NTA (UGC)-NET: Demand Forecasting
Define demand forecasting. Explain types of forecast and steps to be followed in forecasting.
Demand forecasting is a specific type of forecasting, which enables the manager to minimize elements of risk and uncertainty. The likely future event has to be given form and content in terms of projected courses of variable, i.e.. Is forecasting. The manager can conceptualize the future in definite terms. If he is concerned with future events in its order, intensity and duration, he can predict the future. If he is concerned with the course in like of future variable of future variables like demand, price or profit, he can project the future.
Types of Forecasts
- Economic And Non-Economic Forecasts: ‘SOCIAL’ technological and ‘political’ forecasts are all example of non economic forecasts, for example, one can forecast the crime rate, technological obsolescence, election result and so on:
- Micro And Macro-Forecasts: Micro-forecasts are at firm level. E. g. a demand or sales forecast. On the other hand, macro-forecasts are at the industry level or the economy level for e. g. Five year plan projections.
- Active And Passive Forecasts: If the firm extrapolates the demand of previous years to yield the likely estimated demand for the coming year, it is and example of passive forests. If the firm, on the other hand, tries to manipulate demand by changing price, product quality promotional efforts, etc. Then it is an example of active forecast.
- Conditional And Non-Conditional Forecasts: IN ‘conditional’ forecasting we estimate the likely impact of certain known or assumed changes in the independent variable on the dependant variables. ‘Non conditional’ forecasting, in contrast, requires the estimation of the change in the independent variable themselves.
- Short-Run And Long Run Forecasts: In a long term forecast, one has to consider long-term changes in population, tastes preferences of the buyers, technology, and product life cycle etc. By contrast short-run forecasting concentrates on a few selected variables, here simple techniques based o analysis of past experience and information give fairly accurate forecasts.