To forecast the sales for February (period 38), we will follow the steps you outlined, applying the exponential smoothing formula and using the values provided. Here's how we will proceed:

Step 1: Retrieve the Smoothed Value for December (S36)

The smoothed value for December (period 36) is denoted as $S_{36}$. This value is given or needs to be retrieved from previous data, as it will be essential for updating the forecast for January (period 37) and ultimately February (period 38).

Let’s assume the smoothed value for December $S_{36}$ is known, and we’ll proceed to update the forecast.

Step 2: Apply the Exponential Smoothing Formula

The formula for exponential smoothing is:

$S_{37} = \alpha \cdot X_{36} + (1 - \alpha) \cdot S_{36}$

Where:

• $S_{37}$ is the smoothed value for January (period 37),
• $\alpha = 0.0774$ is the smoothing constant,
• $X_{36} = 400$ is the actual sales for January (period 37),
• $S_{36}$ is the smoothed value for December (period 36).

Substituting the known values into the formula:

$S_{37} = 0.0774 \cdot 400 + (1 - 0.0774) \cdot S_{36}$

Simplify the equation:

$S_{37} = 30.96 + 0.9226 \cdot S_{36}$

Now, if we know $S_{36}$, we can calculate $S_{37}$. Let me know if you have the value for $S_{36}$, or if you want me to proceed under an assumption!

Step 3: Use the Updated Smoothed Value for January as the Forecast for February

Once we calculate $S_{37}$, that value will serve as the forecast for February (period 38).

Do you have the value for $S_{36}$, or would you like me to assume a value and proceed?

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Here are 5 related questions for further exploration:

1. How is the smoothing constant $\alpha$ determined in practice?
2. How sensitive is the forecast to changes in the value of $\alpha$?
3. What is the effect of using a higher $\alpha$ on short-term forecasts?
4. How can you adjust the forecast if there’s a known seasonal effect in the data?
5. How would a trend factor be incorporated into exponential smoothing?

Tip: Exponential smoothing is best suited for short-term forecasts, and adjusting the smoothing constant can help control how much weight recent data has in the forecast.