​ Module 4 – Case


Risk: Simple Exponential Smoothing (SES)

Assignment Overview

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.

Case Assignment

Using this Excel template: Data Chart For BUS520 Case 4 (see attached ) 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.

Assignment Expectations


  • Accurate and complete SES analysis in Excel.

Written Report

  • Length requirements: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must submit 4-5 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.”
  • 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.

Note: Please Read attached Chapter 3,4,5 and background Reading to be clear. Also Provide Heading for Each Section of Work.

"Looking for a Similar Assignment? Order now and Get 15% Discount! Use Code "FIRST15"