I see that you will get an A this semester. Department of Business Administration. Chapter 3: Forecasting. FALL 20 10  2011. Outline: What You Will Learn. List the elements of a good forecast. Outline the steps in the forecasting process.
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I see that you willget an A this semester.
Department of Business Administration
FALL 20102011
Outline: What You Will Learn . . .
What is meant by Forecasting and Why?
Marketing
Product
Management
& Finance
Executive
Management
Production &
Inventory
Control
Forecasting Process MapCausal
Factors
Demand
History
Statistical Model
Consensus
Process
Consensus
Forecast
I see that you willget an A this semester.
Features of Forecasts
Step 6 Monitor the forecast
Step 5 Make the forecast
Step 4 Obtain, clean and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast
Steps in the Forecasting ProcessQualitative Forecasts or Judgmental Forecasts
Qualitative Forecasts or Judgmental Forecasts
Qualitative Forecasts or Judgmental Forecasts
Qualitative Forecasts or Judgmental Forecasts
Qualitative Forecasts or Judgmental Forecasts
Atn+ … At2 + At1
Ft = MAn=
n
n= number of period
wnAtn + … wn1At2 + w1At1
Ft = WMAn=
n
n=total amount of number of weights
Ft = Ft1 + (At1  Ft1)
Ft = forecast for period t
Ft1 = forecast for the previous period
= smoothing constant
At1 = actual data for the previous period
1
Ft
Ft = a + bt
0 1 2 3 4 5 t
Linear Trend EquationConstants a and b
The constants a and b are computed using the equations given:
Once the a and b values are computed, a future value of X can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.
Trend Projection Calculating a and bOr If formula b is used first, it may be used formula a in the following format:
Y = 11.90 + 0.394X
Example 2 for Trend Projection
St = 12.06(1.026)t
These forecasts are similar to those obtained by fitting a linear trend
(
Actual
forecast)
MSE
=
n

1
(
Actual
forecast
/ Actual*100)
MAPE
=
n
MAD, MSE, and MAPE
Actual
forecast
=
MAD
n
22/8=2.75
76/81=10.86
10.26/8=1.28 %
Example: Central Call CenterForecast Accuracy  MAD
AP = 3 moving average
AP = 5 moving average
Sqroot of 78.33/9=2.95
RMSE for 3qma=2.95
RMSE for 5qma=2.99
Sqroot of 62.48/7=2.98
Thus threequarter moving average forecast is marginally better than the corresponding five moving average forecast.
F2= 0.3 (20)+(10.3) 21=20.7 with w=0.3
F2= 0.5 (20)+(10.5) 21=20.5 with w=0.5
F2= 0.3 (20)+(10.3) 21=20.7 with w=0.3
F2= 0.5 (20)+(10.5) 21=20.5 with w=0.5
RMSE with w=0.3 is 2.70
RMSE with w=0.5 is 2.91
Both exponential forecasts are better than the previous techniques in terms of average values.
Trend Forecast
Ratio =
SeasonalAdjustment
Average of Ratios forEach Seasonal Period
=
Seasonal VariationRatio to Trend Method
AdjustedForecast
TrendForecast
SeasonalAdjustment
=
Ratio to Trend Method:Example Calculation for Quarter 1
Trend Forecast for 2001.1 = 11.90 + (0.394)(17) = 18.60
Seasonally Adjusted Forecast for 2001.1 = (18.60)(0.887) = 16.50
Deseasonalize data=actual sales/seasonal relative (index)
where
= adjusted time series value at time t
yt = value of the time series at time t
It = index value at time t
Deflating a Time Series: Example
(Total Gross $ = Total domestic gross ticket receipts in $millions)
Deflating a Time Series: Example