Evaluation of Heavy Commercial Vehicles Brand Considering Multi-Attribute Indexes in China

Xiaoqin XIONG; Aiguo CHENG

doi:10.21078/JSSI-2020-291-18

PDF(330 KB)

Journal of Systems Science and Information ›› 2020, Vol. 8 ›› Issue (4) : 291-308. DOI: 10.21078/JSSI-2020-291-18

Evaluation of Heavy Commercial Vehicles Brand Considering Multi-Attribute Indexes in China

Author information +

History +

Abstract

Assessment of brand competitiveness which influences consumer trends and company's sales performance contributes to business development. This paper elaborates the theories and the main investigations of brand competitiveness, and formulates a comprehensive hierarchical structure integrating multi-attribute indexes, i.e., social, technical, managerial, environmental and cultural criteria. An integrated multi-criterion decision-making (MCDM) model combining with analytic hierarchy process (AHP), grey relational analysis (GRA) and VIKOR is presented to determine the weights of influence criteria and to evaluate brand competitiveness. The utilized integrated methodology has been proved to be valid and practical by the empirical application on three enterprises. The results provide an accurate and effective tool for MCDM problem and also a new guideline for the enterprise development.

Key words

heavy commercial vehicles / brand competitiveness / multi-criterion decision-making (MCDM) / grey relational analysis (GRA) / VIKOR

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Xiaoqin XIONG , Aiguo CHENG. Evaluation of Heavy Commercial Vehicles Brand Considering Multi-Attribute Indexes in China. Journal of Systems Science and Information, 2020, 8(4): 291-308 https://doi.org/10.21078/JSSI-2020-291-18

1 Introduction

In recent decades, China's economy has been growing, which leads to the increasing national and household disposable income and demand for private cars. China's auto industry has made great progress. It has steadily occupied the first place in the world for eight years since 2009^[1-3]. Within the thriving industry, there is always an intense competition among automakers to attract the attention of the public^[4]. In the face of the unprecedented outbreak of China's car market, the major automakers are strengthening their brand strategies and campaigns to improve their services by upgrading product technology and accelerating new development models to improve service quality. Automobile companies are setting up a good brand image to attract the attention of consumers and seize the market share^[5].

Brand value is very important to automobile enterprises. It can bring many realistic benefits such as getting a premium, being helpful to extend the production line, gaining more market share, and so on. Automobile giants have adjusted their brand development strategies, implemented brand marketing as the key strategy of the company, and strived to improve their market share with good brand image and brand power^[6]. Based on the above, the way to enhance the company's brand competitiveness is an essential part to get it into the world's largest vehicle market.

Advanced technologies such as self-driving and new energy cars may have great influences on consumers to make a choice among vehicle brands as well as the desire to own a car, and also create potential opportunities and challenges which will present to automotive and non-automotive brand managers and researchers^[7]. Moreover, self-brand personality differences will have a certain impact on consumer's attitudes towards car brands^[8]. The result of that would be the brand-name competitiveness reflecting the incremental value added to a commercial car by its brand identity^[9]. Prestige brands not only earn brand-name premia but also seize high-margin market segments. The global branding strategy of motor manufacturers has made a positive impact on their performance^[10]. Domestic brands are becoming more and more popular especially in China. These brands are developing and expanding rapidly in the largest car market by almost double speed. Besides, corresponding development strategies and industrial objectives have also been formulated for all brands^[11].

The research is devoted to explore the evaluation of commercial vehicles brand competitiveness theories, and to formulate a comprehensive hierarchical structure that integrates impact criteria, i.e., market property, environmental property, communication property, technical property, cultural property. First, we conclude and summarize the theory and practice experience of commercial vehicle brand competitiveness through complex phenomena, and establish a simple and clear knowledge model. Second, the results of this study can optimize scientific and other related aspects of the future brand management and decision-making of commercial vehicle production enterprises in a certain extent of the guidance. With it, enterprises find out their own strengths and weaknesses easily, and potential customers are attracted by their more competitive products and services. It is also of a practical value.

The remainder of this paper is organized as follows. Section 2 proposes the hybrid MCDM method. Section 3 details the method and explores its empirical application in three enterprises. Section 4 analyzes and discusses the results of the previous chapter. The last section shows the conclusions.

2 Solution Methodology

An integrated MCDM approach combining AHP and G-VIKOR is proposed to evaluate the heavy commercial vehicles brand competitiveness considering multi-attribute indexes in China. The calculation procedure of this approach can be summarized in two stages, as described by the detailed flowchart shown in Figure 1. AHP is applied to analyze the influences and interrelationships among each criterion, and to obtain the final weights of each criterion. The optimal alternative will be evaluated via G-VIKOR. Besides, a novel hierarchical structure of criteria for evaluation is proposed. The specific procedures and processes of both phases are summarized in the following sub-sections.

Figure 1 The flowchart of the proposed novel hybrid approach

Full size|PPT slide

2.1 Hierarchical Criteria for Evaluation

To formulate the comprehensive hierarchical structure of criteria for the evaluation of commercial vehicles brand competitiveness in China, we reviewed the existing literature and interviewed experienced experts from colleges/enterprises. Thus, the structure is built as tabulated in Table 1. The structure includes two levels, i.e., attribute and sub-criterion. The attribute level involves market property (A1), environmental property (A2), brand property (A3), technological property (A4) and cultural property (A5). Market property includes market share (M1), market penetration (M2), sales (M3), profit margin (M4), and profit rate (M5). Environmental property includes economic conditions (E1), political and legal environment (E2) and competitive environment (E3). Brand property includes brand positioning (B1), brand communication (B2), brand awareness (B3), brand reputation (B4), brand loyalty (B5) and brand association (B6). Technological property includes investment of R & D (T1), technical developers (T2), success rate of R & D (T3), output value of new products (T4) and number of patents (T5). Cultural property includes enterprise culture construction (C1) and culture forces (C2). Besides, the descriptions of each sub-criteria are shown in Table 1.

