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Short Term Fo Month cummluative avg Jan 1 - Feb 1.3 1.00 - Mar 2.7 1.15 - Apr 3.1 1.67 1.67 May 3.8 2.03 2.37 Jun 4.1 2.38 3.20 Jul 2.67 3.67 USED Stable environment Used Most Frequently Better Forcast Demand in(000) n- point moving avg (3 month moving avg)

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Forecasting

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Page 1: ForeCasting

Short Term Forcasting

Month cummluative avg

Jan 1 -Feb 1.3 1.00 - W1=0.1, W2=0.3, W3=0.6Mar 2.7 1.15 -Apr 3.1 1.67 1.67 2.11May 3.8 2.03 2.37 2.8Jun 4.1 2.38 3.20 3.48Jul 2.67 3.67 3.91

USED Stable environment Used Most Frequently as weight can Moderated Better Forcast

Sum of all Weights equal to 1

Weights may be subjectively ae.g. W1 W2 W31,2,3,4=10(sum)

W1= 1/10W2= 2/10W3= 3/10W4=4/10

The largest weight should be

Demand in(000)

n- point moving avg (3 month moving avg)

n- point weighted moving avg (3 month weighthed

moving avg)

Page 2: ForeCasting

Short Term Forcasting

1.001.001.252.442.983.654.02

New Prodcut LaunchesBetter Forcast

F(t+1)=Forecast next time periodA(t)= Actual of this time periodF(t)=Forcast of the time period

the forcast of the first time period may be taken anyone of the foloowing three ways 1. it may be asssumed or given2. it may be taken as actual of the first time period3. it may be taken to be avg of the first time periods

Single exponential smoothingSES =α= 0.82

Single exponential smoothingSES =α= 0.82

F(t+1) = αA(t)+(1- α)F(t)

Page 3: ForeCasting

Time Series Linear Regression

x Month SUMMARY OUTPUT

1 Jan 12 Feb 1.3 Regression Statistics3 Mar 2.7 Multiple R 0.97828914 Apr 3.1 R Square 0.95704965 May 3.8 Adjusted R 0.946312

6 Jun 4.1Standard Er0.2962464

7 JulObservatio 6

a(intecept) 0.3266667 ANOVAb (slope) 0.6685714 df SS MS

r (Corelation) 0.9782891 Regression 1 7.8222857 7.8222857R Square 0.9570496 Residual 4 0.3510476 0.0877619

Forecast for July 5.0066667 Total 5 8.1733333

CoefficientsStandard Error t StatIntercept 0.3266667 0.2757904 1.1844746x 0.6685714 0.0708164 9.4409091

Demand in(000)

Page 4: ForeCasting

Time Seriesy=a+bx

y= Dependent Variable

F Significance F89.130765 0.0007019 y=.32+.668x

P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%0.3017961 -0.43905 1.0923834 -0.43905 1.09238340.0007019 0.4719535 0.8651893 0.4719535 0.8651893

were a is Constant or Intercept

b= is slope/Radiant /Regression Coefficent

x= independent variable/ predict variabel /treatment variable


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