Module 4 – Case
RISK: EXPONENTIAL SMOOTHING FORECASTING AND VALUE OF
INFORMATION
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, do
the following:
Calculate the MAPE for Year 2 Linear Regression forecast
(use the first spreadsheet tab labeled âYear 2 Forecast â MAPEâ).
Calculate forecasted sales for Year 2 using SES (use the
second spreadsheet tab labeled âSES â MAPEâ). Use 0.15 and 0.90 alphas.
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
Analysis
Accurate and complete SES analysis in Excel.
Written Report
Length requirements: 4â5 pages minimum (not including Cover
and Reference pages). NOTE: You must submit 4â5 pages of written discussion and
analysis. This means that you should avoid use of tables and charts as âspace
fillers.â
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.
ABC Furniture Company
Sales
Year 2 Customers (x)
Actual Y(t) Forecast F(t) Y(t)
– F(t) PE APE
January 215
February 259
March 325
April 354
May 258
June 199
July 254
August 299
September 264
October 198
November 223
December 261
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