Detailed evaluation supplier risk early warning system and early warning method

Abstract

The invention relates to the technical field of supplier early warning, in particular to a detailed evaluation supplier risk early warning system and early warning method. Comprising an information acquisition unit which is interconnected with data updating systems corresponding to different supplier information issuing ends, acquires influence factors influencing enterprise resource acquisition, and converts the influence factors into corresponding different early warning coefficients; the data analysis unit is used for receiving different early warning coefficients influencing enterprise resource acquisition from the information acquisition end, and loading the early warning coefficients into a data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; and the risk assessment unit receives risk assessment data points analyzed by the data analysis unit for different suppliers at any time, calls risk threshold points stored in the database storage unit, and carries out corresponding analysis and assessment to determine whether risk early warning needs to be carried out or not. According to the system, related data of suppliers can be analyzed, and rapid early warning can be carried out on data which are not beneficial to enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of supplier early warning, and in particular to a supplier risk early warning system and a method. Background Art

[0002] Supplier risk warning refers to the early detection of potential supplier risks through an effective risk assessment and monitoring system, and the adoption of appropriate measures to deal with them. Supplier delivery capability risk refers to the potential risk that suppliers cannot complete delivery tasks on time or in quality. This risk may be caused by factors such as internal management problems of suppliers, raw material supply problems, transportation problems, etc. Understanding the importance of supplier risk warning can help companies timely discover and respond to supplier delivery capability risks and ensure the stable operation of the supply chain. With the continuous expansion of globalization and supply chains, supplier risk has become an important challenge facing companies. The supplier's delivery capability risk may have a serious impact on the company's production and supply chain operations. Therefore, establishing an effective supplier risk warning mechanism is crucial to the stable development of the company. This article will explore how supplier risk warning can deal with supplier delivery capability risks and put forward relevant suggestions.

[0003] However, some large enterprises have many departments and need to connect with a large number of suppliers. If they adopt the traditional method of obtaining supplier information in detail and then conducting detailed analysis and evaluation, it will consume a lot of human resources and the evaluation efficiency will be low. It may happen that when an emergency occurs to the supplier, it may not be possible to issue a timely warning. Summary of the invention

[0004] In view of the current problem that it is extremely difficult to monitor and warn a large number of suppliers in large enterprises, and it is difficult to provide timely warnings for emergencies, this application provides a detailed supplier risk warning system and warning method to solve the above problems.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: The present application embodiment discloses a detailed evaluation supplier risk early warning system, including: Information acquisition unit: interconnected with the data update systems corresponding to the information decentralization terminals of different suppliers, acquire the factors affecting the acquisition of enterprise resources, and convert them into corresponding different early warning coefficients; Data analysis unit: receives different early warning coefficients that affect the acquisition of enterprise resources from the information acquisition end, and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; Database storage unit: The database storage unit receives and loads basic data of different suppliers and risk threshold points input by operators; Early warning analysis unit: constantly receives risk assessment data points analyzed by the data analysis unit on different suppliers, and retrieves risk threshold points stored in the database storage unit to conduct corresponding analysis and evaluation to determine whether risk early warning is needed.

[0006] Adopt the above-mentioned technical scheme: In the above-mentioned scheme, through the corresponding information acquisition unit, it is possible to obtain the corresponding influencing factor information of different enterprises for the data analysis unit to analyze, obtain the corresponding risk assessment data points, and provide the corresponding risk assessment data points to the early warning analysis unit for analysis to determine whether there is a risk warning for the corresponding enterprise.

[0007] Preferably, the information acquisition unit includes: Data interconnection terminal: interconnect with the information update systems of different suppliers and connect with the corresponding information ports; Data extraction end: obtain the desired information through the information port in real time, and capture the influencing factor information through clustering algorithm; Warning coefficient analysis end: analyzes the influencing factor information extracted by the data extraction end, and converts the influencing factor information into corresponding warning coefficients.

