Let's walk through the process of finding the Tracking Signal step-by-step.

Formula for Tracking Signal:

$\text{Tracking Signal} = \frac{\text{Cumulative Forecast Error (CFE)}}{\text{Mean Absolute Deviation (MAD)}}$

Step 1: Compute the Forecast Errors

The forecast error for each week is: $\text{Forecast Error} = \text{Actual Demand} - \text{Forecast}$

Here’s the data from the question:

Week	Demand (Actual)	Forecast	Forecast Error
1	19	19.00	0.00
2	23	19.00	4.00
3	29	21.40	7.60
4	38	25.96	12.04
5	25	33.18	-8.18
6	29	28.27	0.73
7	35	28.71	6.29
8	22	32.48	-10.48
9	25	26.19	-1.19
10	29	25.48	3.52

Step 2: Compute the Cumulative Forecast Error (CFE)

$\text{CFE} = \sum (\text{Actual Demand} - \text{Forecast})$

Adding all the forecast errors: $\text{CFE} = 0.00 + 4.00 + 7.60 + 12.04 - 8.18 + 0.73 + 6.29 - 10.48 - 1.19 + 3.52 = 14.33$

Step 3: Use the Given MAD

The Mean Absolute Deviation (MAD) is provided as: $\text{MAD} = 5.40$

Step 4: Calculate the Tracking Signal

$\text{Tracking Signal} = \frac{CFE}{MAD} = \frac{14.33}{5.40} = 2.65$

Final Answer:

The Tracking Signal is: $\boxed{2.65}$

Would you like further explanations or help with any part of this calculation?

---

Here are 5 related questions:

1. How does changing the value of alpha affect the exponential smoothing forecast?
2. What is the significance of a tracking signal outside the range [-4, 4]?
3. How can you adjust your forecasting model if the tracking signal indicates bias?
4. How do MAD and MSE compare in measuring forecast accuracy?
5. What other forecasting techniques could be used instead of exponential smoothing?

Tip: A positive tracking signal suggests the forecast underestimates demand, while a negative signal indicates overestimation.