1. Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two moving average forecasting methods to see which one was better over this period.
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To test the two moving average forecasting methods, you will need to calculate the forecasted demand using each method and then compare the results.
Moving average is a forecasting technique that calculates the average of a specified number of previous data points to estimate future values. In this case, you need to determine which moving average method is better for forecasting demand over a nine-month period.
The steps to compare the two moving average methods are as follows:
1. Choose a specific number of data points to calculate the moving average. For example, you can use a three-month moving average or a five-month moving average. The choice of the number of data points will depend on analyzing the demand patterns and considering the trade-off between responsiveness and stability of the forecast.
2. Calculate the moving average for each data point by summing the demand values of the chosen number of previous periods and dividing by that number. Repeat this calculation for each data point in the nine-month period.
3. Once you have calculated the moving average forecasts for both methods, compare them by analyzing different performance measures such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), or Mean Absolute Percentage Error (MAPE). These measures help assess the accuracy and precision of each forecasting method.
4. Identify which moving average method had a better performance based on the performance measures. The method with lower MAD, MSE, or MAPE values indicates better forecasting accuracy.
By following these steps, you can evaluate the performance of the two moving average forecasting methods and determine which one is better for forecasting the demand over the nine-month period.