or something else you measure in the simulation.
Run the model for the base case, with at least 100 trials. Discuss key results from the Arena analysis. Make sure to highlight the metrics you proposed earlier.
Suggest a recommendation for improving performance of the system. This recommendation must involve a policy class related to a key parameter or input of the model. Explain the rationale for using this policy class. The purpose of this policy class is to identify the relationship between one aspect of the model and various performance metrics you suggested earlier. (As an example, the policy class might be to change the arrival rate. The levels of the policy are the different arrival rates. This is probably a bad example, because arrival rates are not under your control.)
Analyze this policy class using the Arena PAN tool. The policy class must have at least 10 levels for the policy parameter. (This is called a Control in PAN.)
Develop a table to clearly identify the relationship between different levels of the policy class and the metrics proposed earlier.
Present at least 3 trade-off curves between important metrics of the model.
Make a recommendation to management. Provide arguments justifying the recommendation.
CASE 2?
Activities A and B start immediately. The time for each activity follows a Normal distribution with mean 50 days, and standard deviation 10 days. o The completion time for activities A and B is correlated, and the correlation parameter is . We will investigate how varying the correlation between these activities affects completion time of the project. Activity C begins only after both Activities A and B are completed. The time to complete Activity C follows a Uniform distribution between 20 and 40 days. Time for Activity C is not correlated with the other activities.
Formally, we have:
o Time-A ~ N(50 , 102)
o Time-B ~ N(50 , 102)
o Time-C ~ U[20 , 40]
o Corr(Time-A , Time-B) = Develop the analysis in Crystal Ball (or @Risk or Python)
Evaluate completion time for the project, as a function of different levels of correlation between times of Activities A and B. Remember that correlation can be between -1 and +1. Include a printout of the completion times.
If the objective is shortest completion time, what’s the best form of correlation between activities? Explain this result.
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