Organizational quality specific immune evaluation of manufacturing enterprises: Multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation

Abstract

This study adopts multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation, uses linear weighting to integrate and sort out the evaluation information of each evaluated object. The method is applied to the evaluation of organizational quality specific immune of actual large manufacturing enterprises, and the practicability and feasibility of the method are proved by empirical analysis research. The empirical analysis results will provide method guidance and practical inspiration for improving organizational quality specific immune of manufacturing enterprises.

Keywords

Multi-attribute group decision-making method with complete ignorance of weight information evidence distance fuzzy entropy transformation organizational quality specific immune evaluation multi-attribute group decision-making

1. Introduction

Many scholars have carried out a lot of research on the situation that the weight information is unknown or the weight information is ambiguous, but there are corresponding deficiencies in various methods [1, 2, 3]. Therefore, after analyzing a lot of literatures, this study mainly studies the group decision problems. The current literatures use multi-attribute group decision methods to carry out evaluation with the help of subjective multi-attribute weights, put emphasis on the case of the weight of experts and the unknown weight of indicators [1, 2, 3, 4], which ignores the objective multi-attribute weights. The current literatures introduce the distance of evidence or the constant change of entropy weight [4, 5, 6, 7], but the literatures that combine with the distance of evidence or the constant change of entropy weight to form the matrix to determine the weight of choice and the weight of each index are few and lack. The current literatures put emphasis on the importance of the weights of multi-attribute with the help of experts [8, 9, 10, 11], but each expert has own standpoint and opinion, the literatures that integrate the expert weights, the entropy weight matrix of the index to obtain the index weight are relatively lack. The current literatures lay foundation for the weight information [12, 13, 14, 15], the weight information has the close relationships with the matrix of the decision maker [16], which is obtained through the research on the decision matrix [17], but the weight information have the corresponding objectivity, the reliability of the objective weight information can not be guaranteed.

Compared with the current literatures, this study mainly applies the multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation to realize the constant change of evidence distance and fuzzy entropy weight [5, 6, 7, 8]. The core of multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation is to determine the weight of experts and the weight of indicators by decision matrix [9, 10, 11, 12]. The individual decision matrix is standardized, the entropy weight method is used to calculate the weight of each index in the individual decision matrix, the weight value of each expert is calculated by the evidence distance to obtain the expert weight vector, the index weight vector is used to form the index [13, 14], the matrix of entropy weight is combined with the weight vector of the expert to obtain the weight vector of the decision-making index of the group [15]. Finally, the evaluation information of the evaluated object is integrated by the experts by linear weighting method, and the comprehensive evaluation value of the group is obtained [16, 17]. Manufacturing enterprises are important pillars of the development of the national economy, and organizational quality specific immune represents for the life, the competitiveness and continuous development of manufacturing enterprises. Accurate evaluation of organizational quality specific immune of manufacturing enterprise is very important and vital. The research motivation of this study lies in: this study further uses the multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation to realize the constant change of evidence distance and fuzzy entropy weight in order to carry out empirical analysis and evaluation of actual organizational quality specific immune of large-scale manufacturing enterprises. The empirical analysis results prove the practicability and operability of the multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation to realize the constant change of evidence distance and fuzzy entropy weight through empirical analysis research in order to solve the fuzzy or unknown weight value. The evaluation of the situation provides guidance and practical enlightenment for improving the organizational quality specific immune of manufacturing enterprises.

