CN113763154A - Steel trade supply chain financial risk assessment method based on fuzzy grey evaluation - Google Patents
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Abstract
The invention provides a steel trade supply chain financial risk assessment method based on fuzzy grey evaluation, which comprises the following steps of: performing preliminary credit check on financing enterprises, establishing a risk evaluation index set after the check is passed, and determining the weight of each index; reading and collecting all data required by risk assessment; the expert comprehensively scores all indexes based on all data and the fuzzy comment set, and an evaluation matrix is established; and finally, calculating a whitening weight function and an evaluation weight matrix according to the grey indexes to obtain a comprehensive evaluation value of each level of indexes and a comprehensive risk evaluation value of the enterprise. The risk assessment problem containing indexes which are not easy to quantify is achieved, and the expert scoring and the fuzzy grey evaluation method are combined based on the analysis of specific data, so that the assessment result is more scientific and credible; the risk loss of financial institutions is avoided, and simultaneously the financing of steel trade enterprises with the characteristic of slow capital operation is promoted.
Description
Technical Field
The invention relates to supply chain financial risk assessment based on steel storage and an e-commerce trading platform, in particular to a steel trade enterprise supply chain financial risk assessment method based on fuzzy grey comprehensive evaluation.
Background
In recent years, steel trade enterprises in China begin to transform to carry out supply chain management, and steel products belong to the category of bulk commodities and have higher circulation cost and unit price; and the steel industry is capital-intensive, the average repayment period of steel money by terminal engineering in the industry exceeds more than 3 months, huge fund amount is needed to support operation, and financing has to be applied to financial institutions. Because the steel industry is a periodic industry, price fluctuation is large, and particularly, iron ore occupying 80% of cost mainly depends on import. When the price of imported ore suddenly and greatly rises, a large amount of profits can be eroded, so that the survival of the steel industry is difficult; or when the demand of the steel mainly applied to market capital construction, real estate and building industries is suddenly and greatly reduced, the capacity is excessive, and the price of the steel is greatly reduced. At this time, the supply chain financing risk is easily conducted from the core enterprise to the entire supply chain enterprise, forming a systematic risk. In addition, the supply chain involves more main bodies, the information among enterprises is asymmetric, the repayment capacity of each enterprise is good and uneven, and even the bank suffers huge fund loss due to the fact that financing is acquired by a fraudulent means. Therefore, the assessment and control of the supply chain financial risk of the steel trade enterprise are very important.
Application No. CN110472815A, entitled: a risk control method and system for financing enterprise in supply chain financial business includes: obtaining all dimensional variables related to the transaction behavior, and modeling to obtain a model A; comparing the predicted account period with the actual account period, and finding a threshold value according to a statistical method; the out-of-range is defined as an abnormal order; according to the proportion of the abnormal orders, giving a mark of an abnormal trading pair; deriving variables at the trading level; modeling to obtain a final model B; finally, the score is given according to the model B. The invention can effectively monitor the transaction behavior of the financing enterprise, but only scores the risk possibly brought by the transaction behavior without considering other indexes which possibly cause the risk. Application No.: CN111815207B, name: a method for quantitative risk assessment for supply chain finance, comprising: monitoring the supply chains by adopting different monitoring strategies according to the chain attribute and the weight value of each supply chain based on the e-commerce platform to obtain first-class information; meanwhile, reading and analyzing the financial transaction information of each supply chain to obtain second-type information; crawling the self operation information of the e-commerce platform to obtain third-class information; and comprehensively evaluating the three types of risk information based on a quantitative evaluation model to obtain an evaluation result. The method is lack of consideration for external risks in the industry, and risk assessment of indexes which are difficult to quantitatively measure is not mentioned, but the fuzzy gray comprehensive evaluation method can effectively solve the problem of risk assessment of indexes which are difficult to quantitatively measure.
Steel enterprises with strong comprehensive strength can build a steel trade supply chain financial platform based on building a steel storage and e-commerce transaction platform by relying on self dominant status and good credit, and cooperate with financial institutions to provide financing service for medium-sized and small enterprises. When the platform obtains a financing request, the background of a financing enterprise and the authenticity of a contract can be verified, and a risk report is generated and sent to a financial institution according to big data information in the platform and the evaluation of a risk assessment expert, so that the financial institution is helped to reduce potential financial risks and avoid financial risk loss.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a steel trade supply chain financial risk assessment method based on fuzzy grey evaluation.
