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Excellent Consulting Group
Scenario: You are still a consultant for the Excellent Consulting Group. You have completed the first assignment, developing and testing a forecasting method based on linear regression (Case 3). However, your consulting manager at ECG wants to go the next step and investigate another forecasting method. It is important to do a thorough job for the client, and you have the expertise to analyze different forecasting methods. You have decided to look at the sales data for clientAc€?cs lottery app as a single data set and use a time series analysis, namely SES, single exponential smoothing.
Using Excel, use the forecasted sales from Case 3 to compute the MAPE, by doing the following:
Find the MAPE for the first 12 months (assume the forecast for Month 1 Ac€?o or January Ac€?o is equal to JanuaryAc€?cs actual sales). To find the MAPE, you will need to compare actual sales for each month, or Y(t), to forecasted sales, or F(t).
Next, forecast the sales for the next three months (Feb  Apr), and compute the MAPE for this 3month period. Compare this 3month MAPE to the MAPE you calculated for the SES analysis (Case 4).
Then write a report to your boss that briefly describes the results that you obtained. Make a final recommendation on which method to use, SES or Linear Regression.
Data: Use the data that you previously have and generated from your analyses in Case 3.
Analysis
Accurate and complete SES analysis in Excel.
Written Report
Length requirements = 4Ac€?o5 pages minimum (not including Cover and Reference pages)
Provide a brief introduction/ background of the problem.
Complete and accurate Excel analysis.
Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used.
Complete, meaningful, and accurate recommendation(s).
Case 3 Data
Following are the data for website hits and app sales (number of the Lottery apps.)
Month
Hits
Sales
Jan
1200
420
Feb
820
545
Mar
1151
301
Apr
1050
510
May
1180
485
Jun
1047
525
Jul
1102
460
Aug
1054
500
Sep
1254
402
Oct
1071
584
Nov
1120
422
Dec
1287
514
Jan
1164
441
Feb
1159

Mar
1298

April  
IMPORTANT: Be sure to shift the monthly sales up by one month because the theory is that the hits predict the next month sales (e.g., the 1,200 hits in January are paired with FebruaryAc€?cs sales of 545). Therefore, your data will look like this:
Month
Hits
Sales
Jan
1200
545
Feb
820
301
Mar
1151
510
Apr
1050
485
May
1180
525
Jun
1047
460
Jul
1102
500
Aug
1054
402
Sep
1254
584
Oct
1071
422
Nov
1120
514
Dec
1287
441
Use the monthly hits for Jan through Mar to predict the sales for Feb through Apr.
When you have done so, ask your Instructor to provide the data for the actual sales for Jan through Apr.
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