Abstract
The growth rate model of forecasting used so far by business organizations in developing countries can no longer generate satisfactory results owing to maturing markets. Lack of availability of sufficiently long time series data with these organizations limits the use of time series or causal models. While qualitative methods are suitable in such situations, previous studies have suggested systematic use of information to improve the accuracy of forecasts generated from these methods. This paper develops a new model called the structural qualitative method (SQM), which generates forecasts using quantitative and qualitative data. Application of this model by an Indian seed company is also illustrated here. Using this method forecasts at disaggregate levels can also be generated. It is suitable for use by both large and small organizations and does not require any prior expertise. It can also aid decision-making since some variables are endogenous in the model.
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