Qualitative simulation of organization quality specific immune decision-making of manufacturing enterprises based on QSIM algorithm simulation

Abstract

Quality is the core of the enterprise, strengthening organization quality specific immune is the key channel. Organization quality specific immune belongs to science and engineering management field, QSIM qualitative simulation method that refer to computational simulation algorithm is widely used in the science and engineering management field, QSIM qualitative simulation method can solve science and engineering management issues effectively. In this study, qualitative simulation QSIM theory is used to analyze and reason the organization quality specific immune decision of manufacturing enterprises. Combined with the pressure-state-response framework, the management mechanism of organization quality specific immune is analyzed according to state variables, decision variables, system variables and environment variables, and further the qualitative simulation rules for organization quality specific immune decision-making are set according to the causal relationships among variables of organization quality specific immune. This study sets organization quality monitor, organization quality defense and organization quality memory as the decision variables, uses QSIM algorithm for simulating organization quality specific immune decision-making reasoning, compares with the influences of single decision variable and multi-decision variables on organization quality specific immune system through simulation results. Simulation results indicate that QSIM algorithm simulation can be used to simulate and reason organization quality specific immune decision-making in order to help manufacturing enterprises reasonably enhance organization quality specific immune performance and quality performance through three decision variables of organization quality monitor, organization quality defense and organization quality memory. The simulation results will provide new revelation for organization quality specific immune decision-making of manufacturing enterprises.

Keywords

Organization quality specific immune decision-making QSIM algorithm simulation qualitative simulation manufacturing enterprises

1. Introduction

Quality is the core lifeblood of the enterprise. In recent years, with the development progress of science and technology in China, the country has increased policy investment on quality of manufacturing enterprises, and enterprises should strengthen product quality and actively cope with the complex market environment [1, 2]. With the support of national quality policy, the product quality of manufacturing enterprises has been improved and optimized [3]. But quality technology innovation ability of manufacturing enterprise is not high and fragile, the security problem of enterprise quality constantly are exposed in violent incidents and endangering events, the incidents and events brings about fatal crisis to the development of manufacturing enterprise, people also more and more attention to the dynamic change tendency of product quality associated with the quality safety issues triggered by quality incidents and quality events [4, 5, 6, 7]. Enterprises formulate a series of strategic objectives in order to improve customer satisfaction with products, obtain good social reputation in the industry. Therefore, how to improve product quality has become the themes of manufacturing enterprises. Manufacturing enterprise constantly innovates the production technology and products, strengths quality culture, promotes and boosts quality management practice, and conducts multi-field quality cooperation with a view to enhancing product production rates and efficiencies [8, 9, 10]. But manufacturing enterprise often cannot reach the desired effect, which will still inevitably encounter quality problems. The theoretical and practical research results indicate that manufacturing enterprise can solve the above quality management complications from biological immunology theory, many serious quality diseases are caused by virus antigen due to the invalid identification, and the complications lie in that manufacturing enterprise is unable to tamp, stimulate, activate the function of organization quality specific immune system, can not find out malignant cells in a timely manner, restrain and eliminate the bad quality cells and the harmful factors of product quality, and are difficult to improve the organism immune and quality performance. According to the revelation of bio-immunology, we found that with the extension of supply chain of manufacturing enterprise, the operation process makes the products be exposed to various complex and unsafe environment, the products quality is inevitably affected. In order to cope with the fierce competitive external environments and internal environment, enterprises must improve and optimize organization quality specific immune, conduct and guide the appropriate, reasonable organization quality specific immune decision-making, achieve quality health development, continuously enhance quality performance [11].

Currently, most scholars home and abroad study the related function mechanism and system structure of organization quality specific immune, which provides reference for the subsequent research on the theory, framework, related empirical analysis and decision-making simulation of organization quality specific immune. Organization quality specific immune system is a complex system, many variables and uncertainties are involved in the process of system operation. The researches on decision-making and simulation of organization quality specific immune is relatively few, there are a lack of research results related to the influence of single decision variable and multi-decision variable on organization quality specific immune system. Quality is the life of manufacturing enterprise, quality management and organization quality specific immune belong to science and engineering management field, QSIM qualitative simulation method that refer to computational simulation algorithm is widely used in the science and engineering management field, QSIM qualitative simulation method can solve science and engineering management issues effectively [12, 13, 14]. This study adopts the QSIM qualitative simulation method to analyze causal relationships of organization quality specific immune decision-making of manufacturing enterprises with the help of pressure-state-response framework, three decision variables of organization quality monitor, organization quality defense and organization quality memory are introduced based on QSIM algorithm state transition rules. This study further uses QSIM algorithm for simulating organization quality specific immune decision-making and reasoning according to state variables, decision variables, system variables and environment variables, compares with the influences of single decision variables and multi-decision variables on organization quality specific immune system through simulation results, empirically explores the influence of the single decision variable and multi-decision variables change on organization quality specific immune decision-making. The qualitative simulation rules for organization quality specific immune decision-making are set according to the causal relationships among variables of organization quality specific immune. Simulation results indicate that QSIM algorithm simulation can be used to simulate and reason organization quality specific immune decision-making in order to help manufacturing enterprises reasonably enhance organization quality specific immune performance and quality performance through three decision variables of organization quality monitor, organization quality defense and organization quality memory, which will provide theoretical reference and practical guideline for organization quality specific immune decision-making and reasoning of manufacturing enterprises.

2. Relevant theories

2.1 Framework and procedures of QSIM algorithm

Kuipers was the founder of QSIM algorithm method, who proposed QSIM theoretical framework in 1986 [15, 16]. QSIM is the most perfect qualitative simulation method. QSIM, as a simulation method for simulating dynamic reasoning, conducting and generating decision-making, derives its core from the theory of differential equation in mathematics [17, 18]. Using QSIM method, all state variables related to the system can be predicted in advance, and the development trend of system variables can be deduced according to the constraint relations determined by system parameters [19]. QSIM qualitative analog reasoning refers to the process of simulating and reasoning subsequent states according to the initial state and constraint relations. Among them, the conversion criteria of each variable state of the system are divided into two categories of P conversion and I conversion [20, 21, 22]. According to the characteristics of the system, the incompatible state variables of post-order transformation are eliminated to save the computing space of the system and improve the efficiency of the system. Since QSIM algorithm has the advantages of certainty and high efficiency, QSIM algorithm is selected to qualitatively simulate the change trend and transition state of organization quality specific immune system of manufacturing enterprises, which can help the decision-making of organization quality specific immune of manufacturing enterprises.

