An enterprise order procurement management method and system

Abstract

The present application provides an enterprise order procurement management method and system, which relates to the technical field of procurement management. By obtaining the performance status feedback information corresponding to the procurement order; performing multi-dimensional alignment analysis on the performance status feedback information to generate the performance efficiency boundaries of different procurement material types, constructing procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundaries and user signature data; performing anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching, and based on the matching deviation trend and shipping data, performing hierarchical verification on the resources of the supplier to generate supply recommendation rules for hierarchical suppliers; generating a target order corresponding to the procurement demand in the supply chain according to the procurement constraint rules and the supply recommendation rules. The present application can dynamically monitor the performance status and recommend suppliers at different levels under the supply chain collaboration platform to improve the accuracy of supply chain matching in the procurement process.

Description

Technical Field

[0001] This application relates to the technical field of procurement management. More specifically, this application relates to an enterprise order procurement management method and system. Background Art

[0002] Procurement management is an important part of an enterprise's supply chain management, covering the entire process from procurement demand identification to order fulfillment. Its core goal is to ensure that the enterprise can obtain the required materials or services from reliable suppliers at the right time and at a reasonable cost, thus ensuring the continuity of production and operation. Modern procurement management has gradually introduced technologies such as big data, artificial intelligence, and blockchain to achieve intelligent and automated procurement decisions. In the face of market demand fluctuations, the procurement management system needs to have capabilities such as real-time analysis, flexible matching, and intelligent scheduling to enhance the adaptability and resilience of the supply chain, reduce procurement risks, and improve overall operational efficiency.

[0003] However, in existing enterprise order procurement management methods and systems, there is a lack of real-time collection of supplier shipment data and user signature data and feedback on the fulfillment status, making it difficult to accurately monitor the fulfillment progress during the procurement process, resulting in information lag, untimely abnormal identification, and inaccurate evaluation of supplier fulfillment capabilities during the execution of procurement orders, thereby increasing the uncertainty of the supply chain and procurement risks. Therefore, how to dynamically monitor the fulfillment status and recommend supplier grading under the supply chain collaboration platform to improve the accuracy of supply chain matching in the procurement process is an issue faced by the industry. Summary of the Invention

[0004] This application provides an enterprise order procurement management method and system, which can dynamically monitor the fulfillment status and recommend supplier grading under the supply chain collaboration platform to improve the accuracy of supply chain matching in the procurement process.

[0005] In a first aspect, this application provides an enterprise order procurement management method, and the procurement management method includes the following steps:

[0006] Collect the supplier shipment data and user signature data from the supply chain collaboration platform, and obtain the fulfillment status feedback information corresponding to the procurement order in real time;

[0007] In the order procurement management, perform multi-dimensional alignment analysis on the fulfillment status feedback information to generate the fulfillment efficiency boundaries of different procurement material types, and construct procurement constraint rules for each execution stage of the order procurement according to the fulfillment efficiency boundaries and the user signature data;

[0008] Perform anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching. Based on the matching deviation trend and the shipping data, perform hierarchical verification on the resources of the suppliers, and generate supply recommendation rules for hierarchical suppliers;

[0009] Generate a target order corresponding to the procurement demand in the supply chain according to the procurement constraint rules and the supply recommendation rules.

[0010] In this embodiment, in order procurement management, performing multi-dimensional alignment analysis on the performance status feedback information to generate the performance efficiency boundary of different procurement material types specifically includes:

[0011] Determine the supply response intensity of the supplier during order supply according to the performance status feedback information;

[0012] Extract multi-dimensional features from the supply response intensity to obtain dynamic coupling information of different procurement material types;

[0013] Construct a performance efficiency gradient for different procurement material types according to the dynamic coupling information;

[0014] Determine the performance efficiency boundary of different procurement material types from the performance efficiency gradient.

