# Partner Selection in Supply Chain of Vietnam’s Textile and Apparel Industry: The Application of a Hybrid DEA and GM (1,1) Approach

Authors: Chia-Nan Wang; Han-Khanh Nguyen; Ruei-Yuan Liao
Journal: Mathematical Problems in Engineering
 (2017)

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## Abstract

Vietnam is currently among the top-five textile and apparel exporters, and the industry is considered quite attractive to foreign investors. Nevertheless, the global textile and garment industry is experiencing important changes. The three main producing regions in the world are China, Southwest Asia (India, Pakistan, Bangladesh, and Turkey), and ASEAN. In order to maintain its positioning and to establish stable and sustainable Vietnam textile and apparel development, there must be radical changes. Due to this necessity, the authors conducted this study by using grey forecasting to predict and reflect the condition of businesses in the period of 2017–2020, together with combining a DEA model to help businesses select the most appropriate strategic partner in the supply chain in order to achieve economic goals and promote the strength of the businesses partaking in the association. Besides, this helps businesses exploit market opportunities and take advantage of the capabilities of the textile and apparel industry.


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## Body

## 1. Introduction

### 1.1. Overview of Vietnam’s Textile and Apparel Industry

After more than 20 years of continuous development with an average growth rate at 17% per year, the textile and apparel industry has become a major economic industry and is a leading export industry in Vietnam, accounting for 10%–15% gross domestic product (GDP) [1]. By the end of 2014, there were 5,214 textile and apparel companies in Vietnam with the majority of those being small- and medium-sized companies. The textile and apparel work force occupied more than 20% of the nation-wide work force and nearly 5% of the nation-wide work force. Garment companies accounted for the largest share (84%), followed by textile and spinning companies (15%). Vietnam was one of the top 10 textile exporters in the world with the market share at 3.1% in 2014. The country’s main export markets included the United States, European Union (EU), Japan, and Korea (85% of the total export goods), providing the garment for mostly the low and middle classes (see Table 1). Although foreign direct investment (FDI) enterprises represented only 25% in the volume, they contributed to more than 65% of Vietnam’s export turnover [2].Table 1
Main export markets of Vietnam’s textile and apparel in 2016.
Nation
Unit
Value
United States of America
Billions of U.S Dollars
11.45
Japan
Billions of U.S Dollars
2.9
Korea
Billions of U.S Dollars
2.284
China
Millions of U.S Dollars
825
Germany
Millions of U.S Dollars
726.2
England
Millions of U.S Dollars
715
Holland
Millions of U.S Dollars
538
Sources: [14].Currently, Vietnam is involved in the process of the lowest value-added, that is, the “cut–make–trim” with the simple outworks production method (the “cut–make–trim” comprises 85%; see Table2). Vietnam’s garment sector still depends on imported raw materials (70%–80%), mainly from China, Taiwan, and Korea. The size of the Vietnamese textile and apparel market is limited at only $3 billion, and per capita expenditures for the textile and apparel are rather low [2]. Also, the Vietnamese garment enterprises are encountering difficulties in occupying the domestic market, as those companies have to compete with fake and non-quota imported goods. Branding includes just a few small and successful firms; the majority of Vietnamese textile and apparel enterprises are inadequate in absorbing knowledge such as building distribution systems, design, and branding. Free trade agreements, such as the Vietnam-Korea Free Trade Agreement (VKFTA), European Union–Vietnam Free Trade Agreement (EVFTA), and Trans-Pacific Partnership Agreement (TPP), open up many opportunities for the export turnover growth in Vietnam’s textile and apparel industry. The industry is expected to reach $50–$55 billion in 2025 thanks to FTA [2]. On the other hand, Vietnam has to face many difficulties and challenges in obeying origin rules, labor standards, strict social responsibilities, eco labels, environmental protection, and so on. Currently, Vietnam is relying on the supply of main fabrics and fibers from China and Taiwan; those that are not in the TPP. Thus, when the agreements come into force, Vietnam may have little chance to inherit tax incentives. The textile and apparel industry with the majority of small- and medium-sized companies having low financial capacity and competitiveness will experience difficulties when the economy is opened. Hence, in order to promote the overall strength of the industry, the implementation of coalition among textile enterprises has to be driven in force for the enterprises to complete and move up to a higher value chain, aiming to gain benefits from alliances, for example, achieving economies of scale; reducing production costs and transaction costs; increasing access to technology, new production resources, and markets; leveraging negotiation position and competitiveness; improving the organizational and knowledge capacity through the sharing of experience within coalition group; and building risk reduction. Joining alliances is to better serve the market. It is the only way for new textile and apparel enterprises to provide a large number of products with consistent quality and timely delivery for many partners and integrate deeper and wider into the world economy.Table 2
Overview of Vietnam’s textile and apparel industry.
Indicators
Unit
Value
Number of companies
Companies
6.000
Enterprise scale
People
SMEs of 200–500 + Account for a large proportion
Company structure based on ownership
Private (84%), FDI (15%), State-owned (1%).
Company structure based on operation
Sewing (70%), spinning (6%), weaving/knitting (17%), dying (4%), ancillary industries (3%)
Geographical allocation of company
North (30%), Central and plateau (8%), South (62%).
Number of employees
People
2,5 Million
Average income per worker
VND
4,5 Million
Number of working days per week
Day
6
Number of hours worked per week
Hour
48
Number of shifts per day
Shift
2
Value of textile export in 2016
US$
23.841 Billion
Method of production
%
Cut - Make – Trim: 85%; others: 15%
Sources: [2].


### 1.2. Global Textile and Apparel Value Chain

Gereffi introduced the global textile and apparel value chain in 2002 [3]. Whereby the textile and apparel value chain is affected by purchasers, creating finished products must go through many stages, and production activities are often carried out in many countries. In particular, well-known manufacturers, big wholesalers, and retailers play key roles in establishing production networks and shaping mass consumption through a series of strong brands and outsourcing activities to satisfy this demand. The global textile and apparel value chain is divided into five basic stages: (1) supply of raw materials, including natural cotton, thread, and so on; (2) production of intermediate goods; products of this stage are fibers and fabrics provided by weaving, knitting, and dyeing companies; (3) design and manufacture of finished products implemented by garment companies; (4) export by commercial intermediaries; (5) marketing and distribution [3]. (See Figure 1.)Figure 1
Global textile and apparel value chain [3].In order to study and present the context of the whole supply chain, more research is required. Within this study, we feature an in-depth study of consumer products for textile companies and suppliers for apparel companies. Moreover, this study is mainly focused on partner selection, which is just a part of the supply chain.


### 1.3. Literature Review

In 1989, Deng introduced the grey system theory, which is an interdisciplinary scientific area and has become quite common in systems to process and predict partially unknown parameters [4]. The grey model (GM) can estimate the behavior under grey system theory in the future based on a limited amount of available data. The GM (1,1) is one of the most important parts of grey system theory and is considered to be the core of the grey prediction model. The advantage of this model is that it can be used when the amount of data is insufficient to perform statistical analysis methods. It requires only a small amount of data and random sample data to calculate and produce the forecast results. Many studies have applied the grey system. For example, in 1989, Bunn conducted a study about forecasting with more than one model [5]. In 2001, Chen et al. studied application of an innovative combined forecasting method in power [6]. In 2005, Yamaguchi et al. studied new grey relational analysis for finding the invariable structure and its applications [7]. In 2009, Lingbin et al. used the grey theory model to predict the number of international tourists in China [8]. Besides, some researchers have conducted strategic alliance studies. For example, Büyüközkan et al. (2008) provided a decision support to make a careful assessment of an e-logistics partner [9]. This research proposed a multicriteria decision-making approach to effectively evaluate e-logistics based on strategic alliance partners. Candace et al. (2011) affirmed strategic alliances as an efficient means to access the resources needed for innovation in dynamic environments [10]. Several researches used hybrid DEA (data development analysis) and GM (1,1) to study strategic alliance, which revealed high reliability. This was a complementary concept, which assumed that two complementary companies to be good partners could make an alliance and perform better business. Because this method can estimate the behavior of data in the future based on a limited amount of available data, this complementary concept appeared in the hybrid DEA and GM (1,1) to study strategic alliance for the electronic manufacturing service industry [11]. In 2016, Wang et al. used DEA and a grey theory model to analyze the selection strategic alliance partner in the automobile industry [12]. In 2017, Wang and Nguyen used the grey and DEA model to study enhancing urban development quality based on the results of appraising efficient performance of investors in Vietnam [13].Strategic alliances between organizations are now ubiquitous; it has been used in the past for enterprises that have the same expertise and targets. In the current economy of integration and development, along with the limited capacity of enterprises, the technology has not been developed nor has it met the quality and quantity requirements of partners who continue to operate individually; thus, they will encounter difficulties when facing competitors. If we combine each other’s strengths, we will create a value chain in each product line, offer prestigious and quality products, and enhance competitiveness in the domestic and international markets. Therefore, strategic alliances between enterprises are urgent. Thus, combining a hybrid DEA model and grey system theory helps us to choose the strategic alliance for textile and apparel enterprises to make the best of their business goals and possibly exploit the opportunities of the environment, address challenges, and take advantage of the capacity of the textile and apparel industry in Vietnam. This study proposes an effective new method. Through the realities in Vietnam's textile and apparel industry, we would recommend finding the alliance partners for companies to solve those existing problems that they cannot overcome when doing business by using grey theory GM (1,1) to predict what would happen about their business situation in the period 2017–2020. After that, we use a DEA model to evaluate ranking and score of all decision-making units (DMU) in 2016. Combining analytical results from super-SBM model, we put out analysis; alliance suggests bringing high economic efficiency for enterprises as well as Vietnam’s textile and apparel industry.