The invention is realized by the following modes:
a steel trade supply chain financial risk assessment method based on fuzzy grey evaluation comprises the following steps:
s1: performing preliminary credit review on the enterprise;
s2: identifying a risk source, and establishing a risk assessment index set comprising a primary index and a secondary index;
s3: determining each index weight by an analytic hierarchy process;
s4: acquiring all data required by risk assessment, and reading and collecting data required by each index judgment by combining the setting of financing risk index judgment in S2;
s5: determining a fuzzy comment set V, and setting an evaluation standard and an evaluation grade for a risk assessment expert;
s6: establishing a sample evaluation matrix D;
s7: determining evaluation gray classes and establishing a whitening weight function;
s8: determining a grey evaluation weight matrix of each primary index;
s9: calculating a comprehensive gray evaluation value of each secondary index;
s10: calculating a comprehensive gray evaluation value of each level of index;
s11: and calculating the comprehensive credit evaluation value of the enterprise.
Further, S1 is specifically that when the financial platform of the steel trade supply chain receives an enterprise financing application, first, a preliminary credit check is performed on the enterprise to determine whether the average contract performance rate of the enterprise meets the standard, and if not, the financing application of the enterprise is rejected; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract times.
Further, the S2 establishes a risk assessment index set:
first-level indexes: external environment risks, core enterprise integrated capacity risks, supply chain partner cooperation risks, financing asset condition risks, and cloud warehouse service management risks;
secondary indicators of external environmental risk: policy risk, economic risk, supply and demand relationship;
three-level indicators of policy risk: policy support strength, index description: talent policy, subsidy policy;
three-level indicators of economic risk: the price condition of imported iron ore and the price relationship between the iron ore and steel products, and the indexes are described as follows: the current imported iron ore price and the international agreement change rate of the iron ore and the current relationship and the change trend of the iron ore and the steel price;
three-level indexes of supply and demand relations: the steel supply and demand balance relation, index description: the relation between the capacity and the demand of the current steel industry;
secondary indexes of the comprehensive capacity risk of the core enterprise: profitability, growth potential, capital turnover, debt repayment;
three-level indexes of profitability: the profit rate of business income and the index description are as follows: the profit rate of the operating income is the total profit/net operating income;
tertiary indicators of growth potential: the business profit growth rate and the index description are as follows: the business profit growth rate is the business profit growth amount of the current year/the total business profit of the last year;
third-level index of capital turnover: total asset turnover rate, index description: total asset turnover rate ═ sales revenue/[ (initial asset total + end asset total)/2 ];
three-level indexes of repayment capacity: the rate of assets and liabilities, and the index description: the rate of assets liability is total amount of liability/total amount of assets;
secondary indicators of supply chain partner risk of collaboration: partner integrity, partner service level, and partner comprehensive strength;
three-level indexes of partner honesty degree: contract performance rate, index description: contract performance rate is the number of performance times/number of cooperation times;
three-level indicators of partner service level: year of cooperation, index description: year of collaboration with core enterprises;
three-level indexes of the comprehensive strength of the partner: the size of the contract quantity and the index description are as follows: monthly contract amount;
secondary indicators of financing asset condition risk: ability to reveal pledges;
three-level indexes of the expression ability of the pledge: physical characteristics of the pledge, market price stability and evaluation price of the pledge, and index description: whether the quality is easy to be damaged, the average price reduction degree of the market in the service period and whether the evaluation price is deviated from the market price;
secondary indexes of risk of cloud warehouse service management: storage capacity, cloud storage coordination capacity, informatization and intellectualization level;
three-level index of storage capacity: warehousing operation capacity, index description: the intelligent level of warehousing;
three-level indexes of cloud storage synergy: information/organization coordination ability, index description: information coordination capability;
three-level indexes of informatization and intellectualization levels: the capability and the information transmission efficiency of the Internet of things are described in indexes: the construction and application capability of the management platform of the internet of things of the enterprise and the transmission efficiency of the upstream and downstream information systems are good, namely the convenience and accuracy of interface transmission.
Further, the step S3 is specifically to calculate the maximum eigenvalue λ of the discrimination matrix by constructing the discrimination matrix, comparing the importance degrees of the two factors, assuming that the discrimination matrix has n indexesmaxThereby calculating a compatibility index CI ═ λmax-n)/(n-1); different n correspond to different randomness index values IR, when n is<When 3, IR is 0; when n is 3, IR is 0.58; when n is 4, IR is 0.90; when n is 5, IR is 1.12; when n is 6, IR is 1.24; then, a consistency index is calculated, if the consistency index CR is equal to CI/IR<0.1, the consistency is passed, otherwise, a discrimination matrix needs to be reconstructed; after consistency check, normalizing the discrimination matrix, wherein the eigenvector corresponding to the maximum eigenvalue is the weight of the index of the layer; and finally, combining the absolute weight coefficients of all the layers to obtain a final weight set W of all the indexes.
Further, S5 is specifically set to (V ═ V)1,V2,V3,V4,V5)TCorresponding Risk assessmentThe price grades are very high, medium, low, very low; the expert can give V according to the real situation of each index of the financing enterprise1Is divided into V5Scoring to indicate the financing risk.
