OPERATIONAL ANALYSIS OF A SELECT SPINNING MILL
ABSTRACT
The Indian Textile Industry is the second largest in
the world, next to Chinese and is one of the largest foreign exchange
earners for the country. Textile is a key contributor to GDP to the
order of 4%. The textile sector employs over 20 million people and is
the second largest employment generator. Textile businesses are also
affected by the global melt down. The industry in India is experiencing
an increase in the collaboration between national and international
companies. International apparel companies like Hugo Boss, Liz
Claiborne, Diesel, Ahlstorm, Kanz, Baird McNutt, etc have already
started their operations in India and these companies are trying to
increase it to a considerable level.
National and the international companies that are
involved in collaborations include Rajasthan Spinning & Weaving
Mills, Armani, Raymond, Levi Strauss, De Witte Lietaer, Barbara, Jockey,
Vardhman Group, Gokaldas, Vincenzo Zucchi, Arvind brands, Benetton,
Esprit, Marzotto, Welspun, etc. Therefore, it is the right time to
concentrate on operational cost to compete with the global leaders by
concentrating on the world class quality products. An empirical study is
made on the cost aspects of a textile mills discloses the possibility
of cost reduction and improvement for profitability. This paper presents
proactive aspects and relationship of the production with raw material
consumption and yield .Cobb Douglas production function has been used to
find the behavour of costs with the production per spindle shift. It
also presents suggestions for improving productivity and profitability.
1. INTRODUCTION
The Indian textile industry, until the economic
liberalization of Indian economy was predominantly an unorganized
industry. The economic liberalization of Indian economy in the early
1990s led to stupendous growth of this Indian industry. Now Textile
industry is the most prominent industry in India as it supplies cloth to
the populations. It also assists for the survival of other small scale
industries. Textile industry has shown its major growth in the post
quota regime under the WTO agreement. Textile accounts for 14% of the
total Industrial production and contributes nearly 30% of the total
exports. Textile is a key contributor to GDP to the order of 4%. The
intense global competition in textiles has stimulated cost-cutting
measures and new investments that have significantly increased the
efficiency of transforming cotton fiber into yarn. Continuous
improvement in production, waste reduction and productivity would
automatically lead of success of the enterprise.
The Spinning Industry in India is on set to hit the
global market with other fabrics as well like the cotton textiles with
its enthusiasm and consistency in work. It has already reached a
phenomenal status in India by beating the obstacles that caused a
downfall since past few years and is now on its way to cover a wider
area in the spinning sector.
2. STATEMENT OF THE PROBLEM
Indian Spinning Industry has gone from strength to
strength since a very long time now as it was the hub of cotton
manufacturing. Cotton is not only consumed to the highest extent in
India but it has also become one of the most profitable textiles in the
export industry. The productivity in case of spinning mill is confined
to gms per spindle shift. An elaborate and detailed assessment is made
on various sectors of the yarn spinning such as, production,
consumption, and materials. Everyone should try to get increased
production at the least possible cost.
For this purpose it is necessary to find the
behaviour of the production, raw material and yield .Tuning this well
would enhance the productivity. On the other hand the costs associated
with the production should also be evaluated to achieve the production
with the least possible costs. It is necessary for everyone to improve
productivity as well as profitability for their survival in the global
economic crisis. It is right time to find solution to this type of
problems.
3. OBJECTIVES OF STUDY
The following may be taken as the objectives of the study:
3.1.To find the relationship between production and raw material consumption and yield.
3.2.To find the association between Spinning production and costs.
3.3.To predict the spinning production using Cobb Douglas production function.
3.4.To offer plausible suggestions for improvement in cost and efficiency.
4. REVIEW OF LITERATURE
1. Imran Sharif Chaudhry et al (2009) have made study on “Factors Affecting Cotton Production in Pakistan: Empirical Evidence from Multan District”. They examined the factors
affecting cotton production. In that study Cobb-Douglas
Production Function was used to assess the effects of various inputs
like cultivation, seed and sowing, irrigation, fertilizer, plant
production and labour cost on yield.
