Published
Russian Journal of Agricultural and Socio-Economic Sciences, 1(61), January 2017. Pp. 294-304
DOI https://doi.org/10.18551/rjoas.2017-01.33
EFFICIENCY ANALYSIS OF MEAT
PROCESSING INDUSTRY
IN INDONESIA
IN INDONESIA
Jafrizal
Faculty of Economics, University of Sriwijaya
Palembang, 30137,Indonesia
drh_jafrizal@yahoo.co.id
drh_jafrizal@yahoo.co.id
Bernadette Robiani
Faculty of Economics, University of Sriwijaya
Palembang, 30137,Indonesia
robiani64@yahoo.com
Suhel
Faculty of Economics
University of Sriwijaya Palembang, 30137,Indonesia
suhel_feunsri@yahoo.co.id
Abstract
This paper aims
to analyze the efficiency of the meat processing industry in Indonesia
large-scale and medium-year period 1990-2013. The method used data envelopment
analysis (DEA) model of Variable Return to Scale (VRS) Input-oriented. It was
found that, the estimation value Constant Return to Scale (CRS) model of
efficiency with an average of 89.38 percent, which means that the industry is
only able to optimize the resources available inputs to produce a production of
89.38 per cent, in other words there is still potential inputs that they can be
optimized for 10.62 percent assuming all companies operating at an optimal
scale. VRS model results by an average of 95.7 percent, which means that the
efficiency is still below 100, there are inefficiencies at 4.3 percent,
assuming the company is not operating at optimal scale due to factors existing
constraints, medium scale efficiency (Scale) an average of 93.36 per cent less
than 100 percent means that the industry is on a scale of inefficiency. The
implications of negative growth efficiency or below 100 percent is the need for
skills development of workers in order to adapt to technological upgrading and
make the selection of efficient combination of inputs.
Keywords: Efficiency, Data envelopment analysis (DEA), Variable Return to
Scale (VRS), Meat Processing Industry.
"It was inevitable that the future is
in the processing industry," this is what the statement C. P. Timmer,
researcher of the Center for Development Globel can ever Star Services for
research on food security in Indonesia. Processing industry plays an important
role in the nation's economy, including the meat processing industry. Meat
processing industry is one of the food industry which contributes greatly to
the economy (Lambert, 1994; Ali, 2007; Knudson, et al., 2010; Ali and Pappa,
2011).
According to BPS data (2015), the contribution to Gross Domestic Product
of the meat processing industry each year has increased from 8.72 percent in
2007 to 56 percent in 2012, a share which is the 2nd highest of the food and
beverage industry. The increase was not followed by the growth rate of the
Gross Domestic Product of the industry continues to decline from 12.17 percent
in 2007 to 1.13 percent in 2012. Growth in industrial processing and preserving
of meat in Indonesia from 1985 to 2013 year average growth of about 26.6 per
cent per year, the average workforce grew 8 percent and corporate units grew
modestly around an average of 4 percent.
Problems of growth efficiency of the processing industry in Indonesia
has become a concern among researchers in recent decades (see: Aswicahyono,
1998; Basri, 2001; Margono and Sharma, 2006; Modjo, 2007; Probowo and Cabanda
2011; Setiawan, 2013; and Surjaningsih and Permono, 2014). When the competition
is going low, it will cause producers to operate inefficiently so that loss of
efficiency and productivity (Gopinath, et al., 2002; Nurdianto, 2004).
Changes efficiency contributes to
productivity growth as reported Saputra (2011), that in the period 1990-2001
subsector processing industry in general has high efficiency. Bappenas (2010)
find the positive growth of technical efficiency, productivity growth in the
processing industry the period 2000-2007. The same was reported Probowo and
Cabanda (2011), in the period 2000-2005 the processing industry in Indonesia is
experiencing technical inefficiency.
