Question

Two different forecasting techniques (f1 and f2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows:

Answer 1

The question deals with comparing two forecasting techniques for the demand for bottled water. The assessment involves calculating forecast accuracy measures to identify which method is more accurate. This exercise helps in understanding supply and demand shifts and their effects on the market.

Step-by-step explanation:

The student's question pertains to comparing two forecasting techniques for projecting the demand for cases of bottled water, where the actual demand and predictions from both techniques are provided.

This topic falls under the category of business or economics, specifically within operations management or supply chain analytics, where forecasting is a critical activity for managing inventory and meeting customer demand.

To evaluate the accuracy of the forecasting techniques (f1 and f2), one might consider using various forecasting accuracy measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Mean Absolute Percentage Error (MAPE). By computing these metrics, the student can assess which forecasting method provides a closer approximation to the actual demand and thus could be more reliable for future use.

An example of how demand and supply changes affect markets can be seen with a shift in supply for good weather during salmon fishing season, potentially increasing the supply and lowering prices if demand remains constant.

Conversely, a shift in demand could occur due to changes in consumer preferences, such as the shift from traditional news sources to digital sources, impacting the quantity demanded at each price level.

Answer 2

The Mean Absolute Deviation (MAD) for forecast F1 is approximately 4.71, while the MAD for forecast F2 is approximately 6.14. Therefore, forecast F1 appears to be more accurate as it has a lower MAD.

Step-by-step explanation:

Mean Absolute Deviation (MAD) is a measure of the average absolute errors between forecasted values and actual values. To compute MAD, you find the absolute difference between each forecasted value and the corresponding actual value, sum these absolute differences, and then divide by the number of observations.

For forecast F1:

`\[MAD_F1 = (|68-63| + |75-66| + |70-73| + |74-65| + |69-71| + |72-69| + |80-70| + |78-72|)/(8) = (37)/(8) \approx 4.71\]`

For forecast F2:

`\[MAD_F2 = (|68-62| + |75-61| + |70-70| + |74-71| + |69-73| + |72-73| + |80-76| + |78-80|)/(8) = (49)/(8) \approx 6.14\]`

Comparing the two MAD values, a lower MAD indicates better accuracy. Therefore, forecast F1 is more accurate in predicting demand for cases of bottled water in this scenario. This conclusion is drawn from the fact that F1 has a smaller average absolute error compared to F2, suggesting a closer alignment with the actual demand values.

Full Question:

Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows:

PREDICTED DEMAND

Period Demand F1 F2

1 68 63 62

2 75 66 61

3 70 73 70

4 74 65 71

5 69 71 73

6 72 69 73

7 80 70 76

8 78 72 80

Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 2 decimal place.)