General steps of QSIM algorithm are as follows [21]:

(1)
The variables of the system are described in detail, the causal relationships among the variables are clarified by using the model, the decision-making of the system is simulated and analyzed, and the state variables, environment variables and decision variables of the system are found out [23, 24]. According to the change of decision variables to the system, the causal model is made.
(2)
Determine the constraint relationships among variables. According to QSIM algorithm, the binary group of system state at time t is determined through the state of each variable at time $t_{0}$ : $\textit{QS(f,t)}=\left\langle\textit{QVAL(f,t)},\textit{QDIR(f,t)}\right\rangle$ . It is concluded that the qualitative value of the variable at time t is

$\displaystyle\textit{QVAL(f,t)}=\left\{{\begin{array}[]{ll}l_{j}&f=l_{j}\\ (l_{j},l_{j+1})&f(t)\in(l_{j},l_{j+1})\\ \end{array}}\right..$
(3)
A single decision variable is introduced to conduct qualitative simulation of the system, reasoning is carried out at two time points t ${}_{0}$ , t ${}_{1}$ and 1 time interval, and subsequent variables are generated by using the state transition table. QSIM qualitative analog reasoning is the process of simulating and reasoning subsequent states according to the initial state and constraint relations. Among them, the conversion criteria of each variable state of the system are divided into two categories of $P$ conversion and $I$ conversion.

$\displaystyle\text{P conversion rule}:\textit{QS}(f,t_{i})\buildrel{\mathop{k}% \nolimits^{\ast}}\over{\longrightarrow}\textit{QS}(f,t_{i},t_{i+1})$ $\displaystyle\text{I conversion rule}:\textit{QS}(f,t_{i+1})\buildrel{\mathop{% k}\nolimits}\over{\longrightarrow}\textit{QS}(f,t_{i+1})$
(4)
According to the transition results, the subsequent states of environment variables are obtained, the system is analyzed, and the conclusion is drawn.

2.2 Framework of organization quality specific immune

Lv and Wang [11] carried out the definition of organization immune through the empirical analysis of a large number of enterprises, namely the enterprises recognize the unfavorable factors, suppress and eliminate the harmful factors of enterprises through their actions, memorize the process to produce antigen, improve the ability of enterprise crisis management process in order to maintain the healthy development of enterprises. Lv and Wang [11] indicated that organization specific immune and organization specific immune constitute organization immune of enterprises by referring to relevant knowledge of biological immunology. Among them, organization specific immune is specified, acquired, adaptive, non-congenital, postnatal and nurtured according to the enterprise’s own development background and strategic objectives, including organization defense, organization monitor and organization memory [25, 26, 27]. Organization non-specific immune includes three elements of organization culture, enterprise rules and regulations, and internal organization structure of enterprise, which is the first line of defense for enterprises to deal with harmful factors and improve organization performance [26, 27].

Quality is the life of enterprise, organization quality immune refers to the process in which the enterprise discovers malignant factors that affect product quality of enterprise, defenses, controls and eliminates the factors that are not conducive to product quality of enterprise through immune behavior, and generates immune memory, so as to improve quality performance of enterprise and maintain organization health of enterprise [4, 12]. Organization quality specific immune and non-specific immune constitute organization quality immune system [4]. Organization quality specific immune refers to that under the premise of organization quality non-specific immune, enterprises threaten quality factors to external and internal environment [29, 30].

The enterprise identifies, cognizes, transfers, defends, purges, fits, renews, eliminates, removes the threat factors, external aggressions and internal change elements that destroy product quality of enterprise and thus generates quality immune memory behavior and autoimmune homeostasis function, achieves immunological homeostasis state and tendency, forms team and group quality immune. Organization quality non-specific immune behavior treatment is carried out for threat antigen firstly [29, 30], which is an important tool for enterprises to deal with the factors threatening their healthy development. However, due to the atypical nature of organization quality non-specific immune of organization, organization quality non-specific immune behavior cannot guarantee the coordination of its structure with the internal structure, rules, regulations and culture of enterprise, so that it cannot completely resist the invasion of risk factors, organization quality specific immune can successfully solve the above complications. Through the core system, enterprises can successfully cope with the internal and external environment and improve quality performance [12, 30]. The main reasons for the occurrence of malignant quality events in enterprises are that enterprises cannot timely find and identify antigens that endanger product quality of enterprises, that is, enterprises cannot timely develop quality immune behaviors and actions, which leads to the inability to spontaneously deal with and investigate harmful factors of enterprises. Therefore, the healthy development of enterprises is aimed at enhancing organization quality specific immune and improving organization quality specific immune performance [29, 30]. Organization quality monitor, organization quality defense and organization quality memory constitute organization quality specific immune system. Multiple theory and practice results have proved that through three behaviors of organization quality specific immune, the enterprises can timely detect and deal with the factors that endanger product quality and organization health of enterprises, protect enterprise quality from malignant factors [28, 30]. Internal and external organization quality monitor constitute organization quality monitor. The organization quality monitor process refers to timely dealing with and eliminating the adverse factors after identifying the threatening factors through organization quality discovery, discovering the harmful factors affecting product quality [4, 25]. By organizing quality monitor, enterprise managers can find out the abnormal cells, make corresponding management decisions, inhibit the development of adverse factors, and improve quality performance [11]. Aguilar [31] first proposed the concept of organization monitor, which is mainly applied in the field of chemical industry. Later, a great many scholars believed that quality information can be effectively discovered through organization quality monitor. Effectively reducing enterprise quality risks, reducing uncertainty of enterprise quality development can better improve quality performance of enterprise [11]. Organization quality defense is based on organization quality monitor, which is the whole process of active elimination harmful factors of organization quality through immune behavior. The enterprise can find out the various quality-related problems inside and outside the organization. The managers make decisions based on quality problems quickly and eliminate the threat factors through defense behaviors to maintain quality stability of enterprise. Lv [25] held on that organization quality defense was a core tool for enterprises to maintain market stability, and it was the benign development of enterprises to better adapt to the market environment through organization quality defense. The mechanism of organization quality defense is to feed the stimulus factor into the organization through organization quality monitor, at which time the organism rapidly adjusts the replication antigen to form the immune network and produce a series of immune response behaviors [4, 5]. Organization quality defense is the sum total of system behaviors. Organization quality defense can enhance organization system elasticity and enhance organization quality stability [31, 32]. After the emergence of immune behavior, the immune scheme is evaluated to select the optimal treatment scheme and maintain the stability of the organism [30]. March and Simon [33] believed that organization coordination is crucial to organization quality defense. enterprises constantly update the internal and external quality technologies and quality information in line with their own development characteristics, and then separate, repair and remove bad cells to form a complete set of organization quality defense system. Organization quality memory is a tool for enterprises to store immune information. After the process of organization quality monitor and defense, the organization produces antigens and stores the immune information in its own database in the form of immune memory. After the emergence of immunization database, the immunization process is preserved and quality immunization knowledge is recorded and summarized. When the factors that threaten product quality of enterprise occur, the immune knowledge base is quickly reflected and the adverse factors are cleared. Jiang and Xiong, Xiong and Feng, Jiang, Yang and Li [34, 35, 36, 37] constructed the organization immune system model by studying and analyzing the organization immune model. Dai and Ding [38] constructed the enterprise internal evaluation system according to the characteristics of enterprise autoimmune and organization quality memory. Kaynak suggested that quality management practices had impacts on quality performance of enterprises [39], Song et al. also indicated that quality management practice played positive roles in quality performance [40], quality management practices are positively related to organization quality defense.

