Efficiency of the Tanjung Modang Nagari Tanjung Bonai Weaving

Production Process Using the Data Envelopment Analysis Method

Fresi Ariesti, Rizal, Nil Firdaus, Khairulis Shobirin and Chitra Indah Sari

Universitas Islam Negeri Mahmud Yunus Batusangkar, Indonesia

Keywords: Data, Envelopment, Analysis.

Abstract: The main problem of this research is that the Lintau Weaving Village industry has never conducted an

inspection of its production work unit. This research is quantitative. The data collection technique used is

observation. The study's findings indicate that DMU 2, 4, and 5 pertain to the VRS model calculations in both

2018 and 2021. In the input variable that is not optimal, namely the total cost of raw materials so that

improvements can be made by minimizing the use of raw material costs and planning raw material

requirements by analyzing sales records of weaving which have not been done before. The output variable

that is not optimal is the number of products. Improvements can be made by increasing resources, expanding

marketing, and promoting through various social media platforms.

1 INTRODUCTION

Efficiency is a word that describes the success of a

person or organization in the work being done,

according to how many resources are used to achieve

the results of these activities. Efficiency refers to the

ratio of input to output. (Liana, 2019) Regarding

system theory, efficiency is the ratio of input and

output. The output of input processed by a certain

process will be based on certain sizes and parameters.

Production efficiency in an industry such as the

weaving industry is measured by the output in terms

of the number of woven products and consumers. The

achievement of goals requires competence to do a job

in accordance with the planned objectives (Asmarani,

2019).

One way to increase income in the Lintau

Weaving Village, Jorong Tanjung Modang, Nagari

Tanjung Bonai, Lintau Buo Utara District, Tanah

Datar Regency, is to use production inputs as

efficiently as possible in order to maximize the profits

obtained by the workforce in the Lintau Weaving

Village. The efficient utilization of production inputs

can result in an increase in weaving production. So

far, Lintau Weaving Village has never conducted

inspections of production work units and also the

amount of production produced is only based on the

number of existing workers so it is not yet known

whether production output is efficient or not.

Therefore, it is necessary to measure efficiency to

determine efficiency in the production process.

The DEA method was used to conduct a study in

light of these problems. DEA was introduced to the

public by Charnes, Cooper, and Rhodes. The DEA

method is a method that can be used to measure the

efficiency of a company with the advantage of

accommodating many inputs and outputs in various

dimensions, so that a more accurate efficiency

measurement is obtained as a first step in increasing

productivity. Data Envelopment Analysis is a

productivity multi-factor analysis model to measure

the efficiency of a homogeneous Decision Making

Unit (DMU) group, so the DEA method can be used

because it can accommodate many inputs and outputs

in many dimensions and a more accurate efficiency

measurement will be obtained as a first step in

increase the productivity of a company. Therefore we

need measurements that involve multiple inputs. For

example, the number of workers, the hours worked,

and the total cost of raw materials. The efficiency of

the production process in each company is affected by

the number of customers and products involved in

multi-output.

Ariesti, F., Rizal, ., Firdaus, N., Shobirin, K. and Sari, C.

Efﬁciency of the Tanjung Modang Nagari Tanjung Bonai Weaving Production Process Using the Data Envelopment Analysis Method.

DOI: 10.5220/0012643400003798

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd Maritime, Economics and Business International Conference (MEBIC 2023) - Sustainable Recovery: Green Economy Based Action, pages 10-16

ISBN: 978-989-758-704-7

2 LITERATURE REVIEW

2.1 Efficiency

Efficiency is the best ratio between input and output

or in other words the optimal results to be achieved

by using existing or limited resources (Munthe,

2019). The higher the ratio of output to input, the

higher the level of efficiency achieved. Efficiency can

also be understood as the achievement of maximum

output through the utilization of specific resources. If

the resulting output is greater than the resources used,

the higher the efficiency achieved. Therefore, this

efficiency is related to the value chain, which refers

to the linkages between activities carried out in

creating goods and services.

2.2 Efficiency Measurement

The efficiency of a DMU is measured by its relative

efficiency against that of other DMUs in a sample

population. Here the condition applies that the DMUs

contain the same type of inputs and outputs (Munthe,

2019).

2.3 Production

In terminology, the word production means to make

an object and add value to it. The value of a product

increases when it provides new or more benefits than

previously. In general, production is the creation of

goods, which means the ability of a product or service

to meet certain human needs (Lubis, 2017).