Various research efficiency of meat processing industrial has also been
carried out in various countries with different results, such as, among others,
by Xia and Buccola (2002), which conducts research in the United States, found
that the level of productivity of the meat processing industry decreased. Ali
(2007) conducted a study in India found that in the period from 1980 to 2003
occurred inefficient use of capital and labor inputs and the low productivity
growth. Nossal et al., (2008) conducted a study in Australia, found that the
productivity of beef processing industry is increasing every year, driven by a
combination of a moderate expansion of output and a decline in the use of
multiple inputs.
Research is also being done in Europe, including in Ukraine, Goncharuk
(2009) found increased growth of efficiency resulting from the reduction in the
use of capital input, labor input. Keramidou et al., (2011) reported the
results of his research on the meat processing industry in the period 1994-2007
Greece, find growth is inefficient use of capital and labor inputs. In Spain,
Kapelko, et al., (2012) found that a decline in productivity driven by
technical setbacks, despite the growth in technical efficiency and scale
efficiency.
In connection with differences on the above results, this study will
analyze the efficiency of the meat processing industry in Indonesia on period
1990-2013. Researchers gave the title of this research is "Efficiency
Analysis Meat Processing Industry in Indonesia"
Concept Efficiency. Theories efficiency first appeared in 1957, was Farrell with microeconomic study approach. In particular, Farrell (1957) describe new insights into two important things: how to define efficiency and how to calculate a measure of efficiency. In the approach to Farrell, the measurement of economic efficiency associated with use of frontier production function, contrary to the notion underlying performance largely econometric literature on production functions.
Concept Efficiency. Theories efficiency first appeared in 1957, was Farrell with microeconomic study approach. In particular, Farrell (1957) describe new insights into two important things: how to define efficiency and how to calculate a measure of efficiency. In the approach to Farrell, the measurement of economic efficiency associated with use of frontier production function, contrary to the notion underlying performance largely econometric literature on production functions.
Farrell (1957) divides the efficiency
of the company into two components, namely the technical efficiency and
allocative efficiency. The two measures are then combined into economic
efficiency (economic efficiency). Sengupta (1995) and Coelli, et al., (2005)
divides efficiency into three components, namely allocative efficiency (AE),
economic efficiency (EE) and technical efficiency (TE).
Allocative efficiency (AE) reflects
the company's ability to optimize the use of inputs in optimal proportions
given their respective prices and production technology. Economic efficiency
(EE) is defined as the ability of a company to produce the quantity of output
that has been determined by the minimum cost for a certain technological level.
A company can be said to be economically efficient if the company can minimize
the cost of production to produce a specific output with a level of technology
that is commonly used as well as the prevailing market price. Technical
efficiency is a measure of the company's success in producing a maximum output
of the set of inputs available (Sengupta, 1995).
Estimated Value Efficiency.Efficiency can be
estimated with parametric or nonparametric methods. The preferred method of
estimation has become an issue of debate, with some researchers prefer
nonparametric approach (Seiford and Thrall, 1990) and some researchers use a
parametric approach. Parametric measurements including determining and
estimating the stochastic frontier production or cost stochastic frontier in
this method, the output (or cost) is assumed to be a function of the input (or
output), inefficiency and random error. On the other hand, parametric frontier
functions require the definition of a particular functional form for technology
and for the inefficiency error term. Terms of the functional form causing the
problem specification and estimation (Sengupta, 1987). Measurement of technical
efficiency tends to be limited to technical and operational influence in the
process of converting inputs into outputs. As a result, efforts to improve the
technical efficiency requires only micro policy which is internal, namely the
control and optimal resource allocation. In the economical efficiency, the
price can not be considered given, because prices can be affected by macro
policy.
Calculation of efficiency according to
the Coelli Farrel, et al. (2005); Cesaro, et al., (2009), there are two
approaches, with the approach of input and ouput approach. First, the input
approach, we set a target output by selecting the input to a minimum. Input
orientation emphasizes the question of how much the number of inputs can be
reduced (reducing input) proportionally without changing the quantity of output
produced. Second, the output approach to see how big an increase in the number
of output without increasing the amount of use input. Orientation
output emphasis on the question how much output can be increased (output
expanding) proportionally without changing the number of inputs used.