[0008] The above technical solution is adopted: the above solution refines the atmosphere data interconnection end, data extraction end and early warning coefficient analysis end of the information acquisition unit, and can realize the rapid capture of the information that different suppliers want to obtain for evaluating the analysis coefficient for analysis by the early warning analysis unit.

[0009] Further preferably, the data analysis unit comprises: Receiving end: receiving different warning coefficients obtained by the information obtaining unit; First data processing end: loading different early warning coefficients received by the receiving end into the algorithm analysis model for algorithm analysis to obtain the value coefficient of the supplier; The second data processing end: calculating the supplier's delivery coefficient according to the supplier's value coefficient calculated by the first data processing end and the supplier's delivery time difference; The third data processing end calculates corresponding risk assessment data points according to the supplier's value coefficient, the supplier's delivery coefficient calculated by the first data processing end and the second data processing end, and the customer evaluation value obtained by the information acquisition unit.

[0010] Adopt the above technical solution: the above solution can realize the calculation of different influencing factors of suppliers into the analysis model, convert the corresponding fuzzy influencing factors into corresponding actual data for analysis by the system model, and confirm whether there is a risk warning for the corresponding supplier.

[0011] Further preferably, the algorithm formula set in the first data processing end is: , in, is the supplier's value coefficient, is the quantity of goods, For the price of goods, is the impact coefficient corresponding to the commodity price, For the quality of the goods, is the impact coefficient corresponding to the product quality, is the supplier enterprise size coefficient.

[0012] Adopting the above technical solution: the above solution can realize corresponding algorithm calculation and analysis according to the quantity, price and quality of the supplier's goods, realize an algorithm evaluation of the quality of the products provided by the supplier, and convert the quality of the products provided by the supplier into corresponding risk assessment values.

[0013] Further preferably, the algorithm formula set in the second processing end is: , in, is the delivery time coefficient, For the Items supplied, Number of supplier items, For the Set a delivery time for each item. For the The actual delivery time of the item, For the The value coefficient corresponding to the item.

[0014] Adopting the above technical solution: the second processing module provided by the above solution can realize the quality of the goods provided by the supplier, and according to the supplier's delivery time of the goods, convert the situation corresponding to the supplier's delivery time into the corresponding risk assessment coefficient according to the algorithm model.

[0015] Further preferably, the algorithm formula set in the third processing end is: , in, is the risk assessment data point, is the supplier's value coefficient, is the influence coefficient corresponding to the supplier value coefficient, is the proportion of the supplier value coefficient in the data points that affect risk assessment, is the delivery time coefficient, is the impact coefficient corresponding to the supplier delivery time coefficient, is the proportion of supplier delivery time coefficient in the data points affecting risk assessment, is the customer evaluation coefficient of the supplier, is the influence coefficient corresponding to the supplier’s customer evaluation coefficient, It is the proportion of the supplier’s customer evaluation coefficient in the data points that affect risk assessment.

[0016] Adopting the above technical solution: The above solution, through the algorithm model provided by the third processing end, can calculate whether the supplier has corresponding risks based on three data: the quality coefficient of the goods provided by the supplier, the delivery time coefficient of the product, and the customer's evaluation coefficient of the supplier.

[0017] Further preferably, after calculating the risk assessment data points of the supplier, the early warning analysis unit will retrieve the risk threshold points loaded in the database storage unit and perform analysis to confirm whether there is a risk warning for the supplier.

[0018] An early warning method, applied to a detailed evaluation supplier risk early warning system as described in any one of the above, comprising: S1: The information acquisition unit acquires the factors that affect the acquisition of enterprise resources and converts them into corresponding different early warning coefficients; S2: The data analysis unit receives different early warning coefficients and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; S3: The operator loads the basic data of different suppliers and risk threshold points through the database storage unit; S4: The early warning analysis unit constantly receives the risk assessment data points analyzed by the data analysis unit for different suppliers, and retrieves the risk threshold points stored in the database storage unit to perform analysis and evaluation to determine whether a risk early warning is needed.