Further, the step S6 is to invite m experts to score the risk possibly brought by the n indexes, and set dijkFor the k-th expert's risk score of the secondary index j in the primary index i, where k is 1,2, …, m, an evaluation matrix D (n, m) is obtained:
further, in S7, if there are 5 evaluation levels, the method corresponds to gray class e, where e is 1,2, 3, 4, 5; establishing a whitening weight function f;
when the gray class number e is evaluated as 1 and the gray range is phi epsilon [0,9),
when the gray class number e is evaluated as 2 and the gray range is Φ ∈ [0,7, 14),
when the gray class number e is evaluated as 3 and the gray range is phi epsilon [0,5, 10),
when the gray class number e is evaluated as 4 and the gray range is phi epsilon [0,3, 6),
when the gray class number e is evaluated as 5 and the gray range is phi epsilon [0,1, 2),
further, the specific calculation method of S8 is as follows:
primary indexes A, B, C, D and E; secondary indexes A1, A2, A3, B1, B2 … …
Evaluation coefficient of gray color under various gray scales
A2 substitution for A according to the above formula11Is A21，A12Is A22，A13Is A23，A14Is A24，A15Is A25The other secondary indexes such as A3, B1 and the like are correspondingly replaced as above;
grey evaluation weight vector under different grey classes:
each secondary index gray evaluation weight vector r: r isA1＝(rA11，rA12，rA13，rA14，rA15)；rA2＝(rA21，rA22，rA23，rA24，rA25)；rA3＝(rA31，rA32，rA33，rA24，rA35)；rB1＝(rB11，rB12，rB13，rB14，rB15) (ii) a Making corresponding replacement for other secondary indexes;
grey evaluation weight matrix R of each first-level indexA、RB、RC、RD、RE：
Further, the above-mentioned S9 is specifically a combined evaluation value of each secondary index under the primary index A, B, C, D, E, which is a ═ aWA×RA、B＝WB×RB、C＝WC×RC、D＝WD×RD、E＝WE×RE。
Further, the S10 calculates a gray comprehensive evaluation value of each primary index, where a gray comprehensive evaluation matrix of the primary index is Z ═ W × R, where R ═ A, B, C, D, E]T。
Further, the S11 specifically includes: and the comprehensive credit evaluation value Q is Z multiplied by V, wherein Z is a primary index gray comprehensive evaluation matrix, V is a fuzzy comment set, and the larger the Q value obtained by final calculation is, the smaller the financing risk of the representative enterprise is.
The invention has the beneficial effects that: the supply chain financial financing risk of the steel trade enterprise is analyzed, and a reference is provided for solving the supply chain financial financing risk assessment problem in the steel industry. The risk assessment method combines the analysis of specific data of each index of a steel trade enterprise and combines the expert risk assessment and the fuzzy grey comprehensive assessment method, so that the risk assessment result is more scientific and credible. In the risk assessment method, assessment experts can perform quantitative scoring by comprehensively considering risks possibly brought by qualitative indexes, so that conversion from qualitative to quantitative is realized, and the problem of risk assessment of indexes which are difficult to quantitatively measure is effectively solved.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples of the invention.
A steel trade supply chain financial risk assessment method based on fuzzy grey evaluation comprises the following steps:
s1: performing preliminary credit review on the enterprise; the step S1 is specifically that when the financial platform of the steel trade supply chain receives an enterprise financing application, a preliminary credit check is first performed on the enterprise to determine whether the average contract performance rate of the enterprise meets the standard, and if not, the financing application of the enterprise is rejected; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract times.
