PENGAWASAN DAN PENGENDALIAN PERAMALAN (CONTOH SOAL DAN PEMBAHASAN)

Summary

This video explains the process of monitoring and controlling forecasts using a tool called the tracking signal. It highlights how to calculate forecast errors, cumulative errors, absolute errors, and tracking signals to assess the accuracy of predictions. The example involves sales data for a cheese bread shop, where the tracking signal is used to determine if the forecast is reliable. By calculating and comparing forecast errors, the lesson demonstrates how to check if a forecast is on target and within an acceptable range, ensuring the reliability of predictions in forecasting models.

Takeaways

• 😀 Forecasting control and monitoring involve assessing whether forecasts are accurate or need adjustments.
• 😀 The tracking signal is a key tool used to evaluate the performance of forecasts by comparing forecast errors to actual outcomes.
• 😀 A positive tracking signal indicates that the actual value exceeds the forecast, while a negative signal shows the actual value is lower than the forecast.
• 😀 The video uses the example of cheese bread sales over six years to demonstrate the calculation of forecast errors and tracking signals.
• 😀 Forecast errors are calculated as the difference between actual sales and forecasted sales for each year.
• 😀 The Running Sum of Forecast errors (RSF) is the cumulative sum of forecast errors over time, which helps assess long-term trends.
• 😀 Absolute forecast errors are the positive values of the forecast errors, regardless of whether the original error was positive or negative.
• 😀 Cumulative absolute forecast errors are the running total of absolute errors, providing a broader perspective on forecasting accuracy.
• 😀 The Mean Absolute Deviation (MAD) is the average of the cumulative absolute forecast errors, giving a measure of forecast accuracy over time.
• 😀 The tracking signal is calculated as the ratio of RSF to MAD, and it helps determine whether the forecast is performing within acceptable bounds.
• 😀 A tracking signal within an acceptable range (typically between -2 and +2) indicates that the forecast is accurate and within expected limits.

Q & A

What is the purpose of forecasting monitoring and control?

The purpose of forecasting monitoring and control is to ensure that forecasts are accurate and reliable. It involves tracking how well a forecast predicts actual outcomes and making necessary adjustments if discrepancies occur.

What is a tracking signal in the context of forecasting?

A tracking signal is a tool used to measure how well a forecast predicts actual values. It compares the cumulative forecast error to the mean absolute deviation (MAD) and helps assess whether the forecast is within an acceptable range.

How is forecast error calculated?

Forecast error is calculated by subtracting the forecasted value from the actual value. A negative forecast error means the forecast was too high, while a positive error means it was too low.

What is the significance of a cumulative forecast error (RSFE)?

The cumulative forecast error (RSFE) is the running total of all forecast errors over a period. It helps identify trends or biases in the forecast, such as consistent over- or under-forecasting.

Why is absolute forecast error important?

Absolute forecast error is important because it removes the effect of negative or positive forecast errors, allowing the focus to be on the magnitude of the forecast's inaccuracies.

What is the difference between cumulative forecast error and cumulative absolute forecast error?

Cumulative forecast error (RSFE) is the total of forecast errors, which can be positive or negative, while cumulative absolute forecast error is the running total of absolute forecast errors, which are always positive.

How is the mean absolute deviation (MAD) calculated?

MAD is calculated by averaging the absolute forecast errors over a specific period. It provides a measure of how far the forecasts are from the actual values on average.

What does a tracking signal outside the range of -2 to +2 indicate?

A tracking signal outside the range of -2 to +2 indicates that the forecast is consistently overestimating or underestimating actual values, suggesting that the forecast needs to be adjusted.

How was the tracking signal used to evaluate the forecast in the example provided?

In the example, the tracking signal was calculated for each year by dividing the cumulative forecast error by the MAD. The tracking signals remained within the acceptable range of -2 to +2, indicating that the forecast was accurate.

Why is it important to regularly monitor and control forecasts?

Regularly monitoring and controlling forecasts ensures that predictions stay accurate over time. It helps identify when adjustments are needed and maintains the reliability of the forecasting process for decision-making.