## 1.1. Overview of Vietnam’s Textile and Apparel Industry

After more than 20 years of continuous development with an average growth rate at 17% per year, the textile and apparel industry has become a major economic industry and is a leading export industry in Vietnam, accounting for 10%–15% gross domestic product (GDP) [1]. By the end of 2014, there were 5,214 textile and apparel companies in Vietnam with the majority of those being small- and medium-sized companies. The textile and apparel work force occupied more than 20% of the nation-wide work force and nearly 5% of the nation-wide work force. Garment companies accounted for the largest share (84%), followed by textile and spinning companies (15%). Vietnam was one of the top 10 textile exporters in the world with the market share at 3.1% in 2014. The country’s main export markets included the United States, European Union (EU), Japan, and Korea (85% of the total export goods), providing the garment for mostly the low and middle classes (see Table 1). Although foreign direct investment (FDI) enterprises represented only 25% in the volume, they contributed to more than 65% of Vietnam’s export turnover [2].Table 1
Main export markets of Vietnam’s textile and apparel in 2016.
Nation
Unit
Value
United States of America
Billions of U.S Dollars
11.45
Japan
Billions of U.S Dollars
2.9
Korea
Billions of U.S Dollars
2.284
China
Millions of U.S Dollars
825
Germany
Millions of U.S Dollars
726.2
England
Millions of U.S Dollars
715
Holland
Millions of U.S Dollars
538
Sources: [14].Currently, Vietnam is involved in the process of the lowest value-added, that is, the “cut–make–trim” with the simple outworks production method (the “cut–make–trim” comprises 85%; see Table2). Vietnam’s garment sector still depends on imported raw materials (70%–80%), mainly from China, Taiwan, and Korea. The size of the Vietnamese textile and apparel market is limited at only $3 billion, and per capita expenditures for the textile and apparel are rather low [2]. Also, the Vietnamese garment enterprises are encountering difficulties in occupying the domestic market, as those companies have to compete with fake and non-quota imported goods. Branding includes just a few small and successful firms; the majority of Vietnamese textile and apparel enterprises are inadequate in absorbing knowledge such as building distribution systems, design, and branding. Free trade agreements, such as the Vietnam-Korea Free Trade Agreement (VKFTA), European Union–Vietnam Free Trade Agreement (EVFTA), and Trans-Pacific Partnership Agreement (TPP), open up many opportunities for the export turnover growth in Vietnam’s textile and apparel industry. The industry is expected to reach $50–$55 billion in 2025 thanks to FTA [2]. On the other hand, Vietnam has to face many difficulties and challenges in obeying origin rules, labor standards, strict social responsibilities, eco labels, environmental protection, and so on. Currently, Vietnam is relying on the supply of main fabrics and fibers from China and Taiwan; those that are not in the TPP. Thus, when the agreements come into force, Vietnam may have little chance to inherit tax incentives. The textile and apparel industry with the majority of small- and medium-sized companies having low financial capacity and competitiveness will experience difficulties when the economy is opened. Hence, in order to promote the overall strength of the industry, the implementation of coalition among textile enterprises has to be driven in force for the enterprises to complete and move up to a higher value chain, aiming to gain benefits from alliances, for example, achieving economies of scale; reducing production costs and transaction costs; increasing access to technology, new production resources, and markets; leveraging negotiation position and competitiveness; improving the organizational and knowledge capacity through the sharing of experience within coalition group; and building risk reduction. Joining alliances is to better serve the market. It is the only way for new textile and apparel enterprises to provide a large number of products with consistent quality and timely delivery for many partners and integrate deeper and wider into the world economy.Table 2
Overview of Vietnam’s textile and apparel industry.
Indicators
Unit
Value
Number of companies
Companies
6.000
Enterprise scale
People
SMEs of 200–500 + Account for a large proportion
Company structure based on ownership
Private (84%), FDI (15%), State-owned (1%).
Company structure based on operation
Sewing (70%), spinning (6%), weaving/knitting (17%), dying (4%), ancillary industries (3%)
Geographical allocation of company
North (30%), Central and plateau (8%), South (62%).
Number of employees
People
2,5 Million
Average income per worker
VND
4,5 Million
Number of working days per week
Day
6
Number of hours worked per week
Hour
48
Number of shifts per day
Shift
2
Value of textile export in 2016
US$
23.841 Billion
Method of production
%
Cut - Make – Trim: 85%; others: 15%
Sources: [2].


## 1.2. Global Textile and Apparel Value Chain

Gereffi introduced the global textile and apparel value chain in 2002 [3]. Whereby the textile and apparel value chain is affected by purchasers, creating finished products must go through many stages, and production activities are often carried out in many countries. In particular, well-known manufacturers, big wholesalers, and retailers play key roles in establishing production networks and shaping mass consumption through a series of strong brands and outsourcing activities to satisfy this demand. The global textile and apparel value chain is divided into five basic stages: (1) supply of raw materials, including natural cotton, thread, and so on; (2) production of intermediate goods; products of this stage are fibers and fabrics provided by weaving, knitting, and dyeing companies; (3) design and manufacture of finished products implemented by garment companies; (4) export by commercial intermediaries; (5) marketing and distribution [3]. (See Figure 1.)Figure 1
Global textile and apparel value chain [3].In order to study and present the context of the whole supply chain, more research is required. Within this study, we feature an in-depth study of consumer products for textile companies and suppliers for apparel companies. Moreover, this study is mainly focused on partner selection, which is just a part of the supply chain.


## 1.3. Literature Review

In 1989, Deng introduced the grey system theory, which is an interdisciplinary scientific area and has become quite common in systems to process and predict partially unknown parameters [4]. The grey model (GM) can estimate the behavior under grey system theory in the future based on a limited amount of available data. The GM (1,1) is one of the most important parts of grey system theory and is considered to be the core of the grey prediction model. The advantage of this model is that it can be used when the amount of data is insufficient to perform statistical analysis methods. It requires only a small amount of data and random sample data to calculate and produce the forecast results. Many studies have applied the grey system. For example, in 1989, Bunn conducted a study about forecasting with more than one model [5]. In 2001, Chen et al. studied application of an innovative combined forecasting method in power [6]. In 2005, Yamaguchi et al. studied new grey relational analysis for finding the invariable structure and its applications [7]. In 2009, Lingbin et al. used the grey theory model to predict the number of international tourists in China [8]. Besides, some researchers have conducted strategic alliance studies. For example, Büyüközkan et al. (2008) provided a decision support to make a careful assessment of an e-logistics partner [9]. This research proposed a multicriteria decision-making approach to effectively evaluate e-logistics based on strategic alliance partners. Candace et al. (2011) affirmed strategic alliances as an efficient means to access the resources needed for innovation in dynamic environments [10]. Several researches used hybrid DEA (data development analysis) and GM (1,1) to study strategic alliance, which revealed high reliability. This was a complementary concept, which assumed that two complementary companies to be good partners could make an alliance and perform better business. Because this method can estimate the behavior of data in the future based on a limited amount of available data, this complementary concept appeared in the hybrid DEA and GM (1,1) to study strategic alliance for the electronic manufacturing service industry [11]. In 2016, Wang et al. used DEA and a grey theory model to analyze the selection strategic alliance partner in the automobile industry [12]. In 2017, Wang and Nguyen used the grey and DEA model to study enhancing urban development quality based on the results of appraising efficient performance of investors in Vietnam [13].Strategic alliances between organizations are now ubiquitous; it has been used in the past for enterprises that have the same expertise and targets. In the current economy of integration and development, along with the limited capacity of enterprises, the technology has not been developed nor has it met the quality and quantity requirements of partners who continue to operate individually; thus, they will encounter difficulties when facing competitors. If we combine each other’s strengths, we will create a value chain in each product line, offer prestigious and quality products, and enhance competitiveness in the domestic and international markets. Therefore, strategic alliances between enterprises are urgent. Thus, combining a hybrid DEA model and grey system theory helps us to choose the strategic alliance for textile and apparel enterprises to make the best of their business goals and possibly exploit the opportunities of the environment, address challenges, and take advantage of the capacity of the textile and apparel industry in Vietnam. This study proposes an effective new method. Through the realities in Vietnam's textile and apparel industry, we would recommend finding the alliance partners for companies to solve those existing problems that they cannot overcome when doing business by using grey theory GM (1,1) to predict what would happen about their business situation in the period 2017–2020. After that, we use a DEA model to evaluate ranking and score of all decision-making units (DMU) in 2016. Combining analytical results from super-SBM model, we put out analysis; alliance suggests bringing high economic efficiency for enterprises as well as Vietnam’s textile and apparel industry.


## 2. Materials and Methods

### 2.1. DMUs Collection

After researching Vietnam’s textile and apparel industry, this study reveals 15 companies collected from the General Statistics Office’s website [15–29]. We did not choose other enterprises because of this reason: the company scale is small, the uptime does not align with the data requirements, and the business data of those companies do not match with the demand of grey theory model that we used in this research. The synopsis is shown in Table 3.Table 3
List of Companies in textile and apparel industry.
Order
DMUs
Companies name
1
DMU1
Hue Textile Garment Joint Stock Company (J.S.C)
2
DMU2
SaiGon Garment Manufacturing Trade J.S.C
3
DMU3
TNG Investment and Trading J.S.C
4
DMU4
Nha Be Garment Corporation J.S.C
5
DMU5
Ha Dong Textile J.S.C
6
DMU6
DongNai Garment Corporation
7
DMU7
HoaTho Textile–Garment J.S.C
8
DMU8
Phan Thiet Garment Import–Export J.S.C
9
DMU9
Saigon 3 Garment J.S.C
10
DMU10
ThangLoi International Garment J.S.C
11
DMU11
HaNoi Industrial Textile J.S.C
12
DMU12
HaNoi Textile and Garment J.S.C
13
DMU13
March 29 Textile Garment J.S.C
14
DMU14
G.HOME Textile Investment J.S.C
15
DMU15
Viet Tien Garment J.S.C
Source: synthetic by researcher [15–29].


### 2.2. Inputs/Outputs Collection

The inputs/outputs element will directly affect the results of the analysis and evaluation. From the literature review, this research summarizes finance factors that were not previously considered. This study carefully selects four inputs and two outputs, which are collected from financial statements of 15 DMUs.(i)
Input factors include the following:
Total assets (TA), which reflect the entire value of existing assets of the business in reporting.Cost of sold capital (CS), which is the total input cost of the enterprise to produce products, including the cost of inputs, fuel costs, machinery, direct labor, and other expenses.Selling expenses (SE), which include all costs incurred in the process of consuming products, goods, or services.General and administration expenses (AE), which are the total costs of business management, administrative management, and other general services related to the operation of the business.(ii)
Output factors include the following:
Revenue of sales (RS), which reflects sales and services revenue of the enterprise in an accounting period of production and business activities from transactions, sales, and servicing operation.Profit after tax (PT), which reflects the total net profit after tax relating to the enterprise’s business activities.The selected factors reflect wholly a firm’s assets, costs, and profits, which can be used as the foundation for analysis, calculation, and evaluation of this study. Researcher synthetics are shown in Tables4, 5, 6, and 7.Table 4
Data of 15 DMUs in 2013 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
509991
1152459
42110
54446
1306653
30880
DMU2
657777
1062372
18633
90818
1228479
57032
DMU3
961199
962177
26726
102633
1186685
14057
DMU4
1878363
2291270
199623
192508
2818958
88070
DMU5
158855
225771
6666
9543
255462
11518
DMU6
454291
898732
18704
39094
983038
29553
DMU7
974923
2217252
70805
86102
2454787
48340
DMU8
103426
142211
1202
5371
172208
21481
DMU9
968574
1713273
38784
77527
1861925
36189
DMU10
72358
118064
18227
14084
162747
6888
DMU11
140479
398557
14232
24012
459383
11949
DMU12
1633770
1232482
46967
72444
1395956
29079
DMU13
263782
326142
10801
37282
396971
9796
DMU14
201567
178629
2769
1554
196211
1127
DMU15
2456738
4159024
226059
209707
4831173
237117
Source: synthetic by researcher [15–29].Table 5
Data of 15 DMUs in 2014 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
588788
1221869
46946
53530
1379742
35119
DMU2
637070
1201404
21510
115432
1409479
60497
DMU3
1197910
1115111
27499
107228
1377234
53158
DMU4
2127350
2454015
223486
269719
3079348
92309
DMU5
163659
257292
6585
9756
292352
44954
DMU6
581485
1137097
21124
45117
1250242
40105
DMU7
1283842
2336302
82537
79718
2593478
64483
DMU8
112973
169561
1281
6774
200017
21650
DMU9
1144414
1042259
41498
60037
1264576
89755
DMU10
73739
133775
18326
13416
176581
5879
DMU11
230140
522542
17807
27507
592539
14565
DMU12
1573948
1394663
51566
79990
1570431
46171
DMU13
316593
412595
11231
34509
490648
18879
DMU14
5771
200702
3592
2396
227138
5771
DMU15
2908907
4749674
220757
200589
5482404
296592
Source: synthetic by researcher [15–29].Table 6
Data of 15 DMUs in 2015 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
606216
1309806
51544
53208
1480821
44063
DMU2
836542
1249641
35649
136582
1502065
63458
DMU3
1613646
1574939
36668
146519
1923940
71300
DMU4
2735885
3538699
306195
383943
4433111
115164
DMU5
243803
232900
6489
8237
262779
13084
DMU6
458819
976675
20858
47048
1083273
19945
DMU7
1369356
2656957
107899
114281
3005032
74018
DMU8
145849
238988
1694
6378
275864
29061
DMU9
1235441
1145009
47629
62771
1349758
96707
DMU10
73484
127695
18151
13368
171465
5412
DMU11
257599
516094
26273
39332
625326
26404
DMU12
1918836
1510140
54745
79196
1756114
39766
DMU13
487229
526497
8161
47275
627916
23900
DMU14
11659
265671
2831
4351
300890
11659
DMU15
3380138
5645821
221379
237333
6408465
311044
Source: synthetic by researcher [15–29].Table 7
Data of 15 DMUs in 2016 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
679185
1341165
52198
26851
1478313
42778
DMU2
883468
1336254
46980
148299
1611379
60986
DMU3
1846223
1554546
28942
140127
1887749
81179
DMU4
2707671
3412884
309616
405506
4233351
52540
DMU5
229651
212276
6716
8125
248086
11748
DMU6
533483
926722
19855
48261
1013852
15293
DMU7
1917445
2882242
90013
119504
3198584
71244
DMU8
165293
241046
1623
5724
269649
22368
DMU9
1324281
1540328
39342
84403
1738944
137131
DMU10
88101
110327
11531
13629
152988
9199
DMU11
219244
471021
29763
43920
588614
31616
DMU12
2108020
1771020
52150
90745
2000541
51154
DMU13
718718
700224
9827
46771
798022
25493
DMU14
12562
312154
3744
4664
351044
12562
DMU15
3832596
6622654
266807
259384
7526047
376606
Source: synthetic by researcher [15–29].


