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The Role of Forecasting
Forecasting is a vital function and impacts every significant management decision.
Finance and accounting use forecasts as the basis for budgeting and cost control. Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation. Production uses forecasts to select suppliers, determine capacity requirements, and to drive decisions about purchasing, staffing, and inventory.
Four basic types of forecasts include: qualitative, time series analysis (primary focus of this chapter), causal relationships, and simulation.
Time series analysis is based on the idea that data relating to past demand can be used to predict future demand. Components of demand include: average demand for a period of time, seasonal element, random variation, trend, cyclical elements, and autocorrelation.
Choosing an appropriate forecasting model depends upon: time horizon to be forecast, data availability, accuracy required, size of forecasting budget, and availability of qualified personnel.
Simple Moving Average
It is useful when demand is not growing or declining rapidly and no seasonality is present. It removes some of the random fluctuation from the data.
Weighted Moving Average
A weighted moving average allows unequal weighting of prior time periods.
Linear Regression Analysis
Regression is used to identify the functional relationship between two or more correlated variables, usually from observed data.
One variable (the dependent variable) is predicted for given values of the other variable (the independent variable).
Linear regression is a special case which assumes the relationship between the variables can be explained with a straight line.
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