Demand Forecasts
• The three principles of all forecasting
techniques:
– Forecasting is always wrong
– Every forecast should include an estimate of
error
– The longer the forecast horizon the worst is
the forecast
– Aggregate forecasts are more accurate
Two comments frequently made by
managers
• We’ve got to have better forecasts
• I don’t trust these forecasts or
understand where they came from
• These comments suggest that
forecasts are held in disrepute by
many managers
The truth about forecasts
• They are always wrong
• Sophisticated forecasting techniques
do not mean better forecasts
• Forecasting is still an art rather than
an esoteric science
• Avoid single number forecasting
– Single number substitutes for the
decision
Selecting a forecasting
technique
• What is the purpose of the forecast?
• How is it to be used?
• What are the dynamics of the system
for which forecast will be made?
• How important is the past in
estimating the forecast?
Forecasting Techniques
• Judgmental methods
Qualitative
• Market research methods
• Time series methods
Quantitative
• Casual methods
Judgmental methods
• Sales-force composite
• Panels of experts
• Delphi method
Market research method
• Markey testing
• Market survey
Time Series methods
• Moving average
• Exponential smoothing
• Trend analysis
• Seasonality
– Use de-seasonalized data for forecast
– Forecast de-seasonalized demand
– Develop seasonal forecast by applying
seasonal index to base forecast
Components of an
observation
Observed demand (O) =
Systematic component (S) + Random
component (R)
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
Causal methods
• Single Regression analysis
• Multiple Regression analysis
Error measures
• MAD
• Mean Squared Error (MSE)
• Mean Absolute Percentage Error
(MAPE)
• Bias
• Tracking Signal
Collection and preparation
of data
• Record data in the same terms as
needed for forecast
– Demand vs. shipment
– Time interval should be the same
• Record circumstances related to data
• Record demand separately for
different customer groups
Time Series Forecasting
Quarter
II, 1998
III, 1998
IV, 1998
I, 1999
II, 1999
III, 1999
IV, 1999
I, 2000
II, 2000
III, 2000
IV, 2000
I, 2001
Demand Dt
8000
13000
23000
34000
10000
18000
23000
38000
12000
13000
32000
41000
Forecast demand for the
next four quarters.
Time Series Forecasting
50,000
40,000
30,000
20,000
10,000
0
, 2 7, 3 7, 4 8, 1 8, 2 8, 3 8, 4 9, 1 9, 2 9, 3 9, 4 0, 1
7
9
9
9
9
9
9
9
9
9
9
9
0