Forecasting Parameters of a firm (input, output and products) Concept Forecasting process Techniques Fundamentals of forecasting Statistics and graphs

Concept Forecast is the estimation of future value of a variable based on the analysis of time series data. The outcomes are called “forecasts”. The point of time when the forecast is made is called the “base”. The range covers the period between the base and the forecast. In strategic planning activities such as inventory purchasing, harvest planning, consumption of demand of certain products (say plywood, roundwood) forecasts are made.

Forecasting process Collection of relevant data Study the pattern of the data Develop a model for forecasting Apply the model to past data (ex-post) Test the accuracy of model by examining the ex-post discrepancy If the model fits into the confidence level, use the model to forecast future Check the accuracy of model with the actual figures If adequate, re-examine data patterns and choose other options of forecasting

Types of forecasting Long term – covering 5-10 years or more Example trend projection Short term – immediate future – period ranging from few months to 2 years Example prediction

Qualities of forecast What are the concerns Time period relevance Level of aggregation Available budget Relevance of historical data to future Consequences of faulty forecast

Forecasting techniques 1. qualitative versus quantitative 2. Judgment versus Scientific 3 Auto regressive versus casual Autoregressive: if historical data are available and variable of interest appears to be function of time then models like averaging, smoothing and linear regression can be used. Casual: If historical data available and variable of interest appears to be function of something else of time then models like multiple regression and economic models can be used.

Fundamentals of forecasting 1. Forecasts are not always accurate 2. Forecast for the near term tend to be more realistic 3. Forecasts for groups of products or services tend to be more accurate 4. Forecasts are not substitutes for calculated values.

Forecasting is only a method of reducing the future uncertainty but not ensure certainty. Hence, forecasting is a systematic method of obtain an estimate of the future value of a variable based on an analysis of observations on past behavior. It is important if the firm is engaged on a large scale production that involves a long gestation period. In such circumstances firms have to rely on the estimated future demand of their product.

Methods of forecasting Economic knowledge and experiences play an important role in forecasting. 1. Time lag forecasting: Correlation methods are commonly used. For example deficit financing may lead to inflationary pressure, the rise in price may result fall in demand and so on. 2. Extrapolation and forecasting from the trend: It is based on the extrapolation of continuity of the past pattern into future. 3. Long term trend projection: if a trend has been fitted to a given time series, extrapolation can be used to study the future movement.

Linear projection: y = a + bx Exponential forms y = a + bx + c x2 4. Short term trend projection: if the time is less than one year, moving average method can be used. 5. Graphical methods: Free hand line is drawn over the years. This method shows the trend but not the actual quantity.

Regression method Regression analysis is the most frequently employed method of estimating future values. This method combines the economic theory and statistical techniques of estimation. Economic method is employed to specify the determinants that afect the value of output in question. Statistical techniques are employed in making estimates .

Steps: 1. Specify the variable that are supposed to affect the values of output in question. 2. Collect time series data on the independent variables 3. specify equation that appropriately describe the nature and extent of relationship ESTIMATE parameters in the chosen equation.

Least square method: Under this method, a trend line is given by the trend equation that is fitted to the time series data with the aid of statistical techniques. Once the parameters of the equations are estimated it becomes easy to forecast future value. This method is quite popular in business forecasting because the analysis require only working knowledge of statistics.