[0019] Further preferably, the S2 comprises: S21: The receiving end receives different warning coefficients obtained by the information obtaining unit; S22: The first data processing end loads the different warning coefficients received by the receiving end into the algorithm analysis model to perform algorithm analysis to obtain the value coefficient of the supplier; S23: The second data processing end calculates the supplier's delivery coefficient according to the supplier's value coefficient calculated by the first data processing end and the supplier's delivery time difference; S24: Calculate corresponding risk assessment data points according to the supplier's value coefficient, the supplier's delivery coefficient, and the customer evaluation value obtained by the information acquisition unit calculated by the first data processing end and the second data processing end.

[0020] Further preferably, the clustering algorithm process is: S001: Select the initial cluster center, use the K-means algorithm, and randomly select k points from the data set as the initial cluster centroid; S002: Calculate the distance between each data point and each cluster center, and assign each data point to the cluster represented by the cluster center closest to it; S003: In each cluster, calculate the average value of all data points and use the average value as the cluster center position; S004: Repeat S002 and S003 until the cluster center no longer changes or the preset number of iterations is reached; S005: Use the elbow rule or silhouette coefficient to evaluate the effect and select the optimal number of clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is the connection diagram of the risk warning system for this application; Figure 2 This is a flowchart of the early warning method for this application; Figure 3 For this application Figure 2 Flow chart of step S2; Figure 4 Flowchart of the clustering algorithm for this application.

[0023] In the figure: 1. Information acquisition unit; 2. Data analysis unit; 3. Database storage unit; 4. Early warning analysis unit; 5. Data interconnection terminal; 6. Data extraction terminal; 7. Early warning coefficient analysis terminal; 8. Receiving terminal; 9. First data processing terminal; 10. Second data processing terminal; 11. Third data processing terminal. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0026] See also Figure 1-Figure 4 , such as the original supplier system, which is not equipped with a corresponding early warning assessment model, and can only analyze each corresponding staff through the computer to confirm whether the supplier has risks. For some large enterprises, this assessment efficiency is not high, which affects the efficiency of the assessment. Based on the above problems, the embodiment of the present application discloses a detailed evaluation supplier risk early warning system, including: Information acquisition unit 1: interconnects with the data update system corresponding to the information decentralization end of different suppliers, obtains the influencing factors that affect the acquisition of enterprise resources, and converts them into corresponding different early warning coefficients; before setting up, this unit will connect with different suppliers, and carry out targeted capture of the information that suppliers need to obtain, for analysis by data analysis unit 2, Data analysis unit 2: Receives different early warning coefficients that affect the acquisition of enterprise resources from the information acquisition end, and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain the risk assessment data points corresponding to different suppliers; this system can convert the corresponding situations of suppliers into corresponding actual risk assessment data points for analysis by the system model.

[0027] Database storage unit 3: Database storage unit 3 receives and loads basic data of different suppliers and risk threshold points input by the operator; Early warning analysis unit 4: always receives the risk assessment data points analyzed by the data analysis unit 2 for different suppliers, and retrieves the risk threshold points stored in the database storage unit 3, performs corresponding analysis and evaluation, and determines whether a risk warning is needed. In the above scheme, through the corresponding information acquisition unit 1, it is possible to obtain the corresponding influencing factor information of different enterprises for analysis by the data analysis unit 2, obtain the corresponding risk assessment data points, and provide the corresponding risk assessment data points to the early warning analysis unit 4 for analysis, and determine whether the corresponding enterprise has a risk warning.