[0015] In this embodiment, constructing procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundary and the user receipt data specifically includes:

[0016] Determine the dynamic association deviation during order procurement according to the performance efficiency boundary and the user receipt data;

[0017] Generate performance risk characteristics for different procurement stages according to the dynamic association deviation;

[0018] Determine the procurement constraint rules for order procurement at each execution stage from the performance risk characteristics.

[0019] In this embodiment, the procurement constraint rule refers to the control conditions for successful performance in different procurement execution stages.

[0020] In this embodiment, performing anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching specifically includes:

[0021] Obtain the keyword extraction strategy during procurement demand identification;

[0022] Extract the matching evolution path of the procurement demand in the supply chain matching from the keyword extraction strategy;

[0023] Determine the deviation level spectrum of procurement requirements in the supply chain matching according to the described matching evolution path;

[0024] Obtain the response ability characteristics of supply chain nodes;

[0025] Determine the matching deviation trend of procurement requirements in the supply chain matching according to the deviation level spectrum and the response ability characteristics.

[0026] In this embodiment, the matching deviation trend refers to the matching deviation state of procurement requirements in each link of the supply chain.

[0027] In this embodiment, based on the matching deviation trend and the shipping data, performing hierarchical verification on the resources of suppliers and generating supply recommendation rules for hierarchical suppliers specifically includes:

[0028] Based on the matching deviation trend and the shipping data, extract the collaborative supply parameters when the resources of hierarchical suppliers are matched;

[0029] Determine the quality fluctuation compensation amount of the supplier during cross-cycle delivery according to the collaborative supply parameters;

[0030] Determine the resource allocation confidence interval of hierarchical suppliers through the quality fluctuation compensation amount;

[0031] Determine the corrected scheduling level of hierarchical suppliers according to the resource allocation confidence interval.

[0032] In this embodiment, the corrected scheduling level refers to the scheduling strategy for adjusting the delivery plans of suppliers at each node in the supply chain.

[0033] In this embodiment, generating the target order corresponding to the procurement requirement in the supply chain according to the procurement constraint rule and the supply recommendation rule specifically includes:

[0034] Determine the elastic matching path corresponding to the procurement requirement in the supply chain according to the procurement constraint rule and the supply recommendation rule;

[0035] Generate a candidate order set for demand matching through the elastic matching path combined with real-time supply chain disturbance parameters;

[0036] Determine the target order corresponding to the procurement requirement in the supply chain according to the candidate order set.

[0037] In a second aspect, the present application provides an enterprise order procurement management system for executing an enterprise order procurement management method, and the procurement management system includes:

[0038] A data acquisition module for collecting the shipping data of suppliers and the user signature data from the supply chain collaboration platform, and for obtaining the feedback information on the fulfillment status corresponding to the procurement order in real time;

[0039] An information analysis module is configured to perform multi-dimensional alignment analysis on the performance status feedback information in order procurement management, generate the performance efficiency boundaries of different procurement material types, and construct procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundaries and the user signature data;

[0040] A hierarchical verification module is configured to perform anomaly detection on the keyword extraction strategy during procurement requirement identification, obtain the matching deviation trend of procurement requirements in supply chain matching, perform hierarchical verification on the resources of suppliers based on the matching deviation trend and the shipping data, and generate supply recommendation rules for hierarchical suppliers;

[0041] An order generation module is configured to generate a target order corresponding to the procurement requirement in the supply chain according to the procurement constraint rules and the supply recommendation rules.

[0042] The technical solution provided by the disclosed embodiments of the present application has the following beneficial effects:

[0043] Collect the shipping data of suppliers and the user signature data from the supply chain collaboration platform, and obtain the performance status feedback information corresponding to the procurement order in real time; in order procurement management, perform multi-dimensional alignment analysis on the performance status feedback information, generate the performance efficiency boundaries of different procurement material types, and construct procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundaries and the user signature data; perform anomaly detection on the keyword extraction strategy during procurement requirement identification, obtain the matching deviation trend of procurement requirements in supply chain matching, perform hierarchical verification on the resources of suppliers based on the matching deviation trend and the shipping data, and generate supply recommendation rules for hierarchical suppliers; generate a target order corresponding to the procurement requirement in the supply chain according to the procurement constraint rules and the supply recommendation rules.

