CN112184075A - Sustainable supply chain risk analysis method - Google Patents

Sustainable supply chain risk analysis method Download PDF

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CN112184075A
CN112184075A CN202011180746.7A CN202011180746A CN112184075A CN 112184075 A CN112184075 A CN 112184075A CN 202011180746 A CN202011180746 A CN 202011180746A CN 112184075 A CN112184075 A CN 112184075A
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何丽娜
谢雨诗
彭振兴
刘珏
敖瑞鑫
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Abstract

The invention discloses a sustainable supply chain risk analysis method, which introduces sustainable customer demand importance and interval intuitive fuzzy quality house into sustainable supply chain analysis, and firstly determines the sustainable customer demand importance based on a fuzzy Kano model; then constructing an interval intuitive fuzzy quality room between the sustainable customer demand and the risk factor based on an interval intuitive fuzzy theory; and determining the importance of the risk factors by combining the inter-region intuitionistic fuzzy cross entropy. The method is based on the Kano and QFD integrated model, can introduce continuous customer requirements and customer satisfaction into the risk analysis of the continuous supply chain, realizes the risk management of the continuous supply chain facing the customer requirements, and further effectively improves the customer satisfaction; the invention further introduces interval intuition fuzzy numbers into the QFD analysis model, more comprehensively and effectively expresses the subjective uncertainty of the evaluation information, effectively relieves the uncertainty of the evaluation information, and thus improves the accuracy of expression.

Description

Sustainable supply chain risk analysis method
Technical Field
The invention belongs to the technical field of big data application, relates to supply chain management, and particularly relates to sustainable supply chain risk management and control.
Background
Sustainable development refers to enterprise development strategies that enterprises make without affecting the natural environment, society, and business viability. Due to global resource limitations, increased consumer awareness of ecological problems, and strict environmental legislation enacted by governments throughout the world, businesses are beginning to consider sustainability issues throughout supply chain activities. Sustainable Supply Chain Management (SSCM) is becoming a key strategy for companies to enable them to pursue higher sustainable performance in economic, social and environmental aspects. However, due to the uncertainty of global economy, outsourcing/offshore outsourcing activities and the development of information technology, sustainable supply chain management is also facing more risks. Therefore, in sustainable supply chain management, identifying critical Risk Factors (RFs) is an important issue to solve.
Since the performance of a Sustainable Supply Chain (SSC) is mainly dependent on Customer Satisfaction (CS), customer demand plays a crucial role in the Sustainable Supply Chain (SSC). In a customer-oriented market environment, risk factor management of the sustainable supply chain is to meet customer demand and increase customer satisfaction. Customer needs can be divided into different categories, and the contribution of the customer needs of different categories to customer satisfaction is also different. Therefore, knowing the customer needs, taking the Kano category of the customer needs and integrating it into the risk factors of the sustainable supply chain is a prerequisite for the development of sustainable supply chain risk management.
Quality Function Deployment (QFD) is a typical customer-driven method that can efficiently identify the relationship between customer demand and risk factors, and based on this, derive the importance of the risk factors. However, the conventional QFD has some inherent disadvantages, which may have a great influence on the importance of the risk factors:
first, QFD has limitations in systematically, comprehensively understanding and analyzing customer needs; risk factor management policies may not actively and purposefully respond to customer needs if they are not properly analyzed;
secondly, in the traditional QFD, the importance of each risk factor is determined by the incidence matrix of the risk factors and the customer requirements and the importance of the customer requirements, and the information mainly comes from subjective evaluation; the current research on QFD rarely considers objective information and may cause subjective deviation;
third, QFD, a typical multi-attribute decision method, provides evaluation information by experts according to the education background and experience of individuals, and has a large subjectivity and ambiguity.
In addition, the traditional QFD has defects in the uncertainty and ambiguity of processing evaluation information, which seriously affects the importance analysis of risk factors and may result in unreasonable risk factor processing decisions.
In conclusion, research on effective analysis of the risk importance of the sustainable supply chain can be realized, and the method has very important significance for realizing risk control of the sustainable supply chain.
Disclosure of Invention
The invention aims to provide a sustainable supply chain risk analysis method aiming at the defects in the prior art, and the fuzzy kano model, QFD and Interval-valued intuitionistic fuzzy sets (IVIFS) are combined to realize effective analysis of the importance of sustainable supply chain risk factors.
The invention idea is as follows: introducing the sustainable customer demand importance and interval intuitive fuzzy quality house into the sustainable supply chain analysis; firstly, determining the importance of sustainable customer demands based on a fuzzy Kano model; then constructing an interval intuitive fuzzy quality room between the sustainable customer demand and the risk factor based on an interval intuitive fuzzy theory; and determining the importance of the risk factors by combining the inter-region intuitionistic fuzzy cross entropy.
