CN113989050A - Supply chain financial risk assessment method for steel trade enterprise based on TOPSIS (technique for order preference by similarity to Ideal solution) comprehensive analysis - Google Patents

Supply chain financial risk assessment method for steel trade enterprise based on TOPSIS (technique for order preference by similarity to Ideal solution) comprehensive analysis Download PDF

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CN113989050A
CN113989050A CN202111252967.5A CN202111252967A CN113989050A CN 113989050 A CN113989050 A CN 113989050A CN 202111252967 A CN202111252967 A CN 202111252967A CN 113989050 A CN113989050 A CN 113989050A
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enterprise
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夏玉雄
王榕
徐哲壮
黎立璋
陈伯瑜
郭凌欢
张庆东
蔡东洲
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Fujian Huading Zhizao Technology Co ltd
Fujian Sangang Minguang Co Ltd
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Fujian Sangang Minguang Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

The invention relates to a supply chain financial risk assessment method for steel trade enterprises based on TOPSIS comprehensive analysis. The method comprises the following steps: 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 data required by risk assessment, and completing all data if the data is missing; establishing an initial matrix and a weighted standardization matrix based on the index set and the data set; calculating positive and negative ideal solutions of each column of index data in the matrix and calculating the distance from the index data corresponding to each enterprise to the positive and negative ideal solutions; and calculating the relative closeness of each enterprise to finally obtain the risk evaluation value of the enterprise and the rank of the risk level. The invention meets the individual requirements of financing risk assessment of steel trade enterprises, and can generate risk assessment ranks of the enterprises, so that financial institutions can more comprehensively understand the financing enterprises. The invention is beneficial to avoiding the risk loss of financial institutions and promotes the financing of steel trade enterprises with the characteristic of slow capital operation.

Description

Supply chain financial risk assessment method for steel trade enterprise based on TOPSIS (technique for order preference by similarity to Ideal solution) comprehensive analysis
Technical Field
The invention relates to financing risk assessment of steel trade enterprises based on steel warehousing and e-commerce transaction platforms, in particular to a supply chain financial risk assessment method of the steel trade enterprises based on TOPSIS comprehensive analysis.
Background
In recent years, steel trade enterprises in China start to transform to carry out supply chain management, because steel products belong to the category of bulk commodities and have higher circulation cost and unit price, and the 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, the price fluctuation is large, particularly, iron ore occupying 80% of the cost mainly depends on import, and the profit of the steel industry can be eroded when the price of the imported ore is suddenly and greatly increased, so that the survival of the steel industry is difficult; or when the requirements of the steel main application market capital construction and real estate and building industries are suddenly and greatly reduced, the capacity is excessive, and the steel price is greatly dropped. 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.
The Chinese patent application numbers are: CN112200486A, name: a method of supply chain financial risk control. It discloses a supply chain financial risk control method, comprising the following steps: acquiring supply chain risk data in advance, calibrating an early warning range in a risk scene based on a quantitative risk model of big data analysis, and determining a supply chain fact and prediction risk index set; and inputting the risk data as information, and establishing a risk model as an evaluation model based on the acquired risk data. The risk modeling method lays a foundation for risk modeling, but the risk modeling method does not combine with specific industries for modeling and analysis, and has a lack of consideration for problems which may occur in a specific implementation process.
The Chinese patent application numbers are: CN110097252A, name: a risk identification system for supply chain finance and a method thereof. The risk identification system and the method thereof for supply chain finance are disclosed, wherein a user only needs to input an enterprise identification symbol of an evaluation risk object, and make evaluation on the overall risk of a supply chain where a target enterprise is located, the self risk of the target enterprise and the associated risk of the target enterprise by comprehensively acquiring related data of the target enterprise and the supply chain where the target enterprise is located and by utilizing a machine learning algorithm and a model, so that the operation capacity, the tax risk and the repayment risk of the target enterprise are comprehensively evaluated, and a risk evaluation result can be quickly returned to the user.
Steel enterprises with strong comprehensive strength can rely on self dominant status and good credit, build a steel trade supply chain financial platform on the basis of building a steel storage and e-commerce transaction platform, cooperate with financial institutions, and provide financing service for medium and small steel trade enterprises. Meanwhile, when the platform obtains a financing request, the authenticity of the background and the contract of the financing enterprise can be verified, and a risk report is generated and sent to the financial institution according to the big data information in the platform and the evaluation of a risk assessment expert, so that the financial institution is helped to reduce the potential financial risk and avoid the financial risk loss.
Disclosure of Invention
The invention aims to provide a supply chain financial risk assessment method for steel trade enterprises based on TOPSIS comprehensive analysis.
In order to achieve the purpose, the technical scheme of the invention is as follows: a supply chain financial risk assessment method for steel trade enterprises based on TOPSIS comprehensive analysis comprises the following steps:
(1) and (3) enterprise preliminary credit auditing: when a financial platform of a steel trade supply chain receives a financing application of a financing enterprise, firstly, performing primary credit check on the enterprise, judging whether the average contract performance rate of the enterprise meets the standard or not, and if not, rejecting the financing application of the enterprise; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract subscription times;
(2) identifying a risk source, establishing a risk assessment index set: the financing risk index of the steel trade enterprise can be divided into an external environment risk index, a core enterprise comprehensive credit index, a supply chain partner operation risk index, a financing asset condition risk index and a cloud warehouse service management risk index; the external environment risk indexes comprise policy support force, price change conditions of imported iron ores, the current iron ore and steel price relationship and the steel supply and demand balance relationship; the core enterprise comprehensive credit risk indexes comprise profitability, debt paying capacity, growth potential and operation and management capacity; the supply chain partner operation risk indexes comprise partner integrity degree, partner comprehensive strength and partner service level; the risk indexes of the financing asset condition comprise the evaluation price of the pledge steel and the stability of the market price; the cloud warehouse service management risk indexes comprise warehousing capacity, cloud warehouse coordination capacity and informatization and intelligentization levels;
(3) analytic hierarchy processDetermining the weight of each index: by constructing a judgment matrix and comparing the importance degrees of two factors, assuming that the judgment matrix has n indexes, calculating the maximum eigenvalue lambda of the judgment 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; finally, combining the absolute weight coefficients of all layers to obtain a final weight set W of all indexes;
(4) acquiring all data required by risk assessment of steel trade enterprises: if the data is missing, data acquisition is carried out, and the required data is supplemented;
(5) constructing an evaluation matrix: constructing an initial matrix A (k, m) according to the data of m indexes of k steel trade enterprises; dividing the indexes into cost indexes, benefit indexes, intermediate indexes and interval indexes according to the index attributes, and normalizing the matrix A into A':
Figure BDA0003322908440000031
wherein:
firstly, cost-type indexes are as follows: the index value is the best minimum;
Figure BDA0003322908440000032
II, benefit type index: the larger the index value, the better;
Figure BDA0003322908440000033
third, intermediate type index: the index is best at an intermediate M value;
a'ij＝M/(M+|aij-M|)
interval type indexes: the index is optimal in the interval [ a, b ], the lowest limit is lb, and the lowest limit is ub;
Figure BDA0003322908440000034
(6) constructing a weighted standardization matrix Z ═ A' W, and judging positive and negative ideal solutions Z +, Z-; the positive ideal solution Z + is the maximum value in the benefit type index set J and the minimum value in the cost type index set J'; the negative ideal solution Z-refers to the minimum value in the benefit type index set J and the maximum value in the cost type index set J', that is:
Figure BDA0003322908440000035
(7) calculating the distance S from each enterprise to the positive and negative ideal pointsi+、Si-；
Figure BDA0003322908440000036
(8) Calculating the relative closeness degree C of each enterprise to obtain the comprehensive risk score of the enterprise; wherein
Figure BDA0003322908440000037
The smaller the Ci, the greater the risk; and finally, ranking the risks of each enterprise according to the risk scores.
Compared with the prior art, the invention has the following beneficial effects:
(1) the risk coefficient of enterprise financing can be directly calculated according to the risk assessment related data of the steel trade enterprise, expert scoring is not needed after the weight of each index is determined, and the risk assessment method is more efficient under the background of rapid change of enterprise information data.