[0044] It can be seen that in this application, the intelligent level of the procurement process and the closed-loop optimization ability of performance management can be improved. Among them, the real-time monitoring of the supply chain performance process is realized, the accuracy and timeliness of data are guaranteed, the visualization ability of the procurement management for the supplier shipment, logistics distribution, and user receipt links is improved, and the performance risks caused by information lag are reduced. Through multi-dimensional analysis of performance data, the performance efficiency boundaries of different procurement materials are formed, and based on this, the constraint rules in the procurement execution stage are constructed, so that the procurement can accurately identify the matching degree between the procurement requirements and the supplier performance ability through keyword extraction, anomaly detection, and matching deviation analysis, optimize the supplier grading strategy, improve the scientific nature of supplier resource management, and reduce the procurement failure rate caused by supply chain matching deviation. The process is more precise and standardized, the executability of the procurement plan is improved, and the probability of performance anomalies is reduced. Combining intelligent procurement constraints with supplier recommendation rules, automatic order optimization configuration is realized, the matching accuracy of procurement requirements and supply chain resources is improved, the procurement cycle is optimized, the performance success rate is increased, thereby reducing the procurement cost and improving the overall efficiency of the supply chain.

[0045] In summary, the technical solution adopted in this application can dynamically monitor the performance status and recommend supplier grading under the supply chain collaboration platform to improve the accuracy of supply chain matching in the procurement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0047] Figure 1 is a flowchart of the enterprise order procurement management method provided by the present application;

[0048] Figure 2 is a schematic flowchart of determining the performance efficiency boundary provided by the present application;

[0049] Figure 3 is a schematic flowchart of determining the supply recommendation rules provided by the present application;

[0050] Figure 4 is a module structure diagram of the enterprise order procurement management system provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0052] The embodiments of the present application provide an enterprise order procurement management method and system. The core is to collect the shipment data of suppliers and the user signature data from the supply chain collaboration platform, and obtain the feedback information on the performance status corresponding to the procurement order in real time; in the order procurement management, perform multi-dimensional alignment analysis on the performance status feedback information to generate the performance efficiency boundaries of different procurement material types, and construct the procurement constraint rules for the order procurement at each execution stage according to the performance efficiency boundaries and the user signature data; perform anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching, and perform hierarchical verification on the resources of the supplier based on the matching deviation trend and the shipment data to generate the supply recommendation rules for the hierarchical supplier; generate the target order corresponding to the procurement demand in the supply chain according to the procurement constraint rules and the supply recommendation rules.

[0053] Embodiment 1. To better understand the above technical solutions, the following will describe the above technical solutions in detail in conjunction with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 As shown, this figure is an exemplary flowchart of the enterprise order procurement management method according to the present embodiment of the present application. The procurement management method includes the following steps:

[0054] In step S1, collect the shipment data of suppliers and the user signature data from the supply chain collaboration platform, and obtain the feedback information on the performance status corresponding to the procurement order in real time.

[0055] In specific implementation, first, through the API interface of the supply chain collaboration platform, the shipping orders and logistics information uploaded by suppliers are obtained, and message queues such as Kafka are used for real-time streaming transmission, and Airflow is used for data cleaning and storage in a distributed database. The shipping data of suppliers is obtained by reading the distributed database; the receipt time and quality inspection status are collected through OCR recognition of the receipt note, RFID scanning of goods or IOT devices, and then NLP tools such as BERT are used to parse the receipt feedback information, such as "material damage", and the receipt feedback information is used as the user receipt data. In other embodiments, other methods can also be used to collect the shipping data of suppliers and the user receipt data, which is not limited here. Then, the transportation information is collected through the supply chain collaboration platform and the logistics service provider interface, including the status of shipping, in transit, delivery, receipt, etc., and the fulfillment progress provided by the supplier, such as production completion, quality inspection qualified, warehouse outbound, etc., is received through electronic data interchange, and the fulfillment evaluation (such as delay, damage) of the receipt note, email, and customer service system is parsed, and the parsed result is used as the fulfillment status feedback information corresponding to the purchase order.

