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Chapter 5

Demand Forecasting

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1.Importance of Forecasting•Helps planning for long-term growth

•Helps in gauging the economic activity auto sales, new home sales, electricity demand)

•Reduces risk and uncertainty in managerial decisions.

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2.Qualitative Forecasts•surveys and opinion polls are used to:

•Make short-term forecasts when

quantitative data are not available

•Supplement quantitative forecasts

•Forecast demand for new products for which data do not exist.

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3.Methods of Qualitative Forecasts Survey Techniques

•Surveys of business executives plant and equipment expenditure plans

•Surveys of plans for inventory changes and expectations

•Surveys of consumers’ expenditure plans

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Opinion polls•Executive polling•Sales force polling•Consumer intention polling

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4.Quantitative Forecast Methods

• Time Series Analysis - use of past values of an economic variable in order to predict its future value.

Trend Projections (linear trend, growth rate trend).

Time Series Data Fluctuate over time due to four reasons:

• Secular trend - long-run increase or decrease in data series (e.g. Demand for gasoline, health care costs).

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•Cyclical fluctuations - major expansions and contractions in economic data series which recur every several years (Housing construction, auto demand).

•Seasonal variation - regular fluctuations in economic activity during each year as caused by weather or social customs (Housing starts, Christmas sales).

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•Irregular or random fluctuations variation in data series due to unique events such as war, natural disaster, and strikes.

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5. Trend Projection• Extension of past changes in

time series data into the future (sales, interest rate, stock value forecasting)

a)Constant amount of change or growth or

Sales = f(time trend)

St = a + bt => constant

amount growth

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b) Exponential growth function

St = So(1+g)t : constant percentage growth (exponential growth)

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5a. Linear Trend Projection

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Year St t Year St t

92-1 11 1 94-1 14 9

-2 15 2 -2 18 10

-3 12 3 -3 15 11

-4 14 4 -4 17 12

93-1 12 5 95-1 15 13

-2 17 6 -2 20 14

-3 13 7 -3 16 15

-4 16 8 -4 19 16

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St =11.90+.394t; R2=.5

S17 =11.9 + .394(17)=18.60

S18 =11.9 + .394(18)=18.99

S19 =11.9 + .394(19)=19.39

S20 =11.9 + .394(20)=19.78

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5b. Exponential Growth ProjectionModel: St = S0( 1 +g)t

ln St=lnS0 + t ln(1 + g)

Year ln St t

92.1 2.398 1

. . .

. . .

. . .

95.4 2.944 16

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ln St = 2.49 + .026t

Taking the antilog of both sides yields,

St= 12.06(1.026)t; R2= .5

S17 = 12.06(1.026)17 = 18.76

S18 = 12.06(1.026)18 = 19.14

S19 = 12.06(1.026)19 = 19.64

S20 = 12.06(1.026)20 = 20.15

tt gSS )1(0

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Notice that forecasts based on linear trend model tend to be less accurate the further one forecasts into the future.

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6. Methods of Incorporating Seasonal Variation

a.Ratio to trend method• get a forecasted value for each quarter

• Calculate the actual-forecasted ratio for each season or each month.

• Calculate the average of the actual/forecast ratio for each season over the entire period of the study.

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•Multiply each forecasted value of the economic variable by its corresponding seasonal adjusting factor.

b. The dummy variable method•Include n-1 dummy variables in the trend equation and run the regression.

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Time-Series Growth Patterns

Y

t ̂

Time(t)

tg)1(ˆ0

Y

Time(t) Time(t)

Y

2ˆ ctt

(a)Linear trend (b)Constant rate of growth trend

(c)Declining rate of growth trend

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7.Some shortcomings of Time Series Analysis

•Assumes that past behaviors will be repeated in the future

•Cannot forecast turning points•Does not examine the underlying causes of fluctuations in economic variables.

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8.Smoothing Techniques• Refer to the method of predicting future values of a time series on the basis of an average of its past values only

• They are used when the data show

irregular variation (random).

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a. Moving Averages• Help to generate acceptable future period value of a variable when the time series are subject to random fluctuations.

• 3-quarter vs 5-quarter Moving Average Forecasts and Comparison

• Objective: Forecast 13th quarter value, given time series data for 12 quarters

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Choose the appropriate period based on the lowest RMSE.

RMSE= At = actual value of the time series in period t.

Ft = the forecasted value of the time series in period t.

Problem: Gives equal weight to

each period

nFA tt /)(

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b. Exponential smoothing - a smoothing technique in which the forecast for period t+1 is a weighted average of the actual and forecasted values of the time series in period t.

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Ft+1 = wAt + (1-w)Ft

where Ft+1 = the forecast of F in period t.

w = the weight assigned to the actual value of the time series, 0<w<1.

1-w = the weight assigned to the forecasted value of the time series.

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9.Barometric Methods Utilize the economic indicators in

order to forecast short-term changes in economic activity or turning points in business cycles.

The economic indicators are:

Leading indicators- indicators which turn (increase or decline) before the actual cyclical peak or cyclical trough is experienced (Generally 6-9 months in advance).

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•Lagging indicators- economic indicators which turn after the cyclical peak or trough is experienced.

•Coincident indicators - indicators which turn in steps with the business cycles. When individual indices of economic indicators exhibit conflicting movements, one could use one of the composite indices, or diffusion index.

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Composite index - the weighted average of the individual indicators in each group (leading, lagging, coincident).

Diffusion index - the percentage of the indicators in the group (i.e. the twelve leading indicators) which move upward.

0<D<100.

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Shortcomings of composite and diffusion index:

•fail to forecast a turning point

•variability in time can be considerable

•do not provide any indication as to the magnitude of the forecasted change in the level of economic activity.

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10. Using Econometric Models to Forecast

Advantages•Seek to explain the economic phenomenon being forecast- i.e. enables mgt to assess the impact of changes in policies (price, Ad)

•Predict the direction and magnitude of change

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•Models can be modified based on the comparison of actual and forecast value.

Comment: The above advantages have to be weighed against the difficulties of getting the forecast values of each of the explanatory variables.