GCU MIS665 Discussions Latest (Full)

GCU MIS665 Discussions 

GCU MIS665 Week 1 Discussion  

DQ 1

Forecasting techniques rely on the adage that history repeats itself. Many times, though, it has been noted that there is no guarantee that historical patterns will repeat and, even if they do, it can be difficult or impossible to predict when a pattern will repeat itself. Provide two time series examples that seemed stable and predictable until there was a significant change to the pattern. Was there a way to predict the pattern change? Explain.

DQ 2

Technically, once a time series model is built, any future value can be forecasted. For example, assume you built a model that forecasts monthly sales of a product given the last 3 months of sales. Using the model, you forecasted monthly sales for the next 6 months, and the model provided very accurate predicted sales.

Your manager wants to use your model to predict sales for the next 5 years. What are some problems that could occur with extending the forecast horizon out that far? How would this affect your confidence in the forecasted values for a year? Five years? Explain.

 

GCU MIS665 Week 2 Discussion 

DQ 1

Consider the following scenario and address the questions that follow.

Suppose your company wanted to determine the amount of sales of a new SUV model, called Lightning. The sales estimate would be based on the overall demand of various SUV model types. Overall demand is assumed to be normally distributed with a mean of 3 million units and standard deviation of 500,000 units. The share of demand that Lightning will take is assumed to be 4%. After running 1,000 simulated scenarios, a 95% confidence interval was constructed for the expected sales of Lightning units sold with limits of 93,048 and 146,964.

1. If 1,000 new simulated scenarios were run, would you get the exact result? Explain why.
2. Would the results be more stable if you ran 2,000 runs? Explain why.
3. Is there a number of simulations that can be run so that the results do not change if you re-ran the simulations? Explain.

DQ 2

Reconsider the scenario presented in DQ 1 and address the questions that follow.

Suppose your company wanted to determine the amount of sales of a new SUV model, called Lightning. The sales estimate would be based on the overall demand of various SUV model types. Overall demand is assumed to be normally distributed with a mean of 3 million units and standard deviation of 500,000 units. The share of demand that Lightning will take is assumed to be 4%. After running 1,000 simulated scenarios, a 95% confidence interval was constructed for the expected sales of Lightning units sold with limits of 93,048 and 146,964.

1. If a mean of 6 million units was used in the simulation, how would the confidence interval change? Explain.
2. How would the confidence interval change if a standard deviation of 250,000 was used? Explain.

 

GCU MIS665 Week 3 Discussion

DQ 1

Before computers were widespread, almost all risk analysis was done without simulation. Therefore, only a handful of scenarios could be formulated to understand the risk of a decision. Typically, a best-case and worst-case scenario was determined and decisions were based on these two scenarios. What are some of the drawbacks of this decision-making approach? Specifically, how does the capability to summarize 1,000s of simulated scenarios improve the approach?

DQ 2

By definition, simulations require a distribution to be specified (e.g., normal, Poisson). Many times, the exact distribution to be used is unknown, so it must be assumed. One argument against using simulations to perform risk analysis is that there is no real benefit because the set of assumptions is simply shifted from assumed parameter values to assumed distributions of parameters. Comment on this argument and justify your opinions with reasons, facts, and examples.

 

GCU MIS665 Week 4 Discussion 

DQ 1

Many times, linear optimization is used to maximize an objective function because profit, productivity, or efficiency is the outcome of interest. Provide two examples where the goal is to optimize a process by minimizing an objective function. In your examples, identify the outcome and any constraints that would need to be met.

DQ 2

When many constraints are present in a linear optimization problem, there is a greater chance that a redundant constraint exists. Assume you are trying to maximize an objective function and you have two decision variables, X1 and X2. If a redundant constraint exists, does the constraint become necessary if you try to minimize (instead of maximize) the same objective function? Why? Do you need an objective function to determine if a constraint is redundant? Explain.

 

GCU MIS665 Week 5 Discussion

DQ 1

Many linear optimization problems can be solved by finding a graphical solution, but there are some problems that require more advanced spreadsheets and software to find an optimal solution. Describe an optimization problem in which finding a solution would be impossible using the feasible-region approach. Discuss the attributes the problem would have to make it impossible to solve using the feasible-region approach.

DQ 2

Optimization techniques are used in many applications. For example, when customers order products from an online store, the shipper has to determine the optimal way to get the product delivered to the customer. The delivery path that is chosen is the path that minimizes shipping costs while simultaneously satisfying these constraints:

1. The product must arrive by a promised date.
2. The shipper must deliver a finite set of items.
3. The product must originate from one of several warehouse hubs across the country.

Discuss whether there can be multiple solutions (i.e., more than one path to get the product to your house). Explain why. Is there a guarantee that a solution always exists? Explain.

 

GCU MIS665 Week 6 Discussion

DQ 1

Most transshipment network modeling problems assume the costs are constant. For example, the costs of shipping a product from one city to another are assumed fixed. This can change over time if fuel costs change. If you knew the distribution of fuel costs, how could the distribution of fuel costs be incorporated into the transshipment problem? Discuss the benefits of employing this approach.

DQ 2

Minimum spanning trees were initially design to solve electrical grid problems but now have many more applications such as computer networks, transportation networks, and supply networks. Describe a business problem where minimum spanning trees can be used to find a solution.

 

GCU MIS665 Week 7 Discussion

DQ 1

Can linear and nonlinear optimization problems use the same approach to find a solution? For example, if the GRG algorithm is used to solve a nonlinear optimization problem, will it work to solve a linear optimization problem? Discuss whether or not the GRG algorithm will always find a corner point similar to the feasible-region approach.

DQ 2

Nonlinear optimization problems can have multiple solutions, and a solution can be local or global. Can there be multiple local solutions? Explain your answer. Can there be multiple global solutions? Explain our answer.

 

GCU MIS665 Week 8 Discussion 

DQ 1

Betamax (or Beta) was a video recording format developed by Sony in the 1970s. Sony conducted research and found that consumers wanted a high-quality picture when using a Beta cassette with their home recording equipment. Sony developed the technology with video quality in mind and, as a consequence, limited the recording time to only 60 minutes.

At the same time, JVC developed the Video Home System (VHS) but without much consumer research. JVC was more interested in developing a unified standard for broadcast operations. Consequently, they focused more on extending the recording time for their VHS cassettes at the expense of picture quality.

In the end, the VHS format won by eventually squeezing the Beta format out of the market. Discuss the approaches these two companies took in the good decisions/good outcomes context.

DQ 2

Suppose you received two job offers when looking for a job, one from Company A and one from Company B. To make a decision, what type of methodology would you use: probabilistic or nonprobabilistic? Whichever you choose, describe the items to consider and the decision rules you would use before deciding which job offer to accept.