Enterprise supply chain management method based on big data

Abstract

The invention discloses an enterprise supply chain management method based on big data, and particularly relates to the field of supply chain management.The method comprises the steps that firstly, data are automatically collected from multiple channels such as an enterprise internal system, a supply chain management platform, a logistics information system, an industry database and a government statistical data website; the method comprises the following steps of: performing cleaning, integration and standardization processing on data, then calculating a supply chain influence evaluation index, evaluating and classifying supply chains from six dimensions of supply capability, product quality, cost, cooperation relationship, financial condition and innovation capability, and finally formulating differentiated cooperation strategies for the supply chains with different influence levels. According to the invention, through multi-dimensional data acquisition and analysis, comprehensive and accurate evaluation of the influence of the supply chain is realized; a risk early warning mechanism is constructed by means of real-time monitoring of key indexes, and enterprises are helped to actively prevent risks; the differentiated cooperation strategy improves the flexibility and efficiency of supply chain management, and ensures the stable operation of the supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management. More specifically, the present invention relates to an enterprise supply chain management method based on big data. Background Art

[0002] In today's highly competitive business environment, supply chain management has become a key factor for enterprises to enhance their competitiveness. With the rapid development of big data technology, the collection, storage, and analysis of massive amounts of data have become more efficient, bringing new opportunities for enterprise supply chain management. Traditional supply chain management mainly relies on manual experience and simple data statistical analysis, making it difficult to comprehensively and accurately grasp the operating conditions of the supply chain. As the scale of enterprises continues to expand and the market environment becomes increasingly complex, the number of links covered by the supply chain and the amount of information involved have increased exponentially, including internal production, procurement, sales, and other links within the enterprise, as well as multi-faceted information such as external supply chains, logistics partners, and market dynamics.

[0003] The application of big data technology in supply chain management has gradually become a research hotspot. Through big data analysis, enterprises can obtain more accurate market demand forecasts, optimize inventory management, reduce costs, and improve the overall efficiency of the supply chain. For example, by analyzing historical sales data and market trends using big data, enterprises can more accurately predict product demand and avoid inventory backlogs or shortages; by analyzing various data of the supply chain, they can more scientifically select the supply chain to ensure the stable supply of raw materials. However, when it is actually used, there are still some drawbacks. For example, traditional supply chain evaluation methods usually only focus on a few indicators such as price and delivery date, making it difficult to comprehensively evaluate the comprehensive influence of the supply chain. Important factors such as the supply capacity of the supply chain, the stability of product quality, innovation ability, and the closeness of cooperation with the enterprise have not been fully considered. This one-sided evaluation method causes enterprises to be unable to accurately identify high-quality supply chains and also makes it difficult to effectively classify and manage the supply chain, increasing the risk of the supply chain. Existing supply chain management systems often lack an effective risk warning mechanism and cannot detect potential risks in a timely manner. Enterprises usually take countermeasures only after the risk occurs, which puts enterprises in a passive position when dealing with risks and may lead to adverse consequences such as production stagnation, increased costs, and decreased customer satisfaction. Traditional supply chain management methods often adopt fixed strategies and lack flexibility and dynamic adjustment capabilities. When market demand changes or problems occur in the supply chain, enterprises are unable to quickly adjust procurement plans, production arrangements, and inventory strategies, resulting in low supply chain efficiency and an inability to meet customer needs. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, the present invention provides an enterprise supply chain management method based on big data. Through the following solutions, it solves the problems that the traditional supply chain evaluation method is difficult to comprehensively evaluate the comprehensive influence of the supply chain, the existing supply chain management system lacks an effective risk warning mechanism and cannot detect potential risks in time, and the traditional supply chain management method often adopts fixed strategies and lacks flexibility and dynamic adjustment ability as put forward in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: An enterprise supply chain management method based on big data, including a data collection terminal, a data processing terminal, an evaluation and analysis terminal, a strategy decision terminal, and an interaction terminal, specifically including the following steps: S1: Determination of data collection area: Obtain the comprehensive data categories of the enterprise supply chain certification data area and establish an index text; The index text includes a first index text, and the first index text includes supply data categories, product data categories, cost data categories, cooperation data categories, financial data categories, and innovation data categories. The first index text includes a second index text; Construct a supplier data category interval, divide the data interval based on the supplier index text, obtain several corresponding supplier data sub-categories, and mark them as 1, 2, 3...i...n in sequence; S2: Supplier data acquisition: Based on the first index text in S1, perform data screening on the supplier data sub-categories to obtain a first screening text, and based on the second index text in S1, perform data screening on the first screening text to obtain a second screening text; S3: Data cleaning and integration: Establish a standardized processing text, import the second screening text into the standardized processing text to obtain a supply chain data text; S4: Supply chain influence evaluation: Construct a supply chain influence evaluation processing text, import the supply chain data text into the supply chain influence evaluation processing text, and classify the suppliers according to the comprehensive influence evaluation score in the supply chain influence evaluation processing text. The suppliers are divided into three categories: high influence, medium influence, and low influence; S5: Supply chain strategy formulation: For suppliers with different influence levels, formulate cooperation strategies.

