3. Forecasting Homework problems: 2,4,5,6,7,11,12,14.
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Transcript of 3. Forecasting Homework problems: 2,4,5,6,7,11,12,14.
3.1. Providing Appropriate Forecast Information
The forecasting process involves much more than just the estimation of future demand. The forecast also needs to take into consideration the intended use of the forecast, the methodology for aggregating and disaggregating the forecast, and assumptions about future conditions.
Selection of an appropriate forecast method is determined by different levels of aggregation, cost of data acquisition and processing, length of forecast (timeframe), top management involvement, forecast frequency, etc.
Figure 3.1
3.1. Forecast Information
• The forecast information and technique must match the intended application:
For strategic decisions such as capacity or market expansion highly aggregated estimates of general trends are necessary.
Sales and operations planning (SOP) activities require more detailed forecasts in terms of product families and time periods.
Master production scheduling (MPS) and control demand highly detailed forecasts, which only need to cover a short period of time.
3.1.1 Forecasting for Strategic Business Planning
Forecast is presented in general terms (sales dollars, tons, hours)
Aggregation level may be related to broad indicators (gross national product (GNP), income)
Causal models and regression/correlation analysis are typical tools
Managerial insight is critical and top management involvement is intense
Forecast is generally prepared annually and covers a period of years
3.1.2 Forecasting for Sales and Operations Planning
Forecast is presented in aggregate measures (dollars, units)
Aggregation level is related to product families (common family measurement)
Forecast is typically generated by summing forecasts for individual products.
Managerial involvement is moderate, and limited to adjustment of aggregate values
Forecast is generally prepared several times each year and covers a period of several months to a year.
3.1.3 Forecasting for MPS and Control
Forecast is presented in terms of individual products (units, not dollars)
Forecast is typically generated by mathematical procedures, often using softwareProjection techniques are commonAssumption is that the past is a valid predictor of
the future
Managerial involvement is minimal.
Forecast is updated almost constantly and covers a period of days or weeks.
3.2. Regression Analysis & Decomposition
Regression identifies a relationship between two or more correlated variables.Linear regression is a special case where the
relationship is defined by a straight line, used for both time series and causal forecasting.
Data should be plotted to see if they appear linear before using linear regression.
Y = a + bXY is value of dependent variable, a is the y-
intercept of the line, b is the slope, and X is the value of the independent variable.
3.2 Least Squares Method
• Objective–find the line that minimizes the sum of the squares of the vertical distance between each data point and the line
Y – calculated dependent variable value
yi – actual dependent variable point
a – y intercept
b – slope of the line
x – time period
Y = a + bx
2222
211 )()()( ii YyYyYySquaresofSum
See Fig. 3.2
Least Squares Regression Line (Fig.3.2)
Regression errors are the vertical distance from the point to the line
Least Squares Example (Fig. 3.3)
Quarter (x) Sales (y) xy x2 y2 Y
1 600 600 1 360,000 801.3
2 1,550 3,100 4 2,402,500 1,160.9
3 1,500 4,500 9 2,250,000 1,520.5
4 1,500 6,000 16 2,250,000 1,880.1
5 2,400 12,000 25 5,760,000 2,239.7
6 3,100 18,600 36 9,610,000 2,599.4
7 2,600 18,200 49 6,760,000 2,959.0
8 2,900 23,200 64 8,410,000 3,318.6
9 3,800 34,200 81 14,440,000 3,678.2
10 4,500 45,000 100 20,250,000 4,037.8
11 4,000 44,000 121 16,000,000 4,397.4
12 4,900 58,800 144 24,010,000 4,757.1
Sum 78 33,350 268,200 650 112,502,500
Least Squares Example (Fig. 3.2)
6666.441)6153.359(5.617.779,2 xbya
6153.3595.6*12650
17.779,2*5.6*12200,268
)( 222
xnx
yxnxyb
xbxaY 6.35967.441
Least Squares Example
Quarter Calculation Forecast
13 Y13=441.6+359.6(13) 5,119.4
14 Y14=441.6+359.6(14) 5,476.0
15 Y15=441.6+359.6(15) 5,835.6
16 Y16=441.6+359.6(16) 6,195.2
Standard Error of Estimate (Syx) – how well the line fits the data
10
)1.757,4900,4()5.520,1500,1()9.160,1550,1()3.801600(
2
)( 22221
2
n
YyS
n
iii
yx
xbxaY 6.35967.441
=363.9
Time Series Decomposition
A time series can consist of one or more components of demand
Trend–the long term growth (or
decrease) of demand
Seasonal–Changes in
demand associated with portions of the year (may be
additive or multiplicative)
Cyclical–repetitive
patterns not associated with
seasonal demand
Autocorrelation–changes in
demand associated with
previous demand levels
Random–changes in
demand that can’t be linked to a specific cause
Seasonality
Seasonality may or may not be relative to the general demand trend
Additive seasonal variation is constant regardless of changes in average demand
Multiplicative seasonal variation maintains a consistent relationship to the average demand (this is the more common case)
Seasonality
Additive seasonal variation is constant regardless of changes in average demand
Forecast=Trend + Seasonal
Multiplicative seasonal variation maintains a consistent relationship to the average demand (this is the more common case)
Forecast= Trend x Seasonal factors
Seasonal Factor/Index
To account for seasonality within the forecast, the seasonal factor/index is calculated.