3. Causal relationships of organization quality specific immune decision-making under PSR framework

Organization quality specific immune of enterprise is a complex system. By using PSR conceptual model, the factors that affect the decision-making of organization quality specific immune of enterprise are described in detail, and the causal relationships and effects among the factors are clarified [27, 28, 29, 30]. Find out the conflicts among the elements can enhance organization quality specific immune performance of enterprises. The root cause of poor organization quality specific immune performance is the problems of organization quality monitor, organization quality defense and organization quality memory [12, 30].

In the PSR framework, R refers to the specific states related to organization quality specific immune decision-making, which are mainly manifested in the following five aspects in this study: low quality performance of enterprises, low product realization rate, low product quality qualification rate, low quality competitiveness of enterprise and low quality economy of enterprise. P refers to the factors of organization quality and organization quality changes in the context of intensified internal and external environment, market competition, which is mainly manifested in the following six aspects: low team cohesion, poor quality profession skill of staff, staff productivity, quality resource utilization rate, insufficient quality improvement and innovation ability of enterprise, and vague quality culture. S refers to the effective countermeasures of enterprise personnel in improving organization quality specific immune, including organization quality monitor, organization quality defense, organization quality memory. Figure 1 shows the PSR analysis framework for organization quality specific immune decision-making of manufacturing enterprises [21, 22].

Figure 1.

PSR framework for organization quality specific immune decision-making.

Based on the PSR framework, this study analyzes the decision-making problems and solving mechanism of organization quality specific immune of manufacturing enterprises from the three aspects of organization quality monitor, organization quality defense and organization quality memory [12]. Figure 2 shows the causal relationships among three decision variables of organization quality monitor, organization quality defense and organization quality memory, and further expounds the decision state transition of organization quality specific immune. When the relationships between two variables are positively correlated, that is, the increase of one variable leads to the increase of the other variable, which is represented by a positive sign in the figure [41]. When the relationships between two variables are negative correlation, that is, the increase of one variable leads to the decrease of the other variable, which is represented by a negative sign in the figure. For example, when organization quality monitor is optimized, the phenomenon of low team cohesion will be reduced, staff productivity will be increased and quality resource utilization rate will be increased at the same time. When organization quality defense is optimized, the phenomenon of low team cohesion will be reduced, quality profession skill of staff will be improved, staff productivity will be improved and quality improvement and innovation ability of enterprise will be enhanced at the same time. When organization memory is optimized, quality improvement and innovation ability of enterprise is enhanced and quality culture of enterprise is clarified [28, 30, 31].

Figure 2.

Causal relationships of organization quality specific immune decision-making under PSR framework.

4. Simulation rules of organization quality specific immune decision-making based on QSIM algorithm

4.1 Decision variables settings

Three types of variables are set according to PSR framework [31]:

(1)
State variables. The state variables of quality performance of enterprise, product realization rate, product quality qualification rate, quality competitiveness of enterprise and quality economy of enterprise are expressed as follows: $X=\left\{{X_{1},X_{2},X_{3},X_{4},X_{5}}\right\}$ . Values are $-1,0,1$ respectively, Where $-$ 1 means that the low quality performance, product realization rate, product quality qualification rate, quality competitiveness, quality economy of enterprise are slight, 0 means that the low quality performance, product realization rate, product quality qualification rate, quality competitiveness, quality economy of enterprise are common, and 1 means that the low quality performance, product realization rate, product quality qualification rate, quality competitiveness, quality economy of enterprise are serious.
(2)
Decision variables. The decision variables are organization quality monitor, organization quality defense and organization quality memory. The decision variables are the response of senior staff to the related decisions of organization quality specific immune, which are represented by $D=\left\{{D_{1},D_{2},D_{3}}\right\}$ [12]. The state variables of organizational quality specific immune decisions will change with the changes of environmental variables and decision variables.
(3)
Environment variables. Low team cohesion, poor quality profession skills of staff, staff productivity, quality resource utilization rate, quality improvement and innovation ability of enterprise and vague quality culture are environmental variables, which can be expressed as $E=\left\{{E_{1},E_{2},E_{3},E_{4},E_{5},E_{6}}\right\}$ .

4.2 Constraint relations of qualitative variables

In organization quality specific immune decision-making process of manufacturing enterprises, the initial time variable state is determined, and the qualitative state binary group is determined according to QSIM model algorithm $\text{QS}(f,t)=\left\langle{\textit{qval}\left({f,t}\right),\textit{qdir}\left% ({f,t}\right)}\right\rangle$ , whose values and meanings are as follows [31, 32, 45]:

$\displaystyle\textit{QVAL}\left({f,t}\right)=\left\{{{\begin{array}[]{ll}{lj}&% {f\left(t\right)=lj,lj=\left\{{-1,0,1}\right\}}\\ {\left({lj,lj+1}\right)}&{f\left(t\right)=\left({lj,lj+1}\right),lj=\left\{{-1% ,0,1}\right\}}\\ \end{array}}}\right.\textit{QDIR}\left({f,t}\right)=\left\{{-,0,+}\right\}$

Of which, $l_{j}$ is the road mark value, $\left({lj,lj+1}\right)$ is the interval value [42, 43].

The change of decision variables will cause the change of state variables and environment variables [23], the constraint relationships among the change variables of environment variables under the specific situations are shown in Table 1. When the relationships between two variables are positively correlated, that is, the increase of one variable leads to the increase of the other variable, which is represented by positive sign in the table; When the relationships between two variables are negative correlation, that is, the increase of one variable leads to the decrease of the other variable, which is represented by negative sign in the table; When the relationships between two variables are weak, it is represented by zero.

Table 1
Constraints between qualitative variables

	E ${}_{1}$	E ${}_{2}$	E ${}_{3}$	E ${}_{4}$	E ${}_{5}$	E ${}_{6}$	X ${}_{1}$	X ${}_{2}$	X ${}_{3}$	X ${}_{4}$	X ${}_{5}$
D ${}_{1}$	$-$		$+$	$+$			$-$	$-$			0
D ${}_{2}$	$-$	$-$	$+$		$-$		$-$	$-$			$-$
D ${}_{3}$					$-$	$-$				$-$
E ${}_{1}$			$-$	$-$			$+$			$+$
E ${}_{2}$			$-$					$+$	$+$
E ${}_{3}$				$+$			$-$	$-$
E ${}_{4}$								$-$		0
E ${}_{5}$				$-$						$+$	0
E ${}_{6}$	$+$		$-$		0		$+$			0

4.3 Qualitative simulation rules

Since the state transition of organization quality specific immune system changes with the passage of time, QSIM algorithm includes the transition of time interval and time point, which can be divided into I transition and P transition according to the state transition rules [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]. The details are shown in Tables 2 and 3.

