For businesses, it is important to be able to estimate demand. A knowledge of the shape of the demand curve for one's products can be very useful. Basic methods of demand estimation can be divided into two categories:
- Interview and experimental
For the first category, interviewers can ask about consumers' current buying habits. They can ask how those habits would change given various price changes. This method is not very reliable because it doesn't actually test the truth of the responses.
A more empirical test is an experiment. A firm can change their prices and see what happens to the quantity of goods they sell. This is not a great method either. There can be intervening variables that distort the effect of price. Also, choosing the wrong price can lose a lot of money.
The second group of methods is statistical. Here, one looks at actual statistics regarding sales at various prices. This method is limited of course by whether data are available. Someone doing this must also be very careful to think of intervening variables that may have affected various of the data points.
There are many ways to estimate something and I am sure that the ways will differ based on discipline. In other words, estimation in sales may be different than estimation is science. However, it seems to be that one principle is constant. All estimates are comparative. In other words, if you know the fact of something or a few things, then this serves as the basis for comparing other things. In this way, your estimations have some grounding. You can estimate that something is bigger, smaller, heavier, more expensive based on a reference point.
Demand estimation or demand forecast is the process of forming judgment about the quantities to a product or service that will be demanded by customers in the future. Such forecast of demand are used for planning and control of activities in production, procurement, marketing, personnel, and Finance.
Forecasting methods are classified in the following four types:
- Time series
Qualitative forecasting methods are primarily subjective: they rely on human judgment and opinion to make a forecast. They are most appropriate when there ale little historical data available or when experts have market intelligence that is critical in making forecast. Such methods may be necessary to forecast demand several years into the future in a new industry.
Time series forecasting methods use historical demand to make a forecast. They are based on the assumption that past demand history is a good indicator of future demand. These methods are most appropriate when the environment situation is stable and the basic demand pattern does not vary significantly from one year to the next. These are simplest methods to implement and can serve as a good starting point for a demand forecast. These methods fall into two basic categories
Causal forecasting methods involve assuming that that the demand forecast highly correlated with certain factors in the environment. Causal forecasting methods find this correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand.
Simulation forecasting methods imitate the consumer choices that give rise to demand to arrive at a forecast. Using simulation, a firm can combine time series and causal to answer such questions as these: What will the impact of a price promotion be? What will the impact be of a competitor opening a store nearby?