2. Problem description and model construction

Cuo and Li [1], Boran et al. [2], Liu et al. [3], Yang [4], Yang and Xu [5], Sentz and Ferson [6], Deng et al. [7], Jousselme et al. [8], Mostaghimi [9], Shuiabi et al. [10], Chen [11], Li et al. [12], Dempster [13], Yeh and Chang [14], Yager [15], Xu [16], Xu and Cai [17] have deeply explored the relevant contents of multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation. Refer to and draw on relevant literature to describe problems and construct models, the problem description and model construction are as follows:

Object sets with multiple attribute decision problems $A=\{a_{i}|i=1,2,\ldots,M\}$ , indicator set $C=\{c_{j}|j=1,2,\ldots,N\}$ , expert set $E=\{e_{k}|k=1,2,\ldots,L\}$ , indicator weight set $CW=\{cw_{j}|j=1,2,\ldots,N\}$ , expert weight set $EW=\{ew_{k}|k=1,2,\ldots,L\}$ .

$\displaystyle\sum\limits_{j=1}^{N}{cw_{j}=1},\sum\limits_{k=1}^{L}{ew_{k}=1}$ (1)

$x_{i,j}^{k}$ is the evaluation value of the indicator $c_{j}$ of expert $e_{k}$ for the object being evaluated $a_{i}$ , $u_{i,j}$ is the comprehensive evaluation value of the indicator $c_{j}$ of decision group to object $a_{i}$ , $v_{i}^{k}$ is the individual evaluation value of the expert $e_{k}$ for the object being evaluated $a_{i}$ .

$\displaystyle u_{i,j}=\sum\limits_{k=1}^{L}{ew_{k}x_{i,j}^{k}}$ (2) $\displaystyle v_{i}^{k}=\sum\limits_{j=1}^{N}{cw_{j}x_{i,j}^{k}}$ (3)

Among them, $i=1,2,\ldots,M;j=1,2,\ldots,N;k=1,2,\ldots,L$ .

Make $s_{i}$ indicates that the decision group is the object to be evaluated $a_{i}$ . The comprehensive evaluation value can be obtained according to Eqs (2) and (3):

$\displaystyle s_{i}=\sum\limits_{j=1}^{N}{cw_{j}u_{i,j}}=\sum\limits_{j=1}^{N}% {cw_{j}}\sum\limits_{k=1}^{L}{ew_{k}x_{i,j}^{k}}=\sum\limits_{k=1}^{L}{ew_{k}v% _{i}^{k}},i=1,2,\ldots,M$ (4)

According to Eq. (4), the ranking of the evaluation values of the respective evaluation objects can be obtained.

2.1 Individual decision-making evaluation

2.1.1 Individual indicator weights based on entropy weight method

The entropy weight method is an objective method of weighting. It mainly determines the weight of the index based on the amount of information contained in each attribute value, thus effectively avoiding the influence of subjective factors. Referring to Table 1, the language value of $x_{i,j}^{k}$ in the process is quantified, and the results are still $x_{i,j}^{k}$ [8, 9].

Table 1
Quantification tables of language evaluation values

Language evaluation values	Intuitionistic fuzzy number	Quantitative value
Extremely good (EG)/Extremely high (EH)	(1.00, 0.00)	10
Very very good (VVG)/Very very high (VVH)	(0.90, 0.10)	9
Very good (VG)/Very high (VH)	(0.80, 0.10)	8
Good (G)/High (H)	(0.70, 0.20)	7
Medium good (MG)/Medium high (MH)	(0.60, 0.30)	6
Fair (F)/Medium (M)	(0.50, 0.40)	5
Medium bad (MB)/Medium low (ML)	(0.40, 0.50)	4
Bad (B)/Low (L)	(0.25, 0.60)	3
Very bad (VB)/Very low (VL)	(0.10, 0.75)	2
Very very bad (VVB)/Very very low (VVL)	(0.10, 0.90)	1

The determined decision matrix of expert $e_{k}$ is $X^{k}=(x_{i,j}^{k})_{M\times N}$ , this study uses the method of linear proportional transformation to perform standardization processing, and obtain the standardized matrix $Y^{k}=(y_{i,j}^{k})_{M\times N}$ , among them:

