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©The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting Demand Management Qualitative Forecasting Methods Simple & Weighted Moving Average Forecasts Exponential Smoothing Simple Linear Regression
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Page 1: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

2

Chapter 13

Forecasting

Demand Management

Qualitative Forecasting Methods

Simple & Weighted Moving Average Forecasts

Exponential Smoothing

Simple Linear Regression

Page 2: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

5

Types of Forecasts

Qualitative (Judgmental)

Quantitative– Time Series Analysis– Causal Relationships– Simulation

Page 3: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

11

Delphi Method

4. Summarize again, refining forecasts and conditions, and again develop new questions.

5. Repeat Step 4 if necessary. Distribute the final results to all participants.

Page 4: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

6

Components of Demand

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

xx

xx

xx

x

x

Year

Sal

es

What’s going on here?

Page 5: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

7

A Trend is Worth Noting

Start by identifying the trend

What is the trend in the sales of personal computers?

Are there any seasonal effects, cyclical factors or other predicted events that might affect the sales of personal computers?

Page 6: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

10

Delphi Method

l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas.

2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants.

3. Summarize the results and redistribute them to the participants along with appropriate new questions.

Page 7: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

12

Judgmental Forecasting ApplicationsSmall and Large Firms

Technique

LowSales

< $100M

HighSales

> $500M

Manager’s opinion 40.7% 39.6%Jury of executive opinion 40.7% 41.6%Sales force composite 29.6% 35.4%Number of Firms 27 48

Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.

Page 8: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

13

Quantitative Forecasting ApplicationsSmall and Large Firms

Technique

LowSales

< $100M

HighSales

> $500M

Moving average 29.6% 29.2%Straight line projection 14.8% 14.6%Naive 18.5% 14.6%Exponential smoothing 14.8% 20.8%Regression 22.2% 27.1%Simulation 3.7% 10.4%Classical decomposition 3.7% 8.3%Box-Jenkins 3.7% 6.3%Number of Firms 27 48

Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.

Page 9: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

14

Time Series Analysis

Pick a model based on:

1. Time horizon to forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel

Page 10: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

30

Forecast Errors

MAD = A - F

n

t tt=1

n

Study the formula for a moment

Now, what does MAD tell you?

Page 11: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

31

Example--MAD

Month Sales Forecast1 220 n/a2 250 2553 210 2054 300 3205 325 315

Determine the MAD for the four forecast periods

Page 12: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

32

Solution

Month Sales Forecast Abs Error1 220 n/a2 250 255 53 210 205 54 300 320 205 325 315 10

40

Page 13: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

15

Simple Moving Average

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 89210 92011 78912 844

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n

Let’s develop 3-week and 6-week moving average forecasts for demand.

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Page 14: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.3310 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

16©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

Page 15: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Demand Demand

3-Week

6-Week

17©The McGraw-Hill Companies, Inc., 1998Irwin/McGraw-

Hill

Page 16: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

18

In-Class Exercise

Week Demand1 8202 7753 6804 6555 6206 6007 575

Develop 3-week and 5-week moving average forecasts for demand.

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Page 17: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

19

In-Class Exercise (Solution)

Week Demand 3-Week 5-Week1 8202 7753 6804 655 758.335 620 703.336 600 651.67 710.007 575 625.00 666.00

Page 18: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

20

Weighted Moving Average

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n

Determine the 3-period weighted moving average forecast for period 4.

Weights: t-1 .5t-2 .3t-3 .2

Week Demand1 6502 6783 7204

Page 19: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

21

Solution

Week Demand Forecast1 6502 6783 7204 693.4

Page 20: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

22

In-Class Exercise

Determine the 3-period weighted moving average forecast for period 5.

Weights: t-1 .7t-2 .2t-3 .1

Week Demand1 8202 7753 6804 655

Page 21: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

23

Solution

Week Demand Forecast1 8202 7753 6804 6555 672

Page 22: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

24

Exponential Smoothing

Premise--The most recent observations might have the highest predictive value.

Therefore, we should give more weight to the more recent time periods when forecasting

Ft = Ft-1 + (At-1 - Ft-1)

Page 23: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

25

Exponential Smoothing Example

Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 77510

Determine exponential smoothing forecasts for periods 2-10 using =.10 and =.60.

Let F1=D1

Page 24: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.6110 776.69 780.77

26©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

Page 25: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

27

Effect of on Forecast

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10

Week

Demand Demand

0.1

0.6

Page 26: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

28

In-Class Exercise

Determine exponential smoothing forecasts for periods 2-5 using =.50

Let F1=D1

Week Demand1 8202 7753 6804 6555

Page 27: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

29

In-Class Exercise (Solution)

Week Demand 0.51 820 820.002 775 820.003 680 797.504 655 738.755 696.88

Page 28: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

35

Simple Linear Regression Model

b is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight-forward as our usual notion of slope

Yt = a + bx

0 1 2 3 4 5 x (weeks)

Y

Page 29: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

36

Calculating a and b

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

Page 30: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill

37

Regression Equation Example

Week Sales1 1502 1573 1624 1665 177

Develop a regression equation to predict sales based on these five points.

Page 31: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 8853 55 162.4 2499

Average Sum Average Sum

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.538©The McGraw-Hill Companies,

Inc., 1998Irwin/McGraw-Hill

Page 32: © The McGraw-Hill Companies, Inc., 1998 Irwin/McGraw-Hill 2 Chapter 13 Forecasting u Demand Management u Qualitative Forecasting Methods u Simple & Weighted.

y = 143.5 + 6.3t

135140145150155

160165170175180

1 2 3 4 5

Period

Sales

Sales

Forecast

39©The McGraw-Hill Companies, Inc., 1998

Irwin/McGraw-Hill


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