2.4 Production Process

Process is a way, method, or technique for

implementing a certain thing. While production is an

activity designed to add or create benefits, it also

considers the form, timing, and location of production

factors that are beneficial to satisfying consumers.

This can be interpreted that the production process is

a step or stage of activity to make an input into an

output that has added value.

2.5 Data Envelopment Analysis Method

Thanassoulis (2001) stated that DEA is a method for

measuring the relative efficiency of homogeneous

operating units like schools, hospitals, industrial

centers, and so forth. The purpose of DEA is to

determine efficiency in production processes and find

improvement strategies for inefficient production

processes. The advantage of DEA is that it

accommodates many inputs and outputs in various

dimensions, so that a more accurate measurement of

efficiency can be obtained.

3 RESEARCH METHODS

This research is a quantitative study aimed at

analyzing Data Envelopment Analysis in measuring

efficiency. The object of this research is the input and

output data from the weaving village. While the

subject of this research is weaving or songket from

Lintau weaving village as a DMU.

The population used in this study is all woven

production in 2018-2021 in Lintau Weaving Village.

The sample of this research is the Decision Making

Unit (DMU), which consists of 5 DMUs including the

Dewi Weaving Unit, Era Weaving Unit, Riza

Weaving Unit, Siti Weaving Unit and Triya Weaving

Unit because the level of efficiency will be measured.

The instruments in this study were processed by

researchers using company data results. In this study,

LINDO 6.1 is utilized as the software. The data

collection technique that the researchers used was

observation, namely the method of collecting data by

taking data directly in the field and by direct

interviews with the weaving village. In this study,

primary and secondary data were used. Primary data

is data obtained from direct interviews with the

weaving village. Secondary data is data that has been

collected and processed by other parties and doesn't

need to be measured again.

Data Analysis Techniques is an attempt to create

a detailed description for further study. The data that

has been obtained is then calculated and analyzed on

the results obtained. The data needed for this research

are product data, labor data, production working

hours data, raw material cost data, and customer data.

The data obtained is data related to the weaving

production process. The data used is data for 2018-

2021. The variables that are employed are both input

and output variables in the process of production.

4 RESULTS AND DISCUSSION

4.1 What Is the Weaving Efficiency

Level?

Based on the calculations that have been done, in

2018, the results obtained were less than 1

(inefficient), specifically for the Era Weaving Unit,

Siti Weaving Unit, and Triya Weaving Unit. The

Efﬁciency of the Tanjung Modang Nagari Tanjung Bonai Weaving Production Process Using the Data Envelopment Analysis Method

efficiency value for the Era Weaving Unit is

0.8250000, Siti's Weaving Unit is 0.6875000, and

Triya's Weaving Unit is 0.8491666. The variable that

has the highest average weight is the number of

customers with a value of 0.0438332. The customer

variable is the most influential variable among all

other variables. This means that the number of

customers is relatively important in the Lintau

weaving village. Next is the variable amount of raw

material costs, which has an average weight of

0.0000444. Next is the variable amount of working

hours, which has an average weight of 0.000081.

Whereas the variable number of products has an

average weight value of 0 and the variable number of

workers has an average weight of 0. This means that

relatively the labor and product variables do not affect

the value of efficiency in Lintau Weaving Village.

Based on the calculations that have been carried

out, in 2019 the results obtained were less than 1

(inefficient), namely the Siti Weaving Unit with an

efficiency value of 0.9444444The variable that has

the highest average weight is the number of

customers with a value of 0.0533336. The customer

variable is the most influencing variable compared to

other variables. The importance of customers is

relative in Kampung Tenun Lintau. Next is the

variable amount of raw material costs, which has an

average weight value of 0.0002516. Next is the

variable amount of working hours, which has an

average weight value of 0.000044. Meanwhile, the

average weight value of the variable number of

products is 0 and the average weight value of the

variable number of workers is also 0. This means that

relatively the labor and product variables do not affect

the value of efficiency in Lintau Weaving Village.

Based on the calculations that have been done, in

2020 the results obtained are less than 1 (inefficient),

namely the Siti Weaving Unit with an efficiency

value of 0.8875740. While the Triya Weaving Unit

has an efficiency value of 0.9230769. The variable

with the highest average weight is the number of

customers, with a value of 0.0809882. The customer

variable is the most influencing variable compared to

other variables. The number of customers in

Kampung Tenun Lintau is a significant factor. Next,

namely the variable number of workers who have an

average weight with a value of 0.0150474, the

variable total cost of raw materials with an average

weight of 0.0002744, then the variable number of

hours worked with an average weight of 0.000022.

