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Forecasting Demand
ISQA 511Dr. Mellie Pullman
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Forecasting (Basics)Independent vs. Dependent Demand
Qualitative Forecasting Methods
Simple & Weighted Moving Average Forecasts
Exponential Smoothing Forecast
Causal Forecast (Regression)
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Independent vs. Dependent Demand
A
Independent Demand:Finished Goods
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand:Raw Materials, Component parts,Sub-assemblies, etc.
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Types of ForecastsQualitative– Judgmental
methods
Quantitative– Time Series
Analysis
4
Quantitative Method:Time Series Analysis
Uses historical data
Many types of models available
Pick a model based on:
1. Fits previous data best
2. Time horizon to forecast
3. Data availability
4. Accuracy required
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Components of Demand
Average demand for a period of time
Trend
Seasonal element
Cyclical elements
Random variation
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Patterns of DemandQ
ua
nti
ty
Time(a) Horizontal (Random): Data cluster about a horizontal line.
Qu
an
tity
Time(b) Trend: Data consistently increase or decrease. 7
Patterns of DemandQ
ua
nti
ty
| | | | | |1 2 3 4 5 years
(d) Cyclical: Data reveal gradual increases and decreases over extended periods.
Qu
an
tity
| | | | | | | | | | | |J F M A M J J A S O N
D
Year 1
Year 2
(c) Seasonal: Data consistently show peaks and valleys.
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Finding Components of Demand
1 2 3 4
x
x xx
xx
x xx
xxxxx
xxxxxxxx
xx
xxxx
xx
xx
x
xx
xx
xx
xx
xx
xx
x
x
Year
Sal
es
Seasonal variation
Linear
Trend
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Simple Moving Average
n
D+...+D +D +D =F 1n-t2-t1-tt
1t
Dt = actual demand from period t
Ft+1 = forecast of demand for period t+1 (next period that has not occurred yet)
Forecast for the next period t+1 = average from the last n periods of actual demand.
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Simple Moving AverageWeek Demand
1 6502 6783 7204 7855 8596 9207 8508 7589 89210 92011 78912 844
n
D+...+D +D +D =F 1n-t2-t1-tt
1t
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
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Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33
10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83
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In-Class Exercise
Week Demand1 8202 7753 6804 6555 6206 6007 575
Develop 3-week and 5-week moving average forecasts for demand for week 8
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Weighted Moving Average
1-n-t1-n-t2-t2-t1-t1-ttt1t Dw+...+Dw+D w+D w=F
w = 1ii=1
n
Determine the 3-period weighted moving average forecast for period 4.
Weights: t .5t-1 .3t-2 .2
Week Demand1 6502 6783 7204
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Solution
Week Demand Forecast1 6502 6783 7204 693.4
F= .5(720)+.3(678)+.2(650)4
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In-Class Exercise
Determine the 3-period weighted moving average forecast for period 5.
Weights: t .7t-1 .2t-2 .1
Week Demand1 8202 7753 6804 655
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Exponential Smoothing( is the smoothing parameter)
Premise — we should determine how much weight to put on recent information versus older information.
0 < < 1High such as .7 puts weight on recent demandLow such as .2 puts weight on many previous periods
Ft+1 = Dt + (1-)Ft
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Exponential Smoothing Example
Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775
10
Determine exponential smoothing forecasts for periods 2-10 using =.10 and =.60.
Let F1=D1
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Week Demand 0.1 0.61 8202 775 820.00 820.003 680 815.50 793.004 655 801.95 725.205 750 787.26 683.086 802 783.53 723.237 798 785.38 770.498 689 786.64 787.009 775 776.88 728.20
10 776.69 756.28
Forecast
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In-Class Exercise (Solution)
Week Demand 0.51 8202 7753 6804 655
Forecast
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Forecasting with Causal Relationships
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Potential Relationships
Temperature and Sales
Interest rate and number of loans
Average daily temperature or rainfall with acre-feet of water used
Others?
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35
Simple Linear Regression Model
b represents?
a represents?
Yt = a + bx
0 1 2 3 4 5 x (weeks)
Y
23
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Regression Equation Example
Week Sales1 1502 1573 1624 1665 177
Develop a regression equation to predict sales
based on these five points.24
Forecast Accuracy
Forecasts Consist of 2 Numbers
1. The projection of actual demand (D), called the forecast (F) which projects historical patterns or relationships
2. The error (E) which defines deviation between the forecast and the actual demand
Measures of Forecast Error
Et = Dt - Ft
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Example- Error Calculation
Month Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
Determine the Error for the four forecast periods
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Forecast ErrorsStudy the formula for a moment. Now, what does each calculation tell you?
– MFA: mean forecast error
– MAD: mean absolute deviation
n
FD =MFE
n
1=ttt
n
F-D =MAD
n
1=ttt
27
Best Error Measurement(What it the problem with the MAD calculation as an error measurement for long histories?)
Day Demand Forecast Error----------- ----------- ------------ ----------1 200 200.0 0.02 134 200.0 -66.03 157 180.2 -23.24 165 173.2 -8.25 177 170.8 6.26 125 172.6 -47.67 146 158.3 -12.38 150 154.6 -4.69 182 153.2 28.810 197 161.9 35.111 136 172.4 -36.412 163 161.5 1.513 157 161.9 -4.914 169 160.5 8.5
--------- --------- ---------TOTALS 2258.0 2381.3 -123.3
365 days Averaged ?
Solution?
Smoothed MAD
Phi () is a smoothing parameter, which is set in advance.
It is important that we fix (set) phi BEFORE we try to find the best forecasting method. Why?