[0056] It should be noted that in this application, the shipping data represents the material shipping information provided by the supplier during the order fulfillment process, including time, location, item list, and transportation method; the user receipt data represents the receipt time, acceptance status, quality inspection result, and feedback information recorded when the user receives the material; the fulfillment status feedback information refers to the status update of each stage during the order fulfillment process, including shipping, transportation, receipt, quality inspection, and abnormal situations.

[0057] In step S2, in the order procurement management, multi-dimensional alignment analysis is performed on the fulfillment status feedback information to generate the fulfillment efficiency boundaries of different procurement material types, and procurement constraint rules for the order procurement in each execution stage are constructed according to the fulfillment efficiency boundaries and the user receipt data.

[0058] Preferably, in this embodiment, in the order procurement management, multi-dimensional alignment analysis is performed on the fulfillment status feedback information to generate the fulfillment efficiency boundaries of different procurement material types. Refer to Figure 2 As shown, this figure is a schematic flow diagram for determining the fulfillment efficiency boundary in some embodiments of this application. The determination of the fulfillment efficiency boundary in this embodiment can be implemented by the following steps:

[0059] In step S21, the supply response intensity of the supplier during order supply is determined according to the fulfillment status feedback information;

[0060] In step S22, multi-dimensional feature extraction is performed on the supply response intensity to obtain the dynamic coupling information of different procurement material types;

[0061] In step S23, a performance efficiency gradient for different types of procurement materials is constructed based on the dynamic coupling information;

[0062] In step S24, the performance efficiency boundaries for different types of procurement materials are determined from the performance efficiency gradient.

[0063] Specifically, when implementing, first, relevant data on supplier performance is obtained from the procurement system, including: order delivery time, supplier order confirmation time, order shipment time, logistics transportation time, order completion status, and order performance exceptions (such as delays, out-of-stock, quality problems, etc.). Metrics such as the average delivery time and on-time delivery rate of the supplier are calculated, and regression analysis and random forest models are used to predict the response intensity of the supplier, that is, the supply response intensity is obtained. Next, the following feature dimensions are extracted: supplier response duration, performance cycle, order delay rate; pass rate of delivered products, quality complaint rate; historical order performance fluctuations, supplier delivery consistency; supplier's response ability to emergency orders, degree of adaptability of delivery ability to market demand changes. Principal component analysis is used to perform dimensionality reduction analysis on the extracted feature dimensions, and the results after dimensionality reduction analysis are predicted using time series, and the prediction results are used as the dynamic coupling information for different types of procurement materials. Then, a mapping relationship between the supply response intensity and the performance result is established, and a decision tree model is used to classify the performance patterns of different material types. Next, the influence weights of different dimensions (time, quality, stability, etc.) on the performance efficiency are constructed, and a Markov decision process is used to model the change trend of the supplier's performance status. The output results are used as the performance efficiency gradient for different types of procurement materials, where the performance efficiency gradient includes high-efficiency suppliers, medium-efficiency suppliers, and low-efficiency suppliers. Finally, the minimum standard of the supplier's response ability is set, low-efficiency suppliers are screened, the distribution of the supplier's performance ability is calculated, the boundary values of high-efficiency, medium-efficiency, and low-efficiency are determined, and a constrained optimization algorithm is used to determine the optimal performance ability demarcation point, and the optimal performance ability demarcation point is used as the performance efficiency boundary for different types of procurement materials.