Based on the above inventive concept, the method for analyzing the risk of the sustainable supply chain provided by the invention comprises the following steps:
s1, acquiring the importance of sustainable customer demands (SCRs) based on the fuzzy kano model, comprising the following steps:
s11 determining sustainable customer demand;
s12 classifying sustainable customer demands based on the fuzzy Kano model, and constructing a Kano questionnaire and a Kano category evaluation table;
s13, constructing a Kano type evaluation matrix capable of meeting the requirements of customers based on the Kano questionnaire research result;
s14, determining the importance of the sustainable customer requirements according to the constructed kano type evaluation matrix of the sustainable customer requirements;
s2, establishing a zone intuitionistic fuzzy quality house (HoQ), and analyzing the importance of risk factors, wherein the method comprises the following steps:
s21 determining risk factors;
s22, establishing an interval intuitive fuzzy quality room, wherein the interval intuitive fuzzy quality room comprises risk factors, sustainable customer requirements and corresponding importance, an interval intuitive fuzzy relation matrix between the sustainable customer requirements and the risk factors and the importance of the risk factors to be determined;
s23, extracting an associated interval intuitive fuzzy set representing the relationship between the risk factors and the sustainable customer requirements based on the interval direct fuzzy quality room;
s24, constructing an ideal interval intuitive fuzzy set representing an ideal reference sequence of the risk factor and a non-ideal interval intuitive fuzzy set representing a non-ideal reference sequence of the risk factor;
s25, based on the interval intuitive fuzzy cross entropy, respectively obtaining the symmetry difference degree between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set;
s26, determining the importance of the risk factors according to the degree of symmetry difference between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set.
In the method for risk analysis of the sustainable supply chain, step S1 is to identify and classify the customer requirements, and further determine the final importance of each customer requirement.
In step S11, customer-oriented supply chain managementFirst, the primary sustainable customer demand is determined. Because the primary goal of sustainable supply chain management is to achieve economic, environmental, and social sustainability, the needs of supply chain management fall into the three categories of economic, environmental, and social needs. Key sustainable customer needs may be determined by conventional means already disclosed in the art, including group discussions, interview access, questionnaires, cluster analysis, and the like. In the present invention, SCR is usedi(i 1, 2.., N) represents the ith sustainable customer demand, and N represents the number of identified sustainable customer demands.
In step S12, the kano model may classify the sustainable customer demand into six categories (H ═ 6), which are charm demand (a), expectation demand (O), basic demand (M), no-difference demand (I), reverse demand (R), and suspicious result (Q). The traditional Kano questionnaire is experienced by the investigator selecting among standard options, thus ignoring personal experience uncertainty. In contrast to the traditional Kano questionnaire, the fuzzy Kano questionnaire uses membership to indicate the degree of experience of the investigator. And giving the fuzzy relation matrix as:
S＝PT×U (1)；
in the formula, P and U respectively represent membership degree matrixes of a forward problem and a reverse problem, and the sum of all elements in a membership degree set is 1; pTRepresenting the transposed matrix of P. Thus, the Kano questionnaire here consists of forward questions, which are satisfied with a certain requirement, reverse questions, which are not satisfied with a certain requirement, and corresponding experience membership, including satisfaction, rationalization, so-called, acceptable, unsatisfactory, etc.; the positive problem is that the sum of all perceptual membership and the negative problem is 1.
The Kano type evaluation table is used for determining the Kano type required by the sustainable customer according to the fuzzy relation matrix, is a matrix table constructed by using forward problem feeling and reverse problem feeling, corresponds to the element positions in the fuzzy relation matrix in a one-to-one mode, and is one of H types required by the sustainable customer.
In step S13, based on the investigation result, the fuzzy relation matrix of each sustainable customer requirement is obtained according to the formula (1), and then the Kano category and the corresponding importance corresponding to each customer requirement can be determined according to the Kano category evaluation table.
