Demand Forecasting Methods

DEMAND FORECASTING METHODS

There are several assumptions about forecasting:

1.   There is no way to state what the future will be with complete certainty. Regardless of the methods that we use there will always be an element of uncertainty until the forecast horizon has come to pass.

2.     There will always be blind spots in forecasts. We cannot, for example, forecast completely new technologies for which there are no existing paradigms.

3. Providing forecasts to policy-makers will help them formulate social policy. The new social policy, in turn, will affect the future, thus changing the accuracy of the forecast.

i.   OPINION POLLING METHODS

a.  EXPERTS OPINION METHOD

Genius forecasting - This method is based on a combination of intuition, insight, and luck. Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts are based exclusively on intuition. Science fiction writers have sometimes described new technologies with uncanny accuracy

b. CONSUMER ‘S SURVEY METHOD

In this method consumer‘s are contacted personally to disclose their future plans

so that we can able to forecast the future because they are ultimate targeters/buyers

c.      COMPLETE ENUMERATION SURVEY

Here all the units of consumers are taken into account without any cutshorts

So here large number of consumers will be there to get the unbiased information .The main

Advantage of this method is its accuracy and its main drawback is it is time consuming one.

d. SURVEY METHOD

Here from the total population certain number of units will be selected as sample units, then the opinion collection will be made. This method is less tedious and less costly than the above method.

ii.  STATISTICAL METHODS

Fitting trend line by observation

This method of estimating trend is elementary,easy and quick.It involves merely plotting of annual sales on graph and then estimating just by observation where the trend line lies.

Trend extrapolation - These methods examine trends and cycles in historical data, and then use mathematical techniques to extrapolate to the future. The assumption of all these techniques is that the forces responsible for creating the past, will continue to operate in the future. This is often a valid assumption when forecasting short term horizons, but it falls short when creating medium and long term forecasts. The further out we attempt to forecast, the less certain we become of the forecast

b.     Simulation methods - Simulation methods involve using analogs to model complex systems. These analogs can take on several forms. A mechanical analog might be a wind tunnel for modeling aircraft performance. An equation to predict an economic measure would be a mathematical analog. A metaphorical analog could involve using the growth of a bacteria colony to describe human population growth. Game analogs are used where the interactions of the players are symbolic of social interactions

c.      Trend Analysis: Uses linear and nonlinear regression with time as the explanatory variable, it is used where pattern over time have a long-term trend. Unlike most time-series forecasting techniques, the Trend Analysis does not assume the condition of equally spaced time series.

d.     Simple Moving Averages: The best-known forecasting methods is the moving averages or simply takes a certain number of past periods and add them together; then divide by the number of periods. Simple Moving Averages (MA) is effective and efficient approach provided the time series is stationary in both mean and variance. The following formula is used in finding the moving average of order n, MA(n) for a period t+1,

e.      Exponential Smoothing Techniques: One of the most successful forecasting methods is the exponential smoothing (ES) techniques. Moreover, it can be modified efficiently to use effectively for time series with seasonal patterns. It is also easy to adjust for past errors-easy to prepare follow-on forecasts, ideal for situations where many forecasts must be prepared, several different forms are used depending on presence of trend or cyclical variations. In short, an ES is an averaging technique that uses unequal weights; however, the weights applied to past observations decline in an exponential manner

f.       Least-Squares Method: To predict the mean y-value for a given x-value, we need a line which passes through the mean value of both x and y and which minimizes the sum of the distance between each of the points and the predictive line. Such an approach should result in a line which we can call a "best fit" to the sample data. The least-squares method achieves this result by calculating the minimum average squared deviations between the sample y points and the estimated line. A procedure is used for finding the values of a and b which reduces to the solution of simultaneous linear equations. Shortcut formulas have been developed as an alternative to the solution of simultaneous equations..

g.     Regression and Moving Average: When a time series is not a straight line one may use the moving average (MA) and break-up the time series into several intervals with common straight line with positive trends to achieve linearity for the whole time series. The process involves transformation based on slope and then a moving average within that interval. For most business time series, one the following transformations might be effective.