Table 1 The hierarchical structure of the commercial vehicles brand in criteria & sub-criteria

Attribute	No.	Sub-criteria	No.	Description	Ref.
Market property	A1	Market share	M1	Referring to the proportion of an enterprise's sales in the same product market.	[12–17]
		Market penetration	M2	The extent to which a product is recognized and bought by customers in a particular market.
		Sales	M3	Referring to its sale quantity.
		Profit margin	M4	The amount by which revenue from sales exceeds costs in a business.
		Profit rate	M5	The relative profitability of an investment project.
Environ-mental property	A2	Economic conditions	E1	The economy state in a country or region.	[18–20]
		Political and legal environment	E2	Government actions that affect the operations of a company or business.
		Competitive environment	E3	The dynamic external system in which a business competes and functions.
Brand property	A3	Brand positioning	B1	An activity of creating a brand that occupies a distinctive place and value in the target customer's mind.	[21–26]
		Brand communication	B2	The combination of activities that influences customers' view of a company and its products.
		Brand awareness	B3	The level of consumer consciousness of a brand.
		Brand reputation	B4	Referring to how a particular brand is viewed by others.
		Brand loyalty	B5	Positive feelings towards a brand and dedication to purchase the same product or service repeatedly now and in the future from the same brand, regardless of a competitor's actions or changes in the environment.
		Brand association	B6	Brand association is a connection formed in a buyer's mind with the brand.
Technolo-gical property	A4	Investment of R & D	T1	Innovative activities undertaken by corporations in developing new services or products, or improving existing services or products.	[27–31]
		Technical developers	T2	The number of technical developers.
		Success rate of R & D	T3	The percent of new products that introduced to the market and that succeed to meet commercial objectives of the business unit who launched the products.
		Output value of new products	T4	The ratio of the new-product's output value to the total output value.
		Patents	T5	The number of patents.
Cultural property	A5	Enterprise culture construction	C1	Its profound meaning is to condense and activate human capital, cultivate core competence, enhance the brand image, and improve the efficiency of management system.	[32–33]
Cultural property	A5	Culture forces	C2	The influencing mechanisms that exist within a society guide business practices and purchasing behavior.	[32–33]

2.2 AHP Approach

AHP, introduced by Saaty^[34], reveals the principle to obtain the relative importance of several clusters of criteria to lay the foundation for MCDM problems. A hierarchical structure, including different levels and various criteria, formulated based on the characteristics of certain events, could be categorized into three layers, i.e., the target one, the rule one, and the index one^[35]. The pair-wise comparison matrix (PWCM) was structured by related experts with reliable experience based on the fundamental scale of comparison values as shown in Table 2, thus calculating the corresponding weight of each decision criterion in the hierarchical structure^[36]. Additionally, the analysis procedure not only considers subjective preferences but also integrates expert experience with objective information to ensure the rationality and effectiveness. The basic steps could be summarized into five parts: a) Identify the decision problem; b) Formulate the fundamental scale about preferences among criteria; c) Structure a PWCM A for

k

decision criteria by related experts as shown in Equations (1) and (2); d) Obtain the weights vector of each criterion

w = (w_{1}, w_{2}, \dots, w_{k})

in the hierarchical structure, according to Equation (3); e) Check consistency based on the final consistency ratio (CR) where

C R = (λ_{max} - k) / (R I^{*} (k - 1))

^{[37, 38]}. Note that CI is the consistency indicator and RI the random consistency indicator as shown in Table 3

\begin{array}{rcl} A = [a_{i j}], i = 1, 2, \dots, k; j = 1, 2, \dots, k, \end{array}

(1)

\begin{array}{rcl} a_{i i} = 1, a_{i j} > 0, a_{j i} = 1 / a_{i j}, i = 1, 2, \dots, k; j = 1, 2, \dots, k, \end{array}

(2)

\begin{array}{rcl} A w = λ_{max} w, \end{array}

(3)

Table 2 AHP scale for combinations

Numerical scale	Definition	Explanation
1	Equal importance	Two activities contribute equally to the objective.
3	Moderate importance	Experience and judgment slightly favor one over another.
5	Strong importance	Experience and judgment strongly favor one over another.
7	Very strong importance	An activity is strongly favored and its dominance is demonstrated in practice.
9	Absolute importance	Importance of one over another affirmed on the highest possible order.
2, 4, 6, and 8	Intermediate values	Used to represent compromise between the priorities listed above.
Reciprocals (1/ $a_{i j}$ )	A value attributed when activity $i$ is compared to activity $j$ becomes the reciprocal when $j$ is compared to $i$

Table 3 Random consistency index (RI)

$k$	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

where

w

expresses the eigenvector corresponding to the maximal eigenvalue

λ_{max}

of matrix A.

C R < 0.1

, the judgment matrix can be accepted. Otherwise, it is unable to meet the requirements of consistency, which should be reviewed and improved.

2.3 G-VIKOR Approach

The compromise ranking method, i.e., VIKOR, presented by Opricovic^[39] is normally employed to solve the complex MCDM problems of obtaining the optimal alternative against conflicting decision criteria. The method depends on an aggregating function inspired by the

L_{p}

-metric.

L_{p}

-metric reflects the distance to the positive/negative ideal individuals, and determines the ultimate ranking of each alternative on the basis of the compromise between the maximum "group benefit" and the minimum "individual regret"^[40]. Because VIKOR has high computational efficiency and a simpler mathematical form to measure the relative performance for alternatives, it has been widely used in many fields, e.g., material selection^[41-44]. To further strengthen the accuracy and validity of this approach, we involved GRA into the conventional aggregating function to better identify relationships among each alternative. The calculation procedures of this approach are summarized in the following.

Step 1 Formulate the initial decision matrix. Assuming that

A_{i} (i = 1, 2, \dots, n)

represents the

i

th optimal solution, and

B_{j} (j = 1, 2, \dots, m)

represents the

j

th evaluation criterion in the MCDM problem, the initial decision matrix

X

can be expressed concisely in a matrix format as follows:

\begin{array}{rcl} X = \begin{array}{rcccccl} B_{1} & \dots & B_{j} & \dots & B_{m} \\ \begin{array}{c} A_{1} \\ ⋮ \\ A_{i} \\ ⋮ \\ A_{n} \end{array} [ & \begin{matrix} x_{11} \\ ⋮ \\ x_{i 1} \\ ⋮ \\ x_{n 1} \end{matrix} & \begin{matrix} \dots \\ ⋱ \\ \dots \\ ⋱ \\ \dots \end{matrix} & \begin{matrix} x_{1 j} \\ ⋮ \\ x_{i j} \\ ⋮ \\ x_{n j} \end{matrix} & \begin{matrix} \dots \\ ⋱ \\ \dots \\ ⋱ \\ \dots \end{matrix} & \begin{matrix} x_{1 m} \\ ⋮ \\ x_{i m} \\ ⋮ \\ x_{n m} \end{matrix} & ] \begin{array}{c}  \end{array} \end{array}, \end{array}

