To calculate the exponential smoothing forecast for the next period, we use the following formula:

$F_{t+1} = \alpha \cdot A_t + (1 - \alpha) \cdot F_t$

Where:

• $F_{t+1}$ is the forecast for the next period.
• $\alpha$ is the smoothing constant (given as 0.6).
• $A_t$ is the actual demand for the current period (given as 103).
• $F_t$ is the forecast value for the current period (given as 99).

Now, substituting the values:

$F_{t+1} = 0.6 \cdot 103 + (1 - 0.6) \cdot 99$

$F_{t+1} = 0.6 \cdot 103 + 0.4 \cdot 99$

$F_{t+1} = 61.8 + 39.6$

$F_{t+1} = 101.4$

Thus, the exponential smoothing forecast for the next period is 101.4.

Would you like more details or have any questions?

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Here are 5 related questions to consider:

1. How does the choice of alpha affect the smoothing forecast?
2. What are the advantages of using exponential smoothing in forecasting?
3. Can exponential smoothing be applied to seasonal data? If so, how?
4. What happens if alpha is set to a very low value (e.g., 0.1)?
5. How would you adjust your forecast if a sudden change in demand occurs?

Tip: A higher alpha value gives more weight to recent data, making the forecast more responsive to changes in demand.