RISK: EXPONENTIAL SMOOTHING FORECASTING AND VALUE OF INFORMATION Risk: Simple Exponential Smoothin

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)

Download and read
Subjective Assessments of Risk and Uncertainty
(SEE ATTACHED ) on subjective probabilities in risk decisions. This paper will be useful for the discussion questions in 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.

Year 2 Forecast MAPE

ABC Furniture Company

Sales

Year 2

Customers (x)

Actual Y(t)

Forecast F(t)

Y(t) – F(t)

APE

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

MPE

MAPE

MAPE = Mean absolute percentage error

SES – MAPE

ABC Furniture Company

Alpha

0.15

0.9

Year 2

Sales, Y(t)

F(t)

Y(t) – F(t)

APE

F(t)

Y(t) – F(t)

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!

MPE

MAPE

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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RISK: EXPONENTIAL SMOOTHING FORECASTING AND VALUE OF INFORMATION Risk: Simple Exponential Smoothin