Post on 29-Dec-2015
transcript
©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
©The McGraw-Hill Companies, Inc., 1998
Irwin/McGraw-Hill
5
Types of Forecasts
Qualitative (Judgmental)
Quantitative– Time Series Analysis– Causal Relationships– Simulation
©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.
©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?
©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?
©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.
©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.
©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.
©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
©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?
©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
©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
©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
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
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
©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
©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
©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
©The McGraw-Hill Companies, Inc., 1998
Irwin/McGraw-Hill
21
Solution
Week Demand Forecast1 6502 6783 7204 693.4
©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
©The McGraw-Hill Companies, Inc., 1998
Irwin/McGraw-Hill
23
Solution
Week Demand Forecast1 8202 7753 6804 6555 672
©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)
©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
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
©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
©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
©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
©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
©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
)
©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.
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