2 . Moosup Jung, et al (2008) made a study on” Total Factor Productivity of Korean Firms and Catching up with the Japanese firms “.They measured and compared the TFP of both
Korean and Japanese listed firms of 1984 to 2004.They
used the Chain Linked Index Number method developed by Good et.
al.(1999). They found that the average TFP of Korean firms grew about
44.1% between 1984 and 2005, with 2.1% annual growth rates. Industry was
observed to be outstanding.
3. Danish A. Hashim made research on” Cost and Productivity in Indian Textiles” for
Indian Council for Research on International Economic
Relations. His observations and findings are: There is an inverse
relationship between the unit cost and productivity: Industry and
States, which witnessed higher productivity (growth) experienced lower
unit cost (growth) and vice- versa. Better capacity utilization,
reductions in Nominal Rate of Protection and increased availability of
electricity are found to be favourably affecting the productivity in all
the three industries.
4. Gokhale,
G S (1992) an important factor that affects material productivity is
the quality of cotton that is used to produce a particular kind of yarn
or cloth. Using too good a cotton variety would contribute to excessive
cost, but using cotton that is not good enough would mean increased
breakage, a heavier work load for the worker, who consequently can only
attend to a lesser number of machine units. The material productivity is
influenced by a number of factors such as quality of material used,
type of technology used, level of maintenance and life of machinery,
count produced and the like.
5. Productivity
also depends upon such factors as layout of machines, mechanical
transport for material handling and machine maintenance. As a result of
all these factors, productivity of the worker is largely governed by a
proper machine allocation. This can easily be determined by work-study.
6. SITRA (1998) stated that the
size of the mills decides the volume of business and also the economic
viability of the business unit. Selection of suitable size is important
for smooth conduct of business, over-capacity as well as under- capacity would bring pressure on the business.
5. SIGNIFICANCE OF THE STUDY
The new textile policy is to be seen in the backdrop of fast changing international scenario in the post-.GATT
era and its implications on Indian textile sector. Textile and clothing
have been brought within the framework of GATT in the Uruguay round of
the multilateral trade negotiations. The first impact of this
negotiation would be felt by the Indian textile industry after the
expiry of Multi Fiber Agreement (MFA) on December 31, 2004. Expiry of
MFA would have implications on both inter-national as well
as domestic markets. The Indian textile industry has to be globally
competitive to be able to sustain its presence not only in the
international market but also in the domestic.
On the other side technological capability is
embodied in the human and physical capital in command of the industry.
In a dynamic sense it means the ability of the industry to articulate
its business problems in terms of technology and also the ability to
access human, physical, financial and organizational resources to find
solutions to the articulated technological problems.
6. METHODOLOGY:
Comprehensive research work was done to achieve the objectives of the study. Ten year data - 1998-99 to 2007-08
of Sambandam Spinning Mills Salem has been employed for this study. In
the first step the association between productions, raw material
consumption and yield are arrived by employing the multiple regressions.
In the next step the relationship between spinning production per
spindle shift and related costs per spindle shifts are arrived by using
the Cobb Douglass production function. Based on this spinning production
per spindle shifts are estimated and the production achievements are
compared between the two units of the same company.
Statistical tools such as Regressions, Analysis of variance technique has been employed to the test the hypotheses.
6.1. DATA ANALYSIS:
The data related to the two units of SSM Ltd has been
analyzed towards its production and operational costs. The following
table shows the production, raw material consumption and yield of SSM
Ltd Unit-1 and Unit-2 for 10 years from 1998-99 to 2007-08.