[0028] The information acquisition unit 1 comprises: Data interconnection terminal 5: interconnect with the information update systems of different suppliers and connect with the corresponding information ports; Data extraction terminal 6: obtains the desired information through the information port in real time, and captures the influencing factor information through the clustering algorithm; Warning coefficient analysis terminal 7: analyzes the influencing factor information extracted by the data extraction terminal 6, and converts the influencing factor information into the corresponding warning coefficient. The above scheme refines the information acquisition unit 1 to the atmosphere data interconnection terminal 5, the data extraction terminal 6 and the warning coefficient analysis terminal 7, and can quickly capture the information that different suppliers want to obtain for the evaluation and analysis coefficient, so that the warning analysis unit 4 can analyze it.

[0029] The data analysis unit 2 comprises: Receiving end 8: receiving different warning coefficients obtained by information obtaining unit 1; The first data processing terminal 9: loads the different early warning coefficients received by the receiving terminal 8 into the algorithm analysis model to perform algorithm analysis to obtain the value coefficient of the supplier; The second data processing end 10 calculates the supplier's delivery coefficient according to the supplier's value coefficient and the supplier's delivery time difference calculated by the first data processing end 9; The third data processing terminal 11 calculates the corresponding risk assessment data points according to the supplier's value coefficient, supplier's delivery coefficient calculated by the first data processing terminal 9 and the second data processing terminal 10, and the customer evaluation value obtained by the information acquisition unit 1. The above scheme can realize the calculation of different influencing factors of suppliers into the analysis model, convert the corresponding fuzzy influencing factors into corresponding actual data, and provide the system model for analysis to confirm whether the corresponding supplier has risk warning.

[0030] The algorithm formula set in the first data processing terminal 9 is: , in, is the supplier's value coefficient, is the quantity of goods, For the price of goods, is the impact coefficient corresponding to the commodity price, For the quality of the goods, is the impact coefficient corresponding to the product quality, is the supplier enterprise size coefficient.

[0031] The above scheme can realize corresponding algorithm calculation and analysis according to the quantity, price and quality of the supplier's goods, realize an algorithm evaluation of the quality of the products provided by the supplier, and convert the quality of the products provided by the supplier into corresponding risk assessment values.

[0032] The algorithm formula set in the second processing end is: , in, is the delivery time coefficient, For the Items supplied, Number of supplier items, For the Set a delivery time for each item. For the The actual delivery time of the item, For the The value coefficient corresponding to the item.

[0033] The second processing module provided by the above solution can realize the quality of the goods provided by the supplier and convert the status corresponding to the supplier's delivery time into the corresponding risk assessment coefficient according to the algorithm model according to the supplier's delivery time of the goods.

[0034] The algorithm formula set in the third processing end is: , in, is the risk assessment data point, is the supplier's value coefficient, is the influence coefficient corresponding to the supplier value coefficient, is the proportion of the supplier value coefficient in the data points that affect risk assessment, is the delivery time coefficient, is the impact coefficient corresponding to the supplier delivery time coefficient, is the proportion of supplier delivery time coefficient in the data points affecting risk assessment, is the customer evaluation coefficient of the supplier, is the influence coefficient corresponding to the supplier’s customer evaluation coefficient, It is the proportion of the supplier’s customer evaluation coefficient in the data points that affect risk assessment.

[0035] The above scheme, through the algorithm model provided by the third processing end, can calculate whether the supplier has corresponding risks based on three data: the quality coefficient of the goods provided by the supplier, the delivery time coefficient of the product, and the customer's evaluation coefficient of the supplier.

[0036] After calculating the risk assessment data points of the supplier, the early warning analysis unit 4 will retrieve the risk threshold points loaded in the database storage unit 3 and perform analysis to confirm whether the supplier has a risk warning.

[0037] An early warning method, applied to a detailed evaluation supplier risk early warning system as described in any one of the above, comprising: S1: Information acquisition unit 1 acquires the influencing factors affecting the acquisition of enterprise resources and converts them into corresponding different early warning coefficients; S2: The data analysis unit 2 receives different early warning coefficients and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; S3: The operator loads the basic data and risk threshold points of different suppliers through the database storage unit 3; S4: The early warning analysis unit 4 constantly receives the risk assessment data points analyzed by the data analysis unit 2 for different suppliers, and retrieves the risk threshold points stored in the database storage unit 3 to perform analysis and evaluation to determine whether a risk early warning is needed.