For convenience, the ith sustainable customer demand SCRiThe distribution ratio of the Kano class importance is represented as Di＝{Dij，|j＝1，2，...，H}，DijRepresents the percentage (i.e., importance) of the ith sustainable customer demand that belongs to the jth Kano category; di1Indicating the importance of the ith sustainable customer demand as an attractive demand (A), Di2Significance of the ith sustainable customer demand as belonging to the desired demand (O), Di3Representing the importance of the ith sustainable customer demand as being among the basetype demands (M), Di4Representing the importance of the ith sustainable customer demand as a uniform demand (I), Di5Representing the importance of the ith sustainable customer demand as a reverse-type demand (R), Di6And (3) representing the importance of the ith sustainable customer requirement belonging to a suspicious result (Q), and when the suspicious result appears in the Kano type distribution of the ith sustainable customer requirement, the question about the questionnaire itself is shown or the customer has an understanding error when filling in the questionnaire survey, and needs to be investigated again. The Kano category importance of all sustainable customer requirements constitutes a Kano category evaluation matrix of sustainable customer requirements, which can be expressed as matrix D:
D＝(Dij)N×H (2)。
in step S14, the importance analysis of the sustainable customer demand is a key issue for the sustainable supply chain management. On the basis of the fuzzy Kano model analysis result, the satisfaction importance and the objective importance of the customer requirements are determined through quantitative analysis, and the satisfaction importance and the objective importance of the customer requirements are integrated to determine the final importance of the sustainable customer requirements, and the method specifically comprises the following steps:
s141 determining the satisfaction importance of sustainable customer demand
Since the sustainable customer demand of different Kano categories contributes differently to satisfaction, it is necessary to measure the importance of the sustainable customer demand according to its influence on satisfaction, which is defined as the satisfaction importance. Based on holdingThe satisfaction importance of the ith sustainable customer requirement SCRi of the Kano type evaluation matrix obtained by fuzzy Kano analysis of subsequent customer requirements
Figure BDA0002750099010000041
Determined by the following equation:
Figure BDA0002750099010000042
Figure BDA0002750099010000043
Figure BDA0002750099010000044
Figure BDA0002750099010000045
Figure BDA0002750099010000046
expressed as the customer sensitivity factor after the sustainable supply chain meets this requirement,
Figure BDA0002750099010000047
representing the customer sensitivity factor after the sustainable supply chain does not meet the requirement;
Figure BDA0002750099010000048
meaning the satisfaction of the SCRI, is given by GiOf decision, GiThe negative customer sensitivity factor is subtracted from the positive customer sensitivity factor. That is, if a sustainable customer demand has a significant impact on satisfaction, the sustainable customer demand should be assigned a higher importance and be allocated more resources to satisfy it.
S142 determining objective importance of sustainable customer demand
In the analysis of sustainable customer demand, objective importance needs to be utilized to reduce subjective bias, and the objective importance can be determined by solving a mathematical model. In the present invention, the objective importance of sustainable customer demand is determined by the maximum deviation method. Vij(Wob) Indicating SCRiDistribution ratio and SCR in Kano class jiDeviation of distribution ratios in other Kano classes.
Figure BDA0002750099010000049
Figure BDA00027500990100000410
Indicating SCRiObjective importance of.
Is provided with
Figure BDA00027500990100000411
Vi(Wob) Indicating SCRiSum of the deviations of the distribution of the Kano classes.
Then, the Kano class distribution maximum deviation model of sustainable customer demand can be described as
Figure BDA00027500990100000412
Figure BDA00027500990100000413
By solving equation (9), the ith sustainable customer demand SCR can be foundiThe objective importance of (a) is shown in equation (10):
Figure BDA0002750099010000051
s143 determining the final importance of the sustainable customer demand
Considering the satisfaction importance and the objective importance in combination, the most sustainable customer demand can be determined by the following formula (11)
Final importance:
Figure BDA0002750099010000052
finally, the final importance of the sustainable customer demand can be expressed as a vector:
W＝(ω1，ω2，...，ωN) (12)。
in the above method for risk analysis of the sustainable supply chain, step S2 is to use the interval intuitive fuzzy number to describe the strength of association between the sustainable customer demand and the risk factor, and establish an interval intuitive fuzzy quality House (HoQ). And analyzing the risk factors according to the analysis results of the sustainable customer requirements and the risk factors by combining the interval intuitive fuzzy quality room and the interval intuitive fuzzy cross entropy analysis to obtain the importance of the risk factors and finish the risk analysis of the sustainable supply chain.
In step S21, the main risk factors of the sustainable supply chain can be determined by conventional means disclosed in the art, such as risk check list, expert interviews, and literature research. Here, the number of risk factors to be determined is set to M, using RFmM represents the mth risk factor, B ═ RF · M1，RF2，...，RFMIs a set of risk factors.
In step S22, it is important to identify the relationship between the sustainable customer demand and the risk factors in the sustainable supply chain. The specific structure of the interval intuitive fuzzy quality house is shown in fig. 2, and comprises risk factors, sustainable customer requirements and corresponding importance, an interval intuitive fuzzy relation matrix between the sustainable customer requirements and the risk factors, and importance of the risk factors to be determined. Wherein, the interval intuitive fuzzy relation matrix R can be expressed as
Figure BDA0002750099010000053
RimIndicating SCRi(i ═ 1, 2,. N) and RFm(M ═ 1, 2.. times, M) correlation, Rim＝[aim，bim]，[cim，dim]。[aim，bim]Indicating SCRiAnd RFmInterval of strength of association between, [ c ]im，dim]Indicating SCRiAnd RFmThe non-associated intensity interval of (a). In this step, the association strength interval and the non-association strength interval may be obtained by inviting experts to describe the association strength and the non-association strength of each pair of sustainable client requirements and risk factors by using interval intuitive fuzzy numbers, and then normalizing the data.