The number of products is variable and their average

weight is 0. This means that the product variable does

not affect the efficiency value in Lintau Weaving

Village.

Based on the calculations that have been done, in

2021 the results obtained are less than 1 (inefficient),

namely the Era Weaving Unit with an efficiency

value of 0.975, Siti Weaving Unit with an efficiency

value of 0.9 while the Triya Weaving Unit with an

efficiency value of 0.9. The variable with the highest

average weight is the number of customers, with a

value of 0.063. The customer variable is the most

influencing variable compared to other variables. The

number of customers in Kampung Tenun Lintau is a

significant factor. Next is the variable number of

workers who has an average weight with a value of

0.04, then the variable amount of raw material costs

with an average weight of 0.0001016. The variable

number of products and hours worked has an average

weight value of 0. This means that relatively the

product variable does not affect the value of

efficiency in Lintau Weaving Village.

The CRS Dual model, which was carried out

using LINDO 6.1, produces Technical Efficiency

(TE) and Slack variables. The dual CRS (constant

return to scale) model is a continuation of the primal

CRS, but in dual CRS, there is no linear relationship

between output and input variables.

The results of CRS Dual in 2018, namely DMU 1

and DMU 3 have a TE value of 1 because the value

of z = 1 and are considered efficient, while DMU 2

TE is 1.3872992751 , DMU 4 TE is 1.6647591302 ,

DMU 5 TE is 1.3868984969. for DMU 2 there is

Slack at Y1 of 0.000133 , X3 of 0.000041. DMU 4

has Slack at Y1 of 0.000133, X3 of 0.000041 and

DMU 5 has Slack at Y1 of 0.000137, X3 of 0.000063.

The Era Weaving Unit has a CRS Dual efficiency

value of 0.7208250, this means that the Era Weaving

Unit is not optimal based on technical and scale

aspects simultaneously. From the CRS Dual

calculation, the slack output value for the number of

So1 products is 0.000133, and the slack input value

for the total Si3 raw material cost is 0.000041. The

Siti Weaving Unit has a CRS Dual efficiency value of

0.6006875, which means that it is not optimal based

on technical and scale aspects simultaneously. From

the CRS Dual calculation, the slack output value for

So1 is 0.000133 and the slack input value for Si3 is

0.000041. The Triya Weaving Unit has a CRS Dual

efficiency value of 0.7210333, which means that it is

not optimal based on technical and scale aspects

simultaneously. The CRS Dual calculation shows that

So1 has a slack output value of 0.000137 and Si3 has

a slack input value of 0.000063. Meanwhile, the Dewi

Weaving Unit and Riza Weaving Unit have a dual

CRS value that is already efficient and optimal,

namely 1 from a technical and concurrent scale

perspective. A DMU that has slack functions to make

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improvements, by adding or subtracting the value of

each variable in the DMU to achieve an optimal

objective function based on the results of CRS Dual.

The results of CRS Dual in 2019, namely DMU 1,

DMU 2, DMU 3 and DMU 5 have a TE value of 1

because the z=1 value is considered efficient, while

DMU 4 TE is 1.1624054819, for DMU 4 there is

Slack at Y1 of 0.000136, X1 of 0.023767. The Siti

Weaving Unit has a CRS Dual efficiency value of

0.8602850, this means that the Siti Weaving Unit is

not optimal based on technical and scale aspects

simultaneously. The CRS Dual calculation results in

a slack output value of 0.000136 for the number of

So1 products, compared to a slack input value of

0.023767 for Si1. Meanwhile, the Dewi Weaving

Unit, Era Weaving Unit, Riza Weaving Unit and

Triya Weaving Unit have a dual CRS value which is

efficient and optimal, namely 1 from a technical point

of view and concurrent scale. A DMU that has slack

functions to make improvements, by adding or

subtracting the value of each variable in the DMU to

achieve an optimal objective function based on the

results of CRS Dual.