### 2.3. Research Development

The authors use the GM (1,1) model to predict the business situation of all DMUs and the super-SBM-I-V model to select strategic alliance partners for the textile business in the future. The process of conducting this study is shown in Figure2.Figure 2
Research process.Step 1 (objectives identification).
Based on the requirements of professional, managerial, and practical needs of the textile and apparel industry, the authors select the objectives of the study.Step 2 (literature review).
By researching and exploring the chosen topic, the authors examine available resources, identifies research purposes, and develops hypothesis for his/her research topic to implement the research. If the topic, time, and method overlap with another study, the authors revert to Step1.Step 3 (methodology).
Prediction Method. There are many prediction methods, and the authors use the GM (1,1) to predict the data from the DMUs from 2017 to 2020 because this is a highly reliable prediction method and suitable with the data of DMUs.DEA Models. There are many methods to choose an alliance partner, and the authors use the super-SBM-I-V, a method of choosing the right strategic alliance partner and is popular nowadays.Step 4 (research planning).
The authors plan to carry out the main tasks, to manage time well, and to scientifically control the progress.Step 5 (DMUs collection).
The authors collected information of 15 Vietnam textile and apparel companies, which was in line with research objectives through the General Statistics Office’s website.Step 6 (inputs/outputs collection).
Input factors include total assets, cost of capital sold, cost of sale, and general and administration expenses.
Output factors include revenue of sales and profit after tax.Step 7 (grey prediction).
The prediction sequence is as follows.
Forecasting. Use business data of 15 DMUs from 2013 to 2016 and apply GM (1,1) to predict business performance from 2017 to 2020.
Check Accuracy. The authors use mean absolute percentage error (MAPE) to calculate predicted errors, thus ensuring the predicted data are accurate compared with the requirements. In case the accuracy does not meet with the requirements, DMUs must be selected accordingly.Step 8 (check Pearson coefficient).
The authors use the Pearson coefficient to check the correlation between inputs and outputs. Suppose the inputs and outputs have a positive correlation; the result is highly reliable. However, if the correlation coefficient is negative or equals zero, the inputs and outputs must be reselected.Step 9 (DEA analysis (super-SBM-I-V)).
The sequence of DEA analysis according to the super-SBM-I-V model is as follows.Before Alliance. The actual results of the order and business performance of the DMUs serve as a basis for the authors to choose future alliance partner.After Alliance. From the preunion assessment, the authors select the DMU target as a partner alliance with other businesses. When combining DMU target with 14 other DMUs, the authors use the SBM-I-V model to analyze and obtain 29 plans to compare and evaluate analysis and comments.Step 10 (partner selection; conclusion).
Based on the results of analysis after the alliance, the authors proposed the most appropriate alliance solution in the supply chain of the enterprises, which brings the highest economic efficiency as well as the affiliate groups that are not highly effective in helping the businesses with suitable direction.


### 2.4. Grey Forecasting Model

Before using the GM (1,1) model, initial data must be tested against the formula [30]:(1)δi=x0i-1x0i;i=2,3,…,n.If all the δi values are within δ(0)(i)=(e-2/(n+1);e2/(n+1)), we can use the GM (1,1) model to forecast.The GM (1,1) model is calculated based on the following differential equation [31]:(2)dx1kdk+ax1k=b.Within the equation, a and b are coefficients. The initial data is considered as a value chain as follows: (3)X0=x01,x02,…,x0n;n≥4.In this research, X(0) is the actual business metrics of DMUs recorded from 2013 to 2016. Data after being tested will be calculated using the following steps.Step 1.
Calculate theX(1) by using the cumulative method [31]:(4)χ1=χ11,χ12,…,χ1n;n≥4,where χ11=χ01,χ(1)(k)=∑i=1kχ(0)(i);k=1,2,3,…,n.
Calculate seriesZ(1) (mean value) of adjacent neighbor χ(1) by [31]:(5)Z1=Z11,Z12,…,Z1n;n≥4,where Z1(κ)=0.5χ1κ+χ1κ-1); k=2,3,…,n.Step 2.
Set the equation for the GM (1,1) model and calculate theZ(1) values [31]:(6)dX1kdk+aX1k=b,where Z1(κ)=0.5(x1(κ)+x(1)(κ-1));k=2,3,…,n.Step 3.
Calculate the parametera and parameter b.
Parametera and parameter b of the GM (1,1) model are calculated based on the least-squares method as follows [31]:(7)a^=abT=BTB-1BTY-N,where:B=-αZ121⋮⋮-αZ1n1;YN=X02⋮X0n.Step 4.
Set the formula to calculate the predictive value of the model [31]:(8)X^1κ+1=χ01-bae-aκ+baκ=1,2,3,….
Then calculate the predicted values of the GM (1,1) model based on the following formula [31]:(9)X^0k+1=x^1k+1-x^1k,wherex^(0)1=x(0)1;(κ=1,2,3,…,n).


### 2.5. Evaluation of Volatility Forecasts

In this paper, we use MAPE, which is a tool to measure the accuracy value in statistics, to identify the grey prediction models with good performance. The MAPE is small; It is called the slacks-based measure of efficiency (SBM) [32].(10)MAPE=1n∑i=1nAi-FiAi×100.The MAPE is divided into our ranks as shown in Table8.Table 8
The grades of MAPE.
MAPE valuation (%)
≤10
10
÷
20
20
÷
50
≥50
Accuracy
Excellent
Good
Qualified
Unqualified
Sources: [30].


### 2.6. Nonradial Super Efficiency Model (Super-SBM)

In this section, we propose a model by which we discriminate between such DMUs: it is called the slacks-based measure of efficiency (SBM). Here, a super-SBM is expanded by the addition of the super efficiency to the DEA model with the polarities of the inputs and outputs. Then DMUs with the input/output matrices (X,Y) are used in this model [33], in which X=xij∈Rm×n and Y=Yij∈Rs×n. λ is a nonnegative vector in Rn. The vectors S-∈Rm and S+∈Rs represent an excess input and a short falling output, respectively [34]. The SBM model is given by following equation [35]:(11)minρ=1-1/m∑i=1msi-/xi01+1/s∑i=1ssi-/yi0s.t:χ0=Xλ+S-,γ0=Yλ-S+,λ≥0,X≥0,Y≥0.Suppose (p∗,λ∗,s-∗,s+∗) is the optimal condition of SBM, and (χ0,γ0) is SBM efficient of DMU. When p∗=1 so s-∗=0 and s+∗=0 (or there is no excess input and a short falling output). Hence, a super-efficiency model was introduced for ranking DMUs, and it was defined by the following formula [36]:(12)minδ=1/m∑i=1mx¯i/xi01/s∑r=1syr¯/yr0s.tx¯≥∑j=1,≠0nλjxj,y¯≤∑j=1,≠0nλjxj,χ-≥χ0,γ-≤γ0,γ-≥0,λ≥0.Suppose the denominator is 1, so that the objective function is an orientation input of the super-SBM model. The obtained feedback value of the objective function is larger or equivalent to 1. It is straightforward, then, that the inputs are positive; however, the outputs are able to become a negative value. The proper solution to solve this problem is to use DEA-solver pro 4.1 manual [37], as follows:Supposeyro≤0. It has defined γ-r+ and γ--r+ by (13)y¯r+=maxj=1,…,nyrj∣yrj>0,y¯-r+=minj=1,…,nyrj∣yrj>0.If there is no positive component in the outputr, it becomes y¯r+=y-r+=1. The element sr+/γr0 is instead the following, while the γr0 is unchanged:(14)Whenγ-r+>γ--r+the  element  is:sr+γ-r+γ-r+-γr+/γ-r+-γr0Whenγ-r+=γ--r+the  element  becomes:sr+γ-r+2/Bγ-r+-γr0.Here,B represents a large positive value; in the DEA-solver the value of B is about 100.Given the fact that the denominator is certainly lower thanγ-r+, it increases with the decrease of the distance γ-r+-γr0 and vice versa. Hence, this model significantly affects the nonpositive output value. The obtained score is constant, and it is a unidimensional unit for using units of the measurement [35].


## 2.1. DMUs Collection

After researching Vietnam’s textile and apparel industry, this study reveals 15 companies collected from the General Statistics Office’s website [15–29]. We did not choose other enterprises because of this reason: the company scale is small, the uptime does not align with the data requirements, and the business data of those companies do not match with the demand of grey theory model that we used in this research. The synopsis is shown in Table 3.Table 3
List of Companies in textile and apparel industry.
Order
DMUs
Companies name
1
DMU1
Hue Textile Garment Joint Stock Company (J.S.C)
2
DMU2
SaiGon Garment Manufacturing Trade J.S.C
3
DMU3
TNG Investment and Trading J.S.C
4
DMU4
Nha Be Garment Corporation J.S.C
5
DMU5
Ha Dong Textile J.S.C
6
DMU6
DongNai Garment Corporation
7
DMU7
HoaTho Textile–Garment J.S.C
8
DMU8
Phan Thiet Garment Import–Export J.S.C
9
DMU9
Saigon 3 Garment J.S.C
10
DMU10
ThangLoi International Garment J.S.C
11
DMU11
HaNoi Industrial Textile J.S.C
12
DMU12
HaNoi Textile and Garment J.S.C
13
DMU13
March 29 Textile Garment J.S.C
14
DMU14
G.HOME Textile Investment J.S.C
15
DMU15
Viet Tien Garment J.S.C
Source: synthetic by researcher [15–29].