[0064] It should be noted that in this application, the supply response intensity refers to the response ability of the supplier to demand changes during the order supply process; the dynamic coupling information refers to the correlation information between the supplier's performance ability, quality performance, and supply stability under different types of procurement materials; the performance efficiency gradient refers to the hierarchical division of different suppliers in terms of performance ability; the performance efficiency boundary refers to the performance ability range of different categories of suppliers.

[0065] In this embodiment, constructing the procurement constraint rules for order procurement in each execution stage based on the performance efficiency boundary and the user signature data can be implemented by the following steps:

[0066] Determine the dynamic correlation deviation during order procurement based on the performance efficiency boundary and the user signature data;

[0067] Generate performance risk characteristics for different procurement stages based on the dynamic correlation deviation;

[0068] Determine the procurement constraint rules for order procurement in each execution stage based on the performance risk characteristics.

[0069] When specifically implemented, first, obtain the performance efficiency boundary data, including supplier performance capabilities, delivery timeliness, performance quality, and user receipt data, including receipt time, receipt status, and exception feedback; use time series analysis to perform time matching on the performance efficiency boundary and user receipt data, calculate the deviation between the actual delivery time and the predicted delivery time to form a timeliness deviation index, then calculate the deviation between the receipt quality and the performance efficiency boundary to form a quality deviation index, linearly fit the timeliness deviation index and the quality deviation index, and use the fitting result as the dynamic correlation deviation. Then, divide the procurement execution process into multiple key stages, such as procurement application, order confirmation, material transportation, warehousing acceptance, and final receipt; analyze historical performance data to identify fluctuations in supplier performance capabilities at different stages, such as supply instability and delay rates, and then based on transportation historical data, calculate the transportation delay probability for different procurement stages, and combine quality inspection data and user feedback data to identify high-risk material types, where high-risk material types include products that are easily damaged and have a high return rate. Evaluate the impact of demand fluctuations on performance capabilities through a market demand prediction model. Use K-means clustering analysis or principal component analysis to reduce the dimension of risk factors for different procurement stages to form a set of key performance risk characteristics, and obtain the performance risk characteristics from the set of key performance risk characteristics. Finally, based on the performance efficiency boundary and dynamic deviation analysis, set the delivery time window for different stages, including: the longest supplier confirmation time, the shortest transportation time; combine user receipt data to set the minimum acceptable quality standards, including: the upper limit of the non-conformance rate, the threshold of the user complaint rate; based on the performance historical data, set the minimum supplier performance standards for different procurement material types, such as on-time delivery rate > 95%. For high-risk procurement stages, such as supply chain break risk, set an alternative supplier trigger mechanism, use reinforcement learning to dynamically adjust procurement constraints to adapt to market demand fluctuations, and verify the adaptability of the procurement constraint rules through simulation tests to ensure their feasibility in actual procurement execution, and finally output the procurement constraint rules for order procurement in each execution stage.

[0070] It should be noted that in this application, the dynamic correlation deviation refers to the difference between the performance efficiency boundary of different procurement stages and the actual user receipt situation during the order procurement process; the performance risk characteristic refers to the potential risk during the procurement execution process; the procurement constraint rule refers to the control conditions for successful performance in different procurement execution stages.

[0071] In step S3, an anomaly detection is performed on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching. Based on the matching deviation trend and the shipping data, a hierarchical verification of the supplier's resources is carried out to generate a supply recommendation rule for the hierarchical suppliers.

[0072] In this embodiment, the anomaly detection of the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of the procurement demand in the supply chain matching can be achieved by the following steps:

[0073] Obtain the keyword extraction strategy during procurement demand identification;

[0074] Extract the matching evolution path of the procurement demand in the supply chain matching from the keyword extraction strategy;

[0075] Determine the deviation level map of the procurement demand in the supply chain matching according to the matching evolution path;

[0076] Obtain the response ability characteristics of the supply chain nodes;

[0077] Determine the matching deviation trend of the procurement demand in the supply chain matching according to the deviation level map and the response ability characteristics.