[0006] The technical effects and advantages of the present invention: Based on big data, the present invention evaluates the supply chain from six dimensions: supply capacity, product quality, cost, cooperation relationship, financial status, and innovation ability. Each dimension includes multiple specific indicators, and through scientific calculation methods and non-linear transformation, comprehensively and accurately evaluates the comprehensive influence of the supply chain. Classify the supply chain according to the evaluation results, enabling enterprises to clearly identify high, medium, and low influence supply chains, providing a strong basis for formulating differentiated cooperation strategies, optimizing supply chain management, and reducing supply chain risks; The present invention establishes a supply chain risk early warning mechanism based on big data analysis. Through real-time monitoring of various indicators of the supply chain, when the monitored indicators exceed the preset threshold, the system promptly sends out warning information. Enterprises can quickly take corresponding countermeasures according to the warning information, such as enabling alternative supply chains, adjusting production plans, optimizing inventory strategies, etc., changing from passive risk response to active risk prevention, ensuring the stable operation of the supply chain, and reducing the impact of risks on the production and operation of enterprises; According to the classification results of the supply chain, this technical solution formulates targeted cooperation strategies. For high-influence supply chains, establish long-term strategic cooperation relationships and jointly carry out projects such as cost optimization; for medium-influence supply chains, regularly evaluate and adjust cooperation strategies according to performance; for low-influence supply chains, require rectification and establish a supervision mechanism, and at the same time formulate contingency plans. This dynamic adjustment strategy enables enterprises to timely adjust supply chain management strategies according to the actual performance of the supply chain and market changes, improve the flexibility and adaptability of the supply chain, better meet customer needs, and enhance the competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0008] Figure 2 It is a schematic diagram of the connection of the terminal device used in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0010] As shown in the appended Figure 1 A method for enterprise supply chain management based on big data includes a data collection terminal, a data processing terminal, an evaluation and analysis terminal, a strategy decision-making terminal, and an interaction terminal; In a more specific application of the present invention, the data acquisition terminal is used to collect in real time the data related to the enterprise's internal supply chain, as well as market data, industry dynamics data, and supply chain financial data from external data platforms; The data processing terminal is used to clean the collected raw data, remove duplicate data, correct error data, process missing values, standardize the data, and at the same time integrate the cleaned data according to the supply chain dimension, establish a unified supply chain data warehouse, and store the data using HBase to ensure the efficient storage and fast access of the data; The evaluation and analysis terminal is used to calculate various supply chain index values using Spark, calculate the influence score of each supply chain through formulas, generate a supply chain influence evaluation index data set, and classify the supply chains; The strategy decision-making terminal is used to formulate supply chain classification management strategies according to the supply chain influence evaluation results, including the selection of supply chains, the establishment and maintenance of cooperative relationships, and the assessment and elimination of supply chains; The interaction terminal is used to provide a user interaction interface through which enterprise management personnel can query detailed supply chain information, adjust the weights of evaluation indicators, and formulate and implement supply chain management strategies.

[0011] For the connection relationships among the above-mentioned data acquisition terminal, data processing terminal, evaluation and analysis terminal, strategy decision-making terminal, and interaction terminal, please refer to Figure 2 as shown.