Table No: 1 Production, Raw Material Consumption and Yield
SSM LTD
|
SSM LTD
| |||||||
Raw-
| ||||||||
material
| ||||||||
Production
|
Raw material
|
Production
|
Consumed
| |||||
Consumed
|
Yield
|
Yield
| ||||||
Kgs)
|
(Ratio)
|
Kgs)
|
Kgs)
|
(Ratio)
| ||||
20.33
|
26.38
|
0.77
|
21.58
|
32.18
|
0.67
| |||
19.27
|
26.60
|
0.72
|
19.54
|
30.49
|
0.64
| |||
19.19
|
26.12
|
0.73
|
18.56
|
29.02
|
0.64
| |||
19.19
|
26.12
|
0.73
|
18.08
|
28.31
|
0.64
| |||
18.00
|
24.38
|
0.74
|
19.41
|
28.08
|
0.69
| |||
17.52
|
22.90
|
0.77
|
25.10
|
36.18
|
0.69
| |||
20.91
|
26.45
|
0.79
|
23.90
|
36.97
|
0.65
| |||
20.98
|
27.03
|
0.78
|
24.35
|
37.55
|
0.65
| |||
19.25
|
30.63
|
0.63
|
27.65
|
41.78
|
0.66
| |||
26.55
|
40.28
|
0.66
|
29.59
|
45.09
|
0.66
| |||
The following hypotheses have been framed to test the relationship between production and costs of the two units.
HYPOTHESIS.1:
THERE IS NO SIGNIFICANT RELATIONSHIP BETWEEN YARN PRODUCTION, RAW MATERIAL CONSUMPTION AND YIELD RATIO OF SSM LTD UNIT-1.
To study the significant relationship between the
yarn production, raw material consumption and yield ratio multiple
regression analysis is employed.. Here we take the yarn production as
dependent variable and other factors as independent variables. The
following table shows the results of fitting a multiple linear
regression model:
Table .2 | ||||||||||||
Coefficients:
| ||||||||||||
Standard
|
T
| |||
Parameter
|
Estimate
|
Error
|
Statistic
| |
CONSTANT
|
1.76891
|
0.0000
| ||
Raw material consumption
|
0.673339
|
0.0197851
|
34.0326
|
0.0000
|
Yield
|
27.1733
|
1.84076
|
14.762
|
0.0000
|
The fitted multiple regression models involving the explanatory variables are given
below:
PRODUCTION = -18.4159 + 0.673339*RAW MATERIAL CONSUMPTION + 27.1733*YIELD.
From the model it is observed that there is a
positive relationship between yarn production and raw material
consumption. It shows that yarn production would increase by 0.67 units,
if the raw material consumption increases by 1 unit assuming the yield
remains the same.
It also shows that there is a positive relationship
between yield and yarn production. That is the yarn production would
increase by 27 units if the yield increases by 1 unit assuming the
consumption remains the same.
The validity of the model has been tested by ANOVA. The output of the ANOVA is presented as :
Table No.3 - Analysis of Variance
Source
|
Sum of Squares
|
Df
|
Mean Square
| ||
Model
|
56.9084
|
2
|
28.4542
|
646.83
|
0.0000
|
Residual
|
0.307931
|
7
|
0.0439901
| ||
Total (Corr.)
|
57.2163
|
9
|
Since the P-value in the ANOVA table is
less than 0.05, there is a statistically significant relationship
between the variables at the 95.0% or higher confidence level. Hence
fitted model is the most suitable model to describe the relationships of
the variables.
Table.4. Related Statistics
R2
|
Standard
|
Mean Absolute Error
| |
Error of Estimate
| |||
99.4618 percent
|
0.209738
|
0.136684
|
1.30309(P=0.0942)
|
The R-Squared statistic indicates that
the model as fitted explains 99.4618%of the variability in production.
The standard error of the estimate shows the standard deviation of the
residuals to be 0.209738.
The mean absolute error (MAE) of 0.136684is the average value of the residuals. The Durbin-Watson
(DW) statistic tests the residuals to determine if there is any
significant correlation based on the order in which they occur. Since
the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level.