According Coelli, et al., (2005), the output approach there are
three types of additional output that constant return to scale, decreasing
returns to scale, and increasing returns to scale. For input and output
approach will provide similar technical efficiency calculations in the constant
return to scale, but show different results in decreasing / increasing returns
to scale. Efficiencies generated through the output approach indicates the
amount of output can be increased without additional input.
Data Envelopment Analysis (DEA) is a mathematical program
optimization method that measures the technical efficiency of a company and
compares relative to other companies. DEA was originally developed by Farrell
(1957), which measures the technical efficiency one input and one output, into
a multi-input and multi-output, using a framework of values relative
efficiency as a ratio of input (single virtual input) to output (single virtual
output) (Giuffrida & Gravelle , 2001). Initially, DEA popularized by
Charnes, et al., (1978) by the method of constant returns to scale (CRS) and
developed by Banker et al., (1984) for variable returns to scale (VRS), which
eventually famous models CCR and BCC.
The main advantage of DEA is easy to use by combining multiple
inputs and outputs to calculate technical efficiency. DEA models can generate
new alternatives to improve performance compared to other techniques. Linear
programming is the backbone of DEA methodology that is based on the
optimization platform. DEA is different from other methods in identifying ways
of optimal average performance.
METHODOLOGY
The Scope of Research. The scope of this study industrial processing and preservation of meat in Indonesia large scale and are using the categories Classification of Indonesian Business Field (KLUI) 1990 with code 31 112, Standard Industrial Classification of Indonesia (ISIC) 1998, 2000, 2005 with the code 15112 and ISIC 2009 with 10130 code.
Types and Sources of Data. The data used in this research is time series data processing and preservation of meat industry in Indonesia are derived from the annual survey data Large and Medium Manufacturing Statistics Statistics Indonesia-year period 1990-2013 were not published. Chosen in 1990 as in 1990 the state of Indonesia first began importing cattle that became the beginning of the meat processing industry uses imported beef. Chosen in 2013 as the last year's research data due consideration of the availability of annual survey data for 2013 BPS only available in May 2015.
Data used in the study includes data
input and output as well as the value of imports. Input and output variables
are used, among other things: The cost of raw and auxiliary materials (raw
materials); Spending on labor; Electric power purchased by the industry;
Spending fuels and lubricants industries; Other expenses consist of cost of
capital lease; The output value is the output value of the meat processing
industry
Analysis method. The model was developed by
Banker, Charnes, and Cooper (model BCC) in 1984 and is a development of the
model CCR. This model assumes that the company is not yet operating at optimal
scale. The assumption of this model is that the ratio between the input and
output additions are not the same (variable returns to scale). That is, the
addition of x times the input will not cause output increased by x times, can
be smaller or larger than x times. Banker, Charnes and Cooper (1984) have
extended measurement DEA method for the case of variable returns to scale
(VRS). This model distinguishes between pure technical efficiency and scale
efficiency (SE), identify whether increasing, decreasing or constant returns to
scale are found. As a result, assuming a linear CRS should change by adding a
further convexity constraint N1 'ʎ = 1, therefore, form-oriented VRS DEA model
inputs specified as:
TE vrs
Ө, ʎ = min Ө
st -
yi + Yʎ≥ 0
Өxi
-Xλ ≥ 0
N1'λ =
1 λ ≥ 0. ………………………………………………..……………………..(3.1)
where
N 1 is an N x 1 vector of satu.θ is the input value of technical efficiency
under VRS, has a value of 0 ≤ θ ≤ 1. As in the previous case, if the value of θ
is equal to one, the company was on the frontier, while λ vector is N x 1 vector
of weights that define a linear combination of the company's
enterprise-i.Karena to VRS DEA model is more flexible and enveloping data in a
way that is more stringent than the CRS DEA model, the value of the VRS
technical efficiency is equal to or greater than the value of CRS technical
efficiency , This influence can be used to measure the scale of business
efficiency:
SE =
TE CRS/TE VRS. ………………………………………..………….