## 2.2. Inputs/Outputs Collection

The inputs/outputs element will directly affect the results of the analysis and evaluation. From the literature review, this research summarizes finance factors that were not previously considered. This study carefully selects four inputs and two outputs, which are collected from financial statements of 15 DMUs.(i)
Input factors include the following:
Total assets (TA), which reflect the entire value of existing assets of the business in reporting.Cost of sold capital (CS), which is the total input cost of the enterprise to produce products, including the cost of inputs, fuel costs, machinery, direct labor, and other expenses.Selling expenses (SE), which include all costs incurred in the process of consuming products, goods, or services.General and administration expenses (AE), which are the total costs of business management, administrative management, and other general services related to the operation of the business.(ii)
Output factors include the following:
Revenue of sales (RS), which reflects sales and services revenue of the enterprise in an accounting period of production and business activities from transactions, sales, and servicing operation.Profit after tax (PT), which reflects the total net profit after tax relating to the enterprise’s business activities.The selected factors reflect wholly a firm’s assets, costs, and profits, which can be used as the foundation for analysis, calculation, and evaluation of this study. Researcher synthetics are shown in Tables4, 5, 6, and 7.Table 4
Data of 15 DMUs in 2013 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
509991
1152459
42110
54446
1306653
30880
DMU2
657777
1062372
18633
90818
1228479
57032
DMU3
961199
962177
26726
102633
1186685
14057
DMU4
1878363
2291270
199623
192508
2818958
88070
DMU5
158855
225771
6666
9543
255462
11518
DMU6
454291
898732
18704
39094
983038
29553
DMU7
974923
2217252
70805
86102
2454787
48340
DMU8
103426
142211
1202
5371
172208
21481
DMU9
968574
1713273
38784
77527
1861925
36189
DMU10
72358
118064
18227
14084
162747
6888
DMU11
140479
398557
14232
24012
459383
11949
DMU12
1633770
1232482
46967
72444
1395956
29079
DMU13
263782
326142
10801
37282
396971
9796
DMU14
201567
178629
2769
1554
196211
1127
DMU15
2456738
4159024
226059
209707
4831173
237117
Source: synthetic by researcher [15–29].Table 5
Data of 15 DMUs in 2014 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
588788
1221869
46946
53530
1379742
35119
DMU2
637070
1201404
21510
115432
1409479
60497
DMU3
1197910
1115111
27499
107228
1377234
53158
DMU4
2127350
2454015
223486
269719
3079348
92309
DMU5
163659
257292
6585
9756
292352
44954
DMU6
581485
1137097
21124
45117
1250242
40105
DMU7
1283842
2336302
82537
79718
2593478
64483
DMU8
112973
169561
1281
6774
200017
21650
DMU9
1144414
1042259
41498
60037
1264576
89755
DMU10
73739
133775
18326
13416
176581
5879
DMU11
230140
522542
17807
27507
592539
14565
DMU12
1573948
1394663
51566
79990
1570431
46171
DMU13
316593
412595
11231
34509
490648
18879
DMU14
5771
200702
3592
2396
227138
5771
DMU15
2908907
4749674
220757
200589
5482404
296592
Source: synthetic by researcher [15–29].Table 6
Data of 15 DMUs in 2015 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
606216
1309806
51544
53208
1480821
44063
DMU2
836542
1249641
35649
136582
1502065
63458
DMU3
1613646
1574939
36668
146519
1923940
71300
DMU4
2735885
3538699
306195
383943
4433111
115164
DMU5
243803
232900
6489
8237
262779
13084
DMU6
458819
976675
20858
47048
1083273
19945
DMU7
1369356
2656957
107899
114281
3005032
74018
DMU8
145849
238988
1694
6378
275864
29061
DMU9
1235441
1145009
47629
62771
1349758
96707
DMU10
73484
127695
18151
13368
171465
5412
DMU11
257599
516094
26273
39332
625326
26404
DMU12
1918836
1510140
54745
79196
1756114
39766
DMU13
487229
526497
8161
47275
627916
23900
DMU14
11659
265671
2831
4351
300890
11659
DMU15
3380138
5645821
221379
237333
6408465
311044
Source: synthetic by researcher [15–29].Table 7
Data of 15 DMUs in 2016 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
679185
1341165
52198
26851
1478313
42778
DMU2
883468
1336254
46980
148299
1611379
60986
DMU3
1846223
1554546
28942
140127
1887749
81179
DMU4
2707671
3412884
309616
405506
4233351
52540
DMU5
229651
212276
6716
8125
248086
11748
DMU6
533483
926722
19855
48261
1013852
15293
DMU7
1917445
2882242
90013
119504
3198584
71244
DMU8
165293
241046
1623
5724
269649
22368
DMU9
1324281
1540328
39342
84403
1738944
137131
DMU10
88101
110327
11531
13629
152988
9199
DMU11
219244
471021
29763
43920
588614
31616
DMU12
2108020
1771020
52150
90745
2000541
51154
DMU13
718718
700224
9827
46771
798022
25493
DMU14
12562
312154
3744
4664
351044
12562
DMU15
3832596
6622654
266807
259384
7526047
376606
Source: synthetic by researcher [15–29].


## 2.3. Research Development

The authors use the GM (1,1) model to predict the business situation of all DMUs and the super-SBM-I-V model to select strategic alliance partners for the textile business in the future. The process of conducting this study is shown in Figure2.Figure 2
Research process.Step 1 (objectives identification).
Based on the requirements of professional, managerial, and practical needs of the textile and apparel industry, the authors select the objectives of the study.Step 2 (literature review).
By researching and exploring the chosen topic, the authors examine available resources, identifies research purposes, and develops hypothesis for his/her research topic to implement the research. If the topic, time, and method overlap with another study, the authors revert to Step1.Step 3 (methodology).
Prediction Method. There are many prediction methods, and the authors use the GM (1,1) to predict the data from the DMUs from 2017 to 2020 because this is a highly reliable prediction method and suitable with the data of DMUs.DEA Models. There are many methods to choose an alliance partner, and the authors use the super-SBM-I-V, a method of choosing the right strategic alliance partner and is popular nowadays.Step 4 (research planning).
The authors plan to carry out the main tasks, to manage time well, and to scientifically control the progress.Step 5 (DMUs collection).
The authors collected information of 15 Vietnam textile and apparel companies, which was in line with research objectives through the General Statistics Office’s website.Step 6 (inputs/outputs collection).
Input factors include total assets, cost of capital sold, cost of sale, and general and administration expenses.
Output factors include revenue of sales and profit after tax.Step 7 (grey prediction).
The prediction sequence is as follows.
Forecasting. Use business data of 15 DMUs from 2013 to 2016 and apply GM (1,1) to predict business performance from 2017 to 2020.
Check Accuracy. The authors use mean absolute percentage error (MAPE) to calculate predicted errors, thus ensuring the predicted data are accurate compared with the requirements. In case the accuracy does not meet with the requirements, DMUs must be selected accordingly.Step 8 (check Pearson coefficient).
The authors use the Pearson coefficient to check the correlation between inputs and outputs. Suppose the inputs and outputs have a positive correlation; the result is highly reliable. However, if the correlation coefficient is negative or equals zero, the inputs and outputs must be reselected.Step 9 (DEA analysis (super-SBM-I-V)).
The sequence of DEA analysis according to the super-SBM-I-V model is as follows.Before Alliance. The actual results of the order and business performance of the DMUs serve as a basis for the authors to choose future alliance partner.After Alliance. From the preunion assessment, the authors select the DMU target as a partner alliance with other businesses. When combining DMU target with 14 other DMUs, the authors use the SBM-I-V model to analyze and obtain 29 plans to compare and evaluate analysis and comments.Step 10 (partner selection; conclusion).
Based on the results of analysis after the alliance, the authors proposed the most appropriate alliance solution in the supply chain of the enterprises, which brings the highest economic efficiency as well as the affiliate groups that are not highly effective in helping the businesses with suitable direction.


## 2.4. Grey Forecasting Model

Before using the GM (1,1) model, initial data must be tested against the formula [30]:(1)δi=x0i-1x0i;i=2,3,…,n.If all the δi values are within δ(0)(i)=(e-2/(n+1);e2/(n+1)), we can use the GM (1,1) model to forecast.The GM (1,1) model is calculated based on the following differential equation [31]:(2)dx1kdk+ax1k=b.Within the equation, a and b are coefficients. The initial data is considered as a value chain as follows: (3)X0=x01,x02,…,x0n;n≥4.In this research, X(0) is the actual business metrics of DMUs recorded from 2013 to 2016. Data after being tested will be calculated using the following steps.Step 1.
Calculate theX(1) by using the cumulative method [31]:(4)χ1=χ11,χ12,…,χ1n;n≥4,where χ11=χ01,χ(1)(k)=∑i=1kχ(0)(i);k=1,2,3,…,n.
Calculate seriesZ(1) (mean value) of adjacent neighbor χ(1) by [31]:(5)Z1=Z11,Z12,…,Z1n;n≥4,where Z1(κ)=0.5χ1κ+χ1κ-1); k=2,3,…,n.Step 2.
Set the equation for the GM (1,1) model and calculate theZ(1) values [31]:(6)dX1kdk+aX1k=b,where Z1(κ)=0.5(x1(κ)+x(1)(κ-1));k=2,3,…,n.Step 3.
Calculate the parametera and parameter b.
Parametera and parameter b of the GM (1,1) model are calculated based on the least-squares method as follows [31]:(7)a^=abT=BTB-1BTY-N,where:B=-αZ121⋮⋮-αZ1n1;YN=X02⋮X0n.Step 4.
Set the formula to calculate the predictive value of the model [31]:(8)X^1κ+1=χ01-bae-aκ+baκ=1,2,3,….
Then calculate the predicted values of the GM (1,1) model based on the following formula [31]:(9)X^0k+1=x^1k+1-x^1k,wherex^(0)1=x(0)1;(κ=1,2,3,…,n).


## 2.5. Evaluation of Volatility Forecasts

In this paper, we use MAPE, which is a tool to measure the accuracy value in statistics, to identify the grey prediction models with good performance. The MAPE is small; It is called the slacks-based measure of efficiency (SBM) [32].(10)MAPE=1n∑i=1nAi-FiAi×100.The MAPE is divided into our ranks as shown in Table8.Table 8
The grades of MAPE.
MAPE valuation (%)
≤10
10
÷
20
20
÷
50
≥50
Accuracy
Excellent
Good
Qualified
Unqualified
Sources: [30].


## 2.6. Nonradial Super Efficiency Model (Super-SBM)

In this section, we propose a model by which we discriminate between such DMUs: it is called the slacks-based measure of efficiency (SBM). Here, a super-SBM is expanded by the addition of the super efficiency to the DEA model with the polarities of the inputs and outputs. Then DMUs with the input/output matrices (X,Y) are used in this model [33], in which X=xij∈Rm×n and Y=Yij∈Rs×n. λ is a nonnegative vector in Rn. The vectors S-∈Rm and S+∈Rs represent an excess input and a short falling output, respectively [34]. The SBM model is given by following equation [35]:(11)minρ=1-1/m∑i=1msi-/xi01+1/s∑i=1ssi-/yi0s.t:χ0=Xλ+S-,γ0=Yλ-S+,λ≥0,X≥0,Y≥0.Suppose (p∗,λ∗,s-∗,s+∗) is the optimal condition of SBM, and (χ0,γ0) is SBM efficient of DMU. When p∗=1 so s-∗=0 and s+∗=0 (or there is no excess input and a short falling output). Hence, a super-efficiency model was introduced for ranking DMUs, and it was defined by the following formula [36]:(12)minδ=1/m∑i=1mx¯i/xi01/s∑r=1syr¯/yr0s.tx¯≥∑j=1,≠0nλjxj,y¯≤∑j=1,≠0nλjxj,χ-≥χ0,γ-≤γ0,γ-≥0,λ≥0.Suppose the denominator is 1, so that the objective function is an orientation input of the super-SBM model. The obtained feedback value of the objective function is larger or equivalent to 1. It is straightforward, then, that the inputs are positive; however, the outputs are able to become a negative value. The proper solution to solve this problem is to use DEA-solver pro 4.1 manual [37], as follows:Supposeyro≤0. It has defined γ-r+ and γ--r+ by (13)y¯r+=maxj=1,…,nyrj∣yrj>0,y¯-r+=minj=1,…,nyrj∣yrj>0.If there is no positive component in the outputr, it becomes y¯r+=y-r+=1. The element sr+/γr0 is instead the following, while the γr0 is unchanged:(14)Whenγ-r+>γ--r+the  element  is:sr+γ-r+γ-r+-γr+/γ-r+-γr0Whenγ-r+=γ--r+the  element  becomes:sr+γ-r+2/Bγ-r+-γr0.Here,B represents a large positive value; in the DEA-solver the value of B is about 100.Given the fact that the denominator is certainly lower thanγ-r+, it increases with the decrease of the distance γ-r+-γr0 and vice versa. Hence, this model significantly affects the nonpositive output value. The obtained score is constant, and it is a unidimensional unit for using units of the measurement [35].


