Tag: forecasting exercise

Guest Post: A Tip on Teaching How to Convert Quarterly Trends to Annual Trends

 

HowardWeiss2Howard Weiss is Professor of Operations Management at Temple University. He has developed both POM for Windows and Excel OM for our text.

At the recent POMS conference I asked the audience: “If the quarterly trend is an increase of 100 units, then what is the annual trend?” The answer I received was, “It is so obviously 400 units year that this must be a trick question.” This is the same answer my students typically give when I ask the question.

Below is a spreadsheet displaying “perfect” data that starts at 10000 and increases at exactly 100 per quarter.blog.trend.Fig1

 

You can easily see that the annual increases are 1600 since each of the 4 quarters increases by 400 from year to year. Thus, the annual trend is 16 times the quarterly trend.

While the data above is contrived, the analysis holds for “real” data. The spreadsheet below shows the revenue at Coca-Cola (after all, the conference was in Atlanta) from 2008 to 2013.

The quarterly trend is an increase of 242 as shown in the graph, the annual trend using Excel’s SLOPE function is an increase of 3916 per year, and the ratio of the annual trend to the quarterly trend is just above 16 for this “real” data. Typically, I have my students use data from their own companies and more often than not, the ratio is near 16 for quarterly data or 144 for monthly data.blog.regressCocaCola

 

Other ideas on teaching forecasting can be found in “Let’s Put the Seasonality and Trend in Decomposition”, R. L. Nydick and H. J. Weiss.

Guest Post: A Great Classroom Forecasting Exercise

steve harrodDr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton and can be reached at steven.harrod@udayton.edu. This is his 3rd guest post for our OM blog.

This large data set, Excel based, forecasting exercise is suitable for an hour lecture, after students have learned basic time series forecast methods in Chapter 4 of the Heizer/Render text. It gives a “real world” experience, and provides an excellent opportunity to visual the significance of error statistics in more detail. Here are instructions:

  1. Distribute the data (here is the link), but do not reveal its source.
  2. Ask the students to experiment (in Excel or Excel OM) by implementing a variety of time series forecast methods (moving average, exponential, etc.). Provide guidance, as you prefer. Give the students time to work independently (or at least in groups without you lecturing).
  3. Intermittently reveal your own progress in completing the steps on a projection screen. In a typical lecture period, you should be able to progress through two or three forecast models, and then pick one of those for error statistics.
  4. Discuss picking a “best” model according to error statistics (MAD, MSE, etc.).
  5. Pick one model, and demonstrate the calculation of the tracking signal. Chart this signal.

For discussion, ask: What are the data? Why is the tracking signal spiking? Reveal that the data are the recorded miles per gallon of a minivan at each fuel tank filling for a period of about two years. The tracking signal is spiking because the family takes vacations, and the mileage shifts from city driving to highway driving. When the tracking signal spikes, it is an indication that some fundamental change has occurred in the underlying process. The tracking signal measures whether the underlying process of the series data is stable. Since a forecast is simply the generation of a trend from a series of data points, the methodology is dependent on the underlying process being stable. If the underlying process is not stable, or experiencing a fundamental change in behavior, the forecast can not accurately predict the trend.