(2) The method and the system fully combine the characteristics of steel trade enterprises and supply chain finance, can generate risk indexes and corresponding risk ranks of all the steel trade enterprises, can realize longitudinal comparison of risks in the steel industry, and can also realize transverse comparison of financing risks among the steel trade enterprises.
Drawings
Fig. 1 is a flow chart of the steel trade enterprise supply chain financial risk assessment of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
As shown in fig. 1, the invention relates to a TOPSIS comprehensive analysis-based steel trade enterprise supply chain financial risk assessment method, which comprises the following steps:
(1) and (3) enterprise preliminary credit auditing: when a financial platform of a steel trade supply chain receives a financing application of a financing enterprise, firstly, performing primary credit check on the enterprise, judging whether the average contract performance rate of the enterprise meets the standard or not, and if not, rejecting the financing application of the enterprise; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract subscription times;
(2) identifying a risk source, establishing a risk assessment index set: the financing risk index of the steel trade enterprise can be divided into an external environment risk index, a core enterprise comprehensive credit index, a supply chain partner operation risk index, a financing asset condition risk index and a cloud warehouse service management risk index; the external environment risk indexes comprise policy support force, price change conditions of imported iron ores, the current iron ore and steel price relationship and the steel supply and demand balance relationship; the core enterprise comprehensive credit risk indexes comprise profitability, debt paying capacity, growth potential and operation and management capacity; the supply chain partner operation risk indexes comprise partner integrity degree, partner comprehensive strength and partner service level; the risk indexes of the financing asset condition comprise the evaluation price of the pledge steel and the stability of the market price; the cloud warehouse service management risk indexes comprise warehousing capacity, cloud warehouse coordination capacity and informatization and intelligentization levels;
(3) determining the weight of each index by an analytic hierarchy process: by constructing judgment matrix and comparing two factorsAssuming that the decision matrix has n indexes, calculating the maximum eigenvalue lambda of the decision 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; finally, combining the absolute weight coefficients of all layers to obtain a final weight set W of all indexes;
(4) acquiring all data required by risk assessment of steel trade enterprises: if the data is missing, data acquisition is carried out, and the required data is supplemented;
(5) constructing an evaluation matrix: constructing an initial matrix A (k, m) according to the data of m indexes of k steel trade enterprises; dividing the indexes into cost indexes, benefit indexes, intermediate indexes and interval indexes according to the index attributes, and normalizing the matrix A into A':
Figure BDA0003322908440000051
wherein:
firstly, cost-type indexes are as follows: the index value is the best minimum;
Figure BDA0003322908440000052
II, benefit type index: the larger the index value, the better;
Figure BDA0003322908440000053
third, intermediate type index: the index is best at an intermediate M value;
a'ij＝M/(M+|aij-M|)
interval type indexes: the index is optimal in the interval [ a, b ], the lowest limit is lb, and the lowest limit is ub;
Figure BDA0003322908440000054
(6) constructing a weighted standardization matrix Z ═ A' W, and judging positive and negative ideal solutions Z +, Z-; the positive ideal solution Z + is the maximum value in the benefit type index set J and the minimum value in the cost type index set J'; the negative ideal solution Z-refers to the minimum value in the benefit type index set J and the maximum value in the cost type index set J', that is:
Figure BDA0003322908440000055
(7) calculating the distance S from each enterprise to the positive and negative ideal pointsi+、Si-；
Figure BDA0003322908440000061
(8) Calculating the relative closeness degree C of each enterprise to obtain the comprehensive risk score of the enterprise; wherein
Figure BDA0003322908440000062
The smaller the Ci, the greater the risk; and finally, ranking the risks of each enterprise according to the risk scores.
The technical scheme in the embodiment of the invention is described in detail by taking the data of 20 resident enterprises on a financial platform of a supply chain of a certain iron and steel group as an example.
(1) And (5) performing preliminary credit auditing of the enterprise. When a financial platform of a steel trade supply chain receives a financing application of a financing enterprise P, firstly judging whether the average contract performance rate i of the enterprise is larger than 60% of the contract performance times/the total contract signing times or not, and if not, rejecting the financing application of the enterprise; and if so, carrying out financing risk assessment on the enterprise.
(2) And identifying a risk source and establishing a risk assessment index set. The characteristics of steel trade enterprises and supply chain financial financing are considered comprehensively, and the indexes of the embodiment are specifically as shown in table 1:
TABLE 1
Figure BDA0003322908440000071
(3) And determining the weight of each index by an analytic hierarchy process. The indexes of the table are concentrated, the first-level index is the first layer of the hierarchical analysis, the second-level and third-level indexes are the second and third layers respectively, and the hierarchical analysis method needs to calculate the weight of a single layer respectively and then comprehensively calculate the weight of each index. Firstly, a judgment matrix is constructed, the importance degree of two factors is compared, the judgment matrix is assumed to have n indexes, and the maximum characteristic value lambda of the judgment matrix is calculatedmaxThereby 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.
(4) All data required for risk assessment is acquired. And if the data is missing, acquiring the data and completing the required data.
(5) And (5) constructing an evaluation matrix. First, according to the concrete data of 15 indexes of 20 steel trade enterprises hosting platforms in this example, an initial matrix a of 20 rows and 15 columns is constructed:
Figure BDA0003322908440000081
(6) the A matrix is normalized to A'. Dividing the indexes into cost indexes, benefit indexes, intermediate indexes and interval indexes according to the index attributes, and normalizing the matrix A into A':
Figure BDA0003322908440000082
wherein:
firstly, cost-type indexes are as follows: the index value is the best minimum, such as the 'rate of assets liability' index.
Figure BDA0003322908440000083
II, benefit type index: the larger the index value, the better.
Figure BDA0003322908440000084
Third, intermediate type index: the indicator is best at some middle value of M.
a'ij＝M/(M+|aij-M|)
Interval type indexes: the index is optimal in the interval [ a, b ], the worst lower limit lb and the worst upper limit ub.
Figure BDA0003322908440000091
(7) A weighted normalization matrix Z is constructed as a' W. A' is a normalized matrix, W is a weight set of each index obtained by an analytic hierarchy process, and comprises:
Figure BDA0003322908440000092
(8) and judging positive and negative ideal solutions Z +, Z-according to the weighting matrix. The positive ideal solution Z + takes the maximum value in the benefit type index set J and the minimum value in the cost type index set J'; and the negative ideal solution Z-takes the minimum value in the benefit type index set J and the maximum value in the cost type index set J'. Solving the positive and negative ideal solutions of each column:
Figure BDA0003322908440000093
(9) the distances Si +, Si-from each enterprise to the positive and negative ideal points are calculated.
Figure BDA0003322908440000094
(10) And calculating the relative closeness degree C of each enterprise to obtain the comprehensive risk score of the enterprise. C is calculated by the formula Ci＝Si -/(Si ++Si -) And i is 1,2 and … 20, the larger the calculated C value is, the smaller the financing risk of the enterprise is. The risk assessment results of 20 enterprises in this example are shown in table 2:
TABLE 2
Figure BDA0003322908440000095
Figure BDA0003322908440000101
(11) And generating the enterprise financing risk report according to the risk score and the ranking.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (1)