## 3. Results

### 3.1. Results and Analysis of the Grey Forecasting of the Output Values for All DMUs

The GM (1,1) model is utilized to predict the input and output factors values for each decision-making unit from 2017 to 2020. We used factor total assets (TA) – input 1 of DMU1 in Table9 to explain calculation procedure in this part.Table 9
Data of DMU1 from 2013 to 2016 (currency unit: 1,000,000 VND).
Year
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
2013
509991
1152459
42110
54446
1306653
30880
2014
588788
1221869
46946
53530
1379742
35119
2015
606216
1309806
51544
53208
1480821
44063
2016
679185
1341165
52198
26851
1478313
42778
Source: synthetic by researcher [9].First, we use the GM (1,1) model for trying to forecast the variance of primitive series.Create the primitive series:(15)X0=509991;588788;606216;679185.Perform the accumulated generating operation (AGO):(16)X1=509991;1098779;1704995;2384180x11=x01=509991x12=x01+x02=1098779x13=x12+x03=1704995x14=x13+x04=2384180.Create the different equation of GM (1,1).To findX(1) series, the following mean obtained by the mean equation is(17)z12=0.5×509991+1098779=804385z13=0.5×1098779+1704995=1401887z14=0.5×1704995+2384180=2044587.5.Solve equations.To finda and b, the primitive series values are substituted into the grey differential equation to obtain(18)588788+a×804385=b606216+a×1401887=b679185+a×2044587.5=b.Convert the linear equations into the form of a matrix.Let(19)B=-8043851-14018871-2044587.51;θ^=ab;YN=588788606216679185.And then use the least-squares method to finda and b:(20)ab=θ^=BTB-1BTyN=-0.0734520724.6709.Use the two coefficients a and b to generate the whitening equation of the differential equation:(21)dx1dk+0.0734×x1=520724.6709.Find the prediction model from equation(22)X^1κ+1=χ01-ba×e-aκ+ba=509991-520724.6709-0.0734×e--0.0734κ+520724.6709-0.0734=7604289.363×e0.0734×k-7094298.363.Substitute different value of k into the equation(23)X11=509991k=0;X12=1089144.30k=1;X13=1712406.71k=2;X14=2383137.67k=3;X15=3104952.43k=4;X16=3881741.61k=5;X17=4717692.14k=6;X18=5617309.83k=7;derive the predicted value of the original series according to the accumulated generating operation and obtain(24)x^01=x11=509991x^02=x^12-x^11=579153.30x^03=x^13-x^12=623262.42x^04=x^14-x^13=670730.95x^05=x^15-x^14=721814.76—Result  forecast  of  2017.x^06=x^16-x^15=776789.18—Result  forecast  of  2018.x^07=x^17-x^16=835950.53—Result  forecast  of  2019.x^08=x^18-x^17=899617.69—Result  forecast  of  2020.As with above process, we have the result of all DMUs from 2017 to 2020, respectively, in Tables10, 11, 12, and 13.Table 10
Data of 15 DMUs in 2017 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
721814.76
1413714.89
55627.68
25296.03
1546318.61
48544.08
DMU2
1053997.39
1403749.17
67264.71
169108.78
1720725.30
62130.40
DMU3
2294455.62
1881264.48
32380.04
165411.22
2268702.20
100498.51
DMU4
3130664.07
4143249.39
371956.57
502950.15
5121472.13
57986.82
DMU5
281200.84
192406.24
6729.79
7154.80
225963.51
3242.28
DMU6
475353.42
816765.35
19381.36
50028.24
895875.88
7694.20
DMU7
2302526.94
3212821.85
100626.19
148225.35
3579094.18
76730.92
DMU8
200487.46
293300.49
1885.72
5316.81
321723.30
25020.34
DMU9
1424974.01
1844619.97
40822.27
98235.86
2007490.91
166631.40
DMU10
94367.97
102522.46
10619.72
13686.16
145006.42
11240.91
DMU11
225439.67
454380.31
38423.52
55629.26
598318.91
45151.67
DMU12
2453736.30
1978048.44
53396.32
94910.26
2250200.03
51258.86
DMU13
1046331.33
901189.93
8305.48
55816.79
1008742.38
29953.18
DMU14
17985.77
388587.67
3557.42
6423.83
435973.52
17985.77
DMU15
4397864.35
7798797.34
287608.15
296483.83
8786351.84
418309.95
Sources: calculated by researcher.Table 11
Data of 15 DMUs in 2018 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
776789.18
1480059.07
58572.89
19300.11
1599270.79
53131.91
DMU2
1226468.27
1481143.18
96220.54
190968.19
1840070.14
62373.71
DMU3
2813720.28
2178272.62
33076.22
186161.97
2608027.98
122708.61
DMU4
3495299.88
4781494.65
430700.05
604322.45
5878474.45
47705.47
DMU5
324822.64
174740.33
6797.49
6498.75
207884.79
1380.11
DMU6
452735.10
734911.71
18797.32
51732.30
804655.10
4497.72
DMU7
2863522.13
3562321.21
104426.16
177677.19
3962721.45
80419.71
DMU8
240420.59
342986.29
2096.21
4894.19
367301.15
25358.17
DMU9
1532556.94
2270676.18
39861.49
118106.13
2376926.55
209816.16
DMU10
103714.56
93407.36
8711.68
13795.21
135247.46
14709.18
DMU11
220517.09
431990.09
48542.73
68926.94
596408.96
63025.78
DMU12
2824812.20
2235788.48
53686.99
101400.69
2541235.71
54332.00
DMU13
1559205.34
1176387.15
7680.31
63932.10
1284240.82
34505.81
DMU14
24530.38
480066.86
3645.25
8464.64
536697.39
24530.38
DMU15
5041317.17
9198794.29
318016.79
335956.54
10293643.64
474013.17
Sources: calculated by researcher.Table 12
Data of 15 DMUs in 2019 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
835950.53
1549516.73
61674.04
14725.40
1654036.27
58153.32
DMU2
1427161.42
1562804.22
137641.17
215653.21
1967692.42
62617.97
DMU3
3450501.19
2522171.46
33787.36
209515.89
2998106.11
149827.14
DMU4
3902405.69
5518058.16
498720.94
726126.87
6747368.91
39247.05
DMU5
375211.34
158696.43
6865.88
5902.85
191252.51
587.46
DMU6
431193.01
661261.18
18230.88
53494.41
722722.69
2629.18
DMU7
3561200.02
3949840.06
108369.64
212981.01
4387468.04
84285.84
DMU8
288307.62
401088.98
2330.18
4505.17
419335.92
25700.56
DMU9
1648262.18
2795139.59
38923.33
141995.57
2814348.88
264192.82
DMU10
113986.88
85102.67
7146.46
13905.12
126145.28
19247.55
DMU11
215701.99
410703.18
61326.93
85403.31
594505.11
87975.68
DMU12
3252005.51
2527112.08
53979.24
108334.98
2869913.27
57589.39
DMU13
2323471.75
1535621.61
7102.19
73227.30
1634980.85
39750.40
DMU14
33456.42
593081.58
3735.26
11153.81
660691.70
33456.42
DMU15
5778913.76
10850110.93
351640.51
380684.49
12059510.17
537133.97
Sources: calculated by researcher.Table 13
Data of 15 DMUs in 2020 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
899617.69
1622233.95
64939.38
11235.04
1710677.14
63649.31
DMU2
1660695.01
1648967.54
196892.37
243529.08
2104166.24
62863.20
DMU3
4231393.77
2920363.98
34513.79
235799.54
3446527.55
182938.84
DMU4
4356928.06
6368085.31
577484.45
872481.62
7744694.24
32288.35
DMU5
433416.69
144125.61
6934.96
5361.60
175950.92
250.06
DMU6
410675.94
594991.68
17681.50
55316.53
649132.88
1536.91
DMU7
4428862.42
4379514.24
112462.04
255299.58
4857741.34
88337.82
DMU8
345732.79
469034.41
2590.28
4147.07
478742.33
26047.57
DMU9
1772702.95
3440739.56
38007.25
170717.17
3332269.41
332661.92
DMU10
125276.61
77536.33
5862.46
14015.91
117655.67
25186.19
DMU11
210992.04
390465.21
77477.97
105818.21
592607.33
122802.45
DMU12
3743802.81
2856395.20
54273.08
115743.46
3241101.23
61042.07
DMU13
3462354.10
2004555.83
6567.59
83873.95
2081511.77
45792.13
DMU14
45630.44
732701.61
3827.48
14697.32
813332.68
45630.44
DMU15
6624428.33
12797862.80
388819.25
431367.35
14128309.73
608660.09
Sources: calculated by researcher.In this study, the authors use MAPE to check the accuracy of forecasts to ensure appropriate predictive methods and as a basis for highly reliable assessments. The results are shown in Table14.Table 14
Average MAPE of 15 DMUs.
DMUs
Average MAPE (%)
DMU1
3.51
DMU2
1.79
DMU3
5.36
DMU4
7.72
DMU5
5.29
DMU6
3.01
DMU7
3.78
DMU8
4.85
DMU9
3.59
DMU10
4.13
DMU11
3.90
DMU12
2.40
DMU13
3.28
DMU14
7.38
DMU15
1.27
Average MAPE of 15 DMUs
4.08 (%)
Source: calculated by researcher.This research uses a model-based forecasting approach (an approach to obtain information for the future with modeling tools); through resimulating the past and comparing values, the forecast is calculated by actual data and the model; if the error is within the allowable limits, then the model is reliable and usable. As shown in Table14, average MAPE of 15 DMUs is less than 10%. Based on rules, as shown in Table 8, the predicted results in this study have a high level of accuracy (average MAPE of 15 DMUs is 4.08%). This confirms that the GM (1,1) model used in this study is suitable with highly reliable predicted results and analysis.