HYPOTHESIS.2:
THERE IS NO SIGNIFICANT REATIONSHIP BETWEEN YARN PRODUCTION, RAW MATERIAL CONSUMPTION AND YIELD RATIO OF SSM LTD UNIT-2
To study the Significant Relationship between the
Yarn Productions, Raw Material Consumption and yield ratio multiple
regression analysis is employed. Here we take the yarn production as
dependent variable and other factors as independent variables. The
following table shows the results of fitting a multiple linear
regression model:
Table 5
Coefficients:
Standard
|
T
| |||
Parameter
|
Estimate
|
Error
|
Statistic
| |
CONSTANT
|
0.963011
|
0.0000
| ||
s2rawmaterial consumption
|
0.657836
|
0.00502045
|
131.031
|
0.0000
|
s2yield
|
31.8798
|
1.45079
|
21.9741
|
0.0000
|
The fitted multiple regression models involving the explanatory variables are given below:
From the model it is observed that there is a
positive relationship between yarn production and raw material
consumption. It shows that yarn production would increase by 0.66 units,
if the raw material consumption increases by 1 unit assuming the yield
remains the same.
It also shows that there is a positive relationship
between yield and yarn production. That is the yarn production would
increase by 31.88 units if the yield increases by 1 unit assuming the
raw material consumption remains the same. The validity of the model has
been tested by ANOVA.
The output of the ANOVA is presented as
|
:
| ||||
Table No.6
| |||||
Analysis of Variance
| |||||
Source
|
Sum of Squares
|
Df
|
Mean Square
| ||
Model
|
142.31
|
2
|
71.1552
|
8996.15
|
0.0000
|
Residual
|
0.0553666
|
7
|
0.00790951
| ||
Total (Corr.)
|
142.366
|
9
| |||
Since the P-value in the ANOVA table is
less than 0.05, there is a statistically significant relationship
between the variables at the 95.0% or higher confidence level. Hence
fitted model is the most suitable model to describe the relationships of
the variables.
R2
|
Standard
|
Mean Absolute Error
| |
Error of Estimate
| |||
99.9611 percent
|
0.0889355
|
0.0593909
|
3.17366 (P=0.9427)
|
The R-Squared statistic indicates that
the model as fitted explains 99.9611% of the variability in production.
The standard error of the estimate shows the standard deviation of the
residuals to be 0.0889355. The mean absolute error (MAE) of 0.0593909 is
the average value of the residuals. The Durbin-Watson (DW)
statistic tests the residuals to determine if there is any significant
correlation based on the order in which they occur. Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level
COBB DOUGLAS PRODUCTION FUNCTION:
Generally for the same level of input factors,
everyone should get almost the same level of output. In spinning mills
the production and cost related to the spinning department has been
considered as the important indicator of the operational performance.
Therefore production and cost related to the spinning department has
been taken for the analysis. To test production achievement of the
companies, the Cobb-Douglas Production Function is employed. The Cobb-
Douglas Production Function assess the effects of
various inputs like raw material, labour store cost, power, interest,
depreciation and other costs involved in the production of yarn. The log
linear form of production function used is based on the following equation.
LnY =α+β1LnX1+ β2LnX2 + β3LnX3+ β4LnX4 + β5LnX5 + β6LnX6 + β7 LnX7 +uµ Where,
Ln Y = Dependent Variable –Spinning production per Spindle shift. X1= Xn are independent variables;
X1 = Raw Material cost /Spindle shift; X2 = Salaries
and Wages/spindle shift X3= Stores cost / Spindle shift; X4 = Power
cost / Spindle shift
X5 = Other costs/ spindle shift; X6 = Interest cost/ spindle shift
X7 = Depreciation cost/ spindle shift
α = constant /Intercept.
β= co-efficient;
u =Random disturbance term; Ln = Natural Logarithms.
To predict the production, first of all the dependent
and independent variables are to be fixed in the production function
equation and processed to get constants and co efficient. To arrive this
following steps are carried out.
