Agroindustrial project analysi

64 AGROINDUSTRIAL PROJECT ANALYSIS* Exponential smoothing. Same as in weighted moving average,except that the analyst uses a set of weights that decreasesexponentially, thereby giving more recent observations moreweight.* Box-Jenkins method. The analyst uses an autoregressive,moving-average linear model to express forecasts as a linearcombination of past actual values (or errors).* Classical decomposition. With this method the analyst decomposesa time series into seasonal, cyclical, trend, and irregularelements.CAUSAL MODELS. Causal techniques attempt to identify those variableswhich predict sales behavior. Regression analysis is one exampleof a causal technique that improves the accuracy of estimating.Simple regression uses one variable to predict sales, whereasmultiple regression uses several-for example, population, growth,income, and price. The relations between the variables and salescan be plotted, and the points can be connected by a regression line.The relations can be calculated mathematically by the least-squarestechnique, which minimizes the sum of the squared deviations ofthe points from the line. Although regression analyses can be performedmanually, inexpensive computer-program packages are alsoavailable to facilitate the task. It can be seen from the earlier exampleof the proposed Ghanian tinned milk factory that futuresales could be estimated (from the data in table 2-3) by eye (judgmentally)or by using a formal regression analysis. These alternativesare demonstrated in table 2-5 through a comparison of aconsultant's estimates with those of regression analysis. The differ-Table 2-5. Alternative Estimates of per Capita Tinned MilkConsumption in Ghana, 1964-68(ounces)Year Consultant's judgment Regression analysis1964 44.75 45.971965 46.75 48.721966 48.50 51.481967 50.25 54.241968 52.00 56.99Note: Projected from consumption data in table 2-3; I fluid ounce = 29.573milliliters.Source: EDo, Tinned Milk Market Forecast, p. 7.