S2: identifying a risk source, and establishing a risk assessment index set comprising a primary index and a secondary index; the step S2 is specifically that the financing risk source has external environment risk, core enterprise comprehensive capacity risk, supply chain partner cooperation risk, financing item asset condition risk and cloud warehouse service management risk; the following risk evaluation index sets are established by combining the characteristics of the steel industry:
first-level indexes: external environment risks, core enterprise integrated capacity risks, supply chain partner cooperation risks, financing asset condition risks, and cloud warehouse service management risks;
secondary indicators of external environmental risk: policy risk, economic risk, supply and demand relationship;
three-level indicators of policy risk: policy support strength, index description: talent policy, subsidy policy;
three-level indicators of economic risk: the price condition of imported iron ore and the price relationship between the iron ore and steel products, and the indexes are described as follows: the current imported iron ore price and the international agreement change rate of the iron ore and the current relationship and the change trend of the iron ore and the steel price;
three-level indexes of supply and demand relations: the steel supply and demand balance relation, index description: the relation between the capacity and the demand of the current steel industry;
secondary indexes of the comprehensive capacity risk of the core enterprise: profitability, growth potential, capital turnover, debt repayment;
three-level indexes of profitability: the profit rate of business income and the index description are as follows: the profit rate of the operating income is the total profit/net operating income;
tertiary indicators of growth potential: the business profit growth rate and the index description are as follows: the business profit growth rate is the business profit growth amount of the current year/the total business profit of the last year;
third-level index of capital turnover: total asset turnover rate, index description: total asset turnover rate ═ sales revenue/[ (initial asset total + end asset total)/2 ];
three-level indexes of repayment capacity: the rate of assets and liabilities, and the index description: the rate of assets liability is total amount of liability/total amount of assets;
secondary indicators of supply chain partner risk of collaboration: partner integrity, partner service level, and partner comprehensive strength;
three-level indexes of partner honesty degree: contract performance rate, index description: contract performance rate is the number of performance times/number of cooperation times;
three-level indicators of partner service level: year of cooperation, index description: year of collaboration with core enterprises;
three-level indexes of the comprehensive strength of the partner: the size of the contract quantity and the index description are as follows: monthly contract amount;
secondary indicators of financing asset condition risk: ability to reveal pledges;
three-level indexes of the expression ability of the pledge: physical characteristics of the pledge, market price stability and evaluation price of the pledge, and index description: whether the quality is easy to be damaged, the average price reduction degree of the market in the service period and whether the evaluation price is deviated from the market price;
secondary indexes of risk of cloud warehouse service management: storage capacity, cloud storage coordination capacity, informatization and intellectualization level;
three-level index of storage capacity: warehousing operation capacity, index description: the intelligent level of warehousing;
three-level indexes of cloud storage synergy: information/organization coordination ability, index description: information coordination capability;
three-level indexes of informatization and intellectualization levels: the capability and the information transmission efficiency of the Internet of things are described in indexes: the construction and application capability of the management platform of the internet of things of the enterprise and the transmission efficiency of the upstream and downstream information systems are good, namely the convenience and accuracy of interface transmission.
S3: determining each index weight by an analytic hierarchy process; s3 is that the maximum eigenvalue lambda of the discrimination matrix is calculated by constructing the discrimination matrix and comparing the importance degree of the two factors, assuming that the discrimination matrix has n indexesmaxThereby calculating a compatibility index CI ═ λmax-n)/(n-1); different n correspond to different randomness index values IR, when n is<When 3, IR is 0; when n is 3When, IR is 0.58; when n is 4, IR is 0.90; when n is 5, IR is 1.12; when n is 6, IR is 1.24; then, a consistency index is calculated, if the consistency index CR is equal to CI/IR<0.1, the consistency is passed, otherwise, a discrimination matrix needs to be reconstructed; after consistency check, normalizing the discrimination matrix, wherein the eigenvector corresponding to the maximum eigenvalue is the weight of the index of the layer; and finally, combining the absolute weight coefficients of all the layers to obtain a final weight set W of all the indexes.
S4: acquiring all data required by risk assessment, and reading and collecting data required by each index judgment by combining the setting of financing risk index judgment in S2;
s5: determining a fuzzy comment set V, and setting an evaluation standard and an evaluation grade for a risk assessment expert; s5 is specifically defined as V ═ V (V)1,V2,V3,V4,V5)TThe corresponding risk assessment grades are very high, medium, low, very low; the expert can give V according to the real situation of each index of the financing enterprise1Is divided into V5Scoring to indicate the financing risk.
S6: establishing a sample evaluation matrix D; s6 is specifically to invite m experts to score the risk possibly brought by n indexes, and set dijkFor the k-th expert's risk score of the secondary index j in the primary index i, where k is 1,2, …, m, an evaluation matrix D (n, m) is obtained:
s7: determining evaluation gray classes and establishing a whitening weight function; specifically, in S7, assuming that there are 5 evaluation levels, a whitening weight function f is established corresponding to the gray class e (e is 1,2, 3, 4, 5);
when the gray class number e is evaluated as 1 and the gray range is phi epsilon [0,9),
when the evaluation gray series number e is 2, the gray range isφ ∈ [0,7, 14)),