$\displaystyle y_{i,j}^{k}=\left\{{{\begin{array}[]{ll}{\frac{x_{i,j}^{k}}{x_{j% }^{\ast k}}}&{x_{j}^{\ast k}=\mathop{\max}\limits_{1\leqslant i\leqslant M}x_{% i,j}^{k}\neq 0|j\in J^{+}}\\ {\frac{x_{j}^{\ast k}}{x_{i,j}^{k}}}&{x_{j}^{\ast k}=\mathop{\min}\limits_{1% \leqslant i\leqslant M}x_{i,j}^{k}|j\in J^{-}}\\ \end{array}}}\right.$ (5)

$i=1,2,\ldots,M;j=1,2,\ldots,N;k=1,2,\ldots,L$ . $J^{+}$ indicates the benefit indicator, $J^{-}$ represents for the cost indicator. Corrected $Y^{k}$ is normalized:

$\displaystyle z_{i,j}^{k}=\frac{y_{i,j}^{k}}{\sum\limits_{i=1}^{M}{y_{i,j}^{k}% }},i=1,2,\ldots,M,j=1,2,\ldots,N,k=1,2,\ldots,L$ (6)

Carrying out calculation of $X^{k}$ entropy value of index $c_{j}$ decision matrix:

$\displaystyle g_{j}^{k}=-\frac{1}{\ln M}\sum\limits_{i=1}^{M}{z_{i,j}^{k}\ln z% _{i,j}^{k}},j=1,2,\ldots,N,k=1,2,\ldots,L$ (7)

Calculating $c_{j}$ decision matrix $X^{k}$ difference coefficient under the circumstances:

$\displaystyle d_{j}^{k}=1-g_{j}^{k},j=1,2,\ldots,N,k=1,2,\ldots,L$ (8)

According to the final indicator $c_{j}$ decision matrix $X^{k}$ , the weights are:

$\displaystyle cw_{j}^{k}=\frac{d_{j}^{k}}{\sum\limits_{j=1}^{N}{d_{j}^{k}}},j=% 1,2,\ldots,N,k=1,2,\ldots,L$ (9)

Then the weight vector of the individual decision indicator is:

$\displaystyle CW^{k}=(cw_{1}^{k},cw_{2}^{k},\ldots,cw_{N}^{k}),k=1,2,\ldots,L$ (10)

2.1.2 Individual decision evaluation based on evidence synthesis method

Based on decision matrix $X^{k}$ and its standardized matrix $Z^{k}=(z_{i,j}^{k})_{{M}\times{N}}(k=1,2,\ldots,L)$ , we use identify the framework $\Theta=A$ , the value of each attribute in the $Z^{k}$ attribute is the evidence body under different indicators.

$\displaystyle m_{j}^{k}(a_{i})=z_{i,j}^{k}$ (11)

$i=1,2,\ldots,M;j=1,2,\ldots,N;k=1,2,\ldots,L,m_{j}^{k}(\cdot)$ is the recognition framework $\Theta=A$ associated with decision matrix $X^{k}$ of the $j_{th}$ group of the evidence body, the credibility is $cw_{j}^{k}$ , under $X^{k}$ index, evidence body is $IE=\{m_{j}^{k}(\cdot)|j=1,2,\ldots,N\}$ . This study makes corrections:

$\displaystyle m_{j}^{{}^{\prime}k}(a_{i})=cw_{j}^{k}m_{j}^{k}(a_{i}),i=1,2,% \ldots,M$ (12) $\displaystyle m_{j}^{{}^{\prime}k}(\Theta)=1-\sum\limits_{i=1}^{M}{m_{j}^{{}^{% \prime}k}(a_{i})}=1-cw_{j}^{k}\sum\limits_{i=1}^{M}{m_{j}^{k}(a_{i})},i=1,2,% \ldots,M$ (13)

The evidence body that integrates the corrected set of indicator evidence according to the Dempster Rule $IE^{\prime}$ :