[0012] The specific implementation of the present invention includes the following steps: S1: Determination of the data acquisition area: Obtain the comprehensive data categories of the enterprise's supply chain certification data area and establish an index text; The index text includes a first index text, and the first index text includes supply data categories, product data categories, cost data categories, cooperation data categories, financial data categories, and innovation data categories. The first index text includes a second index text; Construct a supplier data category interval, divide the data interval based on the supplier index text, obtain several corresponding supplier data sub-categories, and label them as 1, 2, 3...i...n in sequence; Specifically in this embodiment, the second index text includes: supply data categories, such as the proportion of supply volume, the number of product categories, the average replenishment time, and the number of supply interruptions, which evaluate the supply capacity from aspects of supply quantity, category, time, and stability; product data categories, such as the product qualification rate, defective rate, the number of quality complaints, and the quality certification situation, which measure the product quality from multiple dimensions; cost data categories, such as the supply product price, price fluctuation range, and the proportion of procurement cost, which evaluate the procurement cost and price stability; cooperation data categories, such as the cooperation years, cooperation tightness, and order fulfillment rate, which reflect the depth and stability of cooperation; financial data categories, such as the asset-liability ratio, current ratio, and net profit rate, which reflect the financial health of the supply chain; innovation data categories, such as the proportion of new product R & D investment, the number of patents, and the application situation of technological innovation achievements, which measure the innovation investment, achievements, and transformation ability of the supply chain. S2: Supplier data acquisition: Based on the first index text in S1, data screening is performed on the supplier data sub-categories to obtain a first-screened text, and based on the second index text in S1, data screening is performed on the first-screened text to obtain a second-screened text. Specifically in this embodiment, the supply chain data text includes: supply capacity dimension data, product quality dimension data, cost dimension data, cooperation relationship dimension data, financial condition dimension data, and innovation ability dimension data. The supply capacity dimension data specifically includes: The proportion of supply volume, and the specific collection method is as follows: within one month, the total amount of goods purchased by the enterprise from all supply chains is recorded as , and the amount of goods purchased from the i-th supply chain is recorded as .

[0013] The calculation formula for the proportion of supply volume is: ; It should be further noted that this indicator reflects the share of a single supply chain in the overall procurement volume of the enterprise. The higher the share, the higher the possible importance of this supply chain to the enterprise's supply. The number of product categories, and the specific collection method is as follows: Count the number of different product categories provided by the i-th supply chain for the enterprise, and directly record it as ; It should be further noted that this indicator reflects the degree of supply diversification of the supply chain. The more categories, the stronger the ability of the supply chain to meet the diversified procurement needs of the enterprise. The key degree of supply products, and the specific collection method is as follows: Suppose there are n key production links in the final product produced by the enterprise, and the participation degree of the products supplied by the i-th supply chain in the j-th key production link is , The value range is , fully participating is 1, not participating is 0, and the importance weight of each key production link is ; Then the calculation formula for the criticality of the supplied product is: ; It should be further noted that The definition of is specifically as follows: When the product fully participates in the j-th key production link, that is, the production of this link must rely on this product and is irreplaceable, = 1.

[0014] When the product does not participate in the j-th key production link at all, = 0.

[0015] If the product partially participates in the j-th key production link, the participation degree can be determined by the following methods according to factors such as its role size and usage ratio in this link: Quantify the usage ratio of the product in this link. When the proportion of the product in the raw material input of a certain key link is p, then = p; The acquisition method of is as follows: Determine the weight according to the proportion of each key production link in the total cost. For example, the cost of the i-th key production link is , and the total cost of all key production links is ; Average replenishment time. The specific collection method is as follows: Record the time spent on each replenishment of the i-th supply chain during the statistical period , j represents the j-th replenishment, and the total number of replenishments is m; The calculation formula for the average replenishment time is: ; It should be further noted that this indicator reflects the timeliness of the supply chain in replenishment. The shorter the average replenishment time, the stronger the ability of the supply chain to respond to the replenishment needs of the enterprise; Number of supply interruptions. The specific collection method is as follows: Directly count the number of times that cause supply interruptions to the enterprise by the i-th supply chain during the statistical period, denoted as ; It should be further noted that the number of supply interruptions is an important indicator to measure the supply stability of the supply chain. The more times, the worse the supply stability of the supply chain and the greater the risk to the enterprise's production.

