Demand Management
and Forecasting
Types of Forecasts
 Qualitative
 Time Series
 Causal Relationships
 Simulation
Qualitative Methods
Grass Roots
Executive Judgment
Prediction Markets
Historical Analogy
Delphi Method
Qualitative
Market Research
Methods
Panel Consensus
Prediction Markets
http://abcnews.go.com/video/playerIndex?i
d=4826867
http://www.intrade.com/
Quantitative Approaches
Naïve (time series)
Moving Averages (time series)
Exponential Smoothing (time series)
Trend Projection (time series)
Linear Regression (causal)
Naïve Method
This period’s forecast = Last period’s
observation
Crude but effective
August sales = 1000; September sales = ??
1000!
Moving Averages
This period’s forecast = Average of past n
period’s observations
Example: for n = 3: Sales for Jan through
March were 100, 110, 150
April forecast = (100+110+150)/3 = 120
Example
14
12
Dem and
10
Demand
8
Naïve
6
3 Year Mvg Av
4
2
0
0
2
4
6
Year
8
10
12
Evaluating Forecasts
 Concept: Forecast worth function of how close
forecasts are to observations
 Mean Absolute Deviation (MAD)
 MAD = sum of absolute value of forecast errors /
number of forecasts (e.g. periods)
 MAD is the average of the absolute value of all of the
forecast errors.
Weighted Moving Averages
This period’s forecast = Weighted average
of past n period’s observations
Example: for n = 3: Sales for Jan through
March were 100, 110, 150
Suppose weights for last 3 periods are: .5
(March), .3 (Feb), and .2 (Jan)
April forecast =.5*150+.3*110+.2*100 = 128
Exponential Smoothing
 New Forecast = Last period’s forecast + alpha * (Last
period’s actual observation - last period’s forecast)
 Mathematically: F(t) = F(t-1) + alpha * [A(t-1) - F(t-1)],
where F is the forecast; A is the actual observation, and
alpha is the smoothing constant -- between 0 and 1
 Example: F(t-1) = 100; A(t-1) = 110; alpha = 0.4 -- Find F(t)
 F(t) = 104
 Can add parameters for trends and seasonality
Trend Projections
 Use Linear regression
 Model: yhat = a + b* x
 a = y-intercept: forecast at period 0
 b = slope: rate of change in y for each period x
 Example: Sales = 100 + 10 * t, where t is period
 For t = 15, Find yhat - yhat = 250
 Can find and a and b via Method of Least Squares
Linear Regression
 Model: yhat = a + b1 * x1 + b2 * x2 + … + bk * xk
 a = y-intercept
 bi = slope: rate of change in y for each increase in xi,
given that other xj’s are held constant
 Example: College GPA = 0.2 + 0.5 HS GPA + 0.001
HS SAT
 For a HS student with a 3.0 GPA and 1200 SAT what is the forecast?
 The forecast college GPA = 2.90
 Can find a, b1, and b2 via Method of Least Squares