The results of CRS Dual in 2020, namely DMU 1,

DMU 2, and DMU 3 have a TE value of 1 because

the z=1 value is considered efficient, while DMU 4

TE is 1.2322274298, for DMU 4 there is Slack at Y1

of 0.000158, X1 of 0.038067 . DMU 5 TE is

1.0833333604, for DMU 5 there is Slack at Y1 of

0.000155, X1 of 0.025541 and X3 of 0.000044. The

Siti Weaving Unit is not the optimal choice due to its

CRS Dual efficiency value of 0.8115385, which takes

into account both technical and scale aspects

simultaneously. The CRS Dual calculation yields a

slack output value of 0.000158 for So1 products, and

an input SI1 value of 0.038067. The Triya Weaving

Unit has a CRS Dual efficiency value of 0.9230769,

which means that it is not optimal based on technical

and scale aspects simultaneously. From the CRS Dual

calculation, the slack output value for the number of

So1 products is 0.000155, while the slack input Si1 is

0.025541, and Si3 is 0.000044. Meanwhile, the Dewi

Weaving Unit, Era Weaving Unit, and Riza Weaving

Unit have a dual CRS value which is efficient and

optimal, namely 1 from a technical and concurrent

scale perspective. A DMU that has slack functions to

make improvements, by adding or subtracting the

value of each variable in the DMU to achieve an

optimal objective function based on the results of

CRS Dual.

The results of CRS Dual in 2021 are that DMU 1

and DMU 3 have a TE value of 1 because the value

of z=1 is considered efficient, while DMU 2 TE is

1.2091423251, for DMU 2 there is Slack at Y1 of

0.000124, X3 of 0.000041. DMU 4 TE is

1.3666803335, for DMU 4 there is Slack at Y1 of

0.000126, X1 of 0.025700. DMU 5 TE is

1.204456489, for DMU 5 there is Slack at Y1 of

0.000130, X1 of 0.036650. The Era Weaving Unit has

a Dual CRS efficiency value of 0.8270325. This

means that the Era Weaving Unit is not optimal based

on technical and scale aspects simultaneously. From

the CRS Dual calculation, the slack output value for

the number of So1 products is 0.000124 and the slack

input Si3 is 0.000041. The Siti Weaving Unit has a

CRS Dual efficiency value of 0.7317, which means

that the Siti Weaving Unit is not optimal based on

technical and scale aspects simultaneously. The CRS

Dual calculation shows that the slack output value for

So1 products is 0.000126, compared to the slack input

SI1 being 0.025700. The Triya Weaving Unit has a

CRS Dual efficiency value of 0.83025, which means

that the Triya Weaving Unit is not optimal based on

technical and scale aspects simultaneously. From the

CRS Dual calculation, the slack output value for the

number of So1 products is 0.000130 and the slack

input Si1 is 0.036650. Meanwhile, the Dewi Weaving

Unit and Riza Weaving Unit have a dual CRS value

which is already efficient and optimal, namely 1 from

a technical and concurrent scale perspective. A DMU

that has slack functions to make improvements, by

adding or subtracting the value of each variable in the

DMU to achieve an optimal objective function based

on the results of CRS Dual.

At this stage, the VRS model is calculated to

determine if the DMU efficiency is purely technical

efficiency or influenced by other factors outside the

DMU. This VRS model is a refinement of the CRS

DUAL model by providing a convexity constrain = 1.

In 2018, in the VRS calculation, there was only 1

inefficient DMU, namely DMU 2 Era, where the Era

Weaving Unit had a slack output value of So1 of

0.000107 and slack input of Si3 of 0.000041.

In 2019, in the VRS calculation, there were only

2 inefficient DMUs, namely DMU 2 Era and DMU 3

Riza, where the Era Weaving Unit had a slack output

value of So1 of 0.000118 and slack input Si1 of

0.023767. Meanwhile, the Riza Weaving Unit has a

slack output value of 0.000118 at So1 and 0.000228

at Si2.

In 2020, in the calculation of VRS, all DMUs are

efficient.

In 2021, in the VRS calculation, there are only 3

inefficient DMUs, namely DMU 2 Era, DMU 4 Siti,

and DMU 5 Triya where the Era Weaving Unit has a

slack output value of So1 of 0.000115 and slack input

Si3 of 0.000041, Siti's Weaving Unit has a slack value

the output at So1 is 0.000117 and the slack input at

Efﬁciency of the Tanjung Modang Nagari Tanjung Bonai Weaving Production Process Using the Data Envelopment Analysis Method

Si3 is 0.000057. While the Triya Weaving Unit has a

slack output value at So1 of 0.000120 and a slack

input at Si3 of 0.000081.