[0016] The specific product quality dimension data includes: Product qualification rate, Defective rate, Number of quality complaints, and Quality certification status; The specific acquisition method of the quality certification status is as follows: If the i-th supply chain has obtained ISO9001 quality certification, then = 1; if not, then = 0; It should be further noted that the product qualification rate is the most direct indicator to measure the product quality of the supply chain. The higher the qualification rate, the better the product quality provided by the supply chain; the defective rate is inversely related to the product qualification rate. The lower the defective rate, the more reliable the product quality; the number of quality complaints can reflect the quality problems that occur during the actual use of the product. The more complaints, the more likely there are potential hazards in the product quality; quality certification is a proof that the product quality of the supply chain meets certain standards. The supply chain that has obtained the certification may have more advantages in product quality control.

[0017] The specific cost dimension data includes: Supply product price, and the specific acquisition method is as follows: Directly obtain the unit price of the specific product supplied by the i-th supply chain, denoted as .

[0018] The supply product price is an important part of the enterprise's procurement cost. On the premise of ensuring product quality, the lower the price, the more beneficial it is to the enterprise; Supply product price, and the specific acquisition method is as follows: During the statistical period, record the highest price of the products supplied by the i-th supply chain as , the lowest price as , and the average price as , where is the number of price records, is the price recorded at the k-th time; The calculation formula for the price fluctuation range is: ; It should be specifically noted that the price fluctuation range reflects the stability of the supply chain product price. The larger the fluctuation range, the higher the uncertainty of the procurement cost faced by the enterprise; Procurement cost ratio, and the specific acquisition method is as follows: The procurement cost of the i-th supply chain in a month is recorded as , and the total procurement cost of the enterprise in a month is recorded as 。

[0019] The calculation formula for the proportion of procurement cost is: ; Specifically, this indicator helps the enterprise understand the distribution of procurement costs from each supply chain, so as to conduct cost control and supply chain management; The data of the cooperation relationship dimension specifically includes: The cooperation duration, and the specific collection method is as follows: Record the date when the enterprise starts to cooperate with the i-th supply chain as , and the statistical cut-off date is ; The calculation formula for the cooperation duration is: = - , in years; The cooperation intensity, and the specific collection method is as follows: Count the number of key links such as product design, production plan formulation, and quality control participated by the i-th supply chain in the enterprise as , and the total number of key links of the enterprise is N; The calculation formula for the cooperation intensity is: ; It should be further noted that the higher the cooperation intensity, the deeper the collaborative cooperation between the supply chain and the enterprise, and the better it can meet the personalized needs of the enterprise; The order fulfillment rate, and the specific collection method is as follows: During the statistical period, record the number of orders placed by the enterprise to the i-th supply chain as , and record the number of orders actually delivered by the supply chain as; The calculation formula for the order fulfillment rate is: ; The order fulfillment rate reflects the ability of the supply chain to fulfill orders. The higher the fulfillment rate, the better the supply chain can provide goods according to the enterprise's order requirements; The data of the financial status dimension specifically includes: Asset-liability ratio, Current ratio, and Net profit margin; Specifically, the asset - liability ratio reflects the long - term debt - paying ability of the supply chain. The higher the ratio, the heavier the debt burden of the supply chain and the relatively greater the financial risk. The current ratio measures the short - term debt - paying ability of the supply chain. Generally speaking, the higher the current ratio, the stronger the short - term debt - paying ability of the supply chain. The net profit ratio reflects the profitability of the supply chain. The higher the ratio, the stronger the ability of the supply chain to obtain profits in the operation process. The data of the innovation ability dimension specifically includes: The proportion of new product R & D investment, and the specific acquisition method is as follows: Obtain the amount of new product R & D investment within the statistical period from the financial data of the i - th supply chain and operating income ; The calculation formula for the proportion of new product R & D investment is: ; It should be further noted that this indicator reflects the degree of attention and investment in new product R & D by the supply chain. The higher the proportion, the higher the enthusiasm of the supply chain for innovation. The number of patents, and the specific acquisition method is as follows: Count the number of valid patents owned by the i - th supply chain, denoted as ; It should be further noted that the number of patents is an important indicator to measure the technological innovation achievements of the supply chain. The more the number, the stronger the strength of the supply chain in technological innovation may be. The application situation of technological innovation achievements, and the specific acquisition method is as follows: Count the number of products produced by the i - th supply chain using new technologies as , and the total number of products is ; The calculation formula for the application situation of technological innovation achievements is: = 00%； It should be further noted that this indicator reflects the ability of the supply chain to transform technological innovation achievements into actual products. The better the application situation, the better the innovation achievements of the supply chain can create value for the enterprise.

