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12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality. Use decomposition using least squ

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Answer #1
Year Season Period Demand Seasonal averages Seasonal Factor De-seasonalized demand
2017 Spring 1 2250 3000 0.75 3014
Summer 2 3050 3625 0.90 3381
Fall 3 2900 3400 0.85 3428
Winter 4 5500 6050 1.51 3653
2018 Spring 5 3750 0.75 5023
Summer 6 4200 4018.75 0.90 4656
Fall 7 3900 0.85 4610
Winter 8 6600 1.51 4384
2019 Spring 9 0.75
Slope = 247.47
Intercept = 2905.12

A B C D Year Season Period Demand Seasonal averages Seasonal Factor De-seasonalized demand 2 2017 1 2 =(D2+D6)/2 =(D3+D7)/2 =

Regression Equation: Trend forecast = 2905.12 + 247.47 * Period

For Spring (2019), the Period = 9

So,

Trend forecast = 2905.12 + 247.47 * Period = 2905.12 + 247.47 * 9 = 5132

Seasonal factror = 0.75 because of Spring.

So,

Forecast with trend and seasonality = 5132*0.75 = 3849

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