$\displaystyle m^{{}^{\prime}k}(X)=\left\{\begin{array}[]{ll}0&{X=\phi}\\ \frac{1}{1-k}\sum_{\begin{subarray}{c}\forall X_{l_{j}}\subseteq\Theta\\ \cap X_{l_{j}}-X\end{subarray}}{\mathop{\Pi}\limits_{j=1}^{N}m_{j}^{{}^{\prime% }k}(X_{l_{j}})}&{X\neq\phi}\\ \end{array}\right.$ (14)

$K=\sum_{\begin{subarray}{c}\forall X_{l_{j}}\subseteq\Theta\\ \cap X_{l_{j}}-X\end{subarray}}{\mathop{\Pi}\limits_{j=1}^{N}m_{j}^{{}^{\prime% }k}(X_{l_{j}})}$ represents for the degrees of conflicts among individual evidences of $IE^{\prime}$ . Finally, right $IE^{\prime}$ of the integrated reliability is calculated, the evaluation of each decision body in the program set a is obtained $\forall a_{i}\in A$ :

$\displaystyle m^{k}(a_{i})=\frac{m^{{}^{\prime}k}(a_{i})}{1-m^{{}^{\prime}k}(% \Theta)},k=1,2,\ldots,L$ (15)

$m^{k}(\cdot)$ represents for the individual evaluation evidence of expert $e_{k}$ on programme set a.

2.2 Expert weights based on evidence distance calculation

The definition of the distance between $m^{p},m^{q}$ is:

$\displaystyle d(m^{p},m^{q})=\sqrt{(M^{p}-M^{q})^{T}D(M^{p}-M^{q})/2}$ (16)

$M^{l}=[m^{l}(A_{1}),m^{l}(A_{2}),\ldots,m^{l}(A_{2^{M}})]^{T},l=p,q,D_{st}=% \frac{|A_{s}\cap A_{t}|}{|A_{s}\cup A_{t}|},s,t=1,2,\ldots,2^{M}$ .

The formula for calculating the distance between $m^{p},m^{q}$ is:

$\displaystyle d(m^{p},m^{q})=\sqrt{([M^{p},M^{q}]+[M^{q},M^{p}]-2[M^{p},M^{q}]% )/2}$ (17)

$[M^{p},M^{q}]=\sum\limits_{s=1}^{2^{M}}{\sum\limits_{t=1}^{2^{M}}{m^{p}(A_{s})% }}m^{q}(A_{t})D_{s}$ .

The similarity between $m^{p},m^{q}$ is defined as:

$\displaystyle\textit{sim}(m^{p},m^{q})=1-d(m^{p},m^{q})$ (18)

The support degree of evidence $m^{k}$ is $\sup(m^{k})$ :

$\displaystyle\sup(m^{k})=\sum\limits_{t=1,t\neq k}^{L}{\textit{sim}(m^{k},m^{t% })},k=1,2,\ldots,L$ (19)

The absolute credibility of $\sup(m^{k})$ :

$\displaystyle\textit{crd}_{k}=\frac{\sup(m^{k})}{\mathop{\max}\limits_{1% \leqslant t\leqslant L}[\sup(m^{k})]},k=1,2,\ldots,L$ (20)

After normalization, the relative credibility of $\sup(m^{k})$ is as follows:

$\displaystyle EW=(ew_{1},ew_{2},\ldots,ew_{L})$ (21) $\displaystyle ew_{k}=\frac{\textit{crd}_{k}}{\sum\limits_{t=1}^{L}{\textit{crd% }_{k}}},k=1,2,\ldots,L$ (22)

2.3 The weight of each index according to fuzzy entropy weight change calculation

This study constructs the entropy weight matrix of the single indicator:

$\displaystyle R=\left[{{\begin{array}[]{*{20}c}{CW^{1}}\\ {CW^{2}}\\ \vdots\\ {CW^{L}}\\ \end{array}}}\right]=\left[{{\begin{array}[]{cccc}{cw_{1}^{1}}&{cw_{2}^{1}}&% \cdots&{cw_{N}^{1}}\\ {cw_{1}^{2}}&{cw_{2}^{2}}&\cdots&{cw_{N}^{2}}\\ \vdots&\vdots&&\vdots\\ {cw_{1}^{L}}&{cw_{2}^{L}}&\cdots&{cw_{N}^{L}}\\ \end{array}}}\right]$ (23)