(3.2)
SE = 1 means the scale of
efficiency or SE <1 indicates scale inefficiency that could be caused by
increasing or decreasing returns to scale. As a result, some of the VRS units
that can efficiently be inefficient under the scheme CRS because the size
deviates from the optimum scale. The weakness in this procedure is that it can
give an indication whether the company operates under increasing or decreasing
returns to scale. This can be determined by calculating the equation additional
menggunakannon DEA - increasing returns to scale (NIRS). Model VRS DEA
previously can be changed by changing the boundaries N1 'λ = 1 with N1'λ≤ 1 and
other surfaces that will be able to distinguish between the different scales in
the structure of production. In particular :
• if
TEnirs = TEvrs ≠ TEcrs the units producing in decreasing return to scale
• if
TEnirs ≠ TEvrs = TEcrs the units producing in increasing returns to scale
• if
TEnirs = TEvrs = TEcrs the production unit at constant return to scale.
RESULTS AND DISCUSSIONS
The efficiency value of this research is the value obtained from the
technical efficiency estimation using Data Envelopment Analysis method
-Variable Return to Scale (DEA-VRS) input oriented. Table 4.1 of the visible
results of DEA-VRS input oriented that the overall value of the average
efficiency of industrial processing and preservation of meat in Indonesia
period 1990-2013 CRS model with an average of 89.38 percent, which means that
the industry is only able to optimize resource input available to generate
production by 89.38 percent in other words, there is still potential inputs
that they can be optimized by 10.62 percent, assuming all companies operating
at an optimal scale. VRS model results by an average of 95.7 percent, which
means that the efficiency is still below 100, there are inefficiencies at 4.3
percent, assuming the company is not operating at optimal scale due to factors
existing constraints, medium scale efficiency (Scale) an average of 93.36 per
cent less than 100 percent means that the industry is on a scale of
inefficiency. The implications of negative growth efficiency or below 100
percent is the need for skills development of workers in order to adapt to
technological upgrading.
From Table 4.1 also shows that the minimum value interval efficiency
value VRS models of 85.4 percent and a maximum value of 100 percent, with a
standard deviation of 4 percent which means that there are differences in
sample value to the value of the average of 4 percent during the study period.
The estimation results of the efficiency as follows: Under the conditions of
return to scale industries during the study period of 1990-2013, there are 18
units of the company each year during the study period conditions return to
scale is at λ> 1 means that the degree of change in output as a result of
changes in input called the degree of acquisition ascending (increasing returns
to scale). This condition can occur due to the increased scale of operations,
occurs due to specialization of tasks and functions, as well as the use of
special machines that are more productive and related to the liberalization
policy in the industry especially in 1986 whose effects are still felt the time
until the moment before the economic crisis of 1997 / 1998. The number of
technology and innovation as well as investment and the stable economic
conditions contributed to the growth of the industry not to mention the meat
processing industry.