11 tttt MADFDMAD
Phi
Phi controls the period of time over which we are evaluating forecast accuracy--the smaller the value of phi, the larger the number of historical periods that are considered in the measurement of the "average" forecast error.
What effect would changing phi have while you are trying to compare the accuracy of two different forecasting methods?
Suggested Values for Phi
Forecasting Interval
Good Values of Phi
Daily .02 (149 days)
.03 (99 days)
.04 (74 days)
.05 (59 days)
.10 (29 days)
Weekly .05 (59 weeks)
.10 (29 weeks)
.15 (19 weeks)
.20 (14 weeks)
Monthly .10 (29 months)
.15 (19 months)
.20 (14 months)
.25 (11 months)
.30 (9 months)
Phi 0.3
Month Demand Forecast Error MAD
- - - - -
1 200 200.0 0.0 0.0
2 134 200.0 -66.0 19.8
3 157 180.2 -23.2 20.8
4 165 173.2 -8.2 17.0
5 177 170.8 6.2 13.8
6 125 172.6 -47.6 24.0
7 146 158.3 -12.3 20.5
8 150 154.6 -4.6 15.7
9 182 153.2 28.8 19.6
10 197 161.9 35.1 24.3
11 136 172.4 -36.4 27.9
12 163 161.5 1.5 20.0
13 157 161.9 -4.9 15.5
14 169 160.5 8.5 13.4
--------- --------- --------- ---------
TOTALS 2258.0 2381.3 -123.3 252.3
Quarter Year 1 Year 2 Year 3 Year 4
1 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215
Total 1000 1200 1800 2200 Average 250 300 450 550
Seasonal Index/Factor
We estimate 2600 for Year 5 but need to know how manyto make each quarter.
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Seasonal Factor Method
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Quarter Year 1 Year 2 Year 3 Year 4
1 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215
Total 1000 1200 1800 2200 Average 250 300 450 550
Seasonal Index = Actual Demand
Average Demand
Seasonal Index/Factor
35
Quarter Year 1 Year 2 Year 3 Year 4
1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39
Quarter Average Seasonal Index Forecast
1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 650(0.20) = 1302 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 650(1.30) = 8453 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 650(2.00) = 13004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 650(0.50) = 325
Seasonal Influences
36
In- Class Problem: Forecast Year 3(Overall forecast = 1500)
Qtr
Year 1 Year 2Average
IndexDemand Index Demand Index
1 100 192
2 400 408
3 300 384
4 200 216
Avg
37
Decomposition of Season & Trend
Decompose the data into components– Find seasonal component– Deseasonalize demand– Find Trend component
Forecast future values of each component– Project Trend component into future– Multiply trend component by seasonal
component
38
Example of Deseasonalized Data
Period x Quarter Actual Demand SF for X ASF DeseasonlizeAve SF Demand/ASF
1 I 600 0.47 0.74 809.912 II 1550 1.20 1.13 1376.693 III 1500 1.17 1.01 1479.914 IV 1500 1.17 1.12 1339.635 I 2400 0.87 0.74 3243.246 II 3100 1.13 1.13 2753.387 III 2600 0.95 1.01 2565.178 IV 2900 1.05 1.12 2589.969 I 3800 0.88 0.74 5135.1410 II 4500 1.05 1.13 3996.8411 III 4000 0.93 1.01 3946.4112 IV 4900 1.14 1.12 4376.13
Slope 338.4754Intercept 600.944
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Project Future and Re-seasonalize
Period Forecast SF Seasonalize13 4999.9 0.74 3699.9314 5338.31 1.13 6032.2915 5676.72 1.01 5733.4916 6015.13 1.12 6736.95
Slope 338.41Intercept 600.57
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Trend Adjusted Trend Adjusted Exponential SmoothingExponential Smoothing
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Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
Actual room requests
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Trend-Adjusted Exponential Smoothing
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Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
Guest Arrivals
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
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Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
A1 = T1 =
Guest Arrivals
A0 = 28 g D1 = 27 g T0 = 3 g
= 0.20 = 0.20
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
45
Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
A1 = 0.2(27) + 0.80(28 + 3)= 30.2T1 = 0.2(30.2 - 28) + 0.80(3)= 2.8
Guest Arrivals
A0 = 28 g D1 = 27 g T0 = 3 g
= 0.20 = 0.20
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
46
Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
A1 = 30.2T1 = 2.8
Guest Arrivals
A0 = 28 guests T0 = 3 guests
= 0.20 = 0.20
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
Forecast2 = 30.2 + 2.8 = 33
47
Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
Guest Arrivals
A1 = 30.2 D2 = 44 T1 = 2.8
= 0.20 = 0.20
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
A2 = 0.2(44) + 0.80(30.2 + 2.8)= 35.2T2 = 0.2(35.2 - 30.2) + 0.80(2.8)= 3.2
48
Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
Guest Arrivals
A1 = 30.2 D2 = 44 T1 = 2.8
= 0.20 = 0.20
At = Dt + (1 - )(At-1 + Tt-1)Tt = (At - At-1) + (1 - )Tt-1
A2 = 35.2T2 = 3.2
Forecast = 35.2 + 3.2 = 38.4
49
Trend-Adjusted Exponential Smoothing
| | | | | | | | | | | | | | |0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Gu
est
arr
ival
s
Week
Trend-adjusted forecast
Actual guest arrivals
50
In Class Exercise
Amar = 300,000 cases; Tmar = +8,000 cases
Dapr = 330,000 cases; = 0.20 =.10
What are the forecasts for May and July?
51