The amount of correction needed in a time series to adjust for the season of the year
Season Past Sales
Average Sales for Each Season
Seasonal Factor
Spring 200 1000/4=250 Actual/Average=200/250=0.8
Summer 350 1000/4=250 350/250=1.4
Fall 300 1000/4=250 300/250=1.2
Winter 150 1000/4=250 150/250=0.6
Total 1000
Seasonal Factor/Index
• If we expect (forecast) next year’s sales to be 1,100 units, the seasonal forecast is calculated using the seasonal factors:
Season ExpectedSales
Average Sales for Each Season
Seasonal Factor
Forecast
Spring 1100/4=275 X 0.8 = 220
Summer
1100/4=275 X 1.4 = 385
Fall 1100/4=275 X 1.2 = 330
Winter 1100/4=275 X 0.6 = 165
Total 1,100
Seasonality–Trend and Seasonal Factor
Quarter Amount
I – 2008 300
II – 2008 200
III – 2008 220
IV – 2008 530
I – 2009 520
II – 2009 420
III – 2009 400
IV - 2009 700
Trend = 170 +55t
Estimate of trend, use linear regression software to obtain more precise results
Seasonality–Trend and Seasonal Factor
Seasonal factors are calculated for each season, then averaged for similar seasons
Seasonal Factor = Actual/Trend
Seasonality–Trend and Seasonal Factor
Forecasts for 2010 are calculated by extending the linear regression and then adjusting by the appropriate seasonal factor
FITS–Forecast Including Trend and Seasonal Factors
Decomposition Using Least Squares Regression
1. Decompose the time series into its components
a. Find seasonal component
b. Deseasonalize the demand
c. Find trend component
2. Forecast future values for each componenta. Project trend component into future
b. Multiply trend component by seasonal component
Decomposition Using Least Squares Regression
Period Quarter Actual Demand
Average of Same Quarter of Each Year
Seasonal Factor
1 I 600 (600+2400+3800)/3=2266.7
2 II 1,550
3 III 1,500
4 IV 1,500
5 I 2,400
6 II 3,100
7 III 2,600
8 IV 2,900
9 I 3,800
10 II 4,500
11 III 4,000
12 IV 4,900
Total 33,350
Calculate average of same period values
Decomposition Using Least Squares Regression
Period Quarter Actual Demand
Average of Same Quarter of Each Year
Seasonal Factor
1 I 600 (600+2400+3800)/3=2266.7 2266.7/(33350/12)=0.82
2 II 1,550 (1550+3100+4500)/3=3050
3 III 1,500 (1500+2600+4000)/3=2700
4 IV 1,500 (1500+2900+4900)/3=3100
5 I 2,400
6 II 3,100
7 III 2,600
8 IV 2,900
9 I 3,800
10 II 4,500
11 III 4,000
12 IV 4,900
Total 33,350
Calculate seasonal factor for each period
Decomposition Using Least Squares Regression
Period
Deseasonalized Demand
1 735.7
2 1412.4
3 1544.0
4 1344.8
5 2942.6
6 2824.7
7 2676.2
8 2599.9
9 4659.2
10 4100.4
11 4117.3
12 4392.9
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.929653282
R Square 0.864255225
Adjusted R Square 0.850680748
Standard Error 512.8180268
Observations 12
ANOVA
df SS MS F Significance F
Regression 1 16743469.64 16743469.64 63.66766059 1.20464E-05
Residual 10 2629823.286 262982.3286
Total 11 19373292.92
Coefficients Standard Error t Stat P-value
Intercept 555.0045455 315.6176776 1.758471039 0.109173704
Period 342.1800699 42.88399775 7.979201751 1.20464E-05
Y= 555.0 + 342.2x
Use linear regression to fit trend line to deseasonalized data
Create Forecast by Projecting Trend and Reseasonalizing
Period Quarter Y from Regression Seasonal Factor
Forecast
13 I 555+342.2*13=5003.5 X 0.82 = 4102.87
14 II 555+342.2*14=5345.7 X 1.10 = 5880.27
15 III 555+342.2*15=5687.9 X 0.97 = 5517.26
16 IV 555+342.2*16=6030.1 X 1.12 = 6753.71
Project Linear Trend Project Seasonality
Y= 555.0 + 342.2x
3.3. Short-term Forecasting Technique
Some basic concepts: dependent/independent demand aggregate/disaggregate demand long-term/short-term forecast (regression and correlation vs.