(4)

where

x_{i j}

indicates the optimization value of each optimal solution/alternative

A_{i} (i = 1, 2, \dots, n)

with respect to each criterion

B_{j} (j = 1, 2, \dots, m)

Step 2 Normalize the initial decision matrix via linear scale transformation. Note that

Z

represents the normalized decision matrix as follows:

\begin{array}{rcl} Z = {[z_{i j}]}_{n \times m}, \end{array}

(5)

\begin{array}{rcl} z_{i j} = \frac{x_{i j}}{max_{i} x_{i j}}, i = 1, 2, \dots, n; j = 1, 2, \dots, m . \end{array}

(6)

Step 3 Establish the optimum

f_{i}^{*}

and the worst

f_{i}^{-}

values of the entire criterion functions as follows:

\begin{array}{rcl} f_{j}^{*} = {max_{1 \leq i \leq n} ({z_{i j}}_{i = 1}^{n}) | j \in J^{+}, min_{1 \leq i \leq n} ({z_{i j}}_{i = 1}^{n} | j \in J^{-}}, \end{array}

(7)

\begin{array}{rcl} f_{j}^{-} = {min_{1 \leq i \leq n} ({z_{i j}}_{i = 1}^{n}) | j \in J^{+}, max_{1 \leq i \leq n} ({z_{i j}}_{i = 1}^{n} | j \in J^{-}}, \end{array}

(8)

where

J^{+}

expresses the parameter set which the greater the better, and

J^{-}

expresses the parameter set which the smaller the better.

Step 4 Compute the values of

S_{i}

and

R_{i} (i = 1, 2, \dots, n)

combining the grey correlation coefficient between the

i

th solution and negative/positive-ideal solution regarding the

j

th object on the basis of GRA.

\begin{array}{rcl} S_{i} = \sum_{j = 1}^{m} w_{j} (\frac{min_{i} min_{j} | f_{j}^{*} - z_{i j} | + ρ max_{i} max_{j} | f_{j}^{*} - z_{i j} |}{| f_{j}^{*} - z_{i j} | + ρ max_{i} max_{j} | f_{j}^{*} - z_{i j} |}), \forall j, \end{array}

(9)

\begin{array}{rcl} R_{i} = max_{j} [w_{j} (\frac{min_{i} min_{j} | f_{j}^{-} - z_{i j} | + ρ max_{i} max_{j} | f_{j}^{-} - z_{i j} |}{| f_{j}^{-} - z_{i j} | + ρ max_{i} max_{j} | f_{j}^{-} - z_{i j} |})], \forall j, \end{array}

(10)

where

w_{j} (j = 1, 2, \dots, m)

represents the weight of

j

th object that can be obtained using the AHP approach. In addition,

ρ \in [0, 1]

represents the resolution criterion. In this section,

ρ

is set to 0.5^[43].

Step 5 Calculate the value of

Q_{i} (i = 1, 2, \dots, n)

using the relation.

\begin{array}{rcl} {\begin{array}{l} min \sum_{i = 1}^{n} [{(Q_{i} - μ_{i})}^{2} + {(Q_{i} - η_{i})}^{2}] \\ μ_{i} = (S_{i} - S^{*}) / (S^{-} - S^{*}), \\ η_{i} = (R_{i} - R^{*}) / (R^{-} - R^{*}), \\ \begin{matrix} s . t . min (μ_{i}, η_{i}) \leq Q_{i} \leq max (μ_{i}, η_{i}), \\ \begin{array}{c} 0 < Q_{i} < 1, \end{array} \end{matrix} \end{array} \end{array}

(11)

and then,

S^{*} = max S_{i}, S^{-} = min S_{i}, R^{*} = max R_{i}, R^{-} = min R_{i}

Step 6 Judging criteria as follow:

C1:

Q_{j}^{''} - Q_{j}^{'} \geq 1 / (j - 1)

, where,

Q_{j}^{'}

and

Q_{j}^{''}

are the best and second alternative, respectively.

j

is the number of alternatives.

C2: Sort the first solution

S_{j}

(or

R_{j}

) in

Q_{j}

value must sort than the second at the same time.

3 Verification of the Empirical Case

An empirical application on three enterprises proves that this utilized integrated methodology is valid and practical. The sub-sections, entitled "Background and Review", discusses the weighting of each criterion by AHP approach and the evaluation processes by G-VIKOR.

3.1 Background and Review

3.1.1 Overview of D' long Series of Heavy Commercial Vehicles (Alternative 1)

Shaanxi Automobile Group Co., Ltd began to develop since 1968, producing and selling all kinds of truck, engine and automobile components. Cooperating with foreign ventures, this company has developed an excellent research and development (R & D) system and technologies that reaches the international or domestic advanced level. D' long series of heavy commercial vehicles are high-class trucks which can completely replace the corresponding imported products. The vehicles, designed by German MAN company, symbolize the technical cooperation between China and German. Also, their excellent quality wins public praise.

3.1.2 Overview of Dongfeng Tianlong Series of Heavy Commercial Vehicles (Alternative 2)

The Second Automobile Works, established in 1969, is the predecessor of Dongfeng commercial vehicles Co., Ltd. Since 1975, it started producing heavy commercial vehicles and gained goodwill for high-quality: Low oil consumption, high dynamic performance, good durability and extra security. Dongfeng Tianlong series of heavy commercial vehicles have world leading technologies and processing skills. They are targeted to domestic markets and different users. International cooperation maximizes the power of technical fusion, and upgrades performance of the system performance, especially in its technology, safety, economy, reliability, comfort and other aspects of independent innovation.

3.1.3 Overview of Hualing Heavy Commercial Vehicles (Alternative 3)

Hualing Xingma Automobile Group Co., Ltd. is an important invention industrial base of heavy truck and related core components in China. It produces and sells a wide variety of products including complete vehicles, power trains, axles, heavy special trucks, buses and other components. Their special motor vehicles play a leading role in domestic market, and have also been exported for years. Meanwhile, the industrial-academic-research cooperation has a strong technical force of production and new product development capability. With over forty years of achievement, Hualing has developed a unique culture in the future global competition.

Based on the selected model of the configuration parameters as close as possible principle, the body, weight, output power, maximum horsepower and other parameters of each model are taken into account, and the following three models are selected. Table 4 describes the related configuration parameters.