Expert expressed SCRiAnd RFmThe degree of hesitation of the relation between them is represented by piimRepresents:
Figure BDA0002750099010000061
in the above step S23, the RF is applied to the interval intuitive blur quality roommThe relationship with each sustainable customer requirement can be represented by an associated interval intuitive fuzzy set in an associated matrix of an interval intuitive fuzzy quality room, and the specific form of the associated interval intuitive fuzzy set is as follows:
Bm＝{＜[a1m，b1m]，[c1m，d1m]＞，＜[a2m，b2m]，[c2m，d2m]＞，...，＜[aNm，bNm]，[cNm，dNm]＞}。
thus, RF takes into account sustainable customer demandmSet B can be intuitively blurred with associated intervalsmTo indicate.
In step S24, in order to determine the impact of the risk factors on the sustainable customer demand, the present invention further constructs the risk factorsAn ideal reference sequence and a non-ideal reference sequence. If the risk factor differs less from the ideal reference sequence and more from the non-ideal reference sequence, it indicates that the risk factor has a greater impact on sustainable customer demand and should be given greater importance. In this step, an ideal interval intuitive fuzzy set B is adopted+And a non-ideal interval intuitive fuzzy set B-To represent the ideal and non-ideal reference sequences, respectively, of the risk factor:
Figure BDA0002750099010000062
Figure BDA0002750099010000063
Figure BDA0002750099010000064
and
Figure BDA0002750099010000065
respectively representing ideal and non-ideal points of the risk factor and the ith sustainable customer demand association relationship, and taking the following values:
Figure BDA0002750099010000066
Figure BDA0002750099010000067
in step S25, the interval intuitive blur cross entropy is used to measure the difference between two interval intuitive blur sets. In order to more accurately give the difference degree between the two interval direct fuzzy sets, the invention takes the symmetrical difference degree between the two interval direct fuzzy sets as the final difference degree of the two interval direct fuzzy sets. RF (radio frequency)mAssociated interval intuitive fuzzy set BmAnd ideal interval intuitive fuzzy set of ideal reference sequenceB+The degree of difference therebetween can be determined by equation (19):
Figure BDA0002750099010000068
in the formula, ωiIndicating the ith sustainable customer demand SCRiThe importance of (c).
Likewise, the ideal interval intuitive fuzzy set B of the ideal reference sequence+And RFmAssociated interval intuitive fuzzy set BmThe degree of difference therebetween can be determined by the formula (20):
Figure BDA0002750099010000071
then RFmAssociated interval intuitive fuzzy set BmAnd ideal interval intuitive fuzzy set B of ideal reference sequence+The degree of symmetry difference of (a) can be determined by equation (21):
S+(Bm，B+)＝D(Bm，B+)+D(B+，Bm) (21)。
following the same procedure, RFmAssociated interval intuitive fuzzy set BmAnd a non-ideal interval intuitionistic fuzzy set B of non-ideal reference sequences-The degree of symmetry difference of (a) can be determined by equation (22):
S-(Bm，B-)＝D(Bm，B-)+D(B-，Bm) (22)；
D(Bm，B-) And D (B)-，Bm) And D (B)m，B+) And D (B)+，Bm) The determination method is similar, and the "+" in the formula is changed into the "-" so as to obtain the product.
In the step S26, the importance of the risk factor is determined according to the following formula (23) according to the degree of symmetry difference between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set:
Figure BDA0002750099010000072
in the formula umIndicating the importance of the mth risk factor.
Compared with the prior art, the sustainable supply chain risk analysis method provided by the invention has the following beneficial effects:
(1) the method is based on the Kano and QFD integrated model, can introduce continuous customer requirements and customer satisfaction into the risk analysis of the continuous supply chain, realizes the risk management of the continuous supply chain facing the customer requirements, and further effectively improves the customer satisfaction.
(2) The method introduces interval intuitionistic fuzzy numbers into the QFD analysis model, more comprehensively and effectively expresses subjective uncertainty of evaluation information through the association strength interval, the non-association strength interval and the hesitation degree interval, and can effectively relieve the uncertainty of the evaluation information in the process of risk analysis of the sustainable supply chain, thereby improving the expression accuracy.
(3) The difference degree of the sustainable supply chain risk factors with respect to the customer requirements is measured by applying interval intuitive fuzzy cross entropy, and the importance degree of the risk factors is determined based on the difference degree, so that objective analysis based on evaluation information is realized, the subjectivity in risk factor analysis can be effectively reduced, and the scientificity of decision making is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other embodiments and drawings can be obtained according to the embodiments shown in the drawings without creative efforts.
