Reference no: EM13577828

Provide me a half page response to the below discussion post.

To review, my employment is at a university where my primary function is to work within the admissions department. My previous posts have been regarding the enrollment numbers for a particular quarter. Even though my organization has a tough time separating the target/goal definition with the forecast definition; the method of forecasting we use is time series.

The 2 largest challenges we face in applying a decomposition model is understanding the components and how it will affect our forecast. We typically see the largest enrollment numbers in the fall due to demand market. This makes our time series seasonal and it is difficult to determine if this seasonal fluctuation is due to outside sources; weather, timing, or geographical location. The second area of determination is the cyclical component. We notice increase in ease of enrollments when the unemployment rate is climbing and decrease ease of enrollments when the unemployment rate is falling. The trend and irregular component is equally challenging to determine. Is there a population change in our area? Are there more high school graduates this year than last year? Will the department of education (DOE) change federal loan rates, eligibility, and amount? These are all questions that are very hard to determine. If we are unable to answer these questions we will be unable to determine how these base components will be affecting our end forecast number.

Using any type of forecasting method will be better than not using one at all. The decomposition method is what we have used in the past and we continue to use this type of method (whether we are all aware of it or not). When we are able to answer the unemployment, DOE, population, and marketing efforts we are able to produce a fairly accurate long term model. We look at each individual component and how it will relate to the end forecast. I find that this decomposition method is great for identifying trend, seasonal, and cyclical factors, but falls short on the end forecast.

All data is useful, it is how we turn it into information is the hard part. Upon review of the readings and PowerPoint I find that our answers to some of the above questions may not always fit in the decomposition method. The multiplicative components model is closely related as described above. As time increases there is more variability to our observations; unemployment rate, DOE decisions, population in target areas, and marketing. Furthermore, we clearly have a quarterly series and a trend that defines a seasonal pattern. The Multiplicative model would work well with this component setup.