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 3-month period. Compare this 3-month 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.