Table 4 The related configuration parameters of each alternative

	Alternative 1	Alternative 2	Alternative 3
Automobile Brand	Shaanxi Automobile Group Co., Ltd	Dongfeng commercial Vehicles Co., Ltd	Hualing Xingma Automobile Group Co., Ltd
Type	SX4256NT324Z	DFL4251AX16A	HN4250G37CLM3
Drive form	6*4	6*4	6*4
Wheelbase	3175+1400 mm	3300+1350 mm	3200+1350 mm
Vehicle body (lengthwidthHeight)	6.68 m2.49 m4.0 m	6.96 m2.5 m3.7 m	6.87 m2.495 m3.8 m
Front tread	2036 mm	2040 mm	2065 mm
Rear track	1800/1800 mm	1820/1820 mm	1860/1860 mm
Vehicle weight	8.8 t	8.8 t	8.7 t
Total mass	25.0 t	25.0 t	25.0 t
Traction of the total mass	40.0 t	40.0 t	40.0 t
Maximum speed	99.0 km/h	98.0 km/h	90.0 km/h
Number of cylinders	6	6	6
Fuel type	Diesel	Diesel	Diesel
Displacement	11.596 L	11.12 L	11.596 L
Maximum output power	276.0 kW	283.0 kW	276.0 kW
Torque	1800 N $\cdot$ m	1800 N $\cdot$ m	1800 N $\cdot$ m
Maximum horsepower	375 BHP	385 BHP	375 BHP
Rated speed	1900 rpm	1900 rpm	1900 rpm
Maximum torque speed	1000 $\sim$ 1400 rpm	1100 $\sim$ 1500 rpm	1800 rpm
Quasi passenger number	3	3	3
Seating rows	Row half	Row half	Row half
Shift mode	Manual	Manual	Manual
Gearbox	Fast 12JSD180 T	Fast 12JSD180 TA	Fast 12JSD160 T
Forward gear	12	12	12
Reverse the number	2	2	2
Tire number	10	10	10
Fuel tank / gas cylinder material	Aluminum alloy	Aluminum alloy	Iron.
Fuel tank capacity	400.0 L	400.0 L	350.0 L
Suspension type	Leaf spring	Leaf spring	Leaf spring
Rear axle allowable load	18000 kg	18000 kg	18000 kg

3.2 Weighting of Each Criterion by AHP Approach

The pair-wise comparison matrixes of each attribute in the hierarchical structure was structured by related experts with reliable experience as shown in Table 5, based on the fundamental scale of comparison values demonstrated in Table 2. In the same way, the matrixes from market property (

A_{1}

-M), environmental property (

A_{2}

-E), technological property (

A_{3}

-T), management property (

A_{4}

-F) and cultural property (

A_{5}

-C) are presented in Tables 6

\sim

10, respectively.

Table 5 TPair-wise comparison matrix from each attribute

	A1	A2	A3	A4	A5
A1	1	6	1/3	1/5	4
A2	1/6	1	1/5	1/9	1
A3	3	5	1	1/4	6
A4	5	9	4	1	8
A5	1/4	1	1/6	1/8	1

Table 6 air-wise comparison matrix from market point of view ( $A_{1}$ -M)

	M1	M2	M3	M4	M5
M1	1	5	2	3	3
M2	1/5	1	1/4	1/3	1/4
M3	1/2	4	1	3	1
M4	1/3	3	1/3	1	1/3
M5	1/3	5	1	3	1

Table 7 Pair-wise comparison matrix from environmental point of view ( $A_{2}$ -E)

	E1	E2	E3
E1	1	4	6
E2	1/4	1	3
E3	1/6	1/3	1

Table 8 Pair-wise comparison matrix from communication point of view ( $A_{3}$ -T)

	B1	B2	B3	B4	B5	B6
B1	1	4	3	1/3	1/3	4
B2	1/4	1	1/3	1/6	1/5	1
B3	1/3	3	1	1/4	1/3	3
B4	3	6	4	1	1	6
B5	3	5	3	1	1	5
B6	1/4	1	1/3	1/6	1/5	1

Table 9 Pair-wise comparison matrix from technological point of view ( $A_{4}$ -F)

	T1	T2	T3	T4	T5
T1	1	4	2	3	3
T2	1/4	1	1/3	1	1/2
T3	1/2	3	1	2	2
T4	1/3	1	1/2	1	1
T5	1/3	2	1/2	1	1

Table 10 Pair-wise comparison matrix from cultural point of view ( $A_{5}$ -C)

	C1	C2
C1	1	1/3
C2	3	1

Based on the calculation process of AHP, the weights vector of each criterion in the hierarchical structure and the value of CR can be computed. The importance of each criterion, CR of each material, and the ultimate weights vector of criteria on overall goal of evaluation index can be obtained, which is presented in Table 11.

Table 11 Criteria weight and rank on overall goal

Attribute	Weight	Criteria	Weight	Final weight	Rank
Market property	0.1512	Market share	0.3929	0.0594	7
		Market penetration	0.0543	0.0082	20
		Sales	0.2242	0.0338	10
		Profit margin	0.1073	0.0162	15
		Profit rate	0.2212	0.0334	12
Environmental property	0.0428	Economic conditions	0.6853	0.0293	13
		Political and legal environment	0.2213	0.0095	19
		Competitive environment	0.0934	0.0040	21
Brand property	0.2339	Brand positioning	0.1680	0.0393	9
		Brand communication	0.0471	0.0110	17
		Brand awareness	0.1038	0.0243	14
		Brand reputation	0.3325	0.0778	3
		Brand loyalty	0.3015	0.0705	5
		Brand association	0.0471	0.0110	17
Technological property	0.5270	Investment of R & D	0.4028	0.2123	1
		Technical developers	0.0926	0.0488	8
		Success rate of R & D	0.2454	0.1293	2
		Output value of new products	0.1205	0.0635	6
		Patents	0.1387	0.0731	4
Cultural property	0.0451	Enterprise culture construction	0.2500	0.0113	16
		Culture forces	0.7500	0.0338	10

3.3 Evaluation Processes by G-VIKOR

Initial data and related information can be gathered by experts from various fields, e.g., scholars of college and supervisors of enterprise, through questionnaire surveys. In this research, three experts, including two scholars and a supervisor, were interviewed to obtain the pair-wise comparison matrix of each criterion and the decision matrix for the score of three enterprises. Due to space limitation, the final matrixes are only given as Table 12. The investigation was conducted in June, 2017.