According to the principles of fuzzy transformation and Eq. (21), this study combines the expert weight vector with the entropy weight matrix of the indicator, and obtains the weight vector of the group decision indicator:

$\displaystyle CW=EW\cdot R=(ew_{1}\,\,ew_{2}\,\,\cdots\,\,ew_{L})\cdot\left[{{% \begin{array}[]{*{20}c}{cw_{1}^{1}}&{cw_{2}^{1}}&\cdots&{cw_{N}^{1}}\\ {cw_{1}^{2}}&{cw_{2}^{2}}&\cdots&{cw_{N}^{2}}\\ \vdots&\vdots&&\vdots\\ {cw_{1}^{L}}&{cw_{2}^{L}}&\cdots&{cw_{N}^{L}}\\ \end{array}}}\right]=(cw_{1}\,\,cw_{2}\,\,\cdots\,\,cw_{N})$ (24) $\displaystyle cw_{j}=\sum\limits_{k=1}^{L}{ew_{k}\cdot cw_{j}^{k}},j=1,2,% \ldots,N$ (25)

The weights of each expert and each indicator can be determined according to Eqs (22) and (24), and then the entire evaluated set is sorted according to Eq. (4).

3. Empirical analysis

This study carries out empirical analysis based on three large manufacturing enterprises $a_{1},a_{2},a_{3}$ . Organizational quality specific immune refers to that the organization carries out organizational quality motoring and cognition, organizational quality transmission for the threatening factors and dissidents of quality [18, 19, 20], then implements organizational quality defense, repair, removal and elimination with the help of core quality management practices(namely organizational quality defense, removal and repair of hard elements, including design, process management, statistical control and feedback) and basic quality management practices (namely organizational quality defense, removal and repair of soft elements, including leadership attention, employee participation, customer demand, supplier relationship management) [21, 22, 23], generates organizational quality memory and immune homeostasis in order to improve organizational learning ability, organizational quality performance and organizational health [24, 25, 26]. The four evaluation indicators consist of organizational quality monitoring and cognition ( $c_{1}$ ), organizational quality defense, removal and repair of soft elements ( $c_{2}$ ), organizational quality defense, removal and repair of hard elements ( $c_{3}$ ), organizational quality memory and immune homeostasis ( $c_{4}$ ). The organizational quality specific immune status is the main research target and evaluation object, and four evaluation indicators $c_{1}$ – $c_{4}$ that can reflect the organizational quality specific immune of manufacturing enterprise are selected: organizational quality monitoring and cognition ( $c_{1}$ ), organizational quality defense, removal and repair of soft elements ( $c_{2}$ ), organizational quality defense, removal and repair of hard elements ( $c_{3}$ ), organizational quality memory and immune homeostasis ( $c_{4}$ ). As the indicators of being evaluated, three experts ( $e_{1},e_{2},e_{3}$ ) of manufacturing enterprises give out the evaluation value of each evaluation index, the evaluation results are in Table 2.

Table 2
Expert evaluation level tables

Decision maker	Manufacturing enterprises	$c_{1}$	$c_{2}$	$c_{3}$	$c_{4}$
$e_{1}$	$a_{1}$	MG	G	H	VG
	$a_{2}$	F	G	VG	MH
	$a_{3}$	VVG	MG	G	F
$e_{2}$	$a_{1}$	MG	VG	G	MH
	$a_{2}$	VG	F	MG	G
	$a_{3}$	MG	M	VG	VG
$e_{3}$	$a_{1}$	F	MG	VG	MG
	$a_{2}$	VG	H	M	VG
	$a_{3}$	MG	MH	G	F