[0078] In specific implementation, first, use the term frequency-inverse document frequency to extract keywords, or use a deep learning model to extract keywords after context understanding. According to the context of the procurement requirements, extract information such as key material types, demand quantities, and time requirements. Through the extracted keywords, establish a benchmark keyword set, and record the keyword features of normal procurement requirements, such as demand vocabulary, product classification, quantity units, etc. Then, construct the feature distributions of normal and abnormal procurement requirements, and use the feature distributions of normal and abnormal procurement requirements as the keyword extraction strategy. Next, according to the keywords of the procurement requirements, combined with the nodes in the supply chain, where the nodes in the supply chain include suppliers, warehouses, and transportation, establish a matching path. For example, the procurement requirements may go through the process of "demand identification → supplier selection → supplier quotation → delivery". The matching of each node can be mapped as a part of the evolutionary path. Analyze the possible path evolution of different procurement requirements in the supply chain through graph algorithms, identify the matching path deviation, and use time series analysis methods to model the change trend in the matching path. Use the results output by the model as the matching evolutionary path of the procurement requirements in the supply chain matching. Then, according to the matching deviations of different path nodes, where the matching deviations of different paths include: time delay, the difference between the demand quantity and the supplier's delivery quantity, define the deviation level, use K-means clustering analysis to classify the severity of the matching deviation, obtain the graph of the deviation level, and then use visual methods such as heat maps or radar charts to represent the deviation distribution during the matching process of the procurement requirements, forming the graph of the deviation level. Again, extract the response ability characteristics for each supply chain node according to the supplier's delivery timeliness, inventory level, and transportation capacity, use data analysis methods to reduce the dimensions of these characteristics, extract the most representative response ability indicators, and use the extracted indicators as the response ability characteristics of the supply chain nodes. Finally, according to the graph of the deviation level and the response ability characteristics, combined with multiple linear regression to establish a model, predict the matching deviation trend of different procurement requirements, and use the prediction results as the matching deviation trend of the procurement requirements in the supply chain matching.

[0079] It should be noted that in this application, the keyword extraction strategy refers to the process of extracting important descriptive information from procurement requirements through natural language processing technology; the matching evolutionary path refers to the evolution and matching state of procurement requirements in the entire supply chain process; the graph of the deviation level refers to the diagram of the deviation degree extracted from the procurement requirements in the supply chain matching path; the response ability characteristics refer to the execution ability of each supply chain link during the performance process; the matching deviation trend refers to the matching deviation state of procurement requirements in each link of the supply chain.

[0080] Preferably, in this embodiment, based on the matching deviation trend and the shipping data, the resources of the suppliers are graded and verified, and the supply recommendation rules for graded suppliers are generated, referring to Figure 3As shown, the figure is a schematic flowchart for determining supply recommendation rules in some embodiments of the present application. In this embodiment, the determination of supply recommendation rules can be implemented by the following steps:

[0081] In step S31, based on the matching deviation situation and the shipping data, co - supply parameters during resource matching of hierarchical suppliers are extracted;

[0082] In step S32, according to the co - supply parameters, the quality fluctuation compensation amount during cross - cycle delivery of the supplier is determined;

[0083] In step S33, the resource allocation confidence interval of hierarchical suppliers is determined through the quality fluctuation compensation amount;

[0084] In step S34, according to the resource allocation confidence interval, the corrected scheduling level of hierarchical suppliers is determined.