$\displaystyle P\ \text{conversion rule}:\textit{QS}(f,\;t_{i})\buildrel{% \mathop{k}\nolimits^{\ast}}\over{\longrightarrow}\textit{QS}(f,\;t_{i},\;t_{i+% 1})$ $\displaystyle I\ \text{conversion rule}:\textit{QS}(f,\;t_{i+1})\buildrel{% \mathop{k}\nolimits}\over{\longrightarrow}\textit{QS}(f,\;t_{i+1})$

Table 2
Qualitative state P transition rules

P ${}_{1}$	$\left\langle{1,0}\right\rangle\buildrel{0,+}\over{\longrightarrow}\left\langle% {1,0}\right\rangle$	P ${}_{10}$	$\langle 0,+\rangle\buildrel 0\over{\longrightarrow}\langle(0,1),+\rangle$	P ${}_{19}$	$\langle(-1,0),+\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),+\rangle}\hfill\\ {\langle(-1,0),0\rangle}\hfill\\ \end{array}}$
P ${}_{2}$	$\left\langle{1,0}\right\rangle\buildrel-\over{\longrightarrow}\left\langle{(0,% 1),\;-}\right\rangle$	P ${}_{11}$	$\langle 0,+\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{\langle(% 0,1),+\rangle}\hfill\\ {\langle 0,0\rangle}\hfill\\ {\langle(-1,0),-\rangle}\hfill\\ \end{array}}$	P ${}_{20}$	$\langle(-1,0),-\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),0\rangle}\hfill\\ {\langle(-1,0),-\rangle}\hfill\\ \end{array}}$
P ${}_{3}$	$\begin{array}[]{l}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{1,\;0}\right% \rangle\\ \left\langle{1,-}\right\rangle\buildrel+\over{\longrightarrow}\left\langle{1,% \;-}\right\rangle\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{(0,1),\;-}\right\rangle\\ \end{array}$	P ${}_{12}$	$\langle 0,0\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{\langle(% 0,1),+\rangle}\hfill\\ {\langle 0,+\rangle}\hfill\\ \end{array}}$ $\langle(0,1)\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{\langle% (0,1),+\rangle}\hfill\\ {\langle(0,1),+2\rangle}\hfill\\ \end{array}}$	P ${}_{21}$	$\langle(-1,0),-\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),-\rangle}\hfill\\ {\langle(-1,0),-{2}\rangle}\hfill\\ \end{array}}$
P ${}_{4}$	$\left\langle{1,-}\right\rangle\buildrel 0\over{\longrightarrow}\left\langle{(0% ,1),\;-}\right\rangle$	P ${}_{13}$	$\langle 0,0\rangle\buildrel 0\over{\longrightarrow}\langle 0,0\rangle$	P ${}_{22}$	$\langle{-1}+\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{\langle% ({-1,0),}+\rangle}\hfill\\ {\langle({-1,0),}+2\rangle}\hfill\\ \end{array}}$
P ${}_{5}$	$\langle 1,-\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{\langle(% 0,1),-\rangle}\hfill\\ {\langle(0,1),-2\rangle}\hfill\\ \end{array}}$	P ${}_{14}$	$\begin{array}[]{l}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{(-1,0),\;-}\right% \rangle\\ \left\langle{0,0}\right\rangle\buildrel-\over{\longrightarrow}\left\langle{0,0% }\right\rangle\\ \end{array}$	P ${}_{23}$	$\langle-1,+\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{\langle-% 1,+\rangle}\hfill\\ {\langle-1,0\rangle}\hfill\\ \end{array}}$
P ${}_{6}$		P ${}_{15}$	$\begin{array}[]{l}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{0,\;0}\right% \rangle\\ \left\langle{0,\;-}\right\rangle\buildrel+\over{\longrightarrow}\;\left\langle% {(0,1),\;+}\right\rangle\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{(-1,0),\;-}\right\rangle\\ \end{array}$	P ${}_{24}$	$\langle-1,0\rangle\buildrel+\over{\longrightarrow}\langle(-1,0),+\rangle$
P ${}_{7}$	$\langle(0,1),+\rangle\buildrel 0\over{\longrightarrow}\langle(0,1),+\rangle$	P ${}_{16}$	$\langle{0},-\rangle\buildrel 0\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),-\rangle}\hfill\\ {\langle 0,-\rangle}\hfill\\ \end{array}}$	P ${}_{25}$	$\langle-1,0\rangle\buildrel-\over{\longrightarrow}\langle-1,-\rangle$
P ${}_{8}$	$\langle(0,1),+\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{% \langle(0,1),+\rangle}\hfill\\ {\langle(0,1),0\rangle}\hfill\\ \end{array}}$	P ${}_{17}$	$\langle 0,-\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{\langle(% -1,0),-\rangle}\hfill\\ {\langle 0,-\rangle}\hfill\\ \end{array}}$
P ${}_{9}$	$\langle(0,1),+\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{% \langle(0,1),+\rangle}\hfill\\ {\langle(0,1),+2\rangle}\hfill\\ \end{array}}$	P ${}_{18}$	$\langle(-1,0),+\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),+\rangle}\hfill\\ {\langle(-1,0),+2\rangle}\hfill\\ \end{array}}$

