Demand Forecasting means estimating of future demand depending on the present trend of demand parameter. The manager is interested to know what will be future demand of the product against a particular price or against a particular year of business activity. Demand Forecasting is highly relevant in the modern management decision making process.
Significance of Demand Forecasting in Managerial Economics –
1.
The financial manager is interested to know what will be the future requirement of the project in order to fulfil future demand which is derived from the result of demand forecasting model. It is observed that demand forecasting model is accepted as econometric model where we get 3 components, i.e.,
(1) Knowledge in economic principle
(2) Statistical technique
(3) Mathematical knowledge
The financial manger can identify the rational source of finance in the future from which adequate finance can be obtained at minimum rate of interest. This is possible if future demand can be forecasted at present depending on the existing trend of demand parameter.
2.
The personal manager is interested to know how much labour can be employed in the project in the future in order to fulfil future demand requirement derived from the results of demand forecasting module.
3.
The strategic manager can select the best strategy of production for the future and this can be done depending on the results of demand forecasting model.
There are two techniques of production – Labour Intensive Technique and Capital Intensive Technique. In the Labour Intensive Technique we observe that rate of employment of labour is always more than rate of employment of capital and therefore in this technique of production labour output ratio is always higher. Similarly in the Capital Intensive Technique rate of employment of capital is always more than rate of employment of labour and therefore in this technique of production capital output ratio is always higher. The manager has to select the best strategy of production in order to fulfil future demand requirement derived from the results of demand forecasting model.
Techniques of Demand Forecasting Model:
(1) Demand-Price Technique, and
(2) Demand-Time Technique.
(1) Demand-Price Technique
In this technique of demand forecasting the hypothesis is that y = f (x)
where y = forecasted demand, x = forecasted price and both the variables are inversely related.
In this forecasting model the manager is considering the time series data regarding demand and price where we get several combinations between demand and price as in this model the manager is considering two economic parameters, i.e., demand and price and therefore the manager has to consider two normal equations in order to get the values of two unknowns in the model.
The normal equations are,
∑y = Na + b ∑x .... (i)
∑xy = a∑x + b∑x2 .... (ii)
where,
y = demand for the product (dependent variable)
N = number of combinations between demand and price
a = technological product (constant)
b = rate of change in demand parameter
x = price of the product (independent variable)
By solving these normal equations, we get the value of 2 unknowns, i.e., ‘a’ and ‘b’ and then we put the values of ‘a’ and ‘b’ on the linear trend model, i.e., y = a + bx
where,
y = forecasted demand (dependent variable)
x = forecasted price (independent variable)
(2) Demand-Time Technique
In this technique of demand forecasting the manager is considering two parameters, i.e., Deamand and Time and both the parameters are directly relate. The hypothesis is y = f (x)
where,
y = forecasted demand (dependent variable)
x = forecasted time (independent variable) and both the variables are directly related.
In this econometric model the manger is considering two parameters, i.e., Demand and Time and therefore the manager has to take 2 normal equations in order to get values of 2 unknowns.
The normal equations are,
∑y = Na + b ∑x .... (i)
∑xy = a∑x + b∑x2 .... (ii)
where,
y = demand for the product (dependent variable)
N = number of years taken in the account
a = technological product (constant)
b = rate of change in demand parameter
x = time parameter (independent variable)
By solving these normal equations, we get the value of 2 unknowns, i.e., ‘a’ and ‘b’ and then we put the values of ‘a’ and ‘b’ on the linear trend model, i.e., y = a + bx
where,
y = forecasted demand (dependent variable)
x = forecasted time (independent variable)
(and both the variables are directly related)
Significance of Demand Forecasting in Managerial Economics –
1.
The financial manager is interested to know what will be the future requirement of the project in order to fulfil future demand which is derived from the result of demand forecasting model. It is observed that demand forecasting model is accepted as econometric model where we get 3 components, i.e.,
(1) Knowledge in economic principle
(2) Statistical technique
(3) Mathematical knowledge
The financial manger can identify the rational source of finance in the future from which adequate finance can be obtained at minimum rate of interest. This is possible if future demand can be forecasted at present depending on the existing trend of demand parameter.
2.
The personal manager is interested to know how much labour can be employed in the project in the future in order to fulfil future demand requirement derived from the results of demand forecasting module.
3.
The strategic manager can select the best strategy of production for the future and this can be done depending on the results of demand forecasting model.
There are two techniques of production – Labour Intensive Technique and Capital Intensive Technique. In the Labour Intensive Technique we observe that rate of employment of labour is always more than rate of employment of capital and therefore in this technique of production labour output ratio is always higher. Similarly in the Capital Intensive Technique rate of employment of capital is always more than rate of employment of labour and therefore in this technique of production capital output ratio is always higher. The manager has to select the best strategy of production in order to fulfil future demand requirement derived from the results of demand forecasting model.
Techniques of Demand Forecasting Model:
(1) Demand-Price Technique, and
(2) Demand-Time Technique.
(1) Demand-Price Technique
In this technique of demand forecasting the hypothesis is that y = f (x)
where y = forecasted demand, x = forecasted price and both the variables are inversely related.
In this forecasting model the manager is considering the time series data regarding demand and price where we get several combinations between demand and price as in this model the manager is considering two economic parameters, i.e., demand and price and therefore the manager has to consider two normal equations in order to get the values of two unknowns in the model.
The normal equations are,
∑y = Na + b ∑x .... (i)
∑xy = a∑x + b∑x2 .... (ii)
where,
y = demand for the product (dependent variable)
N = number of combinations between demand and price
a = technological product (constant)
b = rate of change in demand parameter
x = price of the product (independent variable)
By solving these normal equations, we get the value of 2 unknowns, i.e., ‘a’ and ‘b’ and then we put the values of ‘a’ and ‘b’ on the linear trend model, i.e., y = a + bx
where,
y = forecasted demand (dependent variable)
x = forecasted price (independent variable)
(2) Demand-Time Technique
In this technique of demand forecasting the manager is considering two parameters, i.e., Deamand and Time and both the parameters are directly relate. The hypothesis is y = f (x)
where,
y = forecasted demand (dependent variable)
x = forecasted time (independent variable) and both the variables are directly related.
In this econometric model the manger is considering two parameters, i.e., Demand and Time and therefore the manager has to take 2 normal equations in order to get values of 2 unknowns.
The normal equations are,
∑y = Na + b ∑x .... (i)
∑xy = a∑x + b∑x2 .... (ii)
where,
y = demand for the product (dependent variable)
N = number of years taken in the account
a = technological product (constant)
b = rate of change in demand parameter
x = time parameter (independent variable)
By solving these normal equations, we get the value of 2 unknowns, i.e., ‘a’ and ‘b’ and then we put the values of ‘a’ and ‘b’ on the linear trend model, i.e., y = a + bx
where,
y = forecasted demand (dependent variable)
x = forecasted time (independent variable)
(and both the variables are directly related)