Demand Forecasting

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DEMAND FORECASTING :

DEMAND FORECASTING

PowerPoint Presentation:

DEFINATION Demand forecasting is an estimation of sales in money or physical units for a specified future period under a proposed marketing plan. We can thus define demand forecasting as the scientific and analytical estimation of demand for a product(good or service) for a particular period of time.

FEATURES :

FEATURES It is the basis of planning production program. It is an estimate or a forecast of sales in future. It depends on market planning. It tries to find out lines or profitable investment. It is done for a particular period. It tries to arrange appropriate promotional efforts, advertisement, sales etc…

USEFULNESS:

USEFULNESS To produce required quantity. To access probable demand. Sales forecasting. Control of business. Inventory control. To plan investment and employment. To help Govt. to import a export policies. Man power planning. To call for team work

PURPOSE OF SHORT-RUN FORCASTING :

PURPOSE OF SHORT-RUN FORCASTING Appropriate production scheduling. Helping the firm in reducing costs of purchase of raw materials. Determine appropriate price policy to maintain consistent sales. Forecasting short term financial requirements.

PURPOSE OF LONG-RUN FORECASTING :

PURPOSE OF LONG-RUN FORECASTING Planning of new unit or expansion of an existing unit. Planning long term financial requirements. Planning man-power require. Planning a suitable statuary to produce goods in accordance with the changing needs of society.

METHODS :

METHODS There are two methods of demand forecasting. Subjective method consumer’s opinion method - in this method buyers are asked about their future buying intentions of products. sales force method- in this method salespersons are asked about their estimated sales targets in their respective sales territories in a given period of time. Expert opinion method (Delphi method)- in this method a group of experts come to a consensus on the demand of a particular good(generally a new one).It is less expensive. Market simulation- Firms may create artificial market where consumers are instructed to shop with some money. Test marketing- in this market product is actually sold in certain segments of the market, regarded as test market.

Quantitative method :

Quantitative method trend projection method a . Secular trend -change occurring consistently over a long time and is relatively smooth in its path. b. Seasonal trend -seasonal variations of data within a year. e.g. demand for woolen, ice cream. c. Cyclic trend - cyclic movement in demand for a product that may have a tendency to recur in a few year d. Random Events -these are natural calamities, social unrest etc. Different Methods of trend projection- a. Graphical method b. Least square method

Graphical method:

Graphical method This is the simplest technique to determine the trend. All values of output or sells for different years are plotted on a graph. Year Sales 1990 30 1991 40 1992 35 1993 50 1994 45

least square method :

least square method We can find out the trend values for each of the 5 years and also for the subsequent years making use of a statistical equation, the method of Least Squares. In a time series, x denotes time and y denotes variable. With the passage of time, we need to find out the value of the variable. To calculate the trend values i.e., Yc, the regression equation used is- Yc = a+ bx. As the values of ‘a’ and ‘b’ are unknown, we can solve the following two normal equations simultaneously. (i) ∑ Y = Na + b∑x (ii) ∑XY = a∑x + b∑ x2 Where, ∑Y = Total of the original value of sales ( y) N = Number of years, ∑X = total of the deviations of the years taken from a central period. ∑XY = total of the products of the deviations of years and corresponding sales (y) ∑x2 = total of the squared deviations of X values . When the total values of X. i.e., ∑X = 0

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Year = n Sales in Rs Lakhs Y Deviation from assumed year X Square of Deviation X 2 Product sales and time Deviation XY Computed trend values Yc 1990 30 -2 4 -60 32 1991 40 -1 1 -40 36 1992 35 0 0 0 40 1993 50 1 1 50 44 1994 45 2 4 90 48 N=5 ∑Y=200 ∑X=0 ∑x2=10 ∑XY=40 CALCULATION Regression equation = Yc = a + bx To find the value of a = ∑Y/N = 200/5 = 40 To find out the value of b = XY/ ∑x2 = 40/10 = 4 EXAMPLE

PowerPoint Presentation:

For 1990 Y = 40+(4*-2) Y = 40-8= 32 For 1991 Y = 40+(4*-1) Y = 40-4= 36 For 1992 Y = 40+(4*0) Y = 40+0 = 40 For 1993 Y = 40+(4*1) Y = 40+4 = 44 For 1994 Y = 40+(4*2) Y = 40+8 = 48 For the next two years, the estimated sales would be: For 1995 Y = 40+(4*3) Y = 40+12 = 52 For 1996 Y = 40+(4*4) Y = 40+16 = 56

PowerPoint Presentation:

Barometric techniques In barometric forecasting we construct an index of a relevant economic indicator and forecast future trends on the basis of these indicators. Regression analysis Regression analysis relates a dependent variable to one or more independent variable in the form of linear equation. Y = a+bx Where , y indicates future demand a indicates fixed demand b indicates rate of change of demand x indicates value of related variables like price,income of consumer,price of related commodity etc.

limitations of demand forecasting:

limitations of demand forecasting Change in fashion- it is an inevitable consequence of advancement of civilisation.Results of demand forecasting have short lasting impacts especially in a dynamic business environment Consumers’ psychology - results of forecasting depend largely on consumers’ psychology, understanding which itself is difficult. Uneconomical - forecasting requires collection of data in huge volumes and their analysis, which may be too expensive for small firms to efforts. Lack of experts - accurate forecasting necessitates experienced experts who may not be easily available. Lack of past data - demand forecasting requires past sales data, which may not be correctly available.

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