Forecasting AssignmentForecasting AssignmentForecasting AssignmentThere 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 ModelsCompanies may experience demand fluctuations by season. Seasonality is the pattern that repeats for each period or season. Seasonal forecasting is a decomposition of a times series forecast model. Data is gathered for each season to gain a picture of the seasonal factor for the period. Using the simple proportion, the seasonality factor consists of a comparison of the period or season average and the overall average. This allows the company to determine if sales are expected to be above or below average during certain times or seasons of the year. A seasonal model can be as simple or complicated as needed. The seasonal factor can be computed using a simple proportion, a hand-fit straight line or be decomposed using least squares regression.
Incorporating seasonality in a forecast is useful when the time series has both trend and seasonal components. The final step in the forecast is to use the seasonal index to adjust the trend projection. One simple way to forecast using a seasonal adjustment is to use a seasonal factor in combination with an appropriate underlying trend of total value of cycles (Arsham, 1994).
Seasonality charts are most accurate during periods with stable market conditions. Also, data from more than one season is best gathered in order to illustrate any trends in the demand. If the company has only limited data a seasonality chart may miss significant upward or downward demand trends that will have a major impact on the accuracy of the forecast.
Delphi MethodThe Delphi method is a judgmental, qualitative forecasting technique used for decision-making. The Delphi method is designed to systematically collect the advice of experts generally through a series of questionnaires. The basic premise of the Delphi method is that there are many influences on decision-makers that may limit their ability to make unbiased decisions. A mediator or coordinator provides the experts with the questions and tabulates the results. The results may then be returned to the experts for further analysis. This process may continue until a consensus is reached. The Delphi method is most useful for answering one specific question. There is less support for its use to determine complex forecasts concerning multiple factors (The Delphi Method, n.d.). The Delphi method does away with the need for group meetings and reduces bias in the results because of the anonymity of the experts involved in the surveys. The main drawback of the Delphi method is the time needed to reach a final decision. The long period of time that may be needed to achieve the final results may make the participants lose interest and discontinue their participation. If a company needs rapid results the Delphi method is not the best forecasting method. The Delphi method is best employed for long-term rather than short-term planning.
Technological forecastingTechnological forecasting can be quantitative or qualitative and focuses on understanding the future results of current technology and decisions. 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.
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