TE values are produced by the calculations of

CRS and VRS models that can be used to calculate

scale efficiency (SE) values. The TE value is obtained

by dividing the optimum efficiency value (1) with the

efficiency value of each DMU for both CRS and VRS

calculations. To get the SE value for each DMU, the

TECRSDUAL value is divided by TEVRS.

4.2 How are DMUs Targeted for

Improvement?

DMUs that have efficient results can become a

reference for improvement for DMUs that have

inefficient results by forming a peer group. Formation

of peer groups by using hierarchial cluster analysis

using SPSS software by looking at the closest squared

euclideant distance between DMUs. The smaller the

squared euclideant distance between the 2 DMUs, the

more similar the DMUs are. The following is the

result of forming a peer group using SPSS software.

Tabel 1: Hasil Peer Group 2018.

Proximity Matrix

Case

Squared Euclidean Distance

,000

,228

,000

,884

,299

,228

,000

,228

,251

,026

,000

,228

,000

,884

,299

,884

,251

,884

,000

,154

,299

,026

,299

,154

,000

This is a dissimilarity matrix

The table shows that DMU 1 has the smallest

proximity value to DMU 2 of 228. In DMU 2 it has

the closest distance to DMU 3 of 228. Meanwhile,

DMU 4 has the smallest proximity value to DMU 5

of 154. At DMU 5 has the smallest closeness value

with DMU 2 of 026.

DMUs that have an inefficient score can be

considered efficient DMUs, as improvements can be

made to one or more DMUs with the help of a peer

group. Peer groups can be done using SPSS software

using the Hierarchial Cluster Analysis method,

namely by looking at the closest squared Euclidean

distance between DMUs. Where closeness is done to

provide a reference for an inefficient DMU to an

efficient DMU.

The results of the SPSS software discuss the value

of Squared Euclidean Distance, where the smallest

value is between the Triya Weaving Unit and the Era

Weaving Unit, which is .026, while the largest value

is between the Dewi Weaving Unit, Riza Weaving

Unit and Siti Weaving Unit, which is .884.

In 2018, the dual DMU CRS model, which has an

inefficient value, is the Era Weaving Unit, Siti

Weaving Unit, and Triya Weaving Unit. The DMU

VRS model is only inefficient for the Era Weaving

Unit. Therefore the Era Weaving Unit becomes an

inefficient DMU based on the 2 models CRS dual and

VRS. At DMU Era, the variable that experienced

target improvement was y1 from a value of 21600 to

21618.000133 with an improvement of 0.083% and

the x3 variable from a value of 5000 to 3604.124959

with an improvement of 27.91%. Meanwhile, based

on DMU Era's VRS calculations, there were 2

variables that experienced target improvements,

namely the number of products from 21600 to

21618.000107 with an improvement of 0.083% and

the raw material cost variable from 5000 to

4540.749959 with an improvement of 9.1%.

Sensitivity analysis is conducted to observe

changes in efficiency that occur after targeted

improvements are made. The optimization reference

is obtained from the dual price value, as the given

limiting function will bind the target function.

The results of calculations from the dual CRS and

VRS models will be subjected to sensitivity analysis

to find out which model will be used as a reference in

increasing efficiency based on target improvements.

In 2018 based on CRS Dual calculations, the Era

Weaving Unit has 2 variables that are not optimal,

namely the output variable y1 (number of products)

and the input variable x3 (raw material costs). Based

on the results of calculations using LINDO software,

the variable y1 has a dual price value of -0.000100,

while the variable x3 has a dual price value of

0.000141. This means that the variable y1 will

increase the efficiency of the Era Weaving Unit by

0.0018000133 so that it will change the efficiency

value at DMU 2 Era by 0.7226250132. The variable

x3 will increase the efficiency of the Era Weaving

Unit by 0.1968183808, thereby changing the

efficiency value of the Era Weaving Unit by

0.9176433807.

Based on the calculations on the VRS model, the

Era Weaving Unit has 2 variables that are not optimal,

namely the output variable y1 (number of products)

and the input variable x3 (raw material costs).

Calculations from the LINDO software produce a

dual price value for variable y1 (output) of -0.000100,

while for variable x3 it is 0.000141. This means that

the variable y1 will increase the efficiency of the

DMU by 0.0018000107, which will also increase the

efficiency of the Era Weaving Unit by 0.9063499893.

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The variable x3 will increase the efficiency of the Era

Weaving Unit by 0.0647542558, which will change

the efficiency value of DMU 2 by 0.9693042344.

