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Chapter 15. Demand Management and Forecasting
Demand Management Independent vs. dependent demand
Qualitative Techniques Market research, focus groups, Delphi technique,
Quantitative Techniques 1. Time series based models
2. Associative (causal) Models
Accuracy and Control of Forecast (errors)
1. Measuring and comparing forecast errors usingMAD,MAPE, MSE, RMSE
2. Controlling Forecasting Process via Tracking Signal
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A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand:
Raw Materials,Component parts,Sub-assemblies, etc.
Independent Demand:Finished Goods
Demand Management
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DemandManagement
Active role to influence demand. Examples from seasonal goods or services
Campaigns, discounts, etc.
Incentives to sales personnel
Passive role, limited or no action taken. Why?
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Qualitative Methods
Grass RootsMarket Research
Panel Consensus
Executive Judgment
Historical analogy
Delphi Method
Qualitative
Methods
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Quantitative forecasting methods
Time series models
Past predicts future
Uses time series data
Key variable: time (t)
Easier to apply
Less accurate Examples:
Moving averages
Exponential smoothing
Causal models
Examines potential cause=> effectrelationships
Requires cross sectional data
Key variables are usually denotedas X
1, X
2, X
3,
More difficult
Takes more time
But worth it since it providesinsight to the system (process)
under study Examples:
Various regression models
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Quantitative techniques
Basic time series approaches i. Moving averages, simple &weighted
ii. Exponential smoothing, simple & trend adjusted
iii. Linear regression (linear trend model)
iv. Techniques for seasonality and trend -
Decomposition of time series
Causal approach i. Simple Linear Regression
ii. Multiple Linear Regression
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Finding Components of a Time Series
1 2 3 4
x
x xx
xx
x xx
xx x x x
xxxxxx x x
xx
x x xx
x
xx
x
x
xx
xx
xx
x
xx
xx
x
x
x
Year
Sales
Seasonal variation
Linear
Trend
15-8
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What to look for in a time series
Trend - long-term movement in data
Seasonality - short-term regular and repetitive variations in data
Cyclical variations long(er) term, occasionally caused by
unusual circumstances, (war, economic downturn, etc.)
Autocorrelation denotes persistence of occurrence (momentumdriven)
Random variations - caused by chance
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Exponential Smoothing Models
Simple exponential smoothingmodel
Alpha is the smoothing constant
Whenever appropriate more weightcan be given to the more recentdata (time periods)
Double exponential smoothing(Holts model)
Adds trend component,
Tand delta(gamma) as the smoothing constant
for trend
Forecast Including Trend (FIT)
Excel time!
Problem 20, continued.
)FA(FF 111 tttt
10where
)FITF(TT 11 tttt
)FITA(FITF 111 tttt
ttt TFFIT
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Measuring Accuracy, Forecast Errors
To compare different time series techniques or to select thebest set of initial values for the parameters, use a combinationof the the following four metrics:
Mean Absolute Deviation Most popular but
Mean Absolut Percent Error Should be used in tandem with MAD
Mean Square Error
Root Mean Square Error
n
FA
=MAD1
n
i
ii
n
i i
ii
n 1 A
FA100=MAPE
n
FA
=MSE 1
2
n
i
ii
MSERMSE
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Tracking Signal
The Tracking Signal or TS is a measure that indicateswhether the forecast average is keeping pace with anygenuine upward or downward changes in demand.
Depending on the number of MADs selected, the TS can beused like a quality control chart indicating when the modelis generating too much error in its forecasts.
TS is a monitoring system.
The TS formula is:
DeviationAbsoluteMean
ErrorsForecastofSumRunning=TS
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Regression analysis
Identify factors (independent variables) that can be used to
predict the values for the forecast variable (e.g., sales).
Regression applied to causal data requires different kinds ofdata
Regression applied to time series data is also know astrend line analysis
We will use Excel (Tools/Data analysis) to obtain theregression line and all relevant statistics.
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A simple regression example
The first example applies regression to time series data.
Whenever possible, plot and observe the data.
The scatter plot shows a linear relation between advertising andsales. So the following regression model is suggested by the data,which refers to the true relationship between the entire population ofadvertising and sales values.
Other common formats are:
ii 110i XY
tbaYXbaY
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Decomposition of a Time Series
Demand has both trend and seasonal components.
View data via Excel.
1. Compute overall average
2. Compute average of the same seasons of each cycle (e.g., year)
3. Compute seasonal indexes (seasonal averages / overall avg.)
4. Deseasonalize data (actual values /seasonal indexes)
5. Apply regression to deseasonalized data6. Compute (project) deseasonalized forecasts using the regressionequation
7. Reseasonalize the forecasts by multiplying them with theseasonal indexes.
Excel time.
Problem 21.
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Multiple regression
Most regression problems involve more than one independent
variable. If each independent variables varies in a linear manner with Y, the
estimated regression function in this case is:
Where b0 is the intercept (also called constant)
The optimal values for the bi (slopes) can again be found using theleast squares method
kkbbbb XXXY
22110
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Steps in multiple regression analysis
1. Hypotheses for testing whether a general linear model is useful ispredicting Y:
1. Ho :1 = 2 = 3 = ... = k = 0 (means there is NOTHING useful)
2. HA : At least one of the parameters in Ho is nonzero.
2. Test statistic: F-statistic = MSR / MSE
3. If the model is deemed adequate (passes the F-test; rejected H0 )
then go to step 4 (otherwise, none of variables have any impact on Y )4. Conduct t-tests (significance tests) on parameters (slopes).
5. Remove the most insignificant independentvariable, re-run theregression, and go to step 4.
6. Repeat steps 4 & 5 until all remaining independent variable
parameters (slopes) are significant, then go to step 77. If the intercept (0 ) is insignificant then remove it, run regression one
more time.
Excel time!
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What Forecasters Should Do
Determine what elements of historical data provide repeatable
patterns and utilize this to make extrapolations. Make a list of the possible independent variables that may have
influenced the historical data and may influence futureoutcomes.
Statistically correlate the independent variables to the outcome
history using regression analysis to validate their importanceand to calibrate their effects.
Make estimates of forecast error wherever possible using MADor standard deviation measures.
Make clear presentations of the results and assumptions andlisten to feedback.
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Forecasting
Always remember that you (managers) are decision makers and
sound decisions are based on good forecasts
Suggested problems:
2, 3, 4, 7, 11, 12, 14, 17, 20, 21, 27