FIG. 1 is a schematic diagram of a method for risk analysis of a sustainable supply chain according to the present invention.
FIG. 2 is a schematic view of the interval intuitive fuzzy quality room (HoQ) structure of the present invention.
FIG. 3 is a histogram of the importance of risk factors determined in an embodiment of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
In order to illustrate the practical application of the method for analyzing the risk of the sustainable supply chain provided by the present invention, the present embodiment takes an air conditioner manufacturer W in china as an example to perform case research.
Due to different influences of different types of sustainable customer demands on satisfaction and increased risk factors in the sustainable supply chain management, in order to reduce risks and actively meet the sustainable customer demands, a risk management strategy must be established by the W company. Furthermore, management is confused as to how important sustainable customer demand and risk factors can be determined. To this end, the present embodiment provides a method for risk analysis of a sustainable supply chain to determine the importance of risk factors in the sustainable supply chain in consideration of the sustainable customer demand.
The method for analyzing the risk of the sustainable supply chain, as shown in fig. 1, includes the following steps:
s1, acquiring the importance of sustainable customer demands (SCRs) based on the fuzzy kano model;
s2, establishing a zone intuitionistic fuzzy quality house (HoQ), and analyzing the importance of risk factors.
Step S1 is to identify and classify the customer needs, and determine the final importance of each customer need. The implementation of the step specifically comprises the following sub-steps:
s11 determines sustainable customer demand.
In the embodiment, 9 main sustainable customer demands (i.e. N is 9) are determined from three aspects of economy, environment and society through expert interview. Economically, sustainable customer demand includes "cost and price competitiveness" (SCR)1) "quality improvement" (SCR)2) "delivery on time" (SCR)3) And "customer service enhancement" (SCR)4). In the environmental context, sustainable customer demand includes "resource utilization and regeneration" (SCR)5) "pollution reduction" (SCR)6). In addition, "health and safety" (SCR)7) "law and moral" of law conservation (SCR)8) And "commercial trust and reputation" (SCR)9) Is a sustainable customer demand on a social basis.
S12 classifies the sustainable customer demands based on the fuzzy Kano model, and constructs a Kano questionnaire and a Kano category evaluation table.
In this embodiment, the Kano model is used to divide the above sustainable customer demand into six categories (H ═ 6), which are charm demand (a), expectation demand (O), basic demand (M), no-difference demand (I), reverse demand (R), and suspicious result (Q). In this embodiment, the fuzzy Kano questionnaire uses the membership degree to represent the feeling degree of the investigator, and the fuzzy Kano questionnaire is composed of a forward problem, a reverse problem and a corresponding feeling membership degree, wherein the forward problem is to satisfy a certain requirement, the reverse problem is not to satisfy the certain requirement, and the feeling includes satisfaction, should be so, no-so, acceptable and unsatisfied; the sum of all perceptual membership of the forward problem and the sum of all perceptual membership of the reverse problem are 1. The fuzzy Kano questionnaire designed in this example is shown in table 1.
TABLE 1 fuzzy Kano questionnaire
Figure BDA0002750099010000091
And giving the fuzzy relation matrix as:
S＝PT×U (1)：
wherein P and U represent the membership matrix of the forward problem and the reverse problem, respectively, and the membership set is the sum of all elementsAnd is 1; pTRepresenting the transposed matrix of P. In this embodiment, since there are five membership degrees, S is a 5 × 5 fuzzy relation matrix.
In order to determine the corresponding Kano category in each sustainable customer requirement, the embodiment further constructs a Kano category evaluation table. The Kano category evaluation table is a matrix table constructed by using forward problem and reverse problem feelings, and a matrix element is one of 5 categories of sustainable customer demands, which is shown in table 2.
TABLE 2Kano Category evaluation Table
Figure BDA0002750099010000092
S13, constructing a Kano type evaluation matrix capable of meeting the requirements of customers based on the Kano questionnaire research result.
The fuzzy Kano questionnaire designed according to table 1 was distributed to engineers and customers. In the present example, 120 effective questionnaires were used in total, and the detailed evaluation results are shown in table 3. The fuzzy relation matrix S of each sustainable customer requirement is obtained by the formula (1).
By SCR1For example, if the fuzzy Kano questionnaire result is P ═ {0.83, 0.17, 0, 0, 0}, and N ═ 0, 0, 0, 0.48, 0.52}, then
Figure BDA0002750099010000101
TABLE 3 results of fuzzy Kano questionnaire
Figure BDA0002750099010000102
Based on the S matrix obtained by calculation, according to the Kano type evaluation table in the table 2, the Kano type and the corresponding importance of each sustainable customer demand can be determined.