[0038] It is worth mentioning that the above scheme provides a risk warning method, which obtains the influencing factors that affect the enterprise resources and converts the corresponding influencing factors, including the supplier's value coefficient, the supplier's delivery time coefficient and the supplier's customer evaluation coefficient, etc., calculates and analyzes the above influencing factors to obtain the final risk assessment data. Before this, the operator will set up risk threshold points for different suppliers, and the system will calculate and evaluate the risk assessment data and risk thresholds, and finally determine whether the corresponding supplier will generate risks after the relevant data changes.

[0039] The S2 includes: S21: The receiving end 8 receives different warning coefficients obtained by the information obtaining unit 1; S22: the first data processing end 9 loads the different warning coefficients received by the receiving end 8 into the algorithm analysis model to perform algorithm analysis to obtain the value coefficient of the supplier; S23: the second data processing end 10 calculates the supplier's delivery coefficient according to the supplier's value coefficient calculated by the first data processing end 9 and the supplier's delivery time difference; S24: Calculate corresponding risk assessment data points according to the supplier's value coefficient and the supplier's delivery coefficient calculated by the first data processing terminal 9 and the second data processing terminal 10 and the customer evaluation value obtained by the information acquisition unit 1 .

[0040] The clustering algorithm process is as follows: S001: Select the initial cluster center, use the K-means algorithm, and randomly select k points from the data set as the initial cluster centroid; S002: Calculate the distance between each data point and each cluster center, and assign each data point to the cluster represented by the cluster center closest to it; S003: In each cluster, calculate the average value of all data points and use the average value as the cluster center position; S004: Repeat S002 and S003 until the cluster center no longer changes or the preset number of iterations is reached; S005: Use the elbow rule or silhouette coefficient to evaluate the effect and select the optimal number of clusters.

[0041] Unless otherwise specified, the device components involved in the above embodiments are all conventional device components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.

[0042] The present invention has been described in detail above in conjunction with the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be changed to form a plurality of specific embodiments, which are all within the common variation range of the present invention and will not be described in detail herein.

Claims

1. A detailed evaluation supplier risk early warning system, characterized in that: include: Information acquisition unit: interconnected with the data update systems corresponding to the information decentralization terminals of different suppliers, acquire the factors affecting the acquisition of enterprise resources, and convert them into corresponding different early warning coefficients; Data analysis unit: receives different early warning coefficients that affect the acquisition of enterprise resources from the information acquisition end, and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; Database storage unit: The database storage unit receives and loads basic data of different suppliers and risk threshold points input by operators; Early warning analysis unit: constantly receives risk assessment data points analyzed by the data analysis unit on different suppliers, and retrieves risk threshold points stored in the database storage unit to conduct corresponding analysis and evaluation to determine whether risk early warning is needed.

2. A detailed evaluation supplier risk early warning system according to claim 1, characterized in that: The information acquisition unit comprises: Data interconnection terminal: interconnect with the information update systems of different suppliers and connect with the corresponding information ports; Data extraction end: obtain the desired information through the information port in real time, and capture the influencing factor information through clustering algorithm; Warning coefficient analysis end: analyzes the influencing factor information extracted by the data extraction end, and converts the influencing factor information into corresponding warning coefficients.

3. A detailed evaluation supplier risk early warning system according to claim 1, characterized in that: The data analysis unit comprises: Receiving end: receiving different warning coefficients obtained by the information obtaining unit; First data processing end: loading different early warning coefficients received by the receiving end into the algorithm analysis model for algorithm analysis to obtain the value coefficient of the supplier; The second data processing end: calculating the supplier's delivery coefficient according to the supplier's value coefficient calculated by the first data processing end and the supplier's delivery time difference; The third data processing end calculates corresponding risk assessment data points according to the supplier's value coefficient, the supplier's delivery coefficient calculated by the first data processing end and the second data processing end, and the customer evaluation value obtained by the information acquisition unit.