In the first step data related to the production
function as given in the following tables have been applied in the
multiple regression models after converting the same in its log form to
arrive the constants and coefficients. In the second step the resultant
constants and coefficients are applied in the Cobb Douglas production
function to estimate the production. In the third step the ten year
averages of independent variables of all the four companies have been
calculated. In the fourth step such average cost is applied in the
production function along with the constants and co-efficient
to predict the production of each company to evaluate the production
achievement of the companies. The following table shows the unit wise
constants and coefficients variables related to the production function.
Table 8.
Constants and Coefficients – SSM Ltd Unit-1 and Unit-2
COMPANY
| ||||||
CONSTANTS
|
3.66
|
3.92
| ||||
Mat. cost /sple sft
|
0.21
|
0.72
| ||||
S& Wages/sple sft
|
0.29
| |||||
Stores/Sple Sft
|
0.36
| |||||
Power/Sple Sft
|
0.77
| |||||
Other costs/Sple Sft
|
1.77
| |||||
Interest/Sple Sft
| ||||||
Dep/Sple Sft
|
0.01
|
0.93
| ||||
The regression equation for SSM LTD
|
given below:
| |||||
+
| ||||||
1.swpersplesft - 0.0816154*ssm-1.storpersplesft + 0.773897*ssm-1.powerpersplesft - 0.510007*ssm-1.otherpersplesft - 0.287602*ssm-1.intpersplesft + 0.0112882*ssm- 1.deppersplesft.
It shows that there is a positive relationship
between production per spindle shifts and raw material cost, power cost
and depreciation. That is the production per spindle shift increases by
the respective co-efficient level if the raw material, power and depreciation increases by one unit assuming the other variables remain constant.
Similarly the equation shows negative relationship
between production per spindle shifts and salaries and wages, stores,
other cost and interest cost. That is the production per spindle shift
decreases by the respective co-efficient level if salaries
and wages, stores, other cost and interest cost decreases by one unit
assuming the other variables remain constant.
The regression equation for SSM LTD
| ||||||||
pdnpersplesft
|
=
|
3.92029
|
+
|
0.71877*rawpersft
|
+
|
0.294551*swpersplesft
|
+
| |
0.361561*storpersplesft
|
-
|
0.833235*powerpersplesft
|
+
|
1.7669*otherpersplesft
|
-
| |||
0.497305*intpersplesft + 0.933198*deppersplesft
| ||||||||
.
| ||||||||
It shows that there is a positive relationship
between production per spindle shifts and raw material cost, salaries
and wages, stores, other costs and depreciation. That is the production
per spindle shift increases by the respective co-efficient
level if the raw material cost, salaries and wages, stores, other costs
and depreciation by one unit assuming the other variables remain
constant.
Similarly the equation shows negative relationship
between production per spindle shifts and power cost and interest cost.
That is the production per spindle shift decreases by the respective co-efficient level if power cost and interest cost decreases by one unit assuming the other variables remain constant.
Table 9
PREDICTED PRODUCTION--COBB-DOUGLAS PRODUCTION FUNCTION
COMPANY
| |||
PREDICTED PRODUCTION
| |||
1
|
Gms/Sple Sft
|
112.45
|
109.4
|
Average uniform cost applied
| |||
2
|
Mat.cost/Sple Sft
|
7.08
|
7.08
|
3
|
S& Wages/ Sple Sft
|
0.89
|
0.89
|
4
|
Stores/Sple sft
|
0.28
|
0.28
|
5
|
Power/Sple sft
|
2.21
|
2.21
|
6
|
Other costs/sple sft
|
1.43
|
1.43
|
7
|
Interest/sple sft
|
0.67
|
0.67
|
8
|
Dep/sple sft
|
0.72
|
0.72
|
9
|
Total Cost /Sple Shift
|
13.28
|
13.28
|
10
|
Cost per Gms
|
0.1181
|
0.1214
|
11
|
1
|
2
| |
Spindle
| |||
12
|
25,000 spindle and 3 shift working)
|
75000
|
75000
|
13
|
predicted production
|
8434
|
8205
|
14
|
Total
|
996000
|
996000
|
15
|
cost per kg
|
118
|
121
|
It has been observed that predicted production
differs within the units of the same company. The production achievement
ranks given .It also shows that increased production causes reduction
in cost
7. FINDINGS
The following are finding arrived from the data analysis:
1.The
yarn production depends on the raw material input and yield percentage.