Table 1. Values Efficiency
and Preserving Meat Processing Industry for the Period of 1990-2013
|
Period
|
Efficincy ( persent)
|
IRS
|
CRS
|
DRS
|
|||
|
CRS
|
VRS
|
Scale
|
(Unit)
|
(Unit)
|
(Unit)
|
||
|
1990-1992
|
85,07
|
90,81
|
93,68
|
18
|
0
|
0
|
|
|
1993-1995
|
98,72
|
99,71
|
99,01
|
23
|
0
|
1
|
|
|
1996-1998
|
94,08
|
99,06
|
94,97
|
21
|
0
|
2
|
|
|
1999-2001
|
86,04
|
96,37
|
89,28
|
18
|
2
|
3
|
|
|
2002-2004
|
85,6
|
93,72
|
91,34
|
13
|
1
|
5
|
|
|
2005-2007
|
89,7
|
97,06
|
92,42
|
21
|
2
|
5
|
|
|
2008-2010
|
90,52
|
96,21
|
94,09
|
21
|
1
|
7
|
|
|
2011-2013
|
85,29
|
92,61
|
92,1
|
12
|
3
|
14
|
|
|
Average
|
89,38
|
95,7
|
93,36
|
18
|
1
|
5
|
|
|
Std. Dev
|
4,0
|
||||||
|
Minimum
|
85,4
|
||||||
|
Maximum
|
100,0
|
||||||
Diskription:
|
Scale
= Scale Efficiency = crs/vrs
|
IRS
= Increasing Return to Scale
|
|
CRS
= Constant Return to Scale
|
DRS
= Decreasing Return to Scale
|
|
VRS = Variable
Return to Scale
|
Tech = Technology
|
Source: Estimation Result
From the above results it appears that a drop in efficiency from the
period 1993-1995 amounted to 99.71 per cent to 92.61 per cent in 2011-2013
peridoe. The condition is the same as the results of technical efficiency of
food processing industries in Indonesia are conveyed by Margono and Sharma
(2006) in the period from 1993 to 2000, and Ikhsan (2007) in the period
1988-2000, which found that the level of efficiency in the food processing
industry Indonesia has decreased.
Periodically, in the period of 1990-1992 the average efficiency of
85.07 percent CRS models, meaning that there is still potential to improve
efficiency by optimizing the use of inputs in the industry amounted to 14.93
percent, assuming all companies operating at an optimal scale. VRS model of
efficiency average of 90.81 percent, meaning that it is still possible for the
company or the industry to further increase its technical efficiency by
reducing the level of technical inefficiency in the use of inputs by reducing
the use of raw material inputs and fuel, electricity and other expenses that
occur excessive use of 9.19 per cent, assuming the company is not operating at
optimal scale due to factors constraints. Overall the 1990-1992 period were on
a scale of inefficiency because efisiensirata scale value by an average of
93.68 percent, still below 100 percent. This condition may occur related to the
liberalization policy in the industry especially in 1986 whose effects are
still felt the time until just before the financial crisis. The number of
technology and innovation and investment that support the industry at that time
have not been fully utilized by the meat processing industry. The low labor
skills possessed in using technology.
This is consistent with reports Priyanto (2005), the condition of the period 1990-2000 is the implementation of policy finance minister in 1989 that lowered import tariffs on beef. This condition, in which industrial processing and preservation of meat that began using imported meat as raw materials benefited from the imposition of import tariffs on meat decreased from 40 percent in 1989 down to 5 percent in 2000. At the time of the imposition of high import tariffs in 1989 to 40 percent, it also affected the price of imported meat but the meat processing industry is still largely domestic beef used as a raw material. Along with a reduction in tariffs and the price of imported meat imports have also increased the composition of meat used as a raw material. It contributed to the establishment of conditions that efficient in terms of cost on industrial processing and preservation of meat in Indonesia.
This is consistent with reports Priyanto (2005), the condition of the period 1990-2000 is the implementation of policy finance minister in 1989 that lowered import tariffs on beef. This condition, in which industrial processing and preservation of meat that began using imported meat as raw materials benefited from the imposition of import tariffs on meat decreased from 40 percent in 1989 down to 5 percent in 2000. At the time of the imposition of high import tariffs in 1989 to 40 percent, it also affected the price of imported meat but the meat processing industry is still largely domestic beef used as a raw material. Along with a reduction in tariffs and the price of imported meat imports have also increased the composition of meat used as a raw material. It contributed to the establishment of conditions that efficient in terms of cost on industrial processing and preservation of meat in Indonesia.
