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 |
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
12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality....
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