[0020] S3: Data cleaning and integration: Establish a standardized processing text, import the secondary - screened text into the standardized processing text to obtain the supply chain data text; Specifically in this embodiment, the standardized processing text specifically includes the following steps: removing duplicate data, correcting error data, performing imputation processing on missing values, and finally performing standardized processing; S4: Supply Chain Influence Evaluation: Construct a text for supply chain influence evaluation, import the supply chain data text into the text for supply chain influence evaluation, and classify the suppliers according to the comprehensive influence evaluation score in the text for supply chain influence evaluation. The suppliers are divided into three categories: high influence, medium influence, and low influence; Specifically in this embodiment, the text for supply chain influence evaluation specifically includes the following steps: A1: Perform non-linear transformation on some indicators: The specific non-linear transformation is as follows: Non-linear transformation of the supply volume ratio: , this function maps the supply volume ratio to interval to avoid excessive influence of too large a ratio on the result; Non-linear transformation of the number of product types: , where is the maximum value of the number of all supply chain product types. This transformation gives relatively more advantages to supply chains with more product types without being overly prominent; Transformation of the average replenishment time: , the shorter the replenishment time, the closer this value is to 1; Transformation of the number of supply interruptions: , the fewer the number of interruptions, the closer this value is to 1; Transformation of the defective rate: ; Transformation of the number of quality complaints: ; Transformation of the price of supplied products: ; Transformation of the price fluctuation range: ; Transformation of the proportion of procurement cost: ; Perform non-linear transformation on the cooperation years: ; Transformation of the asset-liability ratio: ; Perform non-linear transformation on the number of patents: , where is the maximum value of the number of patents of all supply chains; A2: Calculate the supply capacity dimension score SC, product quality dimension score PC, cost dimension score CC, cooperation relationship dimension score RC, financial status dimension score FC, and innovation ability dimension score IC according to the transformed indicators; The specific calculation method of the supply capacity dimension score SC is as follows: ; The specific calculation method of the product quality dimension score PC is as follows: ; The specific calculation method of the cost dimension score CC is as follows: ; The specific calculation method of the cooperation relationship dimension score RC is as follows: ; The specific calculation method of the financial status dimension score FC is as follows: ; The specific calculation method of the innovation ability dimension score IC is as follows: ; A3: Input the supply capacity dimension score SC, product quality dimension score PC, cost dimension score CC, cooperation relationship dimension score RC, financial status dimension score FC, and innovation ability dimension score IC into the comprehensive influence evaluation function to obtain the comprehensive influence evaluation score ; The specific comprehensive influence evaluation function is as follows: ; Among them, , , are the thresholds of the number of supply interruptions, the number of quality complaints, and the asset - liability ratio respectively. If the number of supply interruptions in the supply chain exceeds , or the number of quality complaints exceeds , or the asset - liability ratio exceeds , then the influence of this supply chain is directly determined to be 0 because these situations will have a serious negative impact on the enterprise; It should be further noted that , , are adaptively set by the enterprise according to its own strategy and business needs, so no specific limitations are made in this embodiment; It should be specifically stated in this implementation that the supply chain ability system is as follows: High - influence supply chain: , where is a pre - set high - influence threshold, determined according to the enterprise's historical data and business needs, so no specific limitations are made in this embodiment. Such supply chains are excellent in terms of supply capacity, product quality, cost, cooperation relationship, financial status, and innovation ability, and have a significant impact on the operation and development of the enterprise; Medium - influence supply chain: , is a preset low - impact threshold, which is determined according to the enterprise's historical data and business requirements. Therefore, no specific limitation is made in this embodiment. The performance of these supply chains in all aspects is relatively balanced, but there is still room for improvement in some dimensions, and it has a certain supporting effect on the enterprise; Low - impact supply chain: , such supply chains perform poorly in multiple dimensions, may bring certain risks to the enterprise's supply chain, and need to be focused on and improved.

[0021] S5: Supply chain strategy formulation: Develop cooperation strategies for suppliers with different impact levels.