For example, SCR is given above1S matrix of (1), SCR1Kano categories of (1) are charm type demand (A, 39.8%), expectation type demand (O, 43.2%), baseRequirement for this type (M, 8.8%), and requirement for no difference type (I, 8.2%).
For convenience, the ith sustainable customer demand SCRiThe distribution ratio of the Kano class importance is represented as Di＝{Dij，|j＝1，2，...，H}，DijIndicating the percentage (i.e., importance) of the ith sustainable customer demand that belongs to the jth Kano category. In this example, the value of H is 5, Di1，Di2，Di3，Di4，Di5And Di6Respectively represent CRiPercentages belonging to Kano categories A, O, M, R, I, Q.
Thus, SCR1The Kano class distribution ratio of Di1＝[0.398 0.432 0.088 0.082 0 0]。
In this embodiment, according to the fuzzy Kano questionnaire result, the Kano category evaluation table in table 2 is combined to obtain the Kano category distribution of each sustainable customer requirement, and then the Kano category evaluation matrix for the sustainable customer requirements formed by the Kano category importance of all the sustainable customer requirements is:
Figure BDA0002750099010000111
s14, determining the importance of the sustainable customer requirements according to the constructed kano type evaluation matrix of the sustainable customer requirements.
The importance of each sustainable customer demand is obtained according to the steps S141-S143 given above.
By SCR1For example, the determination of the final importance comprises the following steps:
s141 based on Kano type distribution matrix D of sustainable customer demands, SCR can be obtained according to formulas (3) - (6)1Degree of satisfaction of
Figure BDA0002750099010000112
Figure BDA0002750099010000113
Figure BDA0002750099010000114
Figure BDA0002750099010000115
S142, according to the formula (10), obtaining the SCR with the sustainable customer demand1The objective importance of (a) is:
Figure BDA0002750099010000116
s143 according to the formula (11), SCR can be continued according to the customer demand1The final importance of (c) is:
Figure BDA0002750099010000117
according to the above steps, the importance of other sustainable customer requirements can be obtained in turn, and the results are shown in table 4. Wherein, SCR7The (health and safety) importance is highest. In contrast, SCR5The importance of resource utilization and regeneration is relatively low.
TABLE 4 fuzzy Kano model results
Figure BDA0002750099010000118
In step S2, the purpose is to analyze the risk factors in combination with the sustainable client importance, the interval direct fuzzy quality room, i.e., the interval intuitive fuzzy cross entropy, to obtain the importance of the risk factors, and to complete the risk analysis of the sustainable supply chain.
The implementation of the step specifically comprises the following sub-steps:
s21 determines risk factors.
This example identifies 10 risk factors (i.e., M10) for a sustainable supply chain by expert consultation and literature studies, as listed in table 5.
TABLE 5 Risk factors for sustainable supply chain
Figure BDA0002750099010000121
S22, establishing an interval intuitive fuzzy quality room based on the interval intuitive fuzzy, wherein the interval intuitive fuzzy quality room comprises risk factors, sustainable customer requirements and corresponding importance, an interval intuitive fuzzy relation matrix between the sustainable customer requirements and the risk factors and the importance of the risk factors to be determined.
After the risk factors are determined, an interval intuitive fuzzy quality house is constructed according to the graph 2, the risk requirements are used as a roof, the sustainable customer requirements and the corresponding importance of the sustainable customer requirements are used as a left wall, and the importance of the risk factors to be determined is used as a floor.
And inviting 6 experts to evaluate the incidence relation between the sustainable client requirements and the risk factors by using the interval intuitive fuzzy numbers, and carrying out normalization processing on the evaluation data to obtain an interval intuitive fuzzy relation matrix R between the sustainable client requirements and the risk factors to fill the room. Finally, the interval intuitive fuzzy HoQ is obtained, as shown in Table 6.
TABLE 6 Interval intuitive fuzzy HoQ of sustainable customer demand and risk factors
Figure BDA0002750099010000131
The interval intuitive fuzzy relationship matrix R between sustainable customer demand and risk factors can be represented by the foregoing equation (13), namely:
Figure BDA0002750099010000132
Rimindicating SCRi(i ═ 1, 2,. 9) and RFmCorrelation between (m ═ 1, 2.., 10)Is a group of Rim＝[aim，bim]，[cim，dim]。[aim，bim]Indicating SCRiAnd RFmInterval of strength of association between, [ c ]im，dim]Indicating SCRiAnd RFmThe non-associated intensity interval of (a).
S23 determines an associated interval intuitive fuzzy set representing a relationship between risk factors and sustainable customer demand based on the interval direct fuzzy quality room.
