Demand forecasting is the scientific and analytical estimation of the demand for a product or service in a particular period of time. It is an art or science of predicting or estimating the future demand for a product, undertaken for the purpose of long-term decision-making and planning.
Demand forecasting is very popular in industrially advanced countries. It is bound to become more important with growing industrialization, and will become necessary for sound planning.
It lays the foundation for operation planning, scheduling, production planning, inventory management, and other production and operation functions. Long-term planning forms the framework for corporate investment planning, capital management, expansion, capacity planning, research, and executive development.
To obtain an accurate forecast, it is essential to check the accuracy of past forecasts against present performance. And of present forecasts against future performance. Additionally, the executive making the forecast should have a good understanding of and confidence in the techniques used. Understanding is also necessary for the proper interpretation of the results.
Image 1: forecasting demand lays the foundation for operational planning and inventory management
Flexibility
Flexibility can be viewed as an alternative to generality when a long-lasting function could be set up in terms of basic natural forces and human motives—a set of variables whose coefficient could be adjusted from time to time to meet changing conditions in a more practical way to keep the routine procedure of forecasting intact.
The immediate availability of data is a vital requirement, and the search for reasonable approximations to relevance in late data is a constant strain on the forecaster's patience. The techniques employed should be able to produce meaningful results quickly. Furthermore, delays in the results will adversely affect the managerial decisions.
Cost is a primary consideration which should weigh against the importance of the forecasts to the business operations. Statistical and econometric models are certainly useful, but they are intolerably complex. To those executives who have a fear of mathematics, these methods may as well be Latin or Greek. The procedure should, therefore, be simple and easy so that the management can appreciate and understand why the forecaster has adopted it.
The forecaster has to deal with various independent components. If he or she does not make an adjustment in one component to bring it in line with a forecast of another, the whole will not appear consistent.
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Cor Tiemens
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Jeff Roybal
Kevin Davis