4. A detailed evaluation supplier risk early warning system according to claim 3, characterized in that: The algorithm formula set in the first data processing end is: ， in, is the supplier's value coefficient, is the quantity of goods, For the price of goods, is the impact coefficient corresponding to the commodity price, For the quality of the goods, is the impact coefficient corresponding to the product quality, is the supplier enterprise size coefficient.

5. A detailed evaluation supplier risk early warning system according to claim 4, characterized in that: The algorithm formula set in the second processing end is: ， in, is the delivery time coefficient, For the Items supplied, Number of supplier items, For the Set a delivery time for each item. For the The actual delivery time of the item, For the The value coefficient corresponding to the item.

6. A detailed evaluation supplier risk early warning system according to claim 5, characterized in that: The algorithm formula set in the third processing end is: ， in, is the risk assessment data point, is the supplier's value coefficient, is the influence coefficient corresponding to the supplier value coefficient, is the proportion of the supplier value coefficient in the data points that affect risk assessment, is the delivery time coefficient, is the impact coefficient corresponding to the supplier delivery time coefficient, is the proportion of supplier delivery time coefficient in the data points affecting risk assessment, is the customer evaluation coefficient of the supplier, is the influence coefficient corresponding to the supplier’s customer evaluation coefficient, It is the proportion of the supplier’s customer evaluation coefficient in the data points that affect risk assessment.

7. A detailed evaluation supplier risk early warning system according to claim 6, characterized in that: After calculating the supplier's risk assessment data points, the early warning analysis unit will retrieve the risk threshold points loaded in the database storage unit and perform analysis to confirm whether the supplier has a risk warning.

8. An early warning method, applied to a detailed evaluation supplier risk early warning system as claimed in any one of claims 1 to 7, characterized in that: include: S1: The information acquisition unit acquires the factors that affect the acquisition of enterprise resources and converts them into corresponding different early warning coefficients; S2: The data analysis unit receives different early warning coefficients and loads the early warning coefficients into the data analysis model for analysis by the data analysis model to obtain risk assessment data points corresponding to different suppliers; S3: The operator loads the basic data of different suppliers and risk threshold points through the database storage unit; S4: The early warning analysis unit constantly receives the risk assessment data points analyzed by the data analysis unit for different suppliers, and retrieves the risk threshold points stored in the database storage unit to perform analysis and evaluation to determine whether a risk early warning is needed.

9. The early warning method according to claim 8, characterized in that: The S2 includes: S21: The receiving end receives different warning coefficients obtained by the information obtaining unit; S22: The first data processing end loads the different warning coefficients received by the receiving end into the algorithm analysis model to perform algorithm analysis to obtain the value coefficient of the supplier; S23: The second data processing end calculates the supplier's delivery coefficient according to the supplier's value coefficient calculated by the first data processing end and the supplier's delivery time difference; S24: Calculate corresponding risk assessment data points according to the supplier's value coefficient, the supplier's delivery coefficient, and the customer evaluation value obtained by the information acquisition unit calculated by the first data processing end and the second data processing end.

10. The early warning method according to claim 8, characterized in that: The clustering algorithm process is as follows: S001: Select the initial cluster center, use the K-means algorithm, and randomly select k points from the data set as the initial cluster centroid; S002: Calculate the distance between each data point and each cluster center, and assign each data point to the cluster represented by the cluster center closest to it; S003: In each cluster, calculate the average value of all data points and use the average value as the cluster center position; S004: Repeat S002 and S003 until the cluster center no longer changes or the preset number of iterations is reached; S005: Use the elbow rule or silhouette coefficient to evaluate the effect and select the optimal number of clusters.

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