Forecasting

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Forecasting

• I think there is a world market for maybe five
  computers. Thomas Watson, Chairman of IBM, 1943.
• I skate to where the puck is going to be, not to
  where it has been. Wayne Gretzky, the greatest
  hockey player of all times.
• A good forecaster is not smarter than anyone
  else, he merely has his ignorance better
  organized. Consultants survival tips.

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Forecasting

• Product demand is the basis for all planning
  activities in a firm. Product demand drives the
  requirements for
• Raw material
• Capacity
• People
• However, future demand is almost never certain.
  It needs to be predicted using
• Past sales history
• Causal factors
• Human judgment
• This is called forecasting (also known as demand
  planning)

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Positioning of forecasting process
ISCM, i2 technologies, inc.
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The use of forecasting
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Independent dependent demand
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Characteristics of forecasts

• They are usually wrong
• A good forecast is more than a single number
• Aggregate forecasts are more accurate
• The longer the forecast horizon, the less
  accurate the forecast will be
• Forecasts should not be used to the exclusion of
  known information

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Types of forecasts

• Subjective (qualitative) methods
• Sales force composites
• Customer surveys
• Jury of executive opinion
• Delphi method
• Objective (quantitative) methods
• Causal models
• Time series models
• Blended methods
• A baseline forecast created by quantitative
  methods to be adjusted by the user using
  subjective methods

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Causal methods

• Predict future using data from other sources than
  the series being predicted
• Predict spare parts demand using the data about
  install base
• Predict housing sales using the interest rate for
  mortgages
• Causal models
• Yf(X1,X2,,Xn)
• Ya0a1X1a2X2anXn

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Time series components
Cycles Randomness

• Average
• Trend
• Seasonality

Seasonal variation
Linear Trend
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Forecasting notation

• D1, D2,,Dt the observed values of demand during
  periods 1,2,,t
• If we are forecasting at the end of period t,
• We observed Dt, Dt-1,Dt-2,,
• We did not observe Dt1, Dt2,,
• Ft is the forecast made for period t in period t-1

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Methods for stationary series

• Time series forecasting where the trend,
  seasonality or cyclic behavior is hard to predict
  or model.
• Each observation is a constant plus a random
  fluctuation

• Mainly two methods
• Moving average
• Exponential smoothing

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Simple Moving Average Formula

• The simple moving average model assumes an
  average is a good estimator of future behavior.
• The formula for the simple moving average is

N number of periods used in averaging MA(N)
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Example
F4(650678720)/3
F7(650678720 785859920)/6
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Moving average forecast
Weighted moving average

• Multiple-step-ahead forecast
• If you are at the end of period 3, and if you
  want to forecast period 6, the forecast would be
  same as the forecast for period 4 (stationary
  series)
• Rounding
• If the numbers are small, a carry forward
  rounding (CFR) scheme can be used
• Consider the series (,1,0,1,1) and the MA(4) is
  used, the forecast for future can be one of
• 1,1,0,1,1,1,0,1,1,1,0,.
• 1,0,1,1,1,0,1,1,1,0,1,.

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Finding the right forecasting method
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Evaluating forecasts

• Forecast error for period, et Ft - Dt

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Forecast errors over time
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Moving average lags behind
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Moving average lags behind
Demand
Is moving average forecast an unbiased forecast,
if there is a known trend?
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Exponential smoothing

• The current forecast is weighted average of the
  last forecast and last observed value
• New forecast Ft
• a (Current observation of demand)(1-a)(last
  forecast)
• a (Dt-1)(1-a)(Ft-1)
• a smoothing constant, weight on current
  observation
• (1- a) weight placed on past observations
• FtFt-1- a(Ft-1-Dt-1)Ft-1- a et-1

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Exponential Smoothing Problem

• Question Given the weekly demand data, what are
  the exponential smoothing forecasts for periods
  2-10 using a0.10 and a0.60?
• Assume F1D1

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Exponential Smoothing Problem
F40.1680(1-0.1)(815.5)
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Smoother forecast with small alpha
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Weights in exponential smoothing

• Ft a Dt-1(1-a)(Ft-1)
• Ft-1 a Dt-2(1-a)(Ft-2)
• Ft a Dt-1 a (1-a)(Dt-2)(1- a)2 Ft-2

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Weights in exponential smoothing
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Example

• Engine failures in a military base

Compare MA(3) and ES(0.1)
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Comparison of two methods
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Comparison of exponential smoothing and moving
average

• Similarities
• Stationary series assumption
• Single parameter
• Lag behind if there is a trend
• When a2/(N1) gives same level of accuracy
• Differences
• Exponential smoothing uses all past data, moving
  average uses only the last N
• Exponential smoothing require less data storage