Forecasting Models

One can classify the various models available for forecasting into three categories:

  • Extrapolative models: They make use of past data and essentially prepare future estimates by some methods of extrapolating the past data. For example, the demand for soft drinks in a city or a locality could be estimated as 110 percent of the average sales during the last three months. Similarly, the sale of new garments during the festive season could be estimated to be a percentage of the festive season sales during the previous year.
  • Casual models: It analyses data from the point view of cause-effect relationship. For instance, to the process of estimating the demand for the new houses, the model will identify the factors that could influence the demand for the new houses and establish the relationship between these factors. The factors, for example, may include real estate prices, housing finance options, disposable income of families, and cost of construction and befits derived from tax laws. Once tea relationship between these variables and the demand is established, it is possible to use it for estimating the demand for new houses.
  • Subjective judgments: Another set of models consist of subjective judgment using qualitative data. In some cases, it could be based on quantitative and qualitative data. In several of these methods special mechanisms incorporated to draw substantially from the expertise of group of senior managers using some collective decision making framework.

Selection of a forecasting technique: The selection of a forecasting technique depends on the following three factors:

  • The characteristics of the decision making situation, which include: (i) The time horizon (ii) Level of detail (iii) Number of items (iv) Control versus planning
  • The characteristics of the forecasting methods: (i) the time horizon (number of periods for which forecasting required) (ii) The pattern of data (horizontal, seasonal trend etc.) (iii) Type of model( casual, time series or sta6tistical) (iv) Cost (v) Accuracy (vi) Ease of application
  • Present situation which includes: (i) The item that is being forecast (ii) Amount of historical data available (iii) Time allowed for preparing forecast.

Although there are the below mentioned forecasting models we shall be concentrating on Weighted moving averages model.

  • Weighted moving averages
  • Casual forecasting model
  • Linear regression analysis
  • Multiple regression analysis

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