[0022] It should be specifically noted in this embodiment that the cooperation strategies are as follows: Sign a long - term strategic cooperation agreement with high - impact supply chains, clarify the cooperation goals, rights and obligations of both parties within a certain period in the future, ensure the stability and sustainability of the supply chain, jointly carry out cost analysis and optimization projects with high - impact supply chains, and achieve cost reduction for both parties by optimizing the procurement process, reducing logistics costs, improving production efficiency, etc.; Conduct a comprehensive assessment of medium - impact supply chains once every six months, adjust the cooperation strategy according to the assessment results, appropriately increase the order share for supply chains with significantly improved performance; For supply chains with declining performance, strengthen supervision and management, and take punitive measures if necessary; Send a written rectification notice to low - impact supply chains, clearly point out the existing problems and improvement requirements, set a rectification deadline, at the same time establish a supervision mechanism, regularly check the progress of the supply chain's rectification, require the supply chain to submit a rectification report regularly to ensure that the rectification work is carried out as planned, and at the same time formulate an emergency plan, search for alternative supply chains in advance, and be able to quickly switch the supply channel when the low - impact supply chain cannot meet the enterprise's needs to ensure the continuity of the enterprise's production; It should be further noted that the specific cooperation strategies with suppliers of different levels can be adjusted according to the enterprise's business objectives and requirements. Therefore, no specific limitation is made in this embodiment.

[0023] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments of the present invention are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above - mentioned are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A big data-based enterprise supply chain management method, characterized in that: include: The data collection terminal, data processing terminal, evaluation and analysis terminal, strategy decision terminal and interactive terminal specifically include the following steps: S1: Determine the data collection area: obtain the comprehensive data categories of the enterprise supply chain certification data area and establish the index text; The index text includes a first index text, the first index text includes a supply data category, a product data category, a cost data category, a cooperation data category, a financial data category and an innovation data category, and the first index text includes a second index text; Construct supplier data category intervals, divide the data intervals based on the supplier index text, obtain several corresponding supplier data subcategories, and mark them as 1, 2, 3…i…n in sequence; S2: Supplier data acquisition: Based on the first index text in S1, the supplier data sub-category is screened to obtain a primary screening text, and based on the second index text in S1, the primary screening text is screened to obtain a secondary screening text; S3: Data cleaning and integration: Establish standardized processing text, import the secondary screening text into the standardized processing text, and obtain the supply chain data text; S4: Supply chain impact assessment: Construct a supply chain impact assessment processing text, import the supply chain data text into the supply chain impact assessment processing text, and classify suppliers according to the comprehensive impact assessment scores in the supply chain impact assessment processing text, dividing the suppliers into three categories: high impact, medium impact and low impact; S5: Supply chain strategy formulation: Develop cooperation strategies for suppliers with different levels of influence.