The data have been tested with the multiple regression analysis. It
shows that there is a significant association between the Production,
raw material consumption and yield ratio.
2.The Cobb
Douglass production function shows the relationship between production
per spindle shift and various component costs per spindle shifts.
Prediction of production is made based on the co-efficient
and constants arrived from the regression equation. It shows that there
is a difference in production though the same uniform costs are
applied. The table also shows the one day production and respective
costs and also the cost per Kg. The cost per kg in SSM Ltd Unit-1 is lesser because of greater production per spindle shift.
8. SUGGESTIONS
The following strategies may help the Textile Mills to meet the global challenges to grow up as a global leader by improving the profitability:
1. Buy high yield raw cottons after testing the samples.
Cottons without contamination would give more yields. Before going for production the cotton has to be tested for its yield and then order for raw materials. This would increase the productivity.
2. Reduction in cost per machine shift would reduce the cost of manufacturing.
Monitor the costs by applying activity based costing and remove the unnecessary activities and save the costs.
3. Increase the spindle utilization and make spin plan before starting the production so that count change, count run outs and other reasons for spindle stoppage may be reduced to optimize the spindle utilization.
4. Interest Charges:
Due to the injection of more debt funds heavy interest charges occurs. Mills are unable to use debt funds to magnify the profit as they are very often subject to so many risks. Borrowing at lower rate of interest and timely use of debt capital shall reduce the interest burden. To avoid heavy interest use matching principle in the mixing of Short term and long term funds. Long-term funds in the form of issue of bonds for specific period and short term funds like commercial papers may be planned in addition to the other sources of finding funds so as to get optimal capital-mix.
5. TUF loan:
Government has been giving Technology Up gradation Fund to modernize the plant at lower rate of interest. This is a boon to the textile sector since it would increase the productivity as well as the profitability.
6. Business Intelligence:
It is a process through which the performance of the organization is monitored with KPI’s (Key Performance Indicators) and reported for immediate action and follow up. It is a “Measure-Monitor-Manage- Analyze-Plan system. Alerts and work flow corrections are also indicated by the business Intelligent Software. Close watch over the financial leverage, Asset leverage, Cash flow management and working capital shall make a concern to reduce the interest burden and at the same time enjoy the benefit of zooming profit by taking timely actions with the help of the business Intelligence software.
7. Unused capacity management:
The actual situation would show the unused capacity in a particular period may be managed by doing job works so as to recover the cost and increase profitability.
8.Close Watch on Yarn Prices and try for Exploring for Value addition
Before making production plan Market trend has to be assessed and to source best customers ,use up-selling, cross selling and diversifying techniques and explore new markets for potential customers to improve profit margins.To review the market prices time to time.
9. Long term deals with Suppliers with better pricing on Raw material /products/ spares.
9. CONCLUSION
The improvement in profitability depends on the improvement in productivity of an individuals and operational performance of a company. In this paper the relationship between production, raw material consumption and yield are arrived to find the behavour of the same. This can be used for improving the productivity. The Cobb Douglass production function and prediction of production also exposes the cost areas to be concentrated. Every company can enhance their productivity and profitability if they improve the operational activities at lesser cost. These are within the control of the management. Hence, one makes drill down approach to increase the profitability as well as the productivity by ascertaining the association successfully.
Courtesy : 1Dr.G.GANESAN & C.DHANAPAL
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