Table 3

Qualitative state I transition rules

I ${}_{1}$	$\left\langle{1,0}\right\rangle\buildrel k\over{\longrightarrow}\left\langle{1,% 0}\right\rangle$	I ${}_{9}$	$\langle{(-1,0),}\;+\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(-1,0),}\;+\rangle}\hfill\\ {\langle{(-1,0),}\;+{2}\rangle}\hfill\\ {\langle{0,}+\rangle}\hfill\\ \end{array}}$
I ${}_{2}$	$\left\langle{1,0}\right\rangle\buildrel k\over{\longrightarrow}\left\langle{1,% k}\right\rangle$	I ${}_{10}$	$\langle{(-1},{0),}+\rangle\buildrel 0\over{\longrightarrow}{\begin{array}[]{c}% {\langle(-1,0),+\rangle}\hfill\\ {\langle 0,0\rangle}\hfill\\ \end{array}}$
I ${}_{3}$	$\begin{array}[]{l}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{(0,1),\;+}% \right\rangle\\ \left\langle{1,\;+}\right\rangle\buildrel+\over{\longrightarrow}\;\left\langle% {(0,1),\;+2}\right\rangle\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{1,0}\right\rangle\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left\langle{1+}\right\rangle\\ \end{array}$	I ${}_{11}$	$\langle(-1,0),+\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),0\rangle}\hfill\\ {\langle(-1,0),+\rangle}\hfill\\ {\langle(-1,0),-\rangle}\hfill\\ \end{array}}$
I ${}_{4}$	$\langle(0,1),+\rangle\buildrel{0,-}\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(0,1),0}\rangle}\hfill\\ {\langle{(0,1),}+\rangle}\hfill\\ {\langle 0,0\rangle}\hfill\\ {\langle{(0,1),-}\rangle}\hfill\\ \end{array}}$	I ${}_{12}$	$\langle(-1,0),-\rangle\buildrel+\over{\longrightarrow}{\begin{array}[]{c}{% \langle(-1,0),-\rangle}\hfill\\ {\langle(-1,0),0\rangle}\hfill\\ {\langle(-1,0),+\rangle}\hfill\\ \end{array}}$
I ${}_{5}$	$\langle{(0,1)},-\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(0,1),}-\rangle}\hfill\\ {\langle{(0,1),}-{2}\rangle}\hfill\\ {\langle{(0,1),0}\rangle}\hfill\\ \end{array}}$	I ${}_{13}$	$\langle-{1,0,}-\rangle\buildrel{0}\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(}-{1,0),}-\rangle}\hfill\\ {\langle-{1,0}\rangle}\hfill\\ \end{array}}$
I ${}_{6}$	$\langle{(0,1),-}\rangle\buildrel 0\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(0,1),}-\rangle}\hfill\\ {\langle 0,0\rangle}\hfill\\ \end{array}}$	I ${}_{14}$	$\langle-{1,0},{-}\rangle\buildrel{-}\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(}-{1,0),}-\rangle}\hfill\\ {\langle{(}-{1,0),}-{2}\rangle}\hfill\\ {\langle-{1,0}\rangle}\hfill\\ {\langle-{1,}-\rangle}\hfill\\ \end{array}}$
I ${}_{7}$	$\langle{(0,1),-}\rangle\buildrel{0}\over{\longrightarrow}{\begin{array}[]{c}{% \langle{(0,1),-}\rangle}\hfill\\ {\langle{(0,1),-2}\rangle}\hfill\\ {\langle{0},{-}\rangle}\hfill\\ \end{array}}$	I ${}_{15}$	$\langle{-1},{0}\rangle\left\{{{\begin{array}[]{c}{\buildrel 0\over{% \longrightarrow}\langle-1,0\rangle}\hfill\\ {\buildrel+\over{\longrightarrow}\langle-1,+\rangle}\hfill\\ \end{array}}}\right.$
I ${}_{8}$	$\left\langle{0,0}\right\rangle\left\{{\begin{array}[]{l}\buildrel-\over{% \longrightarrow}\left\langle{0,-}\right\rangle\\ \buildrel 0\over{\longrightarrow}\left\langle{0,0}\right\rangle\\ \buildrel+\over{\longrightarrow}\left\langle{0,+}\right\rangle\\ \end{array}}\right.$	I ${}_{16}$	$\langle-1,0\rangle\buildrel-\over{\longrightarrow}{\begin{array}[]{c}{\langle-% 1,0\rangle}\hfill\\ {\langle-1,-\rangle}\hfill\\ \end{array}}$

5. Qualitative simulation of organization quality specific immune decision-making

Since the organization quality specific immune system is a complex system, the stable and healthy development of enterprises cannot be separated from organization quality specific immune system. According to research results, the malignant events of quality occur in enterprises are caused by the failure to identify and timely monitor the antigen of malignant factors, which will further lead to the spread and deterioration of malignant factors in the enterprise structure, and result in the inability to deal with and eliminate the factors by themselves, bring about serious quality consequences due to immune behaviors [28, 29]. Therefore, improving organization quality monitor, defense and memory, conducting organization quality specific immune decision-making and improving quality performance are key issues in the current society for enterprises. This study takes organization quality monitor and organization quality defense as examples to carry out empirical analysis and simulation breakthrough standpoints, and furthermore qualitative analysis of organization quality specific immune decision-making is conducted based on QSIM algorithm [51] simulation.

5.1 Organization quality monitor decision-making analysis

Organization quality monitor is an important tool and instrument to improve product quality of enterprise. By collecting enterprise quality information through organization quality monitor, valuable quality information can be interpreted and sorted out, and product quality problems and complications of enterprise can be found to improve quality performance and quality competitiveness of enterprise [21, 28, 29, 30].

D ${}_{1}$ is organization quality monitor by senior staff. E ${}_{1}$ , E ${}_{3}$ and E ${}_{4}$ represent for environmental variables of organization quality specific immune system respectively, and X ${}_{4}$ represents for state variable of low quality competitiveness of enterprise, and the values are as follows [21, 22]:

$\displaystyle E_{1}=\left\{{\begin{array}[]{ll}-1,&\text{low team cohesion is % serious}\\ 0,&\text{low team cohesion is common}\\ 1,&\text{low team cohesion is slight}\\ \end{array}}\right.$ $\displaystyle E_{3}=\left\{{\begin{array}[]{ll}-1,&\text{low staff % productivity}\\ 0,&\text{common staff productivity}\\ 1,&\text{high staff productivity}\\ \end{array}}\right.$ $\displaystyle E_{4}=\left\{{\begin{array}[]{ll}-1,&\text{low quality resource % utilization rate }\\ 0,&\text{common quality resource utilization rate}\\ 1,&\text{high quality resource utilization rate}\\ \end{array}}\right.$ $\displaystyle X_{4}=\left\{{\begin{array}[]{ll}-1,&\text{low quality % competitiveness of enterprise}\\ 0,&\text{common quality competitiveness of enterprise}\\ 1,&\text{high quality competitiveness of enterprise}\\ \end{array}}\right.$

Organization quality monitor plays a key role in solving the problem of low quality competitiveness of enterprises, and which is a decision-making factor [12]. The influence of organization quality monitor on organization quality specific immune system of enterprise is as follows $D_{1}=\left\{{{-},0,+}\right\}$ , Where “ $-$ ” means low organization quality monitor intensity, zero 0 means moderate organization quality monitor intensity, and “ $+$ ” means high organization quality monitor intensity [12, 22].

