Paper: ? RISK: EXPONENTIAL SMOOTHING FORECASTING AND VALUE OF INFORMATION Risk: Simple Exponential Smoothing (SES) Assignmen
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 (see attached ) do the following:
- Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled “Year 2 Forecast – MAPE”).
- Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled “SES – MAPE”). 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: 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.
Module 4 – Background
RISK: EXPONENTIAL SMOOTHING FORECASTING AND VALUE OF INFORMATION
Case Background
What if you cannot find another factor that has a high correlation with the forecasted factor? Are there other forecasting methods other than Linear Regression? How do you determine which method is actually the best one?
Chase, C. W., (2013). Demand-driven forecasting: A structured approach to forecasting. John Wiley & Sons. Somerset, NJ. Retrieved from Ebrary.
From the source above, read: SEE ATTACHED
· Chapter 3, pp. 91–93 (the section Some Causes of Forecast Error)
· Chapter 4, pp. 103–113, which provides information on forecast error measures; pay special attention to the sections on the MAPE measurement
· Chapter 5, pp. 125–147; pay attention the sections on Simple Exponential Smoothing (SES)
Download the Excel file Case 4 Examples-Practice.xlsx (SEE ATTACHED ) that contains an example and a Practice Exercise.
Watch this video that shows how to do SES and calculate MAPE: http://permalink.fliqz.com/aspx/permalink.aspx?at=75d6cc75bbe742159e56ad8836531c1d&a=5fae3cf0f1624f39b0341263a6541ea0
PRACTICE: Do the Practice Exercise in the Excel file: Case 4 Examples – Practice (SEE ATTACHED ). Check your work.
You are ready to do the Case 4 problem.
SLP Background
Consider that you may be pretty good sometimes at estimating future probabilities. But you also acknowledge that you might be biased, too. This is where experts are useful, although they do charge a fee for their services. What is the value of the information you could get from an expert? Is it worth paying this expert for his/her advice? Read the paper Deciding to Use an Expert (SEE ATTACHED ) that explains how to make this decision.
Download the Excel file SLP 4 Examples-Practice.xlsx (SEE ATTACHED ) that provides examples and a Practice Exercise.
Watch this video showing how to determine the value of information: http://permalink.fliqz.com/aspx/permalink.aspx?at=f616909cae2d4d06834359502f672aff&a=5fae3cf0f1624f39b0341263a6541ea0
Practice determining the Value of Information; do the Practice Exercise in the Excel file.
You are ready for SLP 4.
Additional Required Reading
(For Discussions, Module 2, 3, and 4)
Get this journal article from the library. It is lengthy, but you only need to read Section 1.1, pp. 3-5. This section provides a very good review of three major biases that have been studied by the famous team of Kahneman and Tversky.
Laibson, D., & Zeckhauser, R. (1998). Amos Tversky and the ascent of behavioral economics. Journal of Risk & Uncertainty. Feb1998, Vol. 16 Issue 1, p7-47. 41p. Retrieved from Business Source Complete (EBSCO) in the Trident Online Library.
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Year 2 Forecast MAPE
ABC Furniture Company | |||||||||||||
Sales | |||||||||||||
b1 | Year 2 | Customers (x) | Actual Y(t) | Forecast F(t) | Y(t) – F(t) | PE | APE | ||||||
b0 | January | 215 | |||||||||||
February | 259 | ||||||||||||
Y= b0+ b1x | March | 325 | |||||||||||
April | 354 | ||||||||||||
May | 258 | ||||||||||||
June | 199 | ||||||||||||
July | 254 | ||||||||||||
August | 299 | ||||||||||||
September | 264 | ||||||||||||
October | 198 | ||||||||||||
November | 223 | ||||||||||||
December | 261 | ME = Mean error | |||||||||||
MPE = Mean percentage error | |||||||||||||
ME | MPE | MAPE | MAPE = Mean absolute percentage error | ||||||||||