[0085] In specific implementation, first, shipping data is collected. The shipping data includes transportation timeliness, receipt feedback, distribution data, and matching deviation trend data. Among them, the matching deviation trend data includes time delay, quantity error, and quality problems. The data fusion method is used to integrate the two types of data, construct the global supplier matching characteristics, and extract the resource cooperation of suppliers, such as the response speed during the delivery process, inventory management ability, transportation ability, etc. Then, based on the historical matching data, the advantages and disadvantages of each supplier in resource matching are analyzed to form a score for the collaborative supply ability. For example, the scoring criteria are: high collaboration, high response, and high stability. The weighted average method is used to evaluate the collaborative supply ability of each supplier, and the collaborative supply ability of each supplier is used as the collaborative supply parameter when matching resources for classified suppliers. Next, based on the historical delivery data, a prediction model for quality fluctuations is established using quality regression analysis. The factors considered in this model include the supplier's delivery history, seasonal fluctuations, market demand fluctuations, etc., and a quantitative value of quality fluctuations is obtained. Then, according to the quality fluctuation prediction of each supplier during the cross-cycle delivery process, the required compensation amount is calculated, that is, the quality fluctuation compensation amount of the supplier during cross-cycle delivery. For example, if the quality fluctuation exceeds the predetermined threshold, additional inventory needs to be increased, the delivery time needs to be adjusted, or alternative supplier resources need to be added, and a rolling prediction model is used to update the quality fluctuation compensation amount in real time to ensure timely adjustment within the delivery cycle. Then, by combining the historical performance data of the supplier and the quality fluctuation compensation amount, the resource adaptability of each supplier under given conditions is analyzed, and the possible resource adaptation range of the supplier within the delivery cycle is calculated using the normal distribution. For example, on the premise of meeting the quality fluctuation compensation amount, the resource supply ability interval of the supplier is defined, and the suppliers are classified according to their resource adaptability. Suppliers with higher adaptability are classified as high priority, and suppliers with lower adaptability are classified as low priority. A classification decision tree is used to classify the suppliers, and different resource adaptation intervals are set for different levels, and the resource adaptation interval is used as the resource adaptation confidence interval. Finally, a time series prediction model or machine learning method is used to predict the future changes in the resources supplied by the supplier. The scheduling level of the supplier can be classified according to its ability to change the supplied resources. High-priority suppliers: Suppliers with a larger adaptation interval and a stronger ability to adapt to changes in the supplied resources. In the case of large changes in the supplied resources, these suppliers can undertake more tasks and delivery responsibilities. Medium-priority suppliers: Suppliers with a certain adaptation ability, but may need to be adjusted when there are large changes in the supplied resources. Low-priority suppliers: Suppliers with weak adaptation ability, and can only participate in supply tasks when the changes in the supplied resources are small or the demand is relatively stable. The scheduling level of high-priority suppliers can be adjusted to the main supplier in the supply chain plan, and resources are preferentially allocated. Medium-priority suppliers can be arranged among the alternative suppliers and ranked second in the scheduling level.Low-priority suppliers can be scheduled only under secondary requirements or dynamically allocated according to the specific resource supply situation. For example, when the resource adaptation range of a high-priority supplier changes, the algorithm will automatically adjust the task allocation to make the resource allocation in the supply chain balanced, that is, the corrected scheduling level of the hierarchical suppliers is obtained.

[0086] It should be noted that in this application, the collaborative supply parameter refers to the relevant indicators of the collaborative effect between suppliers during the supplier matching process; the quality fluctuation compensation amount refers to the adjustment amount required to make up for the quality fluctuations that occur during the supplier's delivery process; the resource adaptation confidence interval refers to the fluctuation range of the supply quantity and delivery timeliness of the resources that meet the supply demand set based on the quality fluctuation compensation amount during the supplier's delivery process; the corrected scheduling level refers to the scheduling strategy for adjusting the supplier's delivery plan at each node in the supply chain.

[0087] In step S4, a target order corresponding to the procurement demand in the supply chain is generated according to the procurement constraint rule and the supply recommendation rule.

[0088] In this embodiment, generating a target order corresponding to the procurement demand in the supply chain according to the procurement constraint rule and the supply recommendