Business Forecasting Definition, Steps, Modeling and Importance



Forecasting is the process of making prediction of the future based on past and present data.

In many cases a reliable forecast can be worth a lot of money, such as consistently and correctly guessing the behavior of the stock market for enough in advance to act upon such a guess.

Objectives of forecasting

In narrow sense, the objectives of forecasting is to produce better forecast. But in the broader sense, the objective is to improve organizational performance, more revenue, more profit, increased customer satisfaction etc. Better forecast by themselves are no inherent value of those forecast are ignored by management or otherwise not used to improve organizational performance.

Steps of forecasting

There are six steps in business forecasting. They are given below-

  • Identify the problem: This is the most difficult step of forecasting. Defining the problem carefully requires an understanding of the way the forecasts will be used.
  • Collect information: In this steps we collect information not data, because data may not be available if for example the forecast is aimed at a new product. The information comes essentially in two ways: the knowledge gathered by expert and from actual data.
  • Performing a preliminary analysis: An early analysis of data may tell us right away if the data usable or not. It also helps in choosing the model that best fit it.
  • Choose a forecasting model: Once all the information is collected and treated then we may choose the model that will give the best prediction possible. If we may not even have historical data then we have to use qualitative forecasting otherwise quantitative forecasting.
  • Data analysis: This step is very simple. After choosing the suitable model, run the data through it.
  • Verify model performance: Finally, we have to compare forecast to actual data.

Modeling of forecasting

The following flow-chart highlight the systematic developement of modeling and forecasting phases- 

modeling and forcasting phases

Methods of forecasting

There are various important forecasting methods in time series analysis. They are-
  • Historical analogy method
  • Field survey and opinion poll
  • Business barometers
  • Extrapolation
  • Regression analysis
  • Time series analysis
  • Exponential smoothing
  • Econometric model
  • Lead-lag analysis
  • Input-output analysis

Importance of forecasting

  • Formation of new business: Forecasting is utmost important in setting up a new business. with the help of forecasting the promoter can find out whether he can succeed in new business, whether he can face the existing competition.
  • Estimation of financial requirements: Financial estimates can be calculated in the light of probable sales and cost there of. How much capital is needed for expansion, development etc will depend upon accurate forecasting.
  • Correctness of management decision: The correctness of management decisions to a great extent depends upon accurate forecasting. The forecasting is considered as the indispensable components of business, because it helps management to take correct decisions.
  • Plan formation: The importance of correct forecasting apparent from the key role it plays in planning. Infact, planning under all circumstance and in all occassions involve a good deal of forecasting.
  • Success in business: The accurate forecasting of sales helps to produce necessary raw materials on the basis of which many business activities are undertaken. It is difficult to decide as to how much production should be done. Thus the success of a business unit depends on the accurate forecasting.
  • Complete control: Forecasting provides the information which helps in the achievement of effective control. The managers become aware of their weakness during forecasting and through implementing better effective control they can overcome these weakness.

Limitation of forecasting

An accurate forecast requires enough data to develop a good model of the phenomenon to be forecasted and depends for its accuracy upon both the accuracy of data and the assuptions inherent in the model. If either one or both of these are wrong or conditions suddenly changed in a way the model doesn't predict, the result of the forecast will usually be inaccurate.

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