SES – MAPE
ABC Furniture Company | ||||||||||||||
Alpha | Alpha | |||||||||||||
0.15 | 0.9 | |||||||||||||
Year 2 | Sales, Y(t) | F(t) | Y(t) – F(t) | PE | APE | F(t) | Y(t) – F(t) | PE | APE | |||||
January | ||||||||||||||
February | ||||||||||||||
March | ||||||||||||||
April | ||||||||||||||
May | ||||||||||||||
June | ||||||||||||||
July | ||||||||||||||
August | ME = Mean error | |||||||||||||
September | MPE = Mean percentage error | |||||||||||||
October | MAPE = Mean absolute percentage error | |||||||||||||
November | ||||||||||||||
December | ||||||||||||||
ERROR:#DIV/0! | ERROR:#DIV/0! | ERROR:#DIV/0! | ERROR:#DIV/0! | ERROR:#DIV/0! | ERROR:#DIV/0! | |||||||||
ME | MPE | MAPE | ME | MPE | MAPE | |||||||||
,
Example
Alpha | Alpha | |||||||||||
0.25 | 0.85 | |||||||||||
Mo | Sales, Y(t) | F(t) | Y(t) – F(t) | PE | APE | F(t) | Y(t) – F(t) | PE | APE | |||
1356 | 1 | 1798 | 1798 | 0.0 | 0.000 | 0.000 | 1798 | 0.0 | 0.000 | 0.000 | ||
1225 | 2 | 1466 | 1798.0 | -332.0 | -0.226 | 0.226 | 1798.0 | -332.0 | -0.226 | 0.226 | ||
1006 | 3 | 1118 | 1715.0 | -597.0 | -0.534 | 0.534 | 1515.8 | -397.8 | -0.356 | 0.356 | ||
1132 | 4 | 1272 | 1565.8 | -293.8 | -0.231 | 0.231 | 1177.7 | 94.3 | 0.074 | 0.074 | ||
1090 | 5 | 1095 | 1492.3 | -397.3 | -0.363 | 0.363 | 1257.9 | -162.9 | -0.149 | 0.149 | ||
1722 | 6 | 1430 | 1393.0 | 37.0 | 0.026 | 0.026 | 1119.4 | 310.6 | 0.217 | 0.217 | ||
1602 | 7 | 1277 | 1402.2 | -125.2 | -0.098 | 0.098 | 1383.4 | -106.4 | -0.083 | 0.083 | ||
1709 | 8 | 1751 | 1370.9 | 380.1 | 0.217 | 0.217 | 1293.0 | 458.0 | 0.262 | 0.262 | ||
1547 | 9 | 1962 | 1465.9 | 496.1 | 0.253 | 0.253 | 1682.3 | 279.7 | 0.143 | 0.143 | ||
1227 | 10 | 1620 | 1590.0 | 30.0 | 0.019 | 0.019 | 1920.0 | -300.0 | -0.185 | 0.185 | ||
1308 | 11 | 1422 | 1597.5 | -175.5 | -0.123 | 0.123 | 1665.0 | -243.0 | -0.171 | 0.171 | ||
1536 | 12 | 1948 | 1553.6 | 394.4 | 0.202 | 0.202 | 1458.5 | 489.5 | 0.251 | 0.251 | ||
1513.25 | -48.6 | -0.072 | 0.1910 | 7.5 | -0.019 | 0.1764 | ||||||
ME | MPE | MAPE | ME | MPE | MAPE |
Practice Problem
Alpha | Alpha | |||||||||||
0.15 | 0.9 | |||||||||||
Mo | Sales, Y(t) | F(t) | Y(t) – F(t) | PE | APE | F(t) | Y(t) – F(t) | PE | APE | |||
1 | 525 | 525 | 525 | |||||||||
2 | 293 | |||||||||||
3 | 256 | |||||||||||
4 | 425 | |||||||||||
5 | 753 | |||||||||||
6 | 596 | |||||||||||
7 | 391 | |||||||||||
8 | 563 | |||||||||||
9 | 571 | |||||||||||
10 | 633 | |||||||||||
11 | 653 | |||||||||||
12 | 811 | |||||||||||
539.1666666667 | ||||||||||||
ME | MPE | MAPE | ME | MPE | MAPE |
Practice Problem-Solution
Alpha | Alpha | |||||||||||
0.15 | 0.9 | |||||||||||
Mo | Sales, Y(t) | F(t) | Y(t) – F(t) | PE | APE | F(t) | Y(t) – F(t) | PE | APE | |||
1 | 525 | 525 | 0.0 | 0.000 | 0.000 | 525 | 0.0 | 0.000 | 0.000 | |||
2 | 293 | 525.0 | -232.0 | -0.792 | 0.792 | 525.0 | -232.0 | -0.792 | 0.792 | |||
3 | 256 | 490.2 | -234.2 | -0.915 | 0.915 | 316.2 | -60.2 | -0.235 | 0.235 | |||
4 | 425 | 455.1 | -30.1 | -0.071 | 0.071 | 262.0 | 163.0 | 0.383 | 0.383 | |||
5 | 753 | 450.6 | 302.4 | 0.402 | 0.402 | 408.7 | 344.3 | 0.457 | 0.457 | |||
6 | 596 | 495.9 | 100.1 | 0.168 | 0.168 | 718.6 | -122.6 | -0.206 | 0.206 | |||
7 | 391 | 510.9 | -119.9 | -0.307 | 0.307 | 608.3 | -217.3 | -0.556 | 0.556 | |||
8 | 563 | 492.9 | 70.1 | 0.124 | 0.124 | 412.7 | 150.3 | 0.267 | 0.267 | |||
9 | 571 | 503.5 | 67.5 | 0.118 | 0.118 | 548.0 | 23.0 | 0.040 | 0.040 | |||
10 | 633 | 513.6 | 119.4 | 0.189 | 0.189 | 568.7 | 64.3 | 0.102 | 0.102 | |||
11 | 653 | 531.5 | 121.5 | 0.186 | 0.186 | 626.6 | 26.4 | 0.040 | 0.040 | |||
12 | 811 | 549.7 | 261.3 | 0.322 | 0.322 | 650.4 | 160.6 | 0.198 | 0.198 | |||
539.1666666667 | 35.5 | -0.048 | 0.2994 | 25.0 | -0.025 | 0.2730 | ||||||
ME | MPE | MAPE | ME | MPE | MAPE |
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