Risk: Simple Exponential Smoothing (SES)
Scenario: You are a consultant for the Excellent Consulting Group (ECG). You have completed the first assignment, developing and testing a forecasting method that uses Linear Regression (LR) techniques (Module 3 Case). However, the consulting manager at ECG wants to try a different forecasting method as well. Now you decide to try Single Exponential Smoothing (SES) to forecast sales.
Using this Excel template: Data chart for BUS520 Case 4, do the following:
1. Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled "Year 2 Forecast - MAPE").
2. Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled "SES - MAPE"). Use 0.15 and 0.90 alphas.
3. Compare the MAPE calculated for the LR forecast (#1 above) with the MAPEs calculated using SES.
Then write a report to your boss in which you discuss the results obtained above. Using calculated MAPE values, make a recommendation concerning which method appears to be more accurate for the Year 2 data: SES or Linear Regression.
• Accurate and complete SES analysis in Excel.
• Provide a brief introduction to/background of the problem.
• Complete a written analysis that supports your Excel analysis, discussing the assumptions, rationale, and logic used to complete your SES forecast.
• Give complete, meaningful, and accurate recommendation(s) relating to whether LR or SES is more accurate in predicting sales.
I am a consultant for the Excellent Consulting Group. I had previously completed the initial project for my client, the ABC Furniture Company, which was comprised of developing and testing a forecasting method that used Linear Regression techniques. The method used monthly Year 1 sales over a 12-month period to forecast Year 2 sales. The ABC Furniture Company believed that the number of customers who visit the store during any particular month was related to the total sales for the month in question. More specifically, the client believed that there was a positive relationship between higher customer traffic in the store and higher total sales, i.e. the client believed that the higher the number of customers who visited the store, the higher the total sales would be.
The client had provided me with the number of customers who visited the store over the most recent 12-month period from January to December, with the sales corresponding to each of those months. A Linear Regression equation was obtained using the client's data. The Linear Regression equation was then used to forecast the sales for Year 2. The forecast sales were later compared with the actual Year 2 sales when the figures because ...
Answered in 2040 words. An Excel file with all computations is provided.