In the period 1993-1995 the average efficiency of 98.72 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry by 1.28 percent with the
assumption that all companies operating at an optimal scale. VRS model of
efficiency average of 99.71 percent, meaning that there is still potential to
improve efficiency by optimizing the use of inputs by 0.29 per cent, assuming
the company is not operating at optimal scale due to factors constraints.
Overall the 1993-1995 period were on a scale of inefficiency because of the
value scale of an average efficiency of 99 percent, is still below 100 percent.
Conditions return to scale is at λ> 1 means that the degree of change in
output as a result of changes in input called the degree of acquisition
ascending (increasing returns to scale).
In the period 1993-1995 there was an increase of efficiency when
compared to the previous period but still not efficient. The condition can
occur due to the processing of the meat processing industry going technology
development and orientation of higher capital compared with other food
industry, so that the meat processing industry has increased the efficiency of
the previous period. This condition is consistent with the reports Tanuwijaya
and Sharma (2004) Aswicayono and Hill (2002), reported that productivity growth
in the food processing industry that is driven by the positive contribution of
the growth efficiency. Modjo (2006) reported that the productivity of the
industry declined in the period from 1990 to 1995 but there has been growth in
efficiency. In the period 1990-1995 the initial process of learning by doing in
adopting the technology because the company is not operating at full production
capacity. This production growth is a positive contribution of the growth of
efficiency changes.
In the period 1996-1998 the average efficiency of 94.08 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry at 5.92 percent with the
assumption that all companies operating at an optimal scale. VRS model of
efficiency average of 99.06 percent, meaning that there is still potential to
improve efficiency by optimizing the use of inputs by 0.94 percent, assuming
the company is not operating at optimal scale due to factors constraints.
Overall the period 1996-1998 were on a scale of inefficiency due to efficiencies
of scale value by an average of 94.97 percent, still below 100 percent.
Conditions return to scale is at λ> 1 means that the degree of change in
output as a result of changes in input called the degree of acquisition
ascending (increasing returns to scale). These results are consistent with the
results of Klein and Luu (2003) provide evidence of the influence of political
factors with negative technical efficiency during the crisis of 1997-1998, and
only after a positive growth in 1999. Bappenas (2010) also reported that the
Indonesian processing industry in the period the period 1997/1998 inefficiency,
as seen in the growth of productivity is lower than the period before the 1998
crisis.
Conditions in the period 1996-1998 as a result of rising prices of
industrial raw materials due to inflation, but the effect on the efficiency is
only down slightly from the period 1994-1996. Many companies are not
operational during this period due to rising production costs and declining
consumer purchasing power. Companies that can efficiently use a competitive
advantage in lowering production costs to maximize utilization of its available
resources. Companies that use imported inputs will bear a bigger impact on the
rising cost of imported raw materials, which in turn will lower the efficiency.
In the period 1999-2001 the average efficiency of 86.04 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry amounted to 13.98 percent,
assuming all companies operating at an optimal scale. VRS model of efficiency
average of 96.37 percent, meaning that there is still potential to improve
efficiency by optimizing the use of inputs by 3.63 per cent, assuming the
company is not operating at optimal scale due to factors constraints. Overall
the 1999-2001 period were on a scale of inefficiency due to efficiencies of
scale value by an average of 89.28 percent, still below 100 percent. Toscale
return conditions are at λ> 1 means that the degree of change in output as a
result of changes in input called the degree of acquisition ascending
(increasing returns to scale).
In the period of 1999-2001 is still a drop in efficiency compared to
the previous period when the crisis of 1997-1998. This can happen still
allegedly associated with the process of consolidation of economic policy
following the crisis of 1998 and the instability of economic conditions. These
results differ from the findings of Klein and Luu (2003), Margono and Sharma
(2004), Modjo (2007) and Setiawan (2013) who found that after a period of
crisis in 1997/1998 the food processing industry (meat) experienced positive
growth efficiency. Differences in results can be caused because the industry is
still able to take advantage of its resources efficiently, despite an unstable
condition after the domestic political situation, high interest rates and the
exchange rate rendahserta access to financial resources is still low, as well
as the practices and values managerial relatively not professional. The low
value of the rupiah resulted only in capital input but although expensive raw
material procurement can still be obtained, so that the industry can still
increase productivity with the use of technological equipment and resources to
the optimum.