2. According to the enterprise supply chain management method based on big data according to claim 1, it is characterized by: The supply chain data text includes: Supply volume share, Number of product types, Supply product criticality Average replenishment time, Number of supply interruptions, Product qualification rate, Defective rate, Number of quality complaints Quality certification status, Supply product price, Supply product price, Procurement cost ratio Years of cooperation The closeness of cooperation, Order fulfillment rate, Debt-to-asset ratio, Current ratio and Net profit margin, R&D investment ratio of new products Number of patents Application of technological innovation achievements; The specific collection method is as follows: In a month, the total amount of goods purchased by the enterprise from all supply chains is recorded as , the quantity of goods purchased from the i-th supply chain is recorded as ; The formula for calculating the supply volume ratio is: ; The specific collection method is as follows: Count the number of different products provided by the i-th supply chain to the enterprise and record it directly as ; The criticality of the supplied products is collected in the following ways: Assume that the final product produced by the enterprise has n key production links, and the participation degree of the product supplied by the i-th supply chain in the j-th key production link is , The value range is , full participation is 1, non-participation is 0, and the importance weight of each key production link is ; The formula for calculating the criticality of the supply product is: ; Average replenishment time. The specific collection method is as follows: Record the time taken for each replenishment of the i-th supply chain during the statistical period , j represents the jth replenishment, and the total number of replenishments is m; The average replenishment time is calculated as: ; The specific collection method of supply interruption number is as follows: directly count the number of supply interruptions caused by the i-th supply chain during the statistical period, recorded as ; The specific collection method of quality certification information is as follows: If the i-th supply chain obtains ISO9001 quality certification, then =1; if not authenticated, =0 ; Supply product prices, the specific collection method is as follows: Directly obtain the unit price of a specific product supplied by the i-th supply chain, denoted as ; Supply product prices, the specific collection method is as follows: During the statistical period, the highest price of the product supplied by the i-th supply chain is , the lowest price is The average price is ,in is the number of price records, is the price recorded at the kth time; The formula for calculating price fluctuation range is: ; The specific collection method is as follows: The procurement cost of the i-th supply chain in a month is recorded as , the total purchase cost of the enterprise in a month is recorded as ; The calculation formula for the proportion of procurement cost is: ; The specific collection method of cooperation years is as follows: The date when the enterprise starts cooperating with the i-th supply chain is The statistical deadline is ; The calculation formula for the cooperation period is: = - , in years; The degree of cooperation is collected in the following ways: Count the number of key links such as product design, production planning, and quality control of the first i supply chain participating enterprises , the total number of key links of the enterprise is N; The calculation formula for the closeness of cooperation is: ; Order fulfillment rate, the specific collection method is as follows: During the statistical period, the number of orders placed by the enterprise to the i-th supply chain is recorded as , the number of orders actually delivered by the supply chain is recorded as ; The calculation formula for order fulfillment rate is: ; Debt-to-asset ratio, Current ratio and Net profit margin; The proportion of new product R&D investment is collected in the following ways: Obtain the amount of new product R&D investment during the statistical period from the financial data of the i-th supply chain and operating income ; The calculation formula for the proportion of new product R&D investment is: ; The number of patents is collected in the following ways: Count the number of valid patents owned by the i-th supply chain, denoted as ; The application of technological innovation results is collected in the following ways: The number of products produced by the i-th supply chain using new technology is The total number of products is ; The calculation formula for the application of technological innovation results is: = 00%。 3. The enterprise supply chain management method based on big data according to claim 1 is characterized by: The supply chain impact assessment processing text specifically includes the following steps: A1: Perform nonlinear transformation on the indicator: A2: Calculate the supply capability dimension score SC, product quality dimension score PC, cost dimension score CC, partnership dimension score RC, financial status dimension score FC, and innovation capability dimension score IC based on the transformed indicators; A3: Input the supply capacity dimension score SC, product quality dimension score PC, cost dimension score CC, partnership dimension score RC, financial status dimension score FC and innovation capability dimension score IC into the comprehensive impact evaluation function to obtain the comprehensive impact evaluation score. .

4. The enterprise supply chain management method based on big data according to claim 3 is characterized by: The nonlinear transformation is specifically as follows: Nonlinear transformation of supply share: , which maps the supply share to interval, to avoid excessive influence of too large proportion on the results; Nonlinear transformation of the number of product types: ,in It is the maximum number of product types in all supply chains. This transformation gives supply chains with more types a relative advantage, but not too much. Transformation of average replenishment time: , the shorter the replenishment time, the closer the value is to 1; Transformation of the number of supply interruptions: , the fewer the number of interruptions, the closer the value is to 1; Transformation of defective rate: ; Changes in the number of quality complaints: ; Changes in supply product prices: ; Changes in price fluctuations: ; Changes in the proportion of procurement costs: ; The cooperation period is transformed nonlinearly: ; Changes in debt-to-asset ratio: ; The number of patents is transformed nonlinearly: ,in It is the maximum number of all supply chain patents.

5. The enterprise supply chain management method based on big data according to claim 3 is characterized by: The specific calculation method of the supply capacity dimension score SC is as follows: ； The specific calculation method of the product quality dimension score PC is as follows: ； The specific calculation method of the cost dimension score CC is as follows: ； The specific calculation method of the partnership dimension score RC is as follows: ； The specific calculation method of the financial status dimension score FC is as follows: ； The specific calculation method of the innovation capability dimension score IC is as follows: 。 6. The enterprise supply chain management method based on big data according to claim 3 is characterized by: The comprehensive influence evaluation function is as follows: ； in, , , They are the number of supply interruptions, the number of quality complaints, and the asset-liability ratio thresholds. If the number of supply interruptions in the supply chain exceeds , or the number of quality complaints exceeds , or the debt-to-asset ratio exceeds , then the supply chain influence is directly determined to be 0.

7. The enterprise supply chain management method based on big data according to claim 1 is characterized by: Enterprises are scored based on comprehensive impact assessment The specific classification method for enterprises is as follows: High Impact Supply Chain: ,in is a pre-set high impact threshold; Medium Impact Supply Chain: , is a pre-set low impact threshold; Low Impact Supply Chain: .

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