Table 12 The score of each criterion for commercial vehicles brand competitiveness of three enterprises

	Alternative 1	Alternative 2	Alternative 3
Market share	4.67	8.33	1.67
Market penetration	3.67	8.33	1.33
Sales	5.33	6.67	2.67
Profit margin	4.00	3.67	3.33
Profit rate	1.33	3.33	3.67
Economic conditions	2.67	5.00	5.00
Political and legal environment	5.00	4.67	3.33
Competitive environment	5.67	5.33	3.67
Brand positioning	4.33	7.33	6.33
Brand communication	5.00	5.33	5.33
Brand awareness	5.33	3.67	2.00
Brand reputation	5.00	6.67	8.67
Brand loyalty	4.67	5.33	4.00
Brand association	1.33	3.67	1.33
Investment of R & D	5.33	7.33	3.33
Technical developers	4.33	6.00	4.67
Success rate of R & D	5.33	3.67	4.33
Output value of new products	2.67	4.67	3.33
Patents	5.33	5.67	3.33
Enterprise culture construction	7.67	4.33	6.00
Culture forces	4.67	6.67	5.33

The score of each criterion for commercial vehicles brand competitiveness of three enterprises is listed in Table 12. The final rank of the value

Q_{i}

can be obtained via the calculation process of this hybrid approach, i.e., G-VIKOR. Thereby, the optimal alternative is selected based on the results. The concrete procedure for the assessment of empirical example, i.e., three enterprises, is shown below:

The initial decision matrix

X

can be obtained by Table 12. Note that

x_{i i}

indicates the value of each alternative

A_{i} (i = 1, 2, 3)

with respect to each evaluation criterion

B_{j} (j = 1, 2, \dots, 21)

. Then, the normalized decision matrix can be calculated by Equations (5)

\sim

(6). The criteria in this hierarchical structure of commercial vehicles brand are benefit elements. The optimum and the worst values of the entire criterion functions can be obtained according to Equations (7)

\sim

(8). The values of

S_{i}

and

R_{i} (i = 1, 2, 3)

can be calculated by Equations (9)

\sim

(10). Finally, the final rank of each alternative shown in Table 13 can be obtained by the values of

Q_{i} (i = 1, 2, 3)

, which are calculated by Equation (11).

Table 13 The values of $S_{i}$ and $R_{i}$

	Alternative 1	Alternative 2	Alternative 3
$S_{i}$	0.5031	0.5749	0.8519
$R_{i}$	0.2123	0.0995	0.1293

Based on the calculation process, the overall closeness index for D'long series of heavy commercial vehicles is 0.5000, the overall closeness index for Dongfeng Tianlong series of heavy commercial vehicles is 0.8971, and the overall closeness index for Hualing heavy commercial vehicles is 0.3678, i.e., alternative 2

\geq

alternative 1

\geq

alternative 3.

4 Analysis and Discussion

4.1 Comparison and Validation

In this work, AHP-TOPSIS method^[42] and AHP-GRA method^[44] are applied to compare the outcomes of the integrated approach. Note that the same weight is adopted when using three MCDM approaches. Table 14 expresses the assessment results obtained from three decision approaches.

Table 14 Comparison results obtained from three approaches

Alternatives	AHP-TOPSIS method		The proposed method		AHP-GRA method
Alternatives	Value	Order	Value	Order	Value	Order
1	0.4537	2	0.5000	2	0.4841	2
2	0.5749	1	0.8971	1	0.7522	1
3	0.4263	3	0.3678	3	0.2515	3

From Table 14, it can be seen that the results of three methods are consistent and close basically. This shows that the integrated method is reasonable and feasible to evaluate commercial vehicles brand competitiveness in China. However, this approach combines the advantages of GRA and VIKOR to make the evaluation process more objective and realistic.

4.2 Discussion

Brand competitiveness is the core of an enterprise in the commercial vehicle market. Enhancing brand competitiveness offers a company advantages among its competitors, for example, leading it to be a guide and supporting role in the whole industries structure.

The key to enhance brand competitiveness is to make an objective and accurate evaluation and research of brand's comprehensive strength. The evaluation is based on the objectivity and operability of the proposed methods. This section validates the results of the commercial vehicle brand image background analysis in China obtained by the literature and interviewed feedback from different experts.

With the rapid development of economy and society, the competition ability of enterprises depends on high quality products. High quality commercial vehicles would not only increase economic benefits, but also set a good corporate image and show the company's social responsibility. It is the advanced technologies that enable manufacturer to produce high-quality autos. Therefore, the competitive power crucially depends on an enterprise's control of new products and technologies, meanwhile, the competitiveness is determined by the innovative capacity for new products, which comes from the research and development of people^[15]. Chen, et al. considered that Chinese car companies should put more fund, manpower and resource on R & D, independent innovation and personnel training to form an independent innovation platform, develop new products, improve the R & D management system and strengthen the protection of intellectual property for auto company^[28]. Han, et al. presented that technology improvement associated with battery cost reduction has maintained cost competitiveness and played an essential role in the starting up China's BEPV market^[45]. They offered identical views with the proposed results of factor analysis.

In the complex and volatile market, the competition of vehicle is either directly or indirectly linked with brand image and brand communication. Cretu and Brodie indicated that the brand's image has a more specific influence on the customers' perceptions of products and service quality, while the company's reputation has a broader influence on perceptions of customer value and customer loyalty^[46]. Dorcak, et al. tested the relationships among factors by a careful statistics, in order to identify and describe the basic facts that affect company reputation of companies in a hyper competitive market. Analysis conducted on the selected parts of the global market identifies the findings. It can be used in any market for the purpose of increasing competitiveness of entities selected from the automotive industry^[22].

Enterprise culture is the soul and spiritual support for the enterprise development, which provides strong spiritual motive and intellectual support. Jiang, et al. presented that managers should improve the level of talent management and development, fully exert the role of enterprises culture in HRM and increase talents' core competitiveness in enterprises^[32]. The market and environment factors in China will inevitably influence China's enterprises competitive situation where there appears to be of a rapid changing dynamic of trends. Li, et al. pointed out that there has a strong correlativity between competitiveness of regional equipment manufacturing and economic strength. Various regions should adjust measures based on local conditions and take effective measures to improve competitiveness of equipment manufacturing^[19].