In the period 2002-2004 the average efficiency of 85.6 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry amounted to 14.4 percent, assuming
all companies operating at an optimal scale. VRS model of efficiency average of
93.72 percent, meaning that there is still potential to improve efficiency by
optimizing the use of inputs by 6.28 percent, assuming the company is not
operating at optimal scale due to factors constraints. Overall the 2002-2004 period
are inefficiency due to efficiencies of scale value by an average of 91.34
percent, still below 100 percent. Conditions return to scale is at λ> 1
means that the degree of change in output as a result of changes in input
called the degree of acquisition ascending (increasing returns to scale). The
period 2002-2004 was a trend decline in efficiency compared to the previous
period. This condition is similar to that found by Robiani (2008), Setiawan
(2013), Ndari and Permono (2014), reported that the growth occurred in the
period 2000-2004 efficiency.
This condition can occur because of changes in efficiency is strong
in 2000-2004 associated with the ongoing consolidation after the financial
crisis of 1998 aggravated domestic political conditions affecting the
investment climate, making it difficult to increase investor confidence shown
by the low growth and low investment realization investation. Slowing changes
in technical efficiency means a decline in the production frontier, because of
declining production capability of the machine. One possible reason is the
interference with the machine as well as the high price of new machinery
because of the low value of the rupiah against the dollar. The same results
with a research report Bappenas (2010).
In the period 2005-2007, the average efficiency of 89.7 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry amounted to 10.3 percent, assuming
all companies operating at an optimal scale. VRS model of efficiency average of
97.06 percent, meaning that there is still potential to improve efficiency by
optimizing the use of inputs by 2.94 percent, assuming the company is not
operating at optimal scale due to factors constraints. Overall the 2005-2007
period were on a scale of inefficiency due to efficiencies of scale value by an
average of 92.42 percent, still below 100 percent. Conditions return to scale
is at λ> 1 means that the degree of change in output as a result of changes
in input called the degree of acquisition ascending (increasing returns to
scale). In the period 2005-2007 the economy recovers. Meat processing industr
efficiency showed a positive trend compared to the previous period despite
higher interest rates and the exchange rate lower as well as access to
financial resources is still low. Companies improve practices to optimize the
use of technology and the value of managerial professionals to improve
efficiency. These results are consistent with the findings of Setiawan (2013) and
the National Development Planning Agency (2010). The company increased the
efficiency of input use between, improve the layout of the production to
shorten the switching between work stations, align the workflow in the
workplace. Increased capital input engine and building a positive effect on the
efficiency and productivity of the industry in this period.
In the period 2008-2010 the average efficiency of 90.52 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry amounted to 9.48 percent with the
assumption that all companies operating at an optimal scale. VRS model of
efficiency average of 96.21 percent, meaning that there is still potential to
improve efficiency by optimizing the use of inputs by 3.75 per cent, assuming
the company is not operating at optimal scale due to factors constraints.
Overall the 2008-2010 period were on a scale of inefficiency due to
efficiencies of scale value by an average of 94.09 percent, still below 100
percent. Toscaleberada return conditions at λ> 1 means that the degree of
change in output as a result of changes in input called the degree of
acquisition ascending (increasing returns to scale) .In general, the industry
is on the condition of increasing returns to scale (IRS), means it is still
possible for the company to improve more technical efficiency by reducing the
level of technical inefficiency in the use of input and take advantage of
economies of scale.
