This week covers some of the less intensive business applications such as using statistical analysis to develop demand forecasts based on historical data. The questions below address some of the finer points of forecasting, as well as offer you a chance to reflect on the material covered in the course.
Select any one of the following starter bullet point sections. Review the important themes within the sub questions of each bullet point. The sub questions are designed to get you thinking about some of the important issues. Your response should provide a succinct synthesis of the key themes in a way that articulates a clear point, position, or conclusion supported by research.
As a marketing analyst, you are responsible for estimating the level of sales associated with different marketing mix allocation scenarios. You have historical sales data, as well as promotional response data, for each of the elements of the marketing mix.
Describe the differences between the forecasting methods that can be used.
Evaluate the forecasting methods in relation to the given scenario.
Choose a forecasting method and justify your choice. If you make any assumptions, state them explicitly. Support your discussion with relevant examples, research, and rationale.
In general, short-term forecasts are more accurate than long-term forecasts. The same is true for forecasts where cyclical or seasonal factors are fairly well defined and repeatable.
Describe the factors that influence the reliability of time-series forecasts.
Evaluate the circumstances that would cause a time-series model to offer a fairly reliable forecast.
Locate information about a company that uses one of the types of time-series models to forecast demand and describe both the organization and the type of product(s) that they use time-series models to forecast.
Explain why the time-series model that is currently being used is the most reasonable model for the company to use or describe an alternate time-series model that would be more appropriate for the company to use.
If you suggest a change in the time-series model being used by the company, predict how the company you have chosen will improve because of that specific time-series model. Support your discussion with relevant examples, research, and rationale.
Experience teaches us that the bulk of the technical material covered in this course will, unfortunately, be forgotten shortly thereafter. If you were to commit just three concepts you have learned in this course to your long-term memory, which concepts would you select and why?
Evaluate the applicability of each of the concepts that you’ve selected to a business environment in which you have worked in the past (or with which you might be familiar).
Describe how you would apply each of the concepts. Be specific.
Explain your goal in applying each concept to the environment that you have described.
The final paragraph (three or four sentences) of your initial post should summarize the one or two key points that you are making in your initial response.
Your posting should be the equivalent of 1 to 2 single-spaced pages (500–1000 words) in length.
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