smoothing out the random fluctuations)
The underlying assumption of time series models is that the future values of the time series can be predicted based upon previous time series values (i.e., past conditions that produced the historical data won’t change !)
The need for some forecasting techniques (Fig. 3.11)
What’s wrong with drawing a line (i.e., use the regular averaging process)?
3.3. Short-term Forecasting Techniques
Moving Average:
Q: what n to use? large or small (longer or shorter)?
Q: Drawback of (simple) moving average?
Weighted Moving Average:
Example. Use weighted moving average with weights of 0.1, 0.2, and 0.3 to forecast demand for period 33.
Sol:
3.3. Short-term Forecasting Techniques
Exponential Smoothing Forecasting (ESF):
ESFt = ESFt-1 + α(actual demand t – ESFt-1) …….. (3.6)
=α(actual demand t ) + (1- α) ESFt-1 …….. (3.7)
where:α= the proportion of the forecast error, under or over estimate, that will be
incorporated into (next) forecast (i.e., smoothing constant).
ESFt-1 = Exp. smoothing forecast made at the end of period t-1 = Exp. smoothing forecast for period t
3.3. Short-term Forecasting Techniques
Exponential Smoothing Forecasting (ESF):
Q: Why is it called “exponential” smoothing? Proof
Q: What happens when α=0 or α=1?
Q: what αvalue to use? Small or large and the effects.
3.3. Short-term Forecasting Techniques
Bias = Σ(actual demand i – forecast demand i) /n
Bias (mean error) measures consistently high or low forecast
MAD = Σ|actual demand i – forecast demand i| /n
MSE= MAD (mean absolute deviation) measures the magnitude of
forecast error
What is a good (ideal) forecast? Which (Bias or MAD) is more critical? When the forecast errors are normally distributed, the standard
deviation of forecast errors = 1.25 MAD
3.3. Some Insights
Focus forecasting: uses the one forecasting model that would have performed the best in the recent past to make the next forecast.
Simple models usually outperform more complex methods, especially for short-term forecasting.
There is no one model that would consistently outperform all the others.
It might be a good idea to average the forecasts from several models used in each period (combination technique).
3.4. Using the Forecasts
Aggregating Forecasts:
Long-term or product-line forecasts are more accurate than short-term or detailed forecasts.
Theorem: Suppose that X and Y are independent random variables with normal distribution N(μ1 , σ1
2 ) and N(μ2 , σ22 ),
respectively. Let Z=X + Y, then Z is a normal distribution N(μ1+ μ2 ,σ1
2 +σ22 ).
Application: Figure 3.17
3.4. Using the Forecasts
Pyramid Forecasting:
To coordinate, integrate, and assure (force) consistency between forecasts prepared in different parts/levels of the organization and company goals or constraints. Figs. 18~20.
Incorporating External Information:
Change the forecast directly, if we know the activities that will influence demand for sure. e.g., promotions, product changes, competitor’s action, etc.
Change the forecast model, if we are not sure of the impact of the activities. e.g., use larger α to be more
responsive to demand change.