### 3.2. Pearson Correlation

In this study, the authors use the super-SBM-I-V model to analyze and choose a strategic alliance partner for DMUs, where the condition for using DEA is the correlation coefficient, which could not be negative or equal to 0. Hence, before analyzing DEA, the authors used the Pearson correlation coefficient to determine the data used in this study, which is in accordance with the DEA standards. Correlation coefficients are always in the range of (−1) to(1); if this factor is as close to (1), it is a perfect linear relation [37]. The results are shown in Tables 15, 16, 17, and 18.Table 15
Correlation of inputs and outputs in 2013.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9077
0.8869
0.9286
0.9176
0.8134
CS
0.9077
1.0000
0.8977
0.9064
0.9985
0.9108
SE
0.8869
0.8977
1.0000
0.9218
0.9168
0.8809
AE
0.9286
0.9064
0.9218
1.0000
0.9245
0.8329
RS
0.9176
0.9985
0.9168
0.9245
1.0000
0.9175
PT
0.8134
0.9108
0.8809
0.8329
0.9175
1.0000
Source: calculated by researcher.Table 16
Correlation of inputs and outputs in 2014.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9322
0.8973
0.8745
0.9429
0.8528
CS
0.9322
1.0000
0.8894
0.8028
0.9984
0.9176
SE
0.8973
0.8894
1.0000
0.9104
0.9086
0.7912
AE
0.8745
0.8028
0.9104
1.0000
0.8316
0.6814
RS
0.9429
0.9984
0.9086
0.8316
1.0000
0.9181
PT
0.8528
0.9176
0.7912
0.6814
0.9181
1.0000
Source: calculated by researcher.Table 17
Correlation of inputs and outputs in 2015.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9328
0.8608
0.8601
0.9425
0.8548
CS
0.9328
1.0000
0.8774
0.8203
0.9983
0.9284
SE
0.8608
0.8774
1.0000
0.9376
0.8999
0.7319
AE
0.8601
0.8203
0.9376
1.0000
0.8517
0.6716
RS
0.9425
0.9983
0.8999
0.8517
1.0000
0.9185
PT
0.8548
0.9284
0.7319
0.6716
0.9185
1.0000
Source: calculated by researcher.Table 18
Correlation of inputs and outputs in 2016.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9404
0.8370
0.8303
0.9463
0.7818
CS
0.9404
1.0000
0.8667
0.7728
0.9987
0.8835
SE
0.8370
0.8667
1.0000
0.9300
0.8873
0.6227
AE
0.8303
0.7728
0.9300
1.0000
0.8031
0.5075
RS
0.9463
0.9987
0.8873
0.8031
1.0000
0.8714
PT
0.7818
0.8835
0.6227
0.5075
0.8714
1.0000
Source: calculated by researcher.Results of the Pearson correlation coefficient from Tables15 to 18 show that the factors used in this study have a strong linear relationship, which is consistent with the conditions of DEA and can be used for analysis. In the super-SBM-I-V model, strategic partners will be selected for DMUs in the future.


### 3.3. Analysis Alliance

#### 3.3.1. Analysis before Alliance

To identify and select the target DMU for alliance with other DMUs in the future, the authors use actual business data of the DMUs and the analytical applications from the super SBM-I-V of DEA to evaluate the business situation of DMUs before the alliance. The results are shown in Table19.Table 19
Rank and score before alliances.
Rank
DMU
Score
1
DMU14
4.9495
2
DMU8
2.1602
3
DMU9
1.4468
4
DMU10
1.3776
5
DMU1
1.1660
6
DMU3
1.1542
7
DMU7
1.0303
8
DMU11
1.0262
9
DMU13
1.0251
10
DMU6
1.0241
11
DMU4
1.0183
12
DMU2
1.0105
13
DMU15
1.0000
14
DMU5
0.9998
15
DMU12
0.8677
Source: calculated by researcher.


#### 3.3.2. Analysis after Alliance

Based on the actual performance of the business in 2016 and the results of business analysis obtained from the super-SBM-I-V software, the authors establishe DMU5 as the alliance target for the business. If we choose low-rated and performance DMUs, it will be difficult to persuade other DMUs to join the alliance. When combing DMU5 to other 14 DMUs, the authors have 29 combinations. The authors used software of a DEA-solver pro 8.0-super-SBM-I-V model and obtained the results, as shown in Table20.Table 20
Performance ranking of virtual DMUs.
Rank
DMU
Score
1
DMU14
4.5722
2
DMU8
1.9574
3
DMU10
1.3776
4
DMU5 + DMU14
1.0526
5
DMU1
1.0498
6
DMU9
1.0473
7
DMU5 + DMU8
1.0293
8
DMU5 + DMU9
1.0292
9
DMU5 + DMU3
1.0266
10
DMU11
1.0254
11
DMU13
1.0251
12
DMU3
1.0193
13
DMU5 + DMU1
1.0189
14
DMU6
1.0170
15
DMU5 + DMU13
1.0092
16
DMU5 + DMU7
1.0084
17
DMU15
1.0067
18
DMU2
1.0043
19
DMU7
1.0042
20
DMU5 + DMU10
1.0040
21
DMU5 + DMU6
1.0032
22
DMU4
1.0026
23
DMU5 + DMU11
1.0024
24
DMU5 + DMU4
1.0019
25
DMU5 + DMU2
1.0006
26
DMU5 + DMU15
1.0000
27
DMU5
0.9998
28
DMU5 + DMU12
0.8802
29
DMU12
0.8533
Source: calculated by researcher.In this study, DMU5 was chosen as the target DMU for alliances with other enterprises in the textile supply chain because DMU5’s business performance in 2016 was ineffective (specificallyDMU5Rank = 14 (14/15 DMUs); DMU5Score = 0.9998). Hence, the DMU5 management team needs to boldly change the strategy and choose an alliance solution with other DMUs to improve their business efficiency. On the other hand, DMU5 (Ha Dong Textile J.S.C), established in 1956, has a long history in the textile industry, with a large market and modern technology, and can easily persuade other DMUs to join their alliance.Based on the ranking and efficiency score at Table20, we can separate into two groups good alliances cooperative and inappropriate alliances as follows.Group 1.
After strategic alliances, the DMUs get good business efficiency, make the relationship among the coalition parties better, and expand market share. Candidates need to own good characteristics and necessary consistency with one’s desire in business. There are 13 DMUs (DMU14, DMU8, DMU9, DMU3, DMU1, DMU13, DMU7, DMU10, DMU6, DMU11, DMU4, DMU2, DMU15) in this group, which help DMU5 to improve results after the strategic alliance. Alliances DMU5 + DMU14, DMU5 + DMU8, DMU5 + DMU9, DMU5 + DMU3, DMU5 + DMU1, DMU5 + DMU13, DMU5 + DMU7, DMU5 + DMU10, DMU5 + DMU6, DMU5 + DMU11, DMU5 + DMU4, DMU5 + DMU2, DMU5 + DMU15 receive a score of more than 1. These are shown in Table21.Table 21
The good alliances.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU14
27
4
23
DMU5 + DMU8
27
7
20
DMU5 + DMU9
27
8
19
DMU5 + DMU3
27
9
18
DMU5 + DMU1
27
13
14
DMU5 + DMU13
27
15
12
DMU5 + DMU7
27
16
11
DMU5 + DMU10
27
20
7
DMU5 + DMU6
27
21
6
DMU5 + DMU11
27
23
4
DMU5 + DMU4
27
24
3
DMU5 + DMU2
27
25
2
DMU5 + DMU15
27
26
1
Source: calculated by researcher.Group 2.
In this group, DMU12 combined with DMU5 shows no any progress after alliance. Thus, this combined will not be priority with selection of a strategic alliance partner in the future, as shown in Table22.Table 22
The inappropriate alliance.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU12
27
28
(
-
1
)
Source: calculated by researcher.


### 3.4. Discussion of Alliance Partner Selection

DMUs include DMU14, DMU8, DMU9, DMU3, DMU1, DMU13, DMU7, DMU10, DMU6, DMU11, DMU4, DMU2, and DMU15. But these DMUs would not be willing to combine with DMU5 because some companies have a score after an alliance, and ranking is reduced before an alliance.In alliances that bring high performance in group 1, DMU9 (Saigon 3 garment joint stock company, GATEXIM), before alliance, has a ranking at 3 (3/15 DMUs); however, after the alliance, its ranking is at 8 (8/29 DMUs). Hence, the effect given is immensely good; specifically, DMU5 in Hanam (the large economic center in the northern region of Vietnam) specializes in the textile industry and the DMU9 in Ho Chi Minh City (the largest economic center in the south of Vietnam) specializes in the garment industry. Thus, in terms of expertise, these two companies joined the alliance between textile and apparel enterprises when the parties signed the main agreement, which helps to build the supply chain by eliminating intermediaries between suppliers and consumers and shorten the length of the channel consumption. Joining the alliance helps businesses ensure that the factors of supply and consumption remain stable. In terms of geographic location, enterprises can deploy and expand their distribution network so that they can take advantage of the available network and human resources of their partners. This not only helps to save cost but also reduces time entering a market to the fullest. Besides, joining alliances also can increase the customer base by leveraging each other’s customer systems. Because every business has its own characteristics matching its inherent potential, alliances will have their own advantages to exploit and complement each other. Hence, in order to survive, compete, and develop, an alliance is inevitably a trend because it gives firms greater value-added alliance than standing alone. Hence, in order to survive, compete, and develop, an alliance is inevitably a trend because it gives firms greater value-added alliance than standing alone, to gain greater economic benefits on a larger scale, to increase prestige and brand name, to reduce costs, to maximize business advantages of parties involved, and to develop the customer base and distribution network. This will help strengthen the position and competitiveness in the market and improve business efficiency.


