Forecasting Efficient operations require that managers match supply to demand. A

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Forecasting Efficient operations require that managers match supply to demand. As they lack a crystal ball, they must rely on forecasting to help predict future demand. Forecasts then drive decisions regarding purchasing, production, and logistics the key activities in the order fulfillment process. Since most forecasting methods rely on historical data to predict future behavior and are our "best guess, they are almost always decision-making. Three types of simple forecasts-simple moving average, weighted moving average. and exponential smoothing-are described below. imple Moving Average: Uses the average of recent time periods to estimate the next period's demand. Using more time periods, increasing stability. Using fewer time periods, increases responsiveness. Weighted Moving Average: More recent data may better reflect future behavior. Managers acknowledge this b y weighting recent time periods more highly. Managerial judgment (and measurement of forecast error) is used to identify the number of periods and set appropriate weights. Weights must add up to 10 Exponential Smoothing: Sometimes unexpected, and largely random, spikes in demand may occur. To avoid being overly influenced by these spikes, managers may use exponential smoothing, which weights the last period's demand with the last period's forecast. Manager choose a smoothing constant based on whether they have more faith in the actual demand or the previous period's forecast. The formula for exponential smoothing is as follows: Forecast+ oActual Demand + (1-o)Forecast Because forecasts tend to be wrong, it is important to measure how wrong-and then to make adjust to improve the forecasting process. Depending on the industry, forecast errors are often 30-80%. Two basic approachesmean squared error and mean absolute error-are described below . Mean Squared Error Is the average of all the squared errors. The result is not very intuitive . Mean Absolute Deviation: Is the average of absolute values of the difference between the actual and forecast values. Taking the absolute value prevents high and low forecasts from canceling each other out Problems: Using the numbers in the chart below, calculate the following 1. Three-period moving average

Explanation / Answer

a)

original 3 point 4 point exp 48 45 14.4 47 46.66666667 23.58 45 45.66666667 45.9 30.606 40 44 43.4 34.9242

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