MARKET APPRAISAL

Steps in Market Appraisal Situational Analysis and Specification of Objectives Collection of Secondary Information Conduct of Market Survey Characterisation of the Market Methods of Demand Forecasting

Conduct of Market Survey Steps in a Sample Survey: 1)Define the Target Population 2)Select the Sampling Scheme & Sample Size 3)Develop the Questionnaire 4)Recruit and Train the Field Investigators 5)Obtain Information as Per the Questionnaire from the Sample of Respondents 6)Scrutinise the Information Gathered 7)Analyse and Interpret the Information

Characterisation of the Market Here data collected in the previous steps is collated and a detailed description of the market is formulated in terms of the following: 1)Effective demand in the past and present 2)Breakdown of demand 3)Price 4)Methods of distribution and sales promotion 5)Consumers 6)Supply and competition 7)Government policy

Methods of Demand Forecasting QUALITATIVE TIME SERIES CAUSAL METHODS PROJECTION METHODS METHODS -Jury Of Executive Opinion - Delphi Method - Trend Projection Method - Moving Average Method - Exponential Smoothing Method - Chain Ratio Method - Consumption Level Method - End Use Method - Leading Indicator Method - Econometric Method

Time Series Analysis and Projection A Time Series refers to an ordered sequence of values of a variable at equally spaced time intervals. While there are a number of techniques of time series analysis, the commonly used ones include: -Trend Projection -Moving Averages -Exponential Smoothing

Trend Projection Method Statistical trend analysis technique uses regression analysis to determine the underlying pattern of growth, stability or decline in the data. Regression Model:- Yt = a + bT Where, Yt = demand for period t T= time variable a = intercept of the relationship b = slope of the relationship

Moving Average Method The forecast is the average sales for the last ‘X’ periods, where ‘X’ is chosen so that the effects of seasonal factors on sales are eliminated. F t+1 = S t + S t-1 +….+ S t-n+1 n where F t+1 = Forecast for the next period S t = Sales for the current period n = period over which averaging is done

Exponential Smoothing Exponential Smoothing assigns exponentially decreasing weights as the observation get older i.e. recent observations are given relatively more weight in forecasting than the older observations. F t+1 = F t + α e t where F t+1 = Forecast for the next period α = smoothing parameter (lies between 0 & 1) e t = error in the forecast for year t (S t -F t )

Causal Methods A Causal method of sales forecasting involves the development and use of a forecasting model in which changes in the level of sales are the result of changes in one or more variables other than time. a)Chain Ratio Method In the chain ratio method, the potential sales is estimated through a series of questioning and segregation of aggregate demand. b) Consumption Level Method This method uses elasticity coefficients and the technique is best suited for products that are directly consumed. Income elasticity and price elasticity is often used

End Use Method Also referred to as the consumption coefficient method, it is best suited for estimating demand for intermediate products. The following steps are involved in this technique 1.Identify the possible uses of the product. 2.Define the consumption coefficient of the product for various uses. 3.Project the output levels for the consuming industries. 4.Derive the demand for the product.

Leading Indicator Method This refers to a time series whose movements precede those of the series to be predicted. These indicators, however, are best suited for forecasting changes in overall business conditions than for directly forecasting sales for individual companies. Econometric Model This approach combines economic theory with statistical methods to produce a system of interrelated regression equations for forecasting sales (or Profits). Taking time- series or cross-sectional/pooled data, causal relationships could be established between demand and other economic variables.

The dependant variable (eg. demand electricity) is expressed as a function of various economic factors. These variables could be population income per capita or value added or output (in industry or commercial sectors) price of power price(s) of alternative fuels (that could be used as substitutes) Approximations for penetration of appliances/equipment (capture technology effect in case of industries) etc.

Thus, one would have: ED = f (Y, Pi, Pj, POP, T) where, ED = electricity demand Y = output or income Pi = own price Pj = price of related fuels POP = population T = technology Several functional forms and combinations of these and other variables may have to be tried till the basic assumptions of the model are met and the relationship is found statistically significant.