Moving Average Forecasting Technique The person in your firm...

Moving Average Forecasting Technique The person in your firm responsible for forecasting has been absent for three months, so your supervisor asks you to forecast the demand of the most important product. The information you get is a table (see Figure 9.8.2.1) showing the historical data on the demand for the product (January to October) and the forecast for the period January to July based on the moving average forecasting technique. $$ \begin{array}{|l|l|l|l|l|l|l|l|l|l|l|} \hline & \text { Jan. } & \text { Feb. } & \text { Mar. } & \text { Apr. } & \text { May. } & \text { Jun. } & \text { July. } & \text { Aug. } & \text { Sept. } & \text { Oct. } \\ \hline \text { Demand } & 151 & 135 & 143 & 207 & 199 & 175 & 111 & 95 & 119 & 191 \\ \hline \text { Forecast } & 183 & 195 & 177 & 155 & 159 & 171 & 181 & & & \\ \hline \end{array} $$ Moreover, your supervisor asks you to: a. Forecast the demand just as your colleague does. Therefore, you have to calculate the parameter $\mathrm{n}$ from the historical forecast data. b. Calculate the forecast for August, September, and October as well as for the following month, November. c. Compute the standard deviation $\sigma$ of the forecast from January to October and decide if the applied technique fits this product.

Moving Average Forecasting Technique

The person in your firm responsible for forecasting has been absent for three months, so your supervisor asks you to forecast the demand of the most important product. The information you get is a table (see Figure 9.8.2.1) showing the historical data on the demand for the product (January to October) and the forecast for the period January to July based on the moving average forecasting technique.
$$
\begin{array}{|l|l|l|l|l|l|l|l|l|l|l|}
\hline & \text { Jan. } & \text { Feb. } & \text { Mar. } & \text { Apr. } & \text { May. } & \text { Jun. } & \text { July. } & \text { Aug. } & \text { Sept. } & \text { Oct. } \\
\hline \text { Demand } & 151 & 135 & 143 & 207 & 199 & 175 & 111 & 95 & 119 & 191 \\
\hline \text { Forecast } & 183 & 195 & 177 & 155 & 159 & 171 & 181 & & & \\
\hline
\end{array}
$$
Moreover, your supervisor asks you to:
a. Forecast the demand just as your colleague does. Therefore, you have to calculate the parameter $\mathrm{n}$ from the historical forecast data.
b. Calculate the forecast for August, September, and October as well as for the following month, November.
c. Compute the standard deviation $\sigma$ of the forecast from January to October and decide if the applied technique fits this product.

Key Concepts

Evaluation of Forecasting Technique

Assessing whether the moving average technique fits a specific product involves comparing the pattern of forecast errors to acceptable benchmarks. High variability in forecast errors, as indicated by a large standard deviation, may signal that the method does not capture key aspects of the demand pattern, prompting consideration of alternative forecasting models.

Forecast Error and Standard Deviation

Forecast error measures the deviation between the predicted values and the actual observed outcomes. The standard deviation of these errors provides a quantitative measure of the dispersion of forecast errors, helping assess the reliability and accuracy of the forecasting technique.

Forecasting Horizon

This concept refers to the time period into the future for which predictions are made. In a moving average context, once the window size is set, forecasts can be extended into the future by using historical averages, though accuracy may decrease as the horizon extends since the model does not account for trends or shifts.

Window Size (Parameter n)

The parameter n represents the number of past periods included in the moving average. Determining the optimal window size is crucial because it balances responsiveness (a smaller n reacts quickly to changes) and smoothing (a larger n reduces noise), thus affecting the accuracy of the forecast.

Moving Average Forecasting

This is a time series forecasting method that smooths out short-term fluctuations by averaging data points over a fixed number of past periods. It is widely used in practice to generate forecasts when the series exhibits random variations but no strong trend or seasonality patterns.

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