Agroindustrial project analysi

62 AGROINDUSTRIAL PROJECT ANALYSISume are magnified when translated into profit. Accordingly, thisprospective manufacturer should reevaluate sales assumptions becauseof their extensive financial consequences.Forecasting methodsThe characteristics, uses, and limitations of the three principalforecasting methods-judgmental estimates, time-series analyses,and causal models-are discussed in the following section. Referencesto further, detailed explanations of the specific techniquesare given in the second section of the bibliography.JUDGMENTAL ESTIMATES. Some degree of judgment is implicit inall estimates, but, when statistical data are limited, the opinions ofknowledgeable observers must be the basis for the forecast. Opinionsare derived from experience, which is itself a form of historicaldata, and the experiences of industrial operators (for example,manufacturers, distributors, salespeople, bankers, consultants) areoften a reasonable basis for the projection of market dynamics. Experienceis even more valuable when taken from a systematicsampling of industrial experts. The most common judgmental forecastingmethods are the following: 1 7- Sales-force composite. The sales estimates of individual salespersonsare pooled into an aggregate sales forecast.* Executive jury. The managers from different functional areasof the enterprise (for example, marketing, production, finance)jointly prepare sales estimates.* Panel consensus. A group of industrial experts discusses anddevelops a common opinion and prediction.= Delphi. The opinions of experts are gathered by questionnairesand the results are returned to the experts iteratively until convergenceis approximated.* Cross-impact analysis. The forces that will likely affect theforecast are identified, and experts systematically assess theeffects of these forces on each other and on the forecast.17. Definitions are drawn from an excellent literature review by VithalaR. Rao and James E. Cox, Jr., Sales Forecasting Methods: A Survey of RecentDevelopments (Cambridge, Mass.: Marketing Science Institute, 1978), appendixA, pp. 88-94.