In 2008 the global financial crisis which affects the overall
economy is no exception processing and preserving meat industry which affect
the cost of raw materials and capital goods. In addition to the result of the
global financial crisis of 2008, also the impact of the application of the Regulation
of the Minister of Agriculture No. 59 / Permentan / HK.060 / 8/2007 about the
decline of the import quota beef up toward 10 percent. Rising prices of
imported raw materials influence the decrease in efficiency (model VRS).
The results were the same as the Surjaningsih and Permono (2014),
BPS (2015) and Sharif (2013) reported a decrease in the efficiency of the
period 2008-2010 is the result of the increase of raw material usage and
increase capital input. The condition occurs because of the increased
consumption of raw materials and capital input will help drive production
growth despite the global financial crisis. The industry is the only
import-oriented so that by utilizing the domestic market alone is able to grow
because only meet domestic needs.
In the period 2011-2013 the average efficiency of 85.29 percent CRS
models, meaning that there is still potential to improve efficiency by
optimizing the use of inputs in the industry amounted to 10.3 percent, assuming
all companies operating at an optimal scale. VRS model of efficiency average of
92.61 percent, meaning that there is still potential to improve efficiency by
optimizing the use of inputs by 2.94 percent, assuming the company is not
operating at optimal scale due to factors constraints. Overall the 2011-2013
period were on a scale of inefficiency due to efficiencies of scale value by an
average of 92.10 percent, still below 100 percent. Conditions return to scale
is at λ <1, the degree of change in output as a result of changes in input
called the degree of acquisition decreased (decreasing returns to scale).
This condition occurs when the increase in output was less than the
increase in inputs. Decreasing returns to scale may occur due to increased
scale of operations, but the company will occur experiencing processed meat
products are also factors that can increase the productivity of the industry.
On the side of the change in efficiency is seen that the company is still
visible in the process of learning by doing in adopting technology that has not
been able to operate in full capacity, in addition to the many problems the
economy is fueling inflation and rupiah exchange rate so that will affect the
efficiency in the selection of inputs sourced from imports.
In the 2011-2013 upheaval in both industrial raw materials
availability issues and pricing issues. The decline in imports resulting
decreased availability of raw materials and price increases helped to provide
impact for the processing industry is mainly a problem of cost efficiency. The
costs incurred for raw material usage resulting in reduced efficiency. These
results are consistent with reports Aswicayono and Hill (2002).
CONCLUSIONS
Meat processing industry in Indonesia experienced a significant
productivity growth over the last two decades, but the contribution of growth
efficiency is still low. Measurement of the value of efficiency in this study
is the value obtained from the technical efficiency estimated by the method of
Data Envelopment Analysis -Variable Return to Scale (DEA-VRS) input oriented.
Based on estimates found that there are technical inefiseisnsi average of 10.62
percent with a model of Constant Return to Scale (CRS) and 4.3 percent with a
model of Variable Return to Scale (VRS). Scale efficiency average of 93.36 percent,
meaning that there is still potential to increase the efficiency of scale in
the meat processing industry amounted to 6.64 percent.
Inefficiency empirically analyzed by assuming that the industry is
not operating at optimal scale in the production process due to factors
existing constraints both microeconomic and makroekonimi. The analysis showed
that the meat processing industry has been on a scale of inefficient primarily
due to factors related to the input of raw materials, capital and energy use
and labor. This indicates that there has been use of inputs that have not fit
in the meat processing industry. In order to improve the efficiency of the
industry, these results are useful for policy makers and meat processors to
work optimally in determining the combination of input, to rationalize the
process of acquiring the output of input use, as well as to design the right
policy framework to address the problems identified in the sector meat
processing. The results showed that the industry needs to modernize production
systems to improve the capacity utilization of input factors, especially of raw
materials, capital and energy. the need to develop the skills of workers in
order to adapt to the use of technology. The raw material is the composition of
the biggest costs arising in the production, which is around 80 percent of
production costs is primarily a meat raw materials and auxiliary materials.
Governments can help in the method of obtaining the raw material meat by
shortening the supply chain for the meat processing industry.
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