production analysis ppt
DESCRIPTION
TRANSCRIPT
PRODUCTION ANALYSIS
Submitted by:-Urvashi Bhat ID no. 05Dixita Chotalia ID no. 12Vibha Jatav ID no. 24Poonam Nangia ID no. 40Kamal Panchal ID no. 42
INTRODUCTION
The topic has been selected to understand the theoretical
aspect of production analysis in a real company.
Major objectives
To study the production function of Tata steel.
To measure the correlation of volume of output with
labour as well as capital employed.
To study whether the production function of Tata steel is
labour intensive or capital intensive.
PRODUCTION ANALYSIS
Production refers to the transformation of inputs or resources into outputs of goods and services.
Production function is an equation showing the maximum output that a firm can produce per period of time with each set of inputs.
Thus the general equation of this simple production function is :-
Q = f (L, K) Q=no. of comodity produced L=no. of workers employed K=amount of capital employed
Cobb Douglas Production Function
Empirical estimation is the power function of the form
Q = AKa Lb,
Q = total production (the monetary value of all goods produced in a year)
L = labour input
K = capital input
A = total factor productivity
a and b are the output elasticity of labor and capital, respectively.
Properties of Cobb Douglas production Function
The exponents of K and L (i.e. a and b) represent output elasticity of capital and labour respectively.
Sum of exponents measures the returns to scale.
It can be estimated by regression analysis
ln Q = ln A + alnK + blnL
It can easily be extended to deal with more than two inputs.
Assumptions
If either labour or capital vanishes, then so will production.
The marginal productivity of labor is proportional to the amount of production per unit of labor.
The marginal productivity of capital is proportional to the amount of production per unit of Capital.
Difficulties If the firm produces a number of different products, the
output may have to be measured in monetary rather in physical units.
Only the capital consumed in the production should be counted ideally.
A time trend is usually included to take into consideration technological changes over time.
Neither Cobb nor Douglas provided any theoretical reason why the coefficients a and b should be constant over time
REGRESSION ANALYSIS
The statistical tool with the help of which we can estimate the unknown value of dependent variable from the known value of independent variable is called regression.
In economics it is the basic technique for measuring or estimating the relationship among economic variable that constitute the essence of economic theory and economic life
The line describing the tendency to regress is called regression line.
• Regression analysis can be of two types.
Simple regression analysis
Multiple regression analysis
Simple regression analysis is used for estimating the value of dependent variable from the independent variable, when there
is only one independent variable.
Y is the estimation of the dependent variable Y from the independent variable X. a and b are estimation parameters
Multiple regression analysis is used for estimating the value of dependent variable from the independent variable, when there is more than one independent variable.
The a coefficient is vertical intercept and gives the value of Y when value of X1 and X2 = 0.b1 and b2 are slope coefficient, they measure the change in Y per unit change in X1 and X2.
CORRELATION ANALYSIS
The correlation is one of the most useful statistic, a correlation is a single number that describe the degree of relationship between two variables.
Like all statistical technique, correlation is only appropriate for certain kinds of data. Correlation works for data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such as gender, brands purchased or favorite color.
• It ranges from -1.0 to +1.0. The closer r is to +1 to-1, the more closely the two variables are related.
If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an “inverse “correlation).
DATA COLLECTION
TATA STEEL
year capital
employed (K)
in cr.
salary wages
(L) in cr.
volume of output
(Q) in cr.
2002 7411.49 1023.46 8736.35
2003 7898.07 1095.63 10782.7
2004 9799.62 1059.46 14782.71
2005 12271.45 1180 15240.32
2006 23741.49 1236.32 17540.86
2007 45328.07 1390.69 19691.26
2008 57122.68 2615.06 24637.79
2009 62201.14 2074.25 24805.88
SUMMARY STATISTICS
Measures Ln (K)-x1
capital
Ln (L)-x2
labour
Ln (Q)-y
Volume of
prod.
Mean 9.909754549 7.229082567 9.685619
Median 9.745005067 7.096582111 9.701994
Standard
Deviation 0.901075017 0.343045125 0.370419
Sample Variance 0.811936187 0.117679958 0.13721
DATA ANALYSIS & INTERPRETATION
2002 2003 2004 2005 2006 2007 2008 20090
10000
20000
30000
40000
50000
60000
70000
capital employed(K) salary wages(L) volume of output(Q)
year
capi
tal,
ouro
utpu
t &
lab
our
RESULT OF CORRELATION
Correlation between volume of output and labour
employed0.8667
Correlation between volume of output and capital
employed0.942861
RESULT OF REGRESSIONSUMMARY OUTPUT
Regression StatisticsMultiple R 0.935476306R Square 0.875115919
Adjusted R Square 0.825162286
Standard Error 0.154885515Observations 8
ANOVA Df SS MS F Significance F
Regression 2 0.840523996 0.420261998 17.51856424 0.005511473Residual 5 0.119947614 0.023989523Total 7 0.96047161
CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%
Intercept 5.510675639 1.533738734 3.592968944 0.015658925 1.568074711 9.453276567Ln(K)-x1 0.352872931 0.135031279 2.613268081 0.047482013 0.005763978 0.699981884
Ln(L)-x2 0.093796003 0.354686026 0.264447979 0.80198931 0.817953452 1.005545457
From the above regression result the production function of the
Tata steel is as follow
From the above production function of Tata steel it is interpreted
that the volume of production is 5.5107 (Intercept).which mean,
even if no labour or capital is employed in the production. If the
company increases the capital employed by 1 the volume of
production is increased by 0.3529 assume that labour is constant,
and if the company increases labour employed by 1 unit the
volume of production will increase by 0.0938 assume that capital
is constant.
CONCLUSION
From the research of the production analysis of the Tata steel
it is concluded that the production is capital intensive rather
than the labour intensive.
Company can increase its production by employee more unit
of capital instead of the labour.
As the correlation analysis also said that the volume of output
is more related to capital than labour and regression analysis
gives the same result that increase in capital leads to increase
in volume of output more than increase in labour.
SUMMARY
Regression tool has been used to estimate the production function of the Tata steel from the historical data of the last 8 years which has been collected through the secondary data collection method from the official site of the company
To perform regression analysis the collected data has been transformed in to the logarithm form, than the regression analysis is performed which gives the following production function of the Tata steel
From the above result of the production function it is to conclude that the production of the Tata steel is capital intensive rather than the labour intensive.
The production function of the Tata steel through the regression analysis which is helpful to understand that the production is capital intensive and it can also important to estimate the volume of production for the coming years.
BIBLIOGRAPHYBOOKS:
Dominik Salvatore. (2008). managerial economics. new delhi: oxford university press.
S.P.Gupta and M.P.Gupta. (2005). business statistics. new delhi: sultan chand & sons.
P.L. mehta. (1999). managerial economics. new delhi.sultan chand & sons.
WEBSITE:
http://www.tatasteel.com/investors/performance/annual-report.asp
http://www.moneycontrol.com/financials/tatasteel/balance-sheet/TIS
THANK YOU