1159 Words5 Pages

Forecasting Assignment

There are many forecasting methods including seasonal, Delphi, technological and time series. Depending upon the situation, one may work better for a company than another. In describing forecasting, Amara and Salanik (1972) offer the following:

Forecasting is: a statement about the future:, a probabilistic statement about the future: a probabilistic, reasonably definite statement about the future: a probabilistic, reasonably definite statement about the future, based upon an evaluation of alternative possibilities. (p. 415)

All forecasts are made under some conditions of uncertainty, as the future is never entirely predictable.

Seasonal Forecasting Models

Companies may experience demand fluctuations*…show more content…*

As technology moves from the research and development stage to widespread public acceptance, the technological forecasting method moves from qualitative to quantitative, as more data becomes available for analysis. Technological forecasting is useful for planning projects. Technological forecasting methods may be used to decide whether to begin a project immediately using available technology or postpone the project until anticipated technology is available. Quantitative methods for technological forecasting include statistical curve fitting, limit analysis, and trend correlation. These statistical models offer an objective approach to technological forecasting.

Time Series Forecasting

Time series forecasting is a quantitative method used to determine patterns in data collected over time. Time series analysis may detect patterns of change in statistical information over regular intervals of time. These patterns are then used to arrive at estimate for the future. Time series forecasting is a valuable forecasting tool because this method helps eliminate some of the uncertainty about the future. The main limitation of time series forecasting is the dependency on accurate data. If the data collected is not precise the forecast will not be accurate. The data gathering and analysis may be expensive and time consuming. Time series models may be simple or complicated. The most simple time series model is the simple moving average in which values for

There are many forecasting methods including seasonal, Delphi, technological and time series. Depending upon the situation, one may work better for a company than another. In describing forecasting, Amara and Salanik (1972) offer the following:

Forecasting is: a statement about the future:, a probabilistic statement about the future: a probabilistic, reasonably definite statement about the future: a probabilistic, reasonably definite statement about the future, based upon an evaluation of alternative possibilities. (p. 415)

All forecasts are made under some conditions of uncertainty, as the future is never entirely predictable.

Seasonal Forecasting Models

Companies may experience demand fluctuations

As technology moves from the research and development stage to widespread public acceptance, the technological forecasting method moves from qualitative to quantitative, as more data becomes available for analysis. Technological forecasting is useful for planning projects. Technological forecasting methods may be used to decide whether to begin a project immediately using available technology or postpone the project until anticipated technology is available. Quantitative methods for technological forecasting include statistical curve fitting, limit analysis, and trend correlation. These statistical models offer an objective approach to technological forecasting.

Time Series Forecasting

Time series forecasting is a quantitative method used to determine patterns in data collected over time. Time series analysis may detect patterns of change in statistical information over regular intervals of time. These patterns are then used to arrive at estimate for the future. Time series forecasting is a valuable forecasting tool because this method helps eliminate some of the uncertainty about the future. The main limitation of time series forecasting is the dependency on accurate data. If the data collected is not precise the forecast will not be accurate. The data gathering and analysis may be expensive and time consuming. Time series models may be simple or complicated. The most simple time series model is the simple moving average in which values for

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