In a zone-intuitive fuzzy-quality house, RFmThe relationship with each sustainable customer requirement can be represented by an associated interval intuitive fuzzy set extracted from an interval direct fuzzy quality room association matrix, and the specific form of the associated interval intuitive fuzzy set is as follows: b ism＝{＜[a1m，b1m]，[c1m，d1m]＞，＜[a2m，b2m]，[c2m，d2m]＞，...，＜[aNm，bNm]，[cNm，dNm]＞}。
Thus, RF takes into account sustainable customer demandmSet B can be intuitively blurred with associated intervalsmTo indicate.
S24 constructs an ideal interval intuitive fuzzy set representing the ideal reference sequence of the risk factor and a non-ideal interval intuitive fuzzy set representing the non-ideal reference sequence of the risk factor.
In this embodiment, the ideal interval intuitive fuzzy set B is obtained according to the above equations (15) to (18)+And a non-ideal interval intuitive fuzzy set B-Comprises the following steps:
Figure BDA0002750099010000141
Figure BDA0002750099010000142
s25, based on the interval intuitive fuzzy cross entropy, obtaining the symmetry difference degree between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set respectively.
In this embodiment, the associated interval intuitive fuzzy set Bm and the ideal interval intuitive fuzzy set B of each risk factor are obtained according to the above formulas (19) to (22)+And a non-ideal interval intuitive fuzzy set B-The results are shown in Table 7.
TABLE 7 importance analysis of risk factors
Figure BDA0002750099010000143
S26, determining the importance of the risk factors according to the degree of symmetry difference between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set.
In this example, the importance of each risk factor is obtained according to the above formula (23), and the results are shown in table 7. The importance of each risk factor is represented in the form of a histogram, as shown in fig. 3.
As can be seen in fig. 3, the importance of the risk factors is ranked as: RF (radio frequency)5＞RF1＞RF9＞RF10＞RF2＞RF8＞RF3＞RF6＞RF4＞RF7. Wherein, RF5(reduction in market share), RF1(production planning problem) and RF9The enterprise has higher importance (not meeting social and environmental factors), and should pay more attention to the risk factors and adopt relevant risk mitigation strategies.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (10)

1. A sustainable supply chain risk analysis method is characterized by comprising the following steps:
s1, acquiring the importance of sustainable customer demands based on the fuzzy kano model, and the method comprises the following steps:
s11 determining sustainable customer demand;
s12 classifying sustainable customer demands based on the fuzzy Kano model, and constructing a Kano questionnaire and a Kano category evaluation table;
s13, constructing a Kano type evaluation matrix capable of meeting the requirements of customers based on the Kano questionnaire research result;
s14, determining the importance of the sustainable customer requirements according to the constructed kano type evaluation matrix of the sustainable customer requirements;
s2, establishing an interval intuitive fuzzy quality room, analyzing the importance of risk factors, and comprising the following steps:
s21 determining risk factors;
s22, establishing an interval intuitive fuzzy quality room, wherein the interval intuitive fuzzy quality room comprises risk factors, sustainable customer requirements and corresponding importance, an interval intuitive fuzzy relation matrix between the sustainable customer requirements and the risk factors and the importance of the risk factors to be determined;
s23, extracting an associated interval intuitive fuzzy set representing the relationship between the risk factors and the sustainable customer requirements based on the interval direct fuzzy quality room;
s24, constructing an ideal interval intuitive fuzzy set representing an ideal reference sequence of the risk factor and a non-ideal interval intuitive fuzzy set representing a non-ideal reference sequence of the risk factor;
s25, based on the interval intuitive fuzzy cross entropy, respectively obtaining the symmetry difference degree between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set;
s26, determining the importance of the risk factors according to the degree of symmetry difference between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set.
2. The sustainable supply chain risk analysis method of claim 1, wherein in step S12, the fuzzy Kano questionnaire uses membership degree to represent the degree of experience of the investigator, and the fuzzy relation matrix is given as:
S＝PT×U (1)；
in the formula, P and U respectively represent membership degree matrixes of a forward problem and a reverse problem in a kano questionnaire, and the sum of all elements in a membership degree set is 1; pTRepresenting the transposed matrix of P.
3. The method according to claim 2, wherein the Kano type evaluation table is used for determining the Kano type of the sustainable customer demand according to the fuzzy relation matrix, and the Kano type evaluation table is a matrix table constructed by using forward problem and backward problem feelings, and the matrix table corresponds to the element positions in the fuzzy relation matrix in a one-to-one manner and is one of H types of the sustainable customer demand.