when the gray class number e is evaluated as 3 and the gray range is phi epsilon [0,5, 10),
when the gray class number e is evaluated as 4 and the gray range is phi epsilon [0,3, 6),
when the gray class number e is evaluated as 5 and the gray range is phi epsilon [0,1, 2),
s8: determining a grey evaluation weight matrix of each primary index; the specific calculation method of S8 is as follows:
primary indexes A, B, C, D and E; secondary indexes A1, A2, A3, B1, B2 … …
Evaluation coefficient of gray color under various gray scales
A2 substitution for A according to the above formula11Is A21，A12Is A22，A13Is A23，A14Is A24，A15Is A25The other secondary indexes such as A3, B1 and the like are correspondingly replaced as above;
grey evaluation weight vector under different grey classes:
each secondary index gray evaluation weight vector r: r isA1＝(rA11，rA12，rA13，rA14，rA15)；rA2＝(rA21，rA22，rA23，rA24，rA25)；rA3＝(rA31，rA32，rA33，rA24，rA35)；rB1＝(rB11，rB12，rB13，rB14，rB15) (ii) a Making corresponding replacement for other secondary indexes;
grey evaluation weight matrix R of each first-level indexA、RB、RC、RD、RE：
S9: calculating a comprehensive gray evaluation value of each secondary index; the S9 is a comprehensive evaluation value of each secondary index under the primary index A, B, C, D, E, where a is WA×RA、B＝WB×RB、C＝WC×RC、D＝WD×RD、E＝WE×RE；
S10: calculating a comprehensive gray evaluation value of each level of index; and S10, calculating the comprehensive gray evaluation value of each primary index, wherein the comprehensive gray evaluation matrix of the primary index is Z ═ WxR, and R ═ A, B, C, D, E]T；
S11: calculating the comprehensive credit evaluation value of the enterprise; the S11 specifically includes: and the comprehensive credit evaluation value Q is Z multiplied by V, wherein Z is a primary index gray comprehensive evaluation matrix, V is a fuzzy comment set, and the larger the Q value obtained by final calculation is, the smaller the financing risk of the representative enterprise is.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
S1 enterprise preliminary credit review. When a steel supply chain financial platform receives a financing application of a financing enterprise P, judging whether the average contract performance rate i of the enterprise is larger than 60% or not, if not, rejecting the financing application of the enterprise; and if so, performing risk assessment on the enterprise.
And S2 identifying the risk source, and establishing a steel enterprise risk assessment index set. Indicators for the risk assessment of steel type as described in table 1:
TABLE 1 evaluation index
The S3 analytic hierarchy process determines the weights of each index. In the listed index set, the first level index is the first layer of the hierarchical analysis, and the second and third level indexes are the second and third layers respectively; establishing a judgment matrix by comparing the importance degrees of the two factors; if the discrimination matrix has n indexes, calculating the maximum eigenvalue lambda of the discrimination matrixmaxThereby calculating a compatibility index CI ═ λmax-n)/(n-1); different n correspond to different randomness index values IR, when n is<When 3, IR is 0; when n is 3, IR is 0.58; when n is 4, IR is 0.90; when n is 5, IR is 1.12; when n is 6, IR is 1.24; then, a consistency index is calculated, if the consistency index CR is equal to CI/IR<0.1, the consistency is passed, otherwise, a discrimination matrix needs to be reconstructed; after consistency check, normalizing the discrimination matrix, wherein the eigenvector corresponding to the maximum eigenvalue is the weight of the index of the layer; and finally, combining the absolute weight coefficients of all the layers to obtain a final weight set W of all the indexes.
S4 obtains all data needed for risk assessment. Reading data required by the evaluation of each index by combining the setting of the evaluation of the financing risk index of the steel trade enterprise; and if the data is missing, acquiring the data and completing the required data.
S5 determines a fuzzy comment set V and sets evaluation criteria for the risk assessment expert. If V is equal to (V)1,V2,V3,V4,V5)T＝(9,7,5,3,1)TThe corresponding risk assessment ratings are very high, medium, low, very low.The expert can give a score of 1 to 9 points according to the real situation of each index of the financing enterprise so as to represent the height of the financing risk.
S6 builds a sample evaluation matrix D. 5 experts are invited to score the risks brought by the 14 indexes of the P enterprise to obtain an evaluation matrix D with 14 rows and 5 columns. Let dijkThe risk score of the kth expert on the secondary index j in the primary index i comprises the following steps:
s7 determines the evaluation gray class and establishes a whitening weight function. The whitening weight function is established in order to determine to which risk level the respective indicator belongs. Since there are 5 ratings (very high, medium, low, very low), there are corresponding grey classes e (e ═ 1,2, 3, 4, 5).
When the gray class number e is evaluated as 1 and the gray range is phi epsilon [0,9),
when the gray class number e is evaluated as 2 and the gray range is Φ ∈ [0,7, 14),
when the gray class number e is evaluated as 3 and the gray range is phi epsilon [0,5, 10),
when the gray class number e is evaluated as 4 and the gray range is phi epsilon [0,3, 6),
when the gray class number e is evaluated as 5 and the gray range is phi epsilon [0,1, 2),
s8 affirmationDetermining a gray evaluation weight matrix R of each primary indexA、RB、RC、RD、RE. The specific calculation method is shown in table 2:
TABLE 2 detailed technical Process
According to the above formula, it can be obtained:
s9, calculating the gray comprehensive evaluation value of each secondary index. The comprehensive evaluation value of each secondary index under the primary index is as follows:
E＝WE×RE
＝0.062794×[0.31 0.3744 0.2874 0.0282 0]
＝[0.0195 0.0235 0.0180 0.0018 0]
s10 calculates a gray overall evaluation value Z ═ W × R for each primary index, where R ═ a B C D E]T：
And S11, obtaining P enterprise comprehensive credit evaluation value. The comprehensive credit evaluation value is the product of the gray comprehensive evaluation value Z and the fuzzy comment set V, and the larger the calculation result Q is, the smaller the comprehensive risk of enterprise financing is. The calculation result of the P enterprise comprehensive evaluation value Q in this embodiment is:
Q＝Z×V＝(0.1103 0.1154 0.076 0.0027 0)×(9 7 5 3 1)T＝2.1889
and generating the enterprise financing risk report according to the evaluation value of each index and the enterprise comprehensive risk score.