5 Conclusion

In China's vehicle industry, evaluating commercial vehicles brand competitiveness is proved to be a tough challenge. This paper elaborates the theories and the main investigations/studies of brand competitiveness. It formulates a comprehensive hierarchical structure that integrates impact criteria, i.e., social, technical, managerial, environmental and cultural criteria. A novel MCDM model that combines AHP, GRA and VIKOR is presented to determine weights of influence criteria and to evaluate the brand competitiveness of enterprises. Three commercial vehicles brand in China, i.e., D'long series of heavy commercial vehicles, Dongfeng Tianlong series of heavy commercial vehicles and Hualing heavy commercial vehicles, are carried out. The results show that Dongfeng Tianlong series of heavy commercial vehicles have the optimal brand competitiveness in China. In future work, we will add environmental property in the hierarchical structure of commercial vehicles brand. In addition, fuzzy theory will be integrated in the evaluation process.

References

Publishing order | Descend order by publishing year | Descend order by cited within

1	Jing H, Lin J Y, Jing P. Using grey relational analysis with entropy to predict the international financial center of China. Journal of Systems Science and Information, 2017, 5 (1): 88- 96. https://doi.org/10.21078/JSSI-2017-088-09 Cited in this article [1]

2	Xiao Y J, Zhang D X. The command decision method of multiple UUV cooperative task assignment based on contract net protocol. Journal of Systems Science and Information, 2016, 4 (4): 379- 390. https://doi.org/10.21078/JSSI-2016-379-12

3	Liu Z W, Hao H, Zhao F Q. Current situation, development demand and future trend of automotive technologies in China. Automobile Technology, 2017, (1): 1- 6. Cited in this article [1]

4	Liu J. Investigation into logistics outsourcing supplier selection for automobile manufacturers. 9th International Conference on Management Science and Engineering Management (ICMSEM), Advances in Intelligent Systems and Computing, 2015, 362, 1239- 1248. Cited in this article [1]

5	Jo H J, Jeong J H. The governance of the automotive product development organization: A focus of a company's pilot center. Korean Journal of Sociology, 2015, 49 (4): 37. https://doi.org/10.21562/kjs.2015.08.49.4.37 Cited in this article [1]

6	Wagner S M, Silveira-Camargos V. Managing risks in just-in-sequence supply networks: Exploratory evidence from automakers. IEEE Transactions on Engineering Management, 2012, 59 (1): 52- 64. https://doi.org/10.1109/TEM.2010.2087762 Cited in this article [1]

7	Olson E L. Will songs be written about autonomous cars? The implications of self-driving vehicle technology on consumer brand equity and relationship. International Journal of Technology Marketing, 2017, 12 (1): 23. https://doi.org/10.1504/IJTMKT.2017.081506 Cited in this article [1]

8	Moons I, Pelsmacker P D. Self-brand personality differences and attitudes towards electric cars. Sustainability, 2015, 7 (9): 12322- 12339. https://doi.org/10.3390/su70912322 Cited in this article [1]

9	Baltas G, Saridakis C. Brand-name effects, segment differences, and product characteristics: An integrated model of the car market. Journal of Product & Brand Management, 2009, 18 (2): 143- 151. Cited in this article [1]

10	Choi M, Lee Y, Koo K R, et al. A case study of Hyundai motors: Live brilliant campaign for modern premium brand. Asia Marketing Journal, 2015, 16 (4): 75- 87. https://doi.org/10.15830/amj.2015.16.4.75 Cited in this article [1]

11	Wang X S, Fan X H. Analysis and evaluation of the development strategy of China's family cars industrial independent brands. Proceedings of 2010 International Conference on Industry Engineering and Management, Changzhou, 2010: 49-54. Cited in this article [1]

12	Urban G L, Carter T, Gaskin S, et al. Market share rewards to pioneering brands: An empirical analysis and strategic implications. Management Science, 1986, 32 (6): 645- 659. https://doi.org/10.1287/mnsc.32.6.645 Cited in this article [1]

13	Zufryden F S. A composite heterogeneous model of brand choice and purchase timing behavior. Management Science, 1977, 24 (2): 121- 136. https://doi.org/10.1287/mnsc.24.2.121

14	Tsafarakis S, Grigoroudis E, Matsatsinis N. Consumer choice behaviour and new product development: An integrated market simulation approach. Journal of the Operational Research Society, 2011, 62 (7): 1253- 1267. https://doi.org/10.1057/jors.2010.70

15	Kim B. Dynamic effects of learning capabilities and profit structures on the innovation competition. Optimal Control Applications & Methods, 2015, 20 (3): 127- 144. Cited in this article [1]

16	Huang J T, Chen Y Q. A study on synthetic fuzzy measurement model of marketing competitiveness. 2008 International Seminar on Business and Information Management, Wuhan, 2008, 2, 873- 876.

17	Li G. New perspective upon enterprise competitiveness: Competition among enterprises in the product and element markets. China Industrial Economy, 2007, 28 (1): 61- 67. Cited in this article [1]

18	Thanasuta K, Patoomsuwan T, Chaimahawong V, et al. Brand and country of origin valuations of automobiles. Asia Pacific Journal of Marketing & Logistics, 2009, 21 (3): 355- 375. Cited in this article [1]

19	Li R P, Cui H D, Cui Z. The evaluation and analysis of competitiveness in regional equipment manufacturing based on factor analysis. IEEE International Conference on Machine Learning and Cybernetics, 2011: 1031-1036. Cited in this article [1]

20	Wang L F, Wen W, Lai M Y, et al. Measurement of the automobile industrial security degree in China. IEEE International Conference on Management Science & Engineering 17th Annual Conference, 2010: 205-211. Cited in this article [1]

21	Zhang H, Yao L, Liu N. Research on Chinese self-owned auto brand building on the basis of the valuechain theory. Proceedings of 4th International Conference on Software Technology and Engineering (ICSTE 2012), Phuket, ASME Press, 2012. https://doi.org/10.1115/1.860151. Cited in this article [1]

22	Dorcak P, Markovic P, Pollak F. Multifactor analysis of online reputation of selected car brands. Procedia Engineering, 2017, 192, 719- 724. https://doi.org/10.1016/j.proeng.2017.06.124 Cited in this article [1]

23	Huang Y C, Yang C, Huang M H. The research on the growth concept of independent intellectual property famous brand. IEEE International Conference on Information Management, Innovation Management and Industrial Engineering, 2010: 314-317.