4. The method for risk analysis of sustainable supply chain according to claim 3, wherein the fuzzy relation matrix of each sustainable customer requirement is obtained according to formula (1), and then according to the Kano type evaluation table, the Kano type and corresponding importance corresponding to each customer requirement are determined;
SCR the ith sustainable customer demandiThe distribution ratio of the Kano class importance is represented as Di＝{Dij，|j＝1，2，...，H}，DijRepresenting the importance of the ith sustainable customer demand belonging to the jth Kano category; h ═ 6;
the Kano category importance of all sustainable customer requirements constitutes a Kano category evaluation matrix of sustainable customer requirements, which can be expressed as matrix D:
D＝(Dij)N×H (2)。
5. the sustainable supply chain risk analysis method according to claim 4, wherein the step S14 comprises the following sub-steps:
s141 determining the satisfaction importance of sustainable customer demand
The ith sustainable customer demand SCR based on a Kano category evaluation matrix obtained by fuzzy Kano analysis of sustainable customer demandsiDegree of satisfaction of
Figure FDA0002750097000000021
Determined by the following equation:
Figure FDA0002750097000000022
Figure FDA0002750097000000023
Figure FDA0002750097000000024
Figure FDA0002750097000000025
Figure FDA0002750097000000026
expressed as the customer sensitivity factor after the sustainable supply chain meets this requirement,
Figure FDA0002750097000000027
representing the customer sensitivity factor after the sustainable supply chain does not meet the requirement;
Figure FDA0002750097000000028
refers to SCRiThe satisfaction importance of;
s142 determining objective importance of sustainable customer demand
Obtaining the ith sustainable customer demand is SCR according to equation (10) belowiObjective importance of (2):
Figure FDA0002750097000000029
s143 determining the final importance of the sustainable customer demand
Considering the satisfaction importance and the objective importance together, the final importance of each sustainable customer demand can be determined by the following formula (11):
Figure FDA00027500970000000210
finally, the final importance of the sustainable customer demand can be expressed as a vector:
W＝(ω1，ω2，...，ωN) (12)。
6. the sustainable supply chain risk analysis method of claim 1, wherein in step S22, the interval intuitive fuzzy relation matrix R is expressed as
Figure FDA0002750097000000031
RimIndicating SCRi(i ═ 1, 2,. N) and RFm(M ═ 1, 2.. times, M) correlation, Rim＝[aim，bim]，[cim，dim]；[aim，bim]Indicating SCRiAnd RFmInterval of strength of association between, [ c ]im，dim]Indicating SCRiAnd RFmThe non-associated intensity interval of (a).
7. The sustainable supply chain risk analysis method of claim 6, wherein in step S23, the association interval intuitive fuzzy set is embodied as follows:
Bm＝{<[a1m，b1m]，[c1m，d1m]>，<[a2m，b2m]，[c2m，d2m]>，...，<[aNm，bNm]，[cNm，dNm]>}。
8. the sustainable supply chain risk analysis method of claim 1, wherein in step S24, the ideal interval intuitive fuzzy set B+And a non-ideal interval intuitive fuzzy set B-Comprises the following steps:
Figure FDA0002750097000000032
Figure FDA0002750097000000033
Figure FDA0002750097000000034
and
Figure FDA0002750097000000035
ideal and non-ideal points representing risk factors and the ith sustainable customer demand association, respectively;
Figure FDA0002750097000000036
Figure FDA0002750097000000037
9. the sustainable supply chain risk analysis method according to claim 1, wherein in step S25, the final difference degree is determined as a symmetrical difference degree between two interval direct fuzzy sets; mth risk factor RFmAssociated interval intuitive fuzzy set BmAnd ideal interval intuitive fuzzy set B of ideal reference sequence+The degree of difference therebetween can be determined by equation (19):
Figure FDA0002750097000000038
in the formula, ωiIndicating the ith sustainable customer demand SCRiThe importance of (2);
likewise, the ideal interval intuitive fuzzy set B of the ideal reference sequence+And RFmAssociated interval intuitive fuzzy set BmThe degree of difference therebetween can be determined by the formula (20):
Figure FDA0002750097000000041
then RFmAssociated interval intuitive fuzzy set BmAnd ideal interval intuitive fuzzy set B of ideal reference sequence+The degree of symmetry difference of (a) can be determined by equation (21):
S+(Bm，B+)＝D(Bm，B+)+D(B+，Bm) (21)；
following the same procedure, RFmAssociated interval intuitive fuzzy set BmAnd a non-ideal interval intuitionistic fuzzy set B of non-ideal reference sequences-The degree of symmetry difference of (a) can be determined by equation (22):
S-(Bm，B-)＝D(Bm，B-)+D(B-，Bm) (22)；
D(Bm，B-) And D (B)-，Bm) And D (B)m，B+) And D (B)+，Bm) The determination method of (3) is similar.
10. The sustainable supply chain risk analysis method of claim 1, wherein in step S26, the importance of the risk factor is determined according to the following formula (23) according to the degree of symmetry difference between the associated interval intuitive fuzzy set and the ideal interval intuitive fuzzy set and the non-ideal interval intuitive fuzzy set:
Figure FDA0002750097000000042
in the formula umIndicating the importance of the mth risk factor.
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