The invention has the beneficial effects that: the risk assessment method combines the analysis of specific data of each index of the steel trade enterprise and combines the expert risk assessment with the fuzzy grey comprehensive evaluation method, so that the risk assessment result is more scientific and credible. In the risk assessment method, assessment experts can perform quantitative scoring by comprehensively considering risks possibly brought by qualitative indexes, so that conversion from qualitative to quantitative is realized, and the problem of risk assessment of indexes which are difficult to quantitatively measure is effectively solved.
Claims (11)
1. A steel trade supply chain financial risk assessment method based on fuzzy grey evaluation comprises the following steps:
s1: performing preliminary credit review on the enterprise;
s2: identifying a risk source, and establishing a risk assessment index set comprising a primary index and a secondary index;
s3: determining each index weight by an analytic hierarchy process;
s4: acquiring all data required by risk assessment, and reading and collecting data required by each index judgment by combining the setting of financing risk index judgment in S2;
s5: determining a fuzzy comment set V, and setting an evaluation standard and an evaluation grade for a risk assessment expert;
s6: establishing a sample evaluation matrix D;
s7: determining evaluation gray classes and establishing a whitening weight function;
s8: determining a grey evaluation weight matrix of each primary index;
s9: calculating a comprehensive gray evaluation value of each secondary index;
s10: calculating a comprehensive gray evaluation value of each level of index;
s11 calculates the enterprise composite credit rating value.
2. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the step S1 is specifically that when the financial platform of the steel trade supply chain receives an enterprise financing application, a preliminary credit check is first performed on the enterprise to determine whether the average contract performance rate of the enterprise meets the standard, and if not, the financing application of the enterprise is rejected; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract times.
3. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the S2 establishes a risk assessment index set:
first-level indexes: external environment risks, core enterprise integrated capacity risks, supply chain partner cooperation risks, financing asset condition risks, and cloud warehouse service management risks;
secondary indicators of external environmental risk: policy risk, economic risk, supply and demand relationship;
three-level indicators of policy risk: policy support strength, index description: talent policy, subsidy policy;
three-level indicators of economic risk: the price condition of imported iron ore and the price relationship between the iron ore and steel products, and the indexes are described as follows: the current imported iron ore price and the international agreement change rate of the iron ore and the current relationship and the change trend of the iron ore and the steel price;
three-level indexes of supply and demand relations: the steel supply and demand balance relation, index description: the relation between the capacity and the demand of the current steel industry;
secondary indexes of the comprehensive capacity risk of the core enterprise: profitability, growth potential, capital turnover, debt repayment;
three-level indexes of profitability: the profit rate of business income and the index description are as follows: the profit rate of the operating income is the total profit/net operating income;
tertiary indicators of growth potential: the business profit growth rate and the index description are as follows: the business profit growth rate is the business profit growth amount of the current year/the total business profit of the last year;
third-level index of capital turnover: total asset turnover rate, index description: total asset turnover rate ═ sales revenue/[ (initial asset total + end asset total)/2 ];
three-level indexes of repayment capacity: the rate of assets and liabilities, and the index description: the rate of assets liability is total amount of liability/total amount of assets;
secondary indicators of supply chain partner risk of collaboration: partner integrity, partner service level, and partner comprehensive strength;
three-level indexes of partner honesty degree: contract performance rate, index description: contract performance rate is the number of performance times/number of cooperation times;
three-level indicators of partner service level: year of cooperation, index description: year of collaboration with core enterprises;
three-level indexes of the comprehensive strength of the partner: the size of the contract quantity and the index description are as follows: monthly contract amount;
secondary indicators of financing asset condition risk: ability to reveal pledges;
three-level indexes of the expression ability of the pledge: physical characteristics of the pledge, market price stability and evaluation price of the pledge, and index description: whether the quality is easy to be damaged, the average price reduction degree of the market in the service period and whether the evaluation price is deviated from the market price;
secondary indexes of risk of cloud warehouse service management: storage capacity, cloud storage coordination capacity, informatization and intellectualization level;
three-level index of storage capacity: warehousing operation capacity, index description: the intelligent level of warehousing;
three-level indexes of cloud storage synergy: information/organization coordination ability, index description: information coordination capability;
three-level indexes of informatization and intellectualization levels: the capability and the information transmission efficiency of the Internet of things are described in indexes: the construction and application capability of the management platform of the internet of things of the enterprise and the transmission efficiency of the upstream and downstream information systems are good, namely the convenience and accuracy of interface transmission.
4. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the step S3 is specifically to construct a judgment matrix; if the discrimination matrix has n indexes, calculating the maximum eigenvalue lambda of the discrimination matrixmaxThereby calculating a compatibility index CI ═ λmax-n)/(n-1); different n correspond to different randomness index values IR, when n is<When 3, IR is 0; when n is 3, IR is 0.58; when n is 4, IR is 0.90; when n is 5, IR is 1.12; when n is 6, IR is 1.24; then, a consistency index is calculated, if the consistency index CR is equal to CI/IR<0.1, the consistency is passed, otherwise, a discrimination matrix needs to be reconstructed; after consistency check, normalizing the discrimination matrix, wherein the eigenvector corresponding to the maximum eigenvalue is the weight of the index of the layer; and finally, combining the absolute weight coefficients of all the layers to obtain a final weight set W of all the indexes.
5. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: s5 is specifically defined as V ═ V (V)1,V2,V3,V4,V5)TThe corresponding risk assessment grades are very high, medium, low, very low;the expert can give V according to the real situation of each index of the financing enterprise1Is divided into V5Scoring to indicate the financing risk.
6. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: s6 is specifically to invite m experts to score the risk possibly brought by n indexes, and set dijkFor the k-th expert's risk score of the secondary index j in the primary index i, where k is 1,2, …, m, an evaluation matrix D (n, m) is obtained:
7. the steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: s7 specifically refers to gray class e, where e is 1,2, 3, 4, 5, assuming that there are 5 evaluation levels; establishing a whitening weight function f;
when the gray class number e is evaluated as 1 and the gray range is phi epsilon [0,9),
when the gray class number e is evaluated as 2 and the gray range is Φ ∈ [0,7, 14),
when the gray class number e is evaluated as 3 and the gray range is phi epsilon [0,5, 10),
when the gray class number e is evaluated as 4 and the gray range is phi epsilon [0,3, 6),
when the gray class number e is evaluated as 5 and the gray range is phi epsilon [0,1, 2),
8. the steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the specific calculation method of S8 is as follows:
primary indexes A, B, C, D and E; secondary indexes A1, A2, A3, B1, B2 … …
Evaluation coefficient of gray color under various gray scales
A2 substitution for A according to the above formula11Is A21，A12Is A22，A13Is A23，A14Is A24，A15Is A25The other secondary indexes such as A3, B1 and the like are correspondingly replaced as above;
grey evaluation weight vector under different grey classes:
corresponding replacement is carried out on other secondary indexes such as A2, A3, B1 and the like;
each secondary index gray evaluation weight vector r: r isA1＝(rA11，rA12，rA13，rA14，rA15)；rA2＝(rA21，rA22，rA23，rA24，rA25)；rA3＝(rA31，rA32，rA33，rA24，rA35)；rB1＝(rB11，rB12，rB13，rB14，rB15) (ii) a Making corresponding replacement for other secondary indexes;
grey evaluation weight matrix R of each first-level indexA、RB、RC、RD、RE：
9. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the S9 is a comprehensive evaluation value of each secondary index under the primary index A, B, C, D, E, where a is WA×RA、B＝WB×RB、C＝WC×RC、D＝WD×RD、E＝WE×RE。
10. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: and S10, calculating the comprehensive gray evaluation value of each primary index, wherein the comprehensive gray evaluation matrix of the primary index is Z ═ WxR, and R ═ A, B, C, D, E]T。
11. The steel trade supply chain financial risk assessment method based on fuzzy grey evaluation according to claim 1, characterized by: the S11 specifically includes: and the comprehensive credit evaluation value Q is Z multiplied by V, wherein Z is a primary index gray comprehensive evaluation matrix, V is a fuzzy comment set, and the larger the Q value obtained by final calculation is, the smaller the financing risk of the representative enterprise is.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115205044A (en) * | 2022-07-29 | 2022-10-18 | 山东浪潮爱购云链信息科技有限公司 | Supply chain financial risk assessment method, equipment and medium |