Using the method described above, the accurate evaluation of organizational quality specific immune of each large manufacturing enterprise is carried out, so the set of evaluated objects in this study is $A=\{a_{1},a_{2},a_{3}\}$ , the indicator set is $C=\{c_{1},c_{2},c_{3},c_{4}\}$ , expert set is $E=\{e_{1},e_{2},e_{3}\}$ . Therefore, according to the contents of Tables 1 and 2, the expert decision matrix can be obtained:

$\displaystyle X^{1}=\left[{{\begin{array}[]{*{20}c}6&7&7&8\\ 5&7&8&6\\ 9&6&7&5\\ \end{array}}}\right]\xrightarrow{{\textit{Normalized}}}\left[{{\begin{array}[]% {*{20}c}{0.300}&{0.350}&{0.318}&{0.421}\\ {0.250}&{0.350}&{0.364}&{0.263}\\ {0.450}&{0.300}&{0.318}&{0.263}\\ \end{array}}}\right]$ $\displaystyle X^{2}=\left[{{\begin{array}[]{*{20}c}6&8&7&6\\ 8&5&6&7\\ 6&5&8&8\\ \end{array}}}\right]\xrightarrow{\textit{Normalized}}\left[{{\begin{array}[]{*% {20}c}{0.300}&{0.444}&{0.333}&{0.286}\\ {0.400}&{0.278}&{0.286}&{0.333}\\ {0.300}&{0.278}&{0.381}&{0.381}\\ \end{array}}}\right]$ $\displaystyle X^{3}=\left[{{\begin{array}[]{*{20}c}5&6&8&8\\ 8&7&5&8\\ 6&6&7&5\\ \end{array}}}\right]\xrightarrow{{\textit{Normalized}}}\left[{{\begin{array}[]% {*{20}c}{0.450}&{0.316}&{0.400}&{0.250}\\ {0.300}&{0.368}&{0.250}&{0.300}\\ {0.250}&{0.316}&{0.350}&{0.450}\\ \end{array}}}\right]$

Calculate the weight vector of each index according to Eqs (7)–(9):

$\displaystyle CW^{1}=(0.475,0.033,0.016,0.475);CW^{2}=(0.323,0.264,0.073,0.340)$ $\displaystyle CW^{3}=(0.414,0.117,0.082,0.387)$

The weight vector is regarded as the credibility of the evidence body corresponding to the indicator. According to Eqs (11)–(15), the evaluation evidence of each expert individual can be obtained:

$\displaystyle m^{1}(a_{1})=0.427,m^{1}(a_{2})=0.233,m^{1}(a_{3})=0.340$ $\displaystyle m^{2}(a_{1})=0.338,m^{2}(a_{2})=0.275,m^{2}(a_{3})=0.387$ $\displaystyle m^{3}(a_{1})=0.324,m^{3}(a_{2})=0.411,m^{3}(a_{3})=0.265$

According to the distance function of the evidence, we can obtain the full vector of each expert: $EW=(0.333,0.333,0.334)$ . According to the index entropy weight matrix combined with the Eq. (24), the index weight vector of the group decision can be obtained: $CW=(0.404,0.208,0.127,0.261)$ .

Use Eqs (2)–(4) to calculate the comprehensive evaluation value of each scheme of the decision-making group: $s_{1}=6.416$ , $s_{2}=6.285,s_{3}=6.503$ . Sort out the organizational quality specific immune of each manufacturing enterprise: $a_{3}>a_{1}>a_{2}$ . Obviously, $a_{3}$ is the development status and tendency of organizational quality specific immune of manufacturing enterprise is the best and optimal.