1. A supply chain financial risk assessment method for steel trade enterprises based on TOPSIS comprehensive analysis is characterized by comprising the following steps:
(1) and (3) enterprise preliminary credit auditing: when a financial platform of a steel trade supply chain receives a financing application of a financing enterprise, firstly, performing primary credit check on the enterprise, judging whether the average contract performance rate of the enterprise meets the standard or not, and if not, rejecting the financing application of the enterprise; if the standard is reached, entering a risk evaluation link; wherein, the contract performance rate i is contract performance times/total contract subscription times;
(2) identifying a risk source, establishing a risk assessment index set: the financing risk index of the steel trade enterprise can be divided into an external environment risk index, a core enterprise comprehensive credit index, a supply chain partner operation risk index, a financing asset condition risk index and a cloud warehouse service management risk index; the external environment risk indexes comprise policy support force, price change conditions of imported iron ores, the current iron ore and steel price relationship and the steel supply and demand balance relationship; the core enterprise comprehensive credit risk indexes comprise profitability, debt paying capacity, growth potential and operation and management capacity; the supply chain partner operation risk indexes comprise partner integrity degree, partner comprehensive strength and partner service level; the risk indexes of the financing asset condition comprise the evaluation price of the pledge steel and the stability of the market price; the cloud warehouse service management risk indexes comprise warehousing capacity, cloud warehouse coordination capacity and informatization and intelligentization levels;
(3) determining the weight of each index by an analytic hierarchy process: by constructing a judgment matrix and comparing the importance degrees of two factors, assuming that the judgment matrix has n indexes, calculating the maximum eigenvalue lambda of the judgment 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; finally, combining the absolute weight coefficients of all layers to obtain a final weight set W of all indexes;
(4) acquiring all data required by risk assessment of steel trade enterprises: if the data is missing, data acquisition is carried out, and the required data is supplemented;
(5) constructing an evaluation matrix: constructing an initial matrix A (k, m) according to the data of m indexes of k steel trade enterprises; dividing the indexes into cost indexes, benefit indexes, intermediate indexes and interval indexes according to the index attributes, and normalizing the matrix A into A':
Figure FDA0003322908430000011
wherein:
firstly, cost-type indexes are as follows: the index value is the best minimum;
Figure FDA0003322908430000021
II, benefit type index: the larger the index value, the better;
Figure FDA0003322908430000022
third, intermediate type index: the index is best at an intermediate M value;
a'ij＝M/(M+|aij-M|)
interval type indexes: the index is optimal in the interval [ a, b ], the lowest limit is lb, and the lowest limit is ub;
Figure FDA0003322908430000023
(6) constructing a weighted standardization matrix Z ═ A' W, and judging positive and negative ideal solutions Z +, Z-; the positive ideal solution Z + is the maximum value in the benefit type index set J and the minimum value in the cost type index set J'; the negative ideal solution Z-refers to the minimum value in the benefit type index set J and the maximum value in the cost type index set J', that is:
Figure FDA0003322908430000024
(7) calculating the distance S from each enterprise to the positive and negative ideal pointsi+、Si-；
Figure FDA0003322908430000025
(8) Calculating the relative closeness degree C of each enterprise to obtain the comprehensive risk score of the enterprise; wherein C isi＝Si -/(Si ++Si -) The smaller i is 1,2, …, k, the greater the risk; and finally, ranking the risks of each enterprise according to the risk scores.
CN202111252967.5A 2021-10-27 2021-10-27 Supply chain financial risk assessment method for steel trade enterprise based on TOPSIS (technique for order preference by similarity to Ideal solution) comprehensive analysis Pending CN113989050A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
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

Cited By (2)

* Cited by examiner, † Cited by third party
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

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