## 3.1. Results and Analysis of the Grey Forecasting of the Output Values for All DMUs

The GM (1,1) model is utilized to predict the input and output factors values for each decision-making unit from 2017 to 2020. We used factor total assets (TA) – input 1 of DMU1 in Table9 to explain calculation procedure in this part.Table 9
Data of DMU1 from 2013 to 2016 (currency unit: 1,000,000 VND).
Year
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
2013
509991
1152459
42110
54446
1306653
30880
2014
588788
1221869
46946
53530
1379742
35119
2015
606216
1309806
51544
53208
1480821
44063
2016
679185
1341165
52198
26851
1478313
42778
Source: synthetic by researcher [9].First, we use the GM (1,1) model for trying to forecast the variance of primitive series.Create the primitive series:(15)X0=509991;588788;606216;679185.Perform the accumulated generating operation (AGO):(16)X1=509991;1098779;1704995;2384180x11=x01=509991x12=x01+x02=1098779x13=x12+x03=1704995x14=x13+x04=2384180.Create the different equation of GM (1,1).To findX(1) series, the following mean obtained by the mean equation is(17)z12=0.5×509991+1098779=804385z13=0.5×1098779+1704995=1401887z14=0.5×1704995+2384180=2044587.5.Solve equations.To finda and b, the primitive series values are substituted into the grey differential equation to obtain(18)588788+a×804385=b606216+a×1401887=b679185+a×2044587.5=b.Convert the linear equations into the form of a matrix.Let(19)B=-8043851-14018871-2044587.51;θ^=ab;YN=588788606216679185.And then use the least-squares method to finda and b:(20)ab=θ^=BTB-1BTyN=-0.0734520724.6709.Use the two coefficients a and b to generate the whitening equation of the differential equation:(21)dx1dk+0.0734×x1=520724.6709.Find the prediction model from equation(22)X^1κ+1=χ01-ba×e-aκ+ba=509991-520724.6709-0.0734×e--0.0734κ+520724.6709-0.0734=7604289.363×e0.0734×k-7094298.363.Substitute different value of k into the equation(23)X11=509991k=0;X12=1089144.30k=1;X13=1712406.71k=2;X14=2383137.67k=3;X15=3104952.43k=4;X16=3881741.61k=5;X17=4717692.14k=6;X18=5617309.83k=7;derive the predicted value of the original series according to the accumulated generating operation and obtain(24)x^01=x11=509991x^02=x^12-x^11=579153.30x^03=x^13-x^12=623262.42x^04=x^14-x^13=670730.95x^05=x^15-x^14=721814.76—Result  forecast  of  2017.x^06=x^16-x^15=776789.18—Result  forecast  of  2018.x^07=x^17-x^16=835950.53—Result  forecast  of  2019.x^08=x^18-x^17=899617.69—Result  forecast  of  2020.As with above process, we have the result of all DMUs from 2017 to 2020, respectively, in Tables10, 11, 12, and 13.Table 10
Data of 15 DMUs in 2017 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
721814.76
1413714.89
55627.68
25296.03
1546318.61
48544.08
DMU2
1053997.39
1403749.17
67264.71
169108.78
1720725.30
62130.40
DMU3
2294455.62
1881264.48
32380.04
165411.22
2268702.20
100498.51
DMU4
3130664.07
4143249.39
371956.57
502950.15
5121472.13
57986.82
DMU5
281200.84
192406.24
6729.79
7154.80
225963.51
3242.28
DMU6
475353.42
816765.35
19381.36
50028.24
895875.88
7694.20
DMU7
2302526.94
3212821.85
100626.19
148225.35
3579094.18
76730.92
DMU8
200487.46
293300.49
1885.72
5316.81
321723.30
25020.34
DMU9
1424974.01
1844619.97
40822.27
98235.86
2007490.91
166631.40
DMU10
94367.97
102522.46
10619.72
13686.16
145006.42
11240.91
DMU11
225439.67
454380.31
38423.52
55629.26
598318.91
45151.67
DMU12
2453736.30
1978048.44
53396.32
94910.26
2250200.03
51258.86
DMU13
1046331.33
901189.93
8305.48
55816.79
1008742.38
29953.18
DMU14
17985.77
388587.67
3557.42
6423.83
435973.52
17985.77
DMU15
4397864.35
7798797.34
287608.15
296483.83
8786351.84
418309.95
Sources: calculated by researcher.Table 11
Data of 15 DMUs in 2018 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
776789.18
1480059.07
58572.89
19300.11
1599270.79
53131.91
DMU2
1226468.27
1481143.18
96220.54
190968.19
1840070.14
62373.71
DMU3
2813720.28
2178272.62
33076.22
186161.97
2608027.98
122708.61
DMU4
3495299.88
4781494.65
430700.05
604322.45
5878474.45
47705.47
DMU5
324822.64
174740.33
6797.49
6498.75
207884.79
1380.11
DMU6
452735.10
734911.71
18797.32
51732.30
804655.10
4497.72
DMU7
2863522.13
3562321.21
104426.16
177677.19
3962721.45
80419.71
DMU8
240420.59
342986.29
2096.21
4894.19
367301.15
25358.17
DMU9
1532556.94
2270676.18
39861.49
118106.13
2376926.55
209816.16
DMU10
103714.56
93407.36
8711.68
13795.21
135247.46
14709.18
DMU11
220517.09
431990.09
48542.73
68926.94
596408.96
63025.78
DMU12
2824812.20
2235788.48
53686.99
101400.69
2541235.71
54332.00
DMU13
1559205.34
1176387.15
7680.31
63932.10
1284240.82
34505.81
DMU14
24530.38
480066.86
3645.25
8464.64
536697.39
24530.38
DMU15
5041317.17
9198794.29
318016.79
335956.54
10293643.64
474013.17
Sources: calculated by researcher.Table 12
Data of 15 DMUs in 2019 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
835950.53
1549516.73
61674.04
14725.40
1654036.27
58153.32
DMU2
1427161.42
1562804.22
137641.17
215653.21
1967692.42
62617.97
DMU3
3450501.19
2522171.46
33787.36
209515.89
2998106.11
149827.14
DMU4
3902405.69
5518058.16
498720.94
726126.87
6747368.91
39247.05
DMU5
375211.34
158696.43
6865.88
5902.85
191252.51
587.46
DMU6
431193.01
661261.18
18230.88
53494.41
722722.69
2629.18
DMU7
3561200.02
3949840.06
108369.64
212981.01
4387468.04
84285.84
DMU8
288307.62
401088.98
2330.18
4505.17
419335.92
25700.56
DMU9
1648262.18
2795139.59
38923.33
141995.57
2814348.88
264192.82
DMU10
113986.88
85102.67
7146.46
13905.12
126145.28
19247.55
DMU11
215701.99
410703.18
61326.93
85403.31
594505.11
87975.68
DMU12
3252005.51
2527112.08
53979.24
108334.98
2869913.27
57589.39
DMU13
2323471.75
1535621.61
7102.19
73227.30
1634980.85
39750.40
DMU14
33456.42
593081.58
3735.26
11153.81
660691.70
33456.42
DMU15
5778913.76
10850110.93
351640.51
380684.49
12059510.17
537133.97
Sources: calculated by researcher.Table 13
Data of 15 DMUs in 2020 (currency unit: 1,000,000 VND).
DMUs
Inputs
Outputs
(I)TA
(I)CS
(I)SE
(I)AE
(O)RS
(O)PT
DMU1
899617.69
1622233.95
64939.38
11235.04
1710677.14
63649.31
DMU2
1660695.01
1648967.54
196892.37
243529.08
2104166.24
62863.20
DMU3
4231393.77
2920363.98
34513.79
235799.54
3446527.55
182938.84
DMU4
4356928.06
6368085.31
577484.45
872481.62
7744694.24
32288.35
DMU5
433416.69
144125.61
6934.96
5361.60
175950.92
250.06
DMU6
410675.94
594991.68
17681.50
55316.53
649132.88
1536.91
DMU7
4428862.42
4379514.24
112462.04
255299.58
4857741.34
88337.82
DMU8
345732.79
469034.41
2590.28
4147.07
478742.33
26047.57
DMU9
1772702.95
3440739.56
38007.25
170717.17
3332269.41
332661.92
DMU10
125276.61
77536.33
5862.46
14015.91
117655.67
25186.19
DMU11
210992.04
390465.21
77477.97
105818.21
592607.33
122802.45
DMU12
3743802.81
2856395.20
54273.08
115743.46
3241101.23
61042.07
DMU13
3462354.10
2004555.83
6567.59
83873.95
2081511.77
45792.13
DMU14
45630.44
732701.61
3827.48
14697.32
813332.68
45630.44
DMU15
6624428.33
12797862.80
388819.25
431367.35
14128309.73
608660.09
Sources: calculated by researcher.In this study, the authors use MAPE to check the accuracy of forecasts to ensure appropriate predictive methods and as a basis for highly reliable assessments. The results are shown in Table14.Table 14
Average MAPE of 15 DMUs.
DMUs
Average MAPE (%)
DMU1
3.51
DMU2
1.79
DMU3
5.36
DMU4
7.72
DMU5
5.29
DMU6
3.01
DMU7
3.78
DMU8
4.85
DMU9
3.59
DMU10
4.13
DMU11
3.90
DMU12
2.40
DMU13
3.28
DMU14
7.38
DMU15
1.27
Average MAPE of 15 DMUs
4.08 (%)
Source: calculated by researcher.This research uses a model-based forecasting approach (an approach to obtain information for the future with modeling tools); through resimulating the past and comparing values, the forecast is calculated by actual data and the model; if the error is within the allowable limits, then the model is reliable and usable. As shown in Table14, average MAPE of 15 DMUs is less than 10%. Based on rules, as shown in Table 8, the predicted results in this study have a high level of accuracy (average MAPE of 15 DMUs is 4.08%). This confirms that the GM (1,1) model used in this study is suitable with highly reliable predicted results and analysis.


## 3.2. Pearson Correlation

In this study, the authors use the super-SBM-I-V model to analyze and choose a strategic alliance partner for DMUs, where the condition for using DEA is the correlation coefficient, which could not be negative or equal to 0. Hence, before analyzing DEA, the authors used the Pearson correlation coefficient to determine the data used in this study, which is in accordance with the DEA standards. Correlation coefficients are always in the range of (−1) to(1); if this factor is as close to (1), it is a perfect linear relation [37]. The results are shown in Tables 15, 16, 17, and 18.Table 15
Correlation of inputs and outputs in 2013.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9077
0.8869
0.9286
0.9176
0.8134
CS
0.9077
1.0000
0.8977
0.9064
0.9985
0.9108
SE
0.8869
0.8977
1.0000
0.9218
0.9168
0.8809
AE
0.9286
0.9064
0.9218
1.0000
0.9245
0.8329
RS
0.9176
0.9985
0.9168
0.9245
1.0000
0.9175
PT
0.8134
0.9108
0.8809
0.8329
0.9175
1.0000
Source: calculated by researcher.Table 16
Correlation of inputs and outputs in 2014.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9322
0.8973
0.8745
0.9429
0.8528
CS
0.9322
1.0000
0.8894
0.8028
0.9984
0.9176
SE
0.8973
0.8894
1.0000
0.9104
0.9086
0.7912
AE
0.8745
0.8028
0.9104
1.0000
0.8316
0.6814
RS
0.9429
0.9984
0.9086
0.8316
1.0000
0.9181
PT
0.8528
0.9176
0.7912
0.6814
0.9181
1.0000
Source: calculated by researcher.Table 17
Correlation of inputs and outputs in 2015.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9328
0.8608
0.8601
0.9425
0.8548
CS
0.9328
1.0000
0.8774
0.8203
0.9983
0.9284
SE
0.8608
0.8774
1.0000
0.9376
0.8999
0.7319
AE
0.8601
0.8203
0.9376
1.0000
0.8517
0.6716
RS
0.9425
0.9983
0.8999
0.8517
1.0000
0.9185
PT
0.8548
0.9284
0.7319
0.6716
0.9185
1.0000
Source: calculated by researcher.Table 18
Correlation of inputs and outputs in 2016.
TA
CS
SE
AE
RS
PT
TA
1.0000
0.9404
0.8370
0.8303
0.9463
0.7818
CS
0.9404
1.0000
0.8667
0.7728
0.9987
0.8835
SE
0.8370
0.8667
1.0000
0.9300
0.8873
0.6227
AE
0.8303
0.7728
0.9300
1.0000
0.8031
0.5075
RS
0.9463
0.9987
0.8873
0.8031
1.0000
0.8714
PT
0.7818
0.8835
0.6227
0.5075
0.8714
1.0000
Source: calculated by researcher.Results of the Pearson correlation coefficient from Tables15 to 18 show that the factors used in this study have a strong linear relationship, which is consistent with the conditions of DEA and can be used for analysis. In the super-SBM-I-V model, strategic partners will be selected for DMUs in the future.


## 3.3. Analysis Alliance

### 3.3.1. Analysis before Alliance

To identify and select the target DMU for alliance with other DMUs in the future, the authors use actual business data of the DMUs and the analytical applications from the super SBM-I-V of DEA to evaluate the business situation of DMUs before the alliance. The results are shown in Table19.Table 19
Rank and score before alliances.
Rank
DMU
Score
1
DMU14
4.9495
2
DMU8
2.1602
3
DMU9
1.4468
4
DMU10
1.3776
5
DMU1
1.1660
6
DMU3
1.1542
7
DMU7
1.0303
8
DMU11
1.0262
9
DMU13
1.0251
10
DMU6
1.0241
11
DMU4
1.0183
12
DMU2
1.0105
13
DMU15
1.0000
14
DMU5
0.9998
15
DMU12
0.8677
Source: calculated by researcher.