Compared with the current literatures, the empirical analysis results are consistent with the methods in the relevant current literatures, the methods in the relevant current literatures are mainly include multi-attribute group decision methods [1, 2, 3, 4], the distance of evidence or the constant change of entropy weight methods [4, 5, 6, 7], the expert weighting methods [8, 9, 10, 11], weight information methods [12, 13, 14, 15]. This study fuses the advantages of the above methods and avoids the disadvantages of the above methods, compared with the above current literatures, this study mainly applies the multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation to realize the constant change of evidence distance and fuzzy entropy weight in order to carry out empirical analysis organizational quality specific immune evaluation.

4. Conclusions

In the specific decision-making or group decision-making process, it is often the case that the expert’s weight is unknown or the indicator’s weight is unknown. Therefore, the evaluation method in the case of unknown weight is particularly important. In this study, the distance of evidence and the constant change of entropy weight are combined with the matrix to determine the weight of choice and the weight of each index, which are entry points of this study. The information entropy and the distance of the evidence are used to calculate the expert weights, and the expert weights are combined with the entropy weight matrix of the index to obtain the index weight. Furthermore, this study uses multiple-attribute group decision-making theories, adopts multi-attribute group decision-making method with complete ignorance of weight information based on evidence distance and fuzzy entropy transformation, uses the measured matrix to determine the weight of the expert and the weight of each indicator. And the actual empirical analysis results prove that the weight information has the close relationships with the matrix of the decision maker, which is obtained through the research on the decision matrix. The weight information not only has the corresponding objectivity, but also can effectively represent for the characteristics of the decision model, and thus ensure the reliability of the results. This study takes the large-scale manufacturing enterprise as the empirical analysis objects, and evaluates the organizational quality specific immune of manufacturing enterprises when the weight information is unknown. Accurate evaluation results of organizational quality specific immune of manufacturing enterprises prove the effectiveness and operability of the method and ensures the clarity of the method concept. The method is successfully applied to the evaluation of organizational quality specific immune of actual large manufacturing enterprises, and the practicability and feasibility of the method are proved by empirical analysis research. It provides method guidance and practical inspiration for improving organizational quality specific immune of manufacturing enterprises.

Based on the problem description and model construction, according to the actual situation and empirical analysis results of organizational quality specific immune of manufacturing enterprises, this study puts forward the following suggestions and recommendations for improving the organizational quality specific immune level of manufacturing enterprises: (1) Establishing the correct quality culture concept. The organizational quality specific immune of implementation requires not only the complete operating system, but also the corresponding power and tools. Quality culture will guide employees to actively learn and use organizational quality institutions and rules, and provide the development momentum for employees to learn quality knowledge. If the manufacturing enterprise loses the correct view of quality culture, then the evolution of organizational quality specific immune activity of manufacturing enterprise lacks the guidance of correct direction, which can not provide help and support for the immune activities of manufacturing enterprises, and even hinder and interfere with the normal quality innovation activities of manufacturing enterprises. (2) Choosing excellent quality professionals. Professional talents in quality management can not only promote the good development of enterprise quality immunization, but also carry out continuous quality innovation within the enterprise, which provides essential driving force for the sustainable development of manufacturing enterprise. And excellent quality talents not only have professional quality knowledge, but also have skilled quality technology and good quality work attitude, and thus improve the organizational quality specific immune of manufacturing enterprises. (3) Establishing convenient communication platform for quality information. In the process of quality management, quality knowledge and information entropy are very important. Mastering advanced knowledge and the latest quality information is the victory of manufacturing enterprise in the changing market environment. Therefore, through the establishment of quality information exchange platform, manufacturing enterprises can not only promote the information exchange of other internal employees, but also obtain external quality knowledge information, and enable enterprises to quickly understand the market through communication with external information of enterprises. Changes in demand can update the quality immunization knowledge of manufacturing enterprises, and then adjust the direction of the development tendency of manufacturing enterprises, thereby improving the organizational quality specific immune of manufacturing enterprises.

Footnotes

Acknowledgments

This research is funded by social science planning fund project of Liaoning province (No. L21AGL013).