5.2 Organization quality defense decision-making analysis

Organization quality defense refers to the immune behavior of enterprise to effectively control and eliminate the threat factors after identifying and discovering the harmful factors of product quality of enterprise [12]. D ${}_{2}$ represents for the policy intervention of enterprise management on organization quality defense. E ${}_{1}$ , E ${}_{2}$ , E ${}_{3}$ and E ${}_{5}$ respectively represent for the environmental variables of organization quality specific immune decision-making, X ${}_{3}$ represents for the state variable of low product quality qualification rate, and the values are as follows:

$\displaystyle E_{1}=\left\{{\begin{array}[]{ll}-1,&\text{the phenomenon of low% team cohesion is serious}\\ 0,&\text{the phenomenon of low team cohesion is common}\\ 1,&\text{the phenomenon of low team cohesion is slight}\\ \end{array}}\right.$ $\displaystyle E_{2}=\left\{{\begin{array}[]{ll}-1,&\text{poor quality % profession skill of staff is serious}\\ 0,&\text{poor quality profession skill of staff is common}\\ 1,&\text{poor quality profession skill of staff is slight}\\ \end{array}}\right.$ $\displaystyle E_{3}=\left\{{\begin{array}[]{ll}-1,&\text{low staff % productivity}\\ 0,&\text{common staff productivity}\\ 1,&\text{high staff productivity}\\ \end{array}}\right.$ $\displaystyle E_{5}=\left\{{\begin{array}[]{ll}-1,&\text{insufficient quality % improvement and innovation ability of enterprise is serious}\\ 0,&\text{insufficient quality improvement and innovation ability of enterprise% is common}\\ 1,&\text{insufficient quality improvement and innovation ability of enterprise% is slight}\\ \end{array}}\right.$ $\displaystyle X_{3}=\left\{{\begin{array}[]{ll}-1,&\text{low product quality % qualification rate is serious}\\ 0,&\text{low product quality qualification rate is common}\\ 1,&\text{low product quality qualification rate is slight}\\ \end{array}}\right.$

$D_{2}=\left\{{-,0,+}\right\}$ , where “ $-$ ” means low organization quality defense intensity, “0” means moderate organization quality defense intensity, and “ $+$ ” means high organization quality defense intensity.

5.3 Qualitative simulation of decision variables of organization quality monitor

Taking organization quality monitor as the decision variable, the system is qualitatively simulated. In this study, two time points and time intervals $\left[{t_{0},t_{1}}\right]$ are used for reasoning, and state transition table is used to generate subsequent variables [21, 22].

Set the initial variable state as $t=t_{0}$ :

$\displaystyle\textit{QS}\left({E_{1},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\ \textit{QS}\left({E_{3},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({E_{4},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({X_{4},t_{0}}\right)=\left\langle{0,0}\right\rangle$

The state of the decision variable is $\textit{QS}\left({D_{1},t_{0}}\right)=\left\langle-\right\rangle$ , the enterprise management department has a low intensity of organizational quality monitor and adjustment. At this time, organization quality specific immune decision-making system is simulated and reasoned.

When $t=\left({t_{0},t_{1}}\right)$ , this is the transformation from time point to time interval, namely P transformation [31]. According to the conclusion, the subsequent state of the environment variable is:

$\displaystyle{E_{1}:P_{12}\Rightarrow}\textit{QS}\left({0,0}\right)={\left% \langle{\left({0,1}\right),+}\right\rangle}\left\langle{0,0}\right\rangle$

The phenomenon of low team cohesion (E ${}_{1}$ ) is affected by organization quality monitor intensity (D ${}_{1}$ ), and the qualitative value changes dynamically. As (E ${}_{1}$ ) and (D ${}_{1}$ ) are negatively related, it indicates that the phenomenon of low team cohesion changes from “normal” to “serious”, and which may continue to deteriorate.

$\displaystyle E_{3}:P_{14}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({-1,0}\right),-}\right\rangle}\left\langle{0,0}\right\rangle$ $\displaystyle E_{4}:P_{14}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({-1,0}\right),-}\right\rangle}{\left\langle{0,0}\right\rangle}$

Staff productivity (E ${}_{3}$ ) and quality resource utilization rate (E ${}_{4}$ ) are affected by organization quality monitor intensity (D ${}_{1}$ ), and the qualitative value changes from $\left\langle{0,0}\right\rangle$ to $\left\langle{(-1,0),-}\right\rangle$ , indicating that staff productivity and quality resource utilization rate change from “common” to “slight”.

$\displaystyle{X_{4}:P_{12}\Rightarrow}\textit{QS}\left({0,0}\right)={\left% \langle{\left({0,1}\right),+}\right\rangle}{\left\langle{0,0}\right\rangle}$

The formation of low quality competitiveness of enterprise is affected by low team cohesion (E ${}_{1}$ ), employee productivity (E ${}_{3}$ ), and quality resource utilization rate (E ${}_{4}$ ), and the qualitative value changes from $\left\langle{0,0}\right\rangle$ to $\left\langle{(0,1),+}\right\rangle$ , indicating that the state of poor quality competitiveness of enterprise changes from “common” to “serious”, and which may continue to deteriorate.

The new state is described as follows:

$\displaystyle E_{1}:\left\langle{\left({0,1}\right),+}\right\rangle\left% \langle{0,0}\right\rangle$ $\displaystyle E_{3}:\left\langle{\left({-1,0}\right),-}\right\rangle\left% \langle{0,0}\right\rangle$ $\displaystyle E_{4}:\left\langle{\left({-1,0}\right),-}\right\rangle\left% \langle{0,0}\right\rangle$ $\displaystyle X_{4}:\left\langle{\left({0,1}\right),+}\right\rangle\left% \langle{0,0}\right\rangle$

When $t=t_{1}$ , according to Table 3, the subsequent states of environment variables can be obtained:

$\displaystyle{E_{1}:}{I_{3}\Rightarrow}{\left\langle{\left({0,1}\right),+}% \right\rangle\left\langle{1,0}\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,% +}\right\rangle}$ $\displaystyle{E_{3}:}{I_{14}\Rightarrow}{\left\langle{\left({-1,0}\right),-}% \right\rangle\left\langle{\left({-1,0}\right),0}\right\rangle\left\langle{-1,0% }\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,+}\right\rangle}$ $\displaystyle{E_{4}:}{I_{14}\Rightarrow}{\left\langle{\left({-1,0}\right),-}% \right\rangle\left\langle{\left({-1,0}\right),0}\right\rangle\left\langle{-1,0% }\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,+}\right\rangle}$ $\displaystyle X_{4}:I_{3}\Rightarrow\left\langle{\left({0,1}\right),+}\right% \rangle\left\langle{1,0}\right\rangle$

At this point, the state is as follows:

$\displaystyle E_{1}:\left\langle{(0,1),+}\right\rangle\left\langle{1,0}\right% \rangle\left\langle{0,+}\right\rangle$ $\displaystyle E_{3}:\left\langle{(-1,0),-}\right\rangle\left\langle{(-1,0),0}% \right\rangle\left\langle{-1,0}\right\rangle\left\langle{0,+}\right\rangle$ $\displaystyle E_{4}:\left\langle{(-1,0),-}\right\rangle\left\langle{(-1,0),0}% \right\rangle\left\langle{-1,0}\right\rangle\left\langle{0,+}\right\rangle$ $\displaystyle X_{4}:\left\langle{\left({0,1}\right),+}\right\rangle\left% \langle{1,0}\right\rangle$

According to the simulation results, there are two states of low quality competitiveness (X4): $\left\langle{\left({0,1}\right),+}\right\rangle$ and $\left\langle{1,0}\right\rangle$ , among them, $\left\langle{\left({0,1}\right),+}\right\rangle$ indicates that the poor quality competitiveness of enterprise is changing from “common” to “serious”, $\left\langle{1,0}\right\rangle$ indicates that the poor quality competitiveness of enterprise is “serious”. Both of these states indicate that the poor quality competitiveness of enterprise is serious, and which may continue to deteriorate.