### 3.3.2. Analysis after Alliance

Based on the actual performance of the business in 2016 and the results of business analysis obtained from the super-SBM-I-V software, the authors establishe DMU5 as the alliance target for the business. If we choose low-rated and performance DMUs, it will be difficult to persuade other DMUs to join the alliance. When combing DMU5 to other 14 DMUs, the authors have 29 combinations. The authors used software of a DEA-solver pro 8.0-super-SBM-I-V model and obtained the results, as shown in Table20.Table 20
Performance ranking of virtual DMUs.
Rank
DMU
Score
1
DMU14
4.5722
2
DMU8
1.9574
3
DMU10
1.3776
4
DMU5 + DMU14
1.0526
5
DMU1
1.0498
6
DMU9
1.0473
7
DMU5 + DMU8
1.0293
8
DMU5 + DMU9
1.0292
9
DMU5 + DMU3
1.0266
10
DMU11
1.0254
11
DMU13
1.0251
12
DMU3
1.0193
13
DMU5 + DMU1
1.0189
14
DMU6
1.0170
15
DMU5 + DMU13
1.0092
16
DMU5 + DMU7
1.0084
17
DMU15
1.0067
18
DMU2
1.0043
19
DMU7
1.0042
20
DMU5 + DMU10
1.0040
21
DMU5 + DMU6
1.0032
22
DMU4
1.0026
23
DMU5 + DMU11
1.0024
24
DMU5 + DMU4
1.0019
25
DMU5 + DMU2
1.0006
26
DMU5 + DMU15
1.0000
27
DMU5
0.9998
28
DMU5 + DMU12
0.8802
29
DMU12
0.8533
Source: calculated by researcher.In this study, DMU5 was chosen as the target DMU for alliances with other enterprises in the textile supply chain because DMU5’s business performance in 2016 was ineffective (specificallyDMU5Rank = 14 (14/15 DMUs); DMU5Score = 0.9998). Hence, the DMU5 management team needs to boldly change the strategy and choose an alliance solution with other DMUs to improve their business efficiency. On the other hand, DMU5 (Ha Dong Textile J.S.C), established in 1956, has a long history in the textile industry, with a large market and modern technology, and can easily persuade other DMUs to join their alliance.Based on the ranking and efficiency score at Table20, we can separate into two groups good alliances cooperative and inappropriate alliances as follows.Group 1.
After strategic alliances, the DMUs get good business efficiency, make the relationship among the coalition parties better, and expand market share. Candidates need to own good characteristics and necessary consistency with one’s desire in business. There are 13 DMUs (DMU14, DMU8, DMU9, DMU3, DMU1, DMU13, DMU7, DMU10, DMU6, DMU11, DMU4, DMU2, DMU15) in this group, which help DMU5 to improve results after the strategic alliance. Alliances DMU5 + DMU14, DMU5 + DMU8, DMU5 + DMU9, DMU5 + DMU3, DMU5 + DMU1, DMU5 + DMU13, DMU5 + DMU7, DMU5 + DMU10, DMU5 + DMU6, DMU5 + DMU11, DMU5 + DMU4, DMU5 + DMU2, DMU5 + DMU15 receive a score of more than 1. These are shown in Table21.Table 21
The good alliances.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU14
27
4
23
DMU5 + DMU8
27
7
20
DMU5 + DMU9
27
8
19
DMU5 + DMU3
27
9
18
DMU5 + DMU1
27
13
14
DMU5 + DMU13
27
15
12
DMU5 + DMU7
27
16
11
DMU5 + DMU10
27
20
7
DMU5 + DMU6
27
21
6
DMU5 + DMU11
27
23
4
DMU5 + DMU4
27
24
3
DMU5 + DMU2
27
25
2
DMU5 + DMU15
27
26
1
Source: calculated by researcher.Group 2.
In this group, DMU12 combined with DMU5 shows no any progress after alliance. Thus, this combined will not be priority with selection of a strategic alliance partner in the future, as shown in Table22.Table 22
The inappropriate alliance.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU12
27
28
(
-
1
)
Source: calculated by researcher.


## 3.3.1. Analysis before Alliance

To identify and select the target DMU for alliance with other DMUs in the future, the authors use actual business data of the DMUs and the analytical applications from the super SBM-I-V of DEA to evaluate the business situation of DMUs before the alliance. The results are shown in Table19.Table 19
Rank and score before alliances.
Rank
DMU
Score
1
DMU14
4.9495
2
DMU8
2.1602
3
DMU9
1.4468
4
DMU10
1.3776
5
DMU1
1.1660
6
DMU3
1.1542
7
DMU7
1.0303
8
DMU11
1.0262
9
DMU13
1.0251
10
DMU6
1.0241
11
DMU4
1.0183
12
DMU2
1.0105
13
DMU15
1.0000
14
DMU5
0.9998
15
DMU12
0.8677
Source: calculated by researcher.


## 3.3.2. Analysis after Alliance

Based on the actual performance of the business in 2016 and the results of business analysis obtained from the super-SBM-I-V software, the authors establishe DMU5 as the alliance target for the business. If we choose low-rated and performance DMUs, it will be difficult to persuade other DMUs to join the alliance. When combing DMU5 to other 14 DMUs, the authors have 29 combinations. The authors used software of a DEA-solver pro 8.0-super-SBM-I-V model and obtained the results, as shown in Table20.Table 20
Performance ranking of virtual DMUs.
Rank
DMU
Score
1
DMU14
4.5722
2
DMU8
1.9574
3
DMU10
1.3776
4
DMU5 + DMU14
1.0526
5
DMU1
1.0498
6
DMU9
1.0473
7
DMU5 + DMU8
1.0293
8
DMU5 + DMU9
1.0292
9
DMU5 + DMU3
1.0266
10
DMU11
1.0254
11
DMU13
1.0251
12
DMU3
1.0193
13
DMU5 + DMU1
1.0189
14
DMU6
1.0170
15
DMU5 + DMU13
1.0092
16
DMU5 + DMU7
1.0084
17
DMU15
1.0067
18
DMU2
1.0043
19
DMU7
1.0042
20
DMU5 + DMU10
1.0040
21
DMU5 + DMU6
1.0032
22
DMU4
1.0026
23
DMU5 + DMU11
1.0024
24
DMU5 + DMU4
1.0019
25
DMU5 + DMU2
1.0006
26
DMU5 + DMU15
1.0000
27
DMU5
0.9998
28
DMU5 + DMU12
0.8802
29
DMU12
0.8533
Source: calculated by researcher.In this study, DMU5 was chosen as the target DMU for alliances with other enterprises in the textile supply chain because DMU5’s business performance in 2016 was ineffective (specificallyDMU5Rank = 14 (14/15 DMUs); DMU5Score = 0.9998). Hence, the DMU5 management team needs to boldly change the strategy and choose an alliance solution with other DMUs to improve their business efficiency. On the other hand, DMU5 (Ha Dong Textile J.S.C), established in 1956, has a long history in the textile industry, with a large market and modern technology, and can easily persuade other DMUs to join their alliance.Based on the ranking and efficiency score at Table20, we can separate into two groups good alliances cooperative and inappropriate alliances as follows.Group 1.
After strategic alliances, the DMUs get good business efficiency, make the relationship among the coalition parties better, and expand market share. Candidates need to own good characteristics and necessary consistency with one’s desire in business. There are 13 DMUs (DMU14, DMU8, DMU9, DMU3, DMU1, DMU13, DMU7, DMU10, DMU6, DMU11, DMU4, DMU2, DMU15) in this group, which help DMU5 to improve results after the strategic alliance. Alliances DMU5 + DMU14, DMU5 + DMU8, DMU5 + DMU9, DMU5 + DMU3, DMU5 + DMU1, DMU5 + DMU13, DMU5 + DMU7, DMU5 + DMU10, DMU5 + DMU6, DMU5 + DMU11, DMU5 + DMU4, DMU5 + DMU2, DMU5 + DMU15 receive a score of more than 1. These are shown in Table21.Table 21
The good alliances.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU14
27
4
23
DMU5 + DMU8
27
7
20
DMU5 + DMU9
27
8
19
DMU5 + DMU3
27
9
18
DMU5 + DMU1
27
13
14
DMU5 + DMU13
27
15
12
DMU5 + DMU7
27
16
11
DMU5 + DMU10
27
20
7
DMU5 + DMU6
27
21
6
DMU5 + DMU11
27
23
4
DMU5 + DMU4
27
24
3
DMU5 + DMU2
27
25
2
DMU5 + DMU15
27
26
1
Source: calculated by researcher.Group 2.
In this group, DMU12 combined with DMU5 shows no any progress after alliance. Thus, this combined will not be priority with selection of a strategic alliance partner in the future, as shown in Table22.Table 22
The inappropriate alliance.
Virtual
Target DMU5
Virtual alliance
Difference
Alliance
Ranking (a)
Ranking (b)
(a) − (b)
DMU5 + DMU12
27
28
(
-
1
)
Source: calculated by researcher.


## 3.4. Discussion of Alliance Partner Selection

DMUs include DMU14, DMU8, DMU9, DMU3, DMU1, DMU13, DMU7, DMU10, DMU6, DMU11, DMU4, DMU2, and DMU15. But these DMUs would not be willing to combine with DMU5 because some companies have a score after an alliance, and ranking is reduced before an alliance.In alliances that bring high performance in group 1, DMU9 (Saigon 3 garment joint stock company, GATEXIM), before alliance, has a ranking at 3 (3/15 DMUs); however, after the alliance, its ranking is at 8 (8/29 DMUs). Hence, the effect given is immensely good; specifically, DMU5 in Hanam (the large economic center in the northern region of Vietnam) specializes in the textile industry and the DMU9 in Ho Chi Minh City (the largest economic center in the south of Vietnam) specializes in the garment industry. Thus, in terms of expertise, these two companies joined the alliance between textile and apparel enterprises when the parties signed the main agreement, which helps to build the supply chain by eliminating intermediaries between suppliers and consumers and shorten the length of the channel consumption. Joining the alliance helps businesses ensure that the factors of supply and consumption remain stable. In terms of geographic location, enterprises can deploy and expand their distribution network so that they can take advantage of the available network and human resources of their partners. This not only helps to save cost but also reduces time entering a market to the fullest. Besides, joining alliances also can increase the customer base by leveraging each other’s customer systems. Because every business has its own characteristics matching its inherent potential, alliances will have their own advantages to exploit and complement each other. Hence, in order to survive, compete, and develop, an alliance is inevitably a trend because it gives firms greater value-added alliance than standing alone. Hence, in order to survive, compete, and develop, an alliance is inevitably a trend because it gives firms greater value-added alliance than standing alone, to gain greater economic benefits on a larger scale, to increase prestige and brand name, to reduce costs, to maximize business advantages of parties involved, and to develop the customer base and distribution network. This will help strengthen the position and competitiveness in the market and improve business efficiency.


## 4. Conclusions

With what has been happening in the textile and apparel industry, the need for linking is becoming increasingly urgent. In this study, the authors provide a method for finding and selecting the right strategic partner for textile and apparel enterprises. The selected plan is to promote the internal strength of all participating enterprises while promoting the strength of the alliance in accordance with high volume products, high quality, international quality, timely delivery, and competitive price needs. Authorities can rely on these research results to make the correct and appropriate strategic decisions in helping Vietnam’s textile and apparel industry to develop when integrating with the global economy.Although the paper shows that GM (1,1) is a flexible and easy model of use to predict what would happen in the future business and DEA is an efficient tool to help businesses find the right strategic partner, we still cannot deny some restrictions about these two methods for further studies. Different DEA models and optimization algorithm can also be tested to reveal more changes, and other industries can be studied by this model in the future [38]. This study only focuses on a quantitative model. The authors will do more research of external environmental factors in the future. The comparisons with other quantitative and qualitative approaches will be a good research direction as well.



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*Source: 7826840-2017-11-08.xml*