24	Ewing M T, Windisch L, Newton F J. Corporate reputation in the people's republic of China: A B2B perspective. Industrial Marketing Management, 2010, 39 (5): 728- 736. https://doi.org/10.1016/j.indmarman.2010.02.011

25	Zheng X, Ye M A. A study on vehicle dealers competitiveness evaluation matrix based on capability and loyalty. Automotive Engineering, 2009, 31 (1): 94- 99. https://doi.org/10.3321/j.issn:1000-680X.2009.01.021

26	Yao J, Chen J H, Liu Z B. The modeling identification design of rubbish vehicle based on brand culture. Packaging Engineering, 2014, 35 (8): 39- 42. Cited in this article [1]

27	Davis B A, Figliozzi M A. A methodology to evaluate the competitiveness of electric delivery trucks. Transportation Research Part E: Logistics & Transportation Review, 2013, 49 (1): 8- 23. Cited in this article [1]

28	Chen H, Yang Y, Chen L. Analysis and strategy of intellectual property rights innovation of self-brand car in China. IEEE International Conference on Future Information Technology and Management Engineering, 2010: 97-100. Cited in this article [1]

29	Zhou G L. Supporting China private enterprise development by technological innovation. Proceedings of Zhengzhou Conference on Management of Technology (MOT 2009), Zhengzhou, 2009, 1, 166- 170.

30	Li X J, Xie N X, Xu K. Study on technological competitiveness of China-owned brand automotive enterprises. China Soft Science, 2009, 45 (5): 125- 134. https://doi.org/10.3969/j.issn.1002-9753.2009.05.016

31	Meng D H, Li X J, Xu K. Differences of technology absorptive capacity: Evidence from China's automotive industry. Science & Technology Progress & Policy, 2014, 31 (19): 81- 86. Cited in this article [1]

32	Jiang M, Shen Q, Mu Y. To exert the role of enterprises culture, to achieve effective human resources management. IEEE International Conference on Management and Service Science, 2011: 1-3. Cited in this article [2]

33	Ataei V, Sharifirad M S. Organizational culture and innovation culture: Exploring the relationships between constructs. Leadership & Organization Development Journal, 2012, 33 (5): 543- 544. Cited in this article [1]

34	Saaty T L. How to make a decision: The analytic hierarchy process. Interfaces, 1994, (24): 19- 43. Cited in this article [1]

35	Tian G D, Chu J W, Hu H S, et al. Technology innovation system and its integrated structure for automotive components remanufacturing industry development in China. Journal of Cleaner Production, 2014, 85, 419- 432. https://doi.org/10.1016/j.jclepro.2014.09.020 Cited in this article [1]

36	Dagdeviren M, Yavuz S, Kilinc N. Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 2009, 36, 8143- 8151. https://doi.org/10.1016/j.eswa.2008.10.016 Cited in this article [1]

37	Kannan M, Jepsen M B. ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 2016, 250 (1): 1- 29. https://doi.org/10.1016/j.ejor.2015.07.019 Cited in this article [1]

38	Kannan M, Diabat A, Shankar S M. Analyzing the drivers of green manufacturing with fuzzy approach. Journal of Cleaner Production, 2015, 96, 182- 193. https://doi.org/10.1016/j.jclepro.2014.02.054 Cited in this article [1]

39	Opricovic S, Tzeng G H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 2004, 156 (2): 445- 455. https://doi.org/10.1016/S0377-2217(03)00020-1 Cited in this article [1]

40	Zhang N, Wei G. Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 2013, 37 (7): 4938- 4947. https://doi.org/10.1016/j.apm.2012.10.002 Cited in this article [1]

41	Li N N, Zhao H R. Performance evaluation of eco-industrial thermal power plants by using fuzzy GRAVIKOR and combination weighting techniques. Journal of Cleaner Production, 2016, 135, 169- 183. https://doi.org/10.1016/j.jclepro.2016.06.113 Cited in this article [1]

Tian G D, Zhang H H, Feng Y X, et al. Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method. Renewable & Sustainable Energy Reviews, 2018, 81, 682- 692.

https://doi.org/10.3969/j.issn.1671-5292.2018.05.009

Cited in this article [1]

43	Tian G D, Zhang H H, Feng Y X, et al. Operation patterns analysis of automotive components remanufacturing industry development in China. Journal of Cleaner Production, 2017, 164, 1363- 1375. https://doi.org/10.1016/j.jclepro.2017.07.028 Cited in this article [1]

44	Zhang H H, Peng Y, Tian G D, et al. Green material selection for sustainability: A hybrid MCDM approach. PloS One, 2017, 12 (5): e0177578. https://doi.org/10.1371/journal.pone.0177578 Cited in this article [2]

45	Han H, Ou X M, Du J Y, et al. China's electric vehicle subsidy scheme: Rationale and impacts. Energy Policy, 2014, 73, 722- 732. https://doi.org/10.1016/j.enpol.2014.05.022 Cited in this article [1]

46	Cretu A E, Brodie R J. The influence of brand image and company reputation where manufacturers market to small firms: A customer value perspective. Industrial Marketing Management, 2007, 36 (2): 230- 240. https://doi.org/10.1016/j.indmarman.2005.08.013 Cited in this article [1]

PDF(330 KB)

242

Accesses

Citation

Detail

Sections

Recommended

Abstract
Key words
Cite this article
1 Introduction
2 Solution Methodology
Figure 1 The flowchart of the proposed novel hybrid approach
2.1 Hierarchical Criteria for Evaluation
Table 1 The hierarchical structure of the commercial vehicles brand in criteria & sub-criteria
2.2 AHP Approach
Table 2 AHP scale for combinations
Table 3 Random consistency index (RI)
2.3 G-VIKOR Approach
3 Verification of the Empirical Case
3.1 Background and Review
3.1.1 Overview of D' long Series of Heavy Commercial Vehicles (Alternative 1)
3.1.2 Overview of Dongfeng Tianlong Series of Heavy Commercial Vehicles (Alternative 2)
3.1.3 Overview of Hualing Heavy Commercial Vehicles (Alternative 3)
Table 4 The related configuration parameters of each alternative
3.2 Weighting of Each Criterion by AHP Approach
Table 5 TPair-wise comparison matrix from each attribute
Table 6 air-wise comparison matrix from market point of view ( $A_{1}$ -M)
Table 7 Pair-wise comparison matrix from environmental point of view ( $A_{2}$ -E)
Table 8 Pair-wise comparison matrix from communication point of view ( $A_{3}$ -T)
Table 9 Pair-wise comparison matrix from technological point of view ( $A_{4}$ -F)
Table 10 Pair-wise comparison matrix from cultural point of view ( $A_{5}$ -C)
Table 11 Criteria weight and rank on overall goal
3.3 Evaluation Processes by G-VIKOR
Table 12 The score of each criterion for commercial vehicles brand competitiveness of three enterprises
Table 13 The values of $S_{i}$ and $R_{i}$
4 Analysis and Discussion
4.1 Comparison and Validation
Table 14 Comparison results obtained from three approaches
4.2 Discussion
5 Conclusion
References