| CN115660774A (en) * | 2022-10-14 | 2023-01-31 | 国网山东省电力公司物资公司 | Material supply chain system credit evaluation method based on block chain |
| CN116777222A (en) * | 2023-07-06 | 2023-09-19 | 北京中知智慧科技有限公司 | Enterprise risk prevention dimension review system |
| CN118628114A (en) * | 2024-08-13 | 2024-09-10 | 四川丝路易购科技有限公司 | Supply chain financial risk assessment method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778533A (en) * | 2015-03-18 | 2015-07-15 | 刘思峰 | Grey clustering evaluation method based on center point triangle whiten weight function |
| CN105243255A (en) * | 2015-08-11 | 2016-01-13 | 北华航天工业学院 | Evaluation method for soft foundation treatment scheme |
| CN105389646A (en) * | 2015-10-15 | 2016-03-09 | 大连海事大学 | Evaluation method based on variable-weight clustering method of gray whitening function |
| CN110363660A (en) * | 2019-07-18 | 2019-10-22 | 河南金明源信息技术有限公司 | A kind of science and technology medium-sized and small enterprises financing structure capability assessment method |
| JP2021068435A (en) * | 2019-10-17 | 2021-04-30 | 株式会社日立製作所 | Supplier evaluation device and supplier evaluation method |
-
2021
- 2021-09-14 CN CN202111072859.XA patent/CN113763154A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778533A (en) * | 2015-03-18 | 2015-07-15 | 刘思峰 | Grey clustering evaluation method based on center point triangle whiten weight function |
| CN105243255A (en) * | 2015-08-11 | 2016-01-13 | 北华航天工业学院 | Evaluation method for soft foundation treatment scheme |
| CN105389646A (en) * | 2015-10-15 | 2016-03-09 | 大连海事大学 | Evaluation method based on variable-weight clustering method of gray whitening function |
| CN110363660A (en) * | 2019-07-18 | 2019-10-22 | 河南金明源信息技术有限公司 | A kind of science and technology medium-sized and small enterprises financing structure capability assessment method |
| JP2021068435A (en) * | 2019-10-17 | 2021-04-30 | 株式会社日立製作所 | Supplier evaluation device and supplier evaluation method |
Non-Patent Citations (12)
| Title |
|---|
| "商业银行供应链金融信用风险评价－－基于保苑仓模式的实证研究", 《中国优秀硕士论文全文数据库》 * |
| 卫伟: "基于供应链风险的我国大宗战略物资进口贸易安全", 《中国优秀硕士论文全文数据库》 * |
| 周校培;陈建明;: "基于多层次灰色评价模型的建筑供应链风险评估及管理研究", 工程管理学报, no. 02 * |
| 张毕西;陈嘉;: "基于灰色多层次评价模型的供应链风险评估", 科技与管理, no. 05 * |
| 张芳芳: "商业银行供应链金融信用风险研究", 《中国优秀硕士论文全文数据库》 * |
| 樊新民: "金融供应链违约风险问题研究", 《中国优秀硕士学位论文全文数据库 经济与管理科学》, pages 45 - 76 * |
| 王润东: "层次分析法在农产品供应链金融风险评价的应用研究", 《物流科技》 * |
| 王红春等: "基于多层次模糊评价法的装配式建筑供应链风险评价研究", 《北京建筑大学学报》 * |
| 葛莹: "考虑风险因素的SMD公司钢贸业务供应商选择与评价", 《中国优秀硕士论文全文数据库》 * |
| 解磊: "银行供应链金融业务信用风险研究", 《中国优秀硕士论文全文数据库》 * |
| 陈欢云: "中小企业供应链融资模式及风险防控研究", 《中国优秀硕士学位论文全文数据库 经济与管理科学》, pages 39 - 69 * |
| 雷岩: "商业银行线上供应链金融信用风险评价研究", 《中国优秀硕士论文全文数据库》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115205044A (en) * | 2022-07-29 | 2022-10-18 | 山东浪潮爱购云链信息科技有限公司 | Supply chain financial risk assessment method, equipment and medium |
| CN115205044B (en) * | 2022-07-29 | 2024-02-13 | 山东浪潮爱购云链信息科技有限公司 | Method, equipment and medium for evaluating financial risk of supply chain |
| CN115660774A (en) * | 2022-10-14 | 2023-01-31 | 国网山东省电力公司物资公司 | Material supply chain system credit evaluation method based on block chain |
| CN115660774B (en) * | 2022-10-14 | 2023-09-19 | 国网山东省电力公司物资公司 | Block chain-based material supply chain system credit evaluation method |
| CN116777222A (en) * | 2023-07-06 | 2023-09-19 | 北京中知智慧科技有限公司 | Enterprise risk prevention dimension review system |
| CN116777222B (en) * | 2023-07-06 | 2024-04-02 | 北京中知智慧科技有限公司 | Enterprise risk prevention dimension review system |
| CN118628114A (en) * | 2024-08-13 | 2024-09-10 | 四川丝路易购科技有限公司 | Supply chain financial risk assessment method and system |
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