Based on the results of the sensitivity analysis, it can

be seen that the repair value has not reached its

optimal value. The two results indicate that the

improvement will be attributed to the VRS model due

to its higher efficiency value than the CRS dual

efficiency value.

4.3 The Right Strategy in Increasing

Efficiency?

Based on the results of the calculations that the

researcher has performed, the solutions that will be

provided only refer to DMUs that have non-optimal

or inefficient efficiency values. In 2018, the CRS

Dual model has 3 inefficient DMUs, namely the Era,

Siti and Triya Weaving Units. Whereas in the DMU

VRS model, only the Era Weaving Unit has non-

optimal values. The improvements made are 2

solutions, namely by using the CRS Dual or VRS

model. Referring to the results of the sensitivity

analysis, it was found that VRS's improvement is

better than CRS Dual, so the solution to be given

should be based on the VRS model. In the calculation

of the VRS model, there is an Era Weaving Unit

DMU which has a non-optimal value. The Era

Weaving Unit's efficiency values are influenced by

two variables: y1 (number of products) and x3 (raw

material costs). The improvement solution required is

for variables y1 (number of products) and x3 (raw

material costs). The increase in target improvements

from DMU 2 or Era Weaving Units also takes into

account the results of peer groups as shown in table

4.27 which shows that Era Weaving Units can

perform benchmarking referring to DMUs with the

lowest and closest value, namely DMU 1 or Dewi

Weaving Unit and DMU 3 or Unit Riza Weaving. In

the Era Weaving Unit there are 18 products while in

the Dewi Weaving Unit there are 25, therefore DMU

Era can refer to DMU Dewi or DMU Riza by

increasing sales results in order to increase the value

of variable y1 (number of products). Increasing sales

can be done by promoting or cooperating with other

companies. The raw material costs of the Era

Weaving Unit will be raised by 9.1% in relation to the

Dewi Weaving Unit. The Dewi Weaving Unit's raw

materials cost is the lowest among all DMUs.

Therefore, by referring to the Dewi Weaving Unit, the

cost of raw materials in the Era Weaving Unit can

make improvements by minimizing the use of raw

material costs and planning raw material

requirements by analyzing records of weaving sales

which have not previously been carried out by the Era

Weaving Unit.

5 CONCLUSIONS

From the results of the study it can be concluded as

follows:

1. The results of measuring the efficiency of the

weaving production process in Tanjung Modang

using Data Envelopment Analysis in 2018 show

that the Era Weaving Unit is a DMU that has an

inefficient value with an efficiency value of

0.9081500. In 2019 it shows that the Siti Weaving

Unit is a DMU that has an inefficient value with a

value of 0.8602850. In 2020 it shows that the Siti

Weaving Unit and the Triya Weaving Unit have

an inefficient value. In 2021 it shows that the Era

Weaving Unit, Siti Weaving Unit and Triya

Weaving Unit are DMUs that have an inefficient

value with values of 0.9250975, 0.8895 and 0.999.

2. The 2018 improvement target for the Era

Weaving Unit is because the value obtained is <1

with the variables that are the target for

improvement, namely Y1 (number of products)

and X3 (raw material costs). In 2019, after

improvements were made to the VRS model, the

Siti Weaving Unit, which was previously

inefficient, became efficient. In 2020, after

improvements were made with the VRS DMU

model, which was previously inefficient, it will

become efficient. The target for improvement in

2021 for the Era Weaving Unit and Siti Weaving

Unit is because the values obtained are <1 with the

variables that are the target of improvement,

namely Y1 (number of products) and X3 (raw

material costs). The Triya Weaving Unit is the

optimal DMU after target improvement on y1 and

x3.

3. Based on the results of data processing and

analysis carried out in 2018 and 2021. The Era

Weaving Unit, Siti Weaving Unit and Triya

Weaving Unit refer to the calculation of the VRS

model. In the input variable that does not yet have

an optimal value, namely the variable amount of

raw material costs so that the three DMUs can

make improvements by minimizing use of raw

material costs and planning raw material

requirements by analyzing records of weaving

sales that had not previously been carried out. The

output variable that is not optimal is the number

of products so that improvements can be made by

increasing resources, expanding marketing and

promoting from various social media platforms.

Efﬁciency of the Tanjung Modang Nagari Tanjung Bonai Weaving Production Process Using the Data Envelopment Analysis Method

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