Received	Accepted	Published
2020-05-09	2020-06-28	2020-08-25
Issue Date
2020-08-25

	A1	A2	A3	A4	A5
A1	1	6	1/3	1/5	4
A2	1/6	1	1/5	1/9	1
A3	3	5	1	1/4	6
A4	5	9	4	1	8
A5	1/4	1	1/6	1/8	1

	M1	M2	M3	M4	M5
M1	1	5	2	3	3
M2	1/5	1	1/4	1/3	1/4
M3	1/2	4	1	3	1
M4	1/3	3	1/3	1	1/3
M5	1/3	5	1	3	1

	B1	B2	B3	B4	B5	B6
B1	1	4	3	1/3	1/3	4
B2	1/4	1	1/3	1/6	1/5	1
B3	1/3	3	1	1/4	1/3	3
B4	3	6	4	1	1	6
B5	3	5	3	1	1	5
B6	1/4	1	1/3	1/6	1/5	1

	T1	T2	T3	T4	T5
T1	1	4	2	3	3
T2	1/4	1	1/3	1	1/2
T3	1/2	3	1	2	2
T4	1/3	1	1/2	1	1
T5	1/3	2	1/2	1	1

	A1	A2	A3	A4	A5
A1	1	6	1/3	1/5	4
A2	1/6	1	1/5	1/9	1
A3	3	5	1	1/4	6
A4	5	9	4	1	8
A5	1/4	1	1/6	1/8	1

	M1	M2	M3	M4	M5
M1	1	5	2	3	3
M2	1/5	1	1/4	1/3	1/4
M3	1/2	4	1	3	1
M4	1/3	3	1/3	1	1/3
M5	1/3	5	1	3	1

	B1	B2	B3	B4	B5	B6
B1	1	4	3	1/3	1/3	4
B2	1/4	1	1/3	1/6	1/5	1
B3	1/3	3	1	1/4	1/3	3
B4	3	6	4	1	1	6
B5	3	5	3	1	1	5
B6	1/4	1	1/3	1/6	1/5	1

	T1	T2	T3	T4	T5
T1	1	4	2	3	3
T2	1/4	1	1/3	1	1/2
T3	1/2	3	1	2	2
T4	1/3	1	1/2	1	1
T5	1/3	2	1/2	1	1

模态框（Modal）标题

Please choose a citation manager

Content to export

Abstract

Key words

Cite this article

1 Introduction

2 Solution Methodology

Figure 1 The flowchart of the proposed novel hybrid approach

2.1 Hierarchical Criteria for Evaluation

Table 1 The hierarchical structure of the commercial vehicles brand in criteria & sub-criteria

2.2 AHP Approach

Table 2 AHP scale for combinations

Table 3 Random consistency index (RI)

2.3 G-VIKOR Approach

3 Verification of the Empirical Case

3.1 Background and Review

3.1.1 Overview of D' long Series of Heavy Commercial Vehicles (Alternative 1)

3.1.2 Overview of Dongfeng Tianlong Series of Heavy Commercial Vehicles (Alternative 2)

3.1.3 Overview of Hualing Heavy Commercial Vehicles (Alternative 3)

Table 4 The related configuration parameters of each alternative

3.2 Weighting of Each Criterion by AHP Approach

Table 5 TPair-wise comparison matrix from each attribute

Table 6 air-wise comparison matrix from market point of view (A1-M)

Table 7 Pair-wise comparison matrix from environmental point of view (A2-E)

Table 8 Pair-wise comparison matrix from communication point of view (A3-T)

Table 9 Pair-wise comparison matrix from technological point of view (A4-F)

Table 10 Pair-wise comparison matrix from cultural point of view (A5-C)

Table 11 Criteria weight and rank on overall goal

3.3 Evaluation Processes by G-VIKOR

Table 12 The score of each criterion for commercial vehicles brand competitiveness of three enterprises

Table 13 The values of Si and Ri

4 Analysis and Discussion

4.1 Comparison and Validation

Table 14 Comparison results obtained from three approaches

4.2 Discussion

5 Conclusion

{{custom_sec.title}}

{{custom_sec.title}}

References

{{custom_fnGroup.title_en}}

Footnotes

Table 6 air-wise comparison matrix from market point of view ( $A_{1}$ -M)

Table 7 Pair-wise comparison matrix from environmental point of view ( $A_{2}$ -E)

Table 8 Pair-wise comparison matrix from communication point of view ( $A_{3}$ -T)

Table 9 Pair-wise comparison matrix from technological point of view ( $A_{4}$ -F)

Table 10 Pair-wise comparison matrix from cultural point of view ( $A_{5}$ -C)

Table 13 The values of $S_{i}$ and $R_{i}$

	A1	A2	A3	A4	A5
A1	1	6	1/3	1/5	4
A2	1/6	1	1/5	1/9	1
A3	3	5	1	1/4	6
A4	5	9	4	1	8
A5	1/4	1	1/6	1/8	1

	M1	M2	M3	M4	M5
M1	1	5	2	3	3
M2	1/5	1	1/4	1/3	1/4
M3	1/2	4	1	3	1
M4	1/3	3	1/3	1	1/3
M5	1/3	5	1	3	1

	B1	B2	B3	B4	B5	B6
B1	1	4	3	1/3	1/3	4
B2	1/4	1	1/3	1/6	1/5	1
B3	1/3	3	1	1/4	1/3	3
B4	3	6	4	1	1	6
B5	3	5	3	1	1	5
B6	1/4	1	1/3	1/6	1/5	1

	T1	T2	T3	T4	T5
T1	1	4	2	3	3
T2	1/4	1	1/3	1	1/2
T3	1/2	3	1	2	2
T4	1/3	1	1/2	1	1
T5	1/3	2	1/2	1	1