References

Guo

. A method for multiple attribute group decision making with complete unknown weight information and its extension. Chin J Manage Sci. 2011; 19(5): 94-103.

Boran

Genc

Kurt

Akay

. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl. 2009; 36(8): 11363-11368.

Liu

Jiang

. Method of adaptive adjustment weights in multi-attribute group decision-making. Syst Eng Electron. 2007; 29(1): 45-58.

Yang

. Rule and utility-based evidential reasoning approach for multi-attribute decision analysis under uncertainties. Eur J Oper Res. 2001; 131(1): 31-61.

Yang

. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans Syst Man Cybern Part A: Syst Humans. 2002; 32(3): 289-304.

Sentz

Ferson

. Combination of evidence in Dempster-Shafer theory. Albuquerque: Sandia National Lab; 2002.

Deng

Shi

Zhu

Liu

. Combining belief functions based on distance of evidence. Decis Support Syst. 2004; 38(3): 489-493.

Jousselme

Grenier

Bosse

. A new distance between two bodies of evidence. Inf Fusion. 2001; 2(2): 91-101.

Mostaghimi

. Bayesian estimation of a decision using information theory. IEEE Trans Syst Man Cybern Part A: Syst Humans. 1997; 27(4): 506-517.

10.

Shuiabi

Thomson

Bhuiyan

. Entropy as a measure of operational flexibility. Eur J Oper Res. 2005; 165(3): 696-707.

11.

Chen

. Research and application of evidence theory decision-making method under the environment of group decision-making [Dissertation]. Hefei: Hefei University of Technology; 2006.

12.

Wang

Duan

Xue

. Engineering fuzzy mathematics methods and applications [Dissertation]. Tianjin: Tianjin Science and Technology Press; 1993.

13.

Dempster

. Upper and lower probabilities induced by a multi-valued mapping. Annuals Math Stat. 1967; 38(4): 325-339.

14.

Yeh

Chang

. Modeling subjective evaluation for fuzzy group multi-criteria decision making. Eur J Oper Res. 2009; 194(2): 464-473.

15.

Yager

. OWA aggregation over a continuous interval argument with applications to decision making. IEEE Trans Syst Man Cybern (Part B). 2004; 34: 1952-1963.

16.

. Consistency of interval fuzzy preference relations in group decision making. Appl Soft Comput. 2011; 11(5): 3898-3909.

17.

Cai

. Nonlinear optimization models for multiple attribute group decision making with intuitionistic fuzzy information. Int J Intell Syst. 2010; 25(6): 489-513.

18.

Pacana

Siwiec

. Analysis of the possibility of used of the quality management techniques with non-destructive testing. Tehnicki Vjesnik-Tech Gazette. 2021; 28(1): 45-51.

19.

Fang

Lin

. A multi-objective optimal decision model for a green closed-loop supply chain under uncertainty: A real industrial case study. Adv Prod Eng Manage. 2021; 16(2): 161-172.

20.

Parast

. A learning perspective of supply chain quality management: empirical evidence from US supply chains. Supply Chain Manage-An Int J. 2020; 25(1): 17-34.

21.

Salimian

Rashidirad

Soltani

. Supplier quality management and performance: The effect of supply chain oriented culture. Prod Plann Control. 2020; 32(11): 942-958.

22.

Kogan

. False quality claims: Prevention and supply chain implications. J Oper Res Soc. 2020; 72(6): 1347-1357.

23.

Strmenik

Wall

Mitsch

Modritscher

. Volume allocation in multi-sourcing: Effects of the quantity-quality trade-off. Cent Eur J Oper Res. 2020; 29(2): 753-771.

24.

Roy

Giri

. A three-echelon supply chain model with price and two-level quality dependent demand. Rairo-Oper Res. 2020; 54(1): 37-52.

25.

Hong

Zhou

Lau

. Supply chain quality management and firm performance in China’s food industry-the moderating role of social co-regulation. Int J Logistics Manage. 2020; 31(1): 99-122.

26.