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The Year 4 forecast is written as 357 because the instructor rounds the exact calculated value of 357.2 to the nearest whole number. Demand is treated as discrete units in this context, so fractional forecasts are rounded off before being recorded in the table.

Conditions: The raw calculated value for Year 4 is 357.2.; The instructor applies a rounding convention to record whole numbers in the table.; Demand is assumed to be measured in discrete units.

The Year 4 forecast is written as 357 because the instructor rounds the exact calculated value of 357.2 to the nearest whole number. Demand is treated as discrete units in this context, so fractional forecasts are rounded off before being recorded in the table.

Conditions: The raw calculated value for Year 4 is 357.2.; The instructor applies a rounding convention to record whole numbers in the table.; Demand is assumed to be measured in discrete units.

The Year 3 forecast is calculated by applying the exponential smoothing formula to the Year 2 actual demand and the Year 2 forecast. The actual demand of 365 is weighted by $\alpha=0.4$, and the previous forecast of 310 is weighted by $(1-\alpha)=0.6$, yielding a new forecast of 332.

Conditions: The smoothing constant $\alpha$ is 0.4.; The actual demand for Year 2 ($A_2$) is 365.; The forecast for Year 2 ($F_2$) is 310.

The Year 3 forecast is calculated by applying the exponential smoothing formula to the Year 2 actual demand and the Year 2 forecast. The actual demand of 365 is weighted by $\alpha=0.4$, and the previous forecast of 310 is weighted by $(1-\alpha)=0.6$, yielding a new forecast of 332.

Conditions: The smoothing constant $\alpha$ is 0.4.; The actual demand for Year 2 ($A_2$) is 365.; The forecast for Year 2 ($F_2$) is 310.

To calculate exponential smoothing forecasts step-by-step, first initialize the first forecast equal to the first actual demand. Then, for each subsequent period, substitute the previous period's actual demand and previous forecast into the formula $F_{t+1} = \alpha A_t + (1-\alpha)F_t$ to compute the next forecast, repeating this recursive process until the target period is reached.

Conditions: The smoothing constant $\alpha$ is fixed across all periods.; An initial forecast $F_1$ is established from $A_1$.; Each new forecast depends only on the immediately preceding actual demand and forecast.

To calculate exponential smoothing forecasts step-by-step, first initialize the first forecast equal to the first actual demand. Then, for each subsequent period, substitute the previous period's actual demand and previous forecast into the formula $F_{t+1} = \alpha A_t + (1-\alpha)F_t$ to compute the next forecast, repeating this recursive process until the target period is reached.

Conditions: The smoothing constant $\alpha$ is fixed across all periods.; An initial forecast $F_1$ is established from $A_1$.; Each new forecast depends only on the immediately preceding actual demand and forecast.