When the state of the decision variable is $\textit{QS}\left({D_{1},t_{0}}\right)=\left\langle+\right\rangle$ , organization quality monitor intensity is enhanced. After $P$ transition and $I$ transition, the subsequent state of the state variable is:

When $t=\left({t_{0},t_{1}}\right)$ , through P transformation, we can obtain the following formulation:

$\displaystyle E_{1}:P_{14}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({-1,0}\right),-}\right\rangle}{\left\langle{0,0}\right\rangle}$ $\displaystyle E_{3}:P_{12}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({0,1}\right),+}\right\rangle}{\left\langle{0,0}\right\rangle}$ $\displaystyle E_{4}:P_{12}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({0,1}\right),+}\right\rangle}{\left\langle{0,0}\right\rangle}$ $\displaystyle X_{4}:P_{14}\Rightarrow\textit{QS}\left({0,0}\right)={\left% \langle{\left({-1,0}\right),-}\right\rangle}{\left\langle{0,0}\right\rangle}$

When $t=t_{1}$ , by I transformation, we can obtain the following formulation:

$\displaystyle{E_{1}:}{I_{14}\Rightarrow}{\left\langle{\left({-1,0}\right),-}% \right\rangle\left\langle{\left({-1,0}\right),0}\right\rangle\left\langle{-1,0% }\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,+}\right\rangle}$ $\displaystyle{E_{3}:}{I_{3}\Rightarrow}{\left\langle{\left({0,1}\right),+}% \right\rangle\left\langle{1,0}\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,% +}\right\rangle}$ $\displaystyle{E_{4}:}{I_{3}\Rightarrow}\left\langle\left({0,1}\right),+\right% \rangle\left\langle{1,0}\right\rangle{I_{8}\Rightarrow}{\left\langle{0,+}% \right\rangle}$ $\displaystyle{X_{4}:}{I_{14}\Rightarrow}{\left\langle{\left({-1,0}\right),-}% \right\rangle\left\langle{\left({-1,0}\right),0}\right\rangle\left\langle{-1,0% }\right\rangle}{I_{8}\Rightarrow}{\left\langle{0,+}\right\rangle}$

At this time, organization quality monitor of manufacturing enterprises should be strengthened, and the state variable (X ${}_{4}$ ) of low quality competitiveness of enterprise refers to three states $\left\langle{\left({-1,0}\right),-}\right\rangle,\left\langle{\left({-1,0}% \right),0}\right\rangle$ and $\left\langle{-1,0}\right\rangle$ , ${\left\langle{\left({-1,0}\right),-}\right\rangle}$ indicates that the state of low quality competitiveness of enterprise is changing from “common” to “slight” and showing a trend of improvement, ${\left\langle{\left({-1,0}\right),0}\right\rangle}$ indicates that the state of low quality competitiveness of enterprise is changing from “common” to “slight” without any improvement trend, ${\left\langle{-1,0}\right\rangle}$ indicates that the state of low quality competitiveness of enterprise has been greatly improved.

5.4 Qualitative simulation of decision variables of organization quality defense

It can be seen from the qualitative simulation of organization quality monitor adjustment that enhancing organization quality monitor can improve the problem of poor product quality. Due to the extremely complex decision of organization quality specific immune, the decision of organization quality specific immune and organization quality defense involves multiple decision variables. This study introduces the decision variable of organization quality specific immune and organization quality defense respectively [4, 12, 21, 22]:

Set the initial state as $t=t_{0}$ :

$\displaystyle\textit{QS}\left({E_{1},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({E_{2},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({E_{3},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({E_{4},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({E_{5},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({X_{3},t_{0}}\right)=\left\langle{0,0}\right\rangle$ $\displaystyle\textit{QS}\left({X_{4},t_{0}}\right)=\left\langle{0,0}\right\rangle$

When the state of the decision variable is $\textit{QS}\left({D_{1},t_{0}}\right)=\left\langle+\right\rangle$ , $\textit{QS}\left({D_{2},t_{0}}\right)=\left\langle+\right\rangle$ , organization quality monitor is strengthened and organization quality defense of manufacturing enterprise is strengthened. The qualitative simulation results are shown in Table 4.

Table 4
The qualitative simulation results

	$t_{0}$	$(t_{0},t_{1})$	$t_{1}$
E1	$\langle{0},0\rangle$	$\langle(-1,0),-\rangle\langle(-1,0),-2\rangle$	$\langle(-1,0),-2\rangle\langle-1,0\rangle\langle{-1,-}\rangle$
E2	$\langle{0},0\rangle$	$\langle{(}-{1,0)},-\rangle\langle 0,-\rangle$	$\langle{(}-{1,0),}-\rangle\langle{(}-{1,0),}-{2}\rangle\langle-{1,0}\rangle% \langle-{1,}-\rangle$
E3	$\langle{0},0\rangle$	$\langle{(1,0),}+\rangle\langle{(0,1),}+{2}\rangle$	$\langle{(0,1),}+{2}\rangle\langle{1,0}\rangle\langle{1,}+\rangle$
E4	$\langle{0},0\rangle$	$\langle{(1,0),}+\rangle\langle{(0,1),}+{2}\rangle$	$\langle{(0,1),}+{2}\rangle\langle{1,0}\rangle\langle{1,}+\rangle$
E5	$\langle{0},0\rangle$	$\langle{(}-1,0{),-}\rangle\langle{(-1,0),-2}\rangle$	$\langle{(-1,0),-2}\rangle\langle{-1,0}\rangle\langle{-1,-}\rangle$
X3	$\langle{0},0\rangle$	$\left\langle{\left({-1,0}\right),-2}\right\rangle\left\langle{-1,0}\right% \rangle\left\langle{-1,-}\right\rangle$	$\left\langle{\left({-1,0}\right)?-2}\right\rangle\left\langle{-1,{-}}\right% \rangle\left\langle{-1,-2}\right\rangle$
X4	$\langle{0},0\rangle$	$\left\langle{\left({-1,0}\right),-2}\right\rangle\left\langle{-1,0}\right% \rangle\left\langle{-1,-}\right\rangle\left\langle{-1,-2}\right\rangle$	$\left\langle{-1,-}\right\rangle\left\langle{-1,-2}\right\rangle$

According to the qualitative simulation results in Table 4, it can be concluded that the phenomenon of low quality performance and low quality competitiveness of enterprise can be effectively avoided by adopting the organization quality specific immune measures to strengthen organization quality monitor and organization quality defense. Through qualitative simulation, the phenomenon of low team cohesion by setting initial state $<$ 0, 0 $>$ into the three states of $<$ ( $-$ 1, 0), $-$ 2 $>$ , $<$ $-$ 1, 0 $>$ , $<$ $-$ 1, $-$ $>$ :

(1)

$<$ ( $-$ 1, 0), $-$ 2 $>$ indicates that the phenomenon of low team cohesion rapidly changes from “common” to “slight”, showing a strong reverse direction.

(2)

$<$ $-$ 1, 0 $>$ means that the low team cohesion changes from “common” to “slight” and remains “slight”.

(3)

$<$ $-$ 1, $-$ $>$ indicates that the phenomenon of low team cohesion changes from “common” to “slight” and continues to show the opposite trend, that is, team cohesion is effectively improved.

We study the related change dynamically of low quality competitiveness of enterprise, setting initial state $<$ 0, 0 $>$ into the three states of $<$ ( $-$ 1, 0), $-$ 2 $>$ , $<$ $-$ 1, $-$ $>$ , $<$ $-$ 1, $-$ 2 $>$ :

(1)

$<$ ( $-$ 1, 0), $-$ 2 $>$ indicates that the low quality competitiveness of enterprise changes rapidly from “common” to “slight”, and presents a strong reverse state.

(2)

$<$ $-$ 1, $-$ $>$ indicates that the low quality competitiveness of enterprise changes from “common” to “slight” and maintains the state of enhanced quality competitiveness of enterprise.

(3)

$<$ $-$ 1, $-$ 2 $>$ indicates that the low quality competitiveness of enterprise changes from “common” to “slight” quickly, and remains in a strong reverse state, which indicates that quality competitiveness of enterprise improves rapidly in a positive state.

Compared with single decision variable, the influences of multiple decision variables on state variables are more profound and complex. When there is only a single decision variable, qualitative simulation results for strengthening organization quality monitor on quality competitiveness of enterprise are $<$ ( $-$ 1, 0), $-$ $>$ , $<$ ( $-$ 1, 0), 0 $>$ , $<$ $-$ 1, 0 $>$ :

(1)

$<$ ( $-$ 1, 0), $-$ $>$ means that low quality competitiveness of enterprise changes from “common” to “slight”, and continues to change in an opposite trend.

(2)

$<$ ( $-$ 1, 0), 0 $>$ means that low quality competitiveness of enterprise continues to change from “common” to “slight”, maintaining a stable situation;

(3)

$<$ $-$ 1, 0 $>$ indicates that quality competitiveness of enterprise is “serious”, that is, quality competitiveness of enterprise improves and maintains a stable situation.

When decision variable of organization quality defense is introduced, qualitative simulation results of low quality competitiveness of enterprise are as follows: $<$ $-$ 1, $-$ $>$ , $<$ $-$ 1, $-$ 2 $>$ :

(1)

$<$ $-$ 1, $-$ $>$ means that low quality competitiveness of enterprise changes from “common” to “slight”, and maintains the reverse trend;

(2)

$<$ $-$ 1, $-$ 2 $>$ indicates that low quality competitiveness of enterprise has changed from “slight” to “slight”, and continues to change rapidly in the strong opposite direction, indicating that quality competitiveness of enterprise has changed from “slight” at the beginning to “common”, and continues to rise in a positive state.

6. Conclusion

This study uses QSIM algorithm simulation to simulate the decision-making of organization quality specific immune of manufacturing enterprises, and adopts the “pressure-state-response” model to carry out the causal relationships analysis of organization quality specific immune decision making of manufacturing enterprises, and further determines organization quality specific immune decision-making rules of manufacturing enterprise under the QSIM algorithm and simulation. This study firstly introduces the single decision variable of organization quality monitor, qualitatively simulates the variable, and analyzes the influence of the decision variable on the system decision. Then, refer to the previous literatures and references, this study introduces the dual decision variables of organization quality monitor and organization quality defense, conducts qualitative simulation of the two decision variables, and analyzes the influence of the two decision variables on the system decision. Qualitative simulation results show that compared to the influence of multi-decision variables and single decision variable of system on state variables, it can be concluded that adopting single decision variable of organization quality specific immune such as organization quality monitor can effectively change the negative factors and negative states, low team cohesion and the passive status quo of low enterprise competitiveness. When organization quality defense is formed into multiple decision variables, the phenomenon of low quality competitiveness of enterprises is greatly improved, and the trend of improving quality competitiveness of enterprises is obvious and significant, and the transformation speed is rapid. Manufacturing enterprise can take a variety of effective measures of organization quality monitor, organization quality defense, organization quality memory to enhance organization quality specific immune performance in order to improve negative factors (low team cohesion, poor quality profession skills of staff, low staff efficiency, low quality resource utilization rate, insufficient quality improvement and innovation ability, low qualified rate of product quality, low enterprise competitiveness), and then optimize and improve the low status of quality performance of manufacturing enterprises. As a result, QSIM algorithm can be used to simulate and reason organization quality specific immune decision-making of manufacturing enterprises, the influences of single decision variable and multiple decision variables on the organization quality specific immune system can be compared, QSIM algorithm can effectively predict the trend of quality performance and organization quality specific immune performance of manufacturing enterprise and provide reference for enterprise managers to manage and make decisions.

Footnotes

Acknowledgments

This research is funded by National Social Science Fund Project (17CGL020), 2019 Science Research Fund of Department of Education of Liaoning Province (JQW201915402), 2022 Economic and Social Development Research Project of Liaoning Province (20221s1qnwzzkt-011).

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Qualitative simulation of organization quality specific immune decision-making of manufacturing enterprises based on QSIM algorithm simulation

Abstract

Keywords

1. Introduction

2. Relevant theories

2.1 Framework and procedures of QSIM algorithm

3. Causal relationships of organization quality specific immune decision-making under PSR framework

4.1 Decision variables settings

Table 1 Constraints between qualitative variables

Table 2 Qualitative state P transition rules

5.1 Organization quality monitor decision-making analysis

5.2 Organization quality defense decision-making analysis

5.3 Qualitative simulation of decision variables of organization quality monitor

5.4 Qualitative simulation of decision variables of organization quality defense

Table 4 The qualitative simulation results

Footnotes

Acknowledgments

References

Table 1
Constraints between qualitative variables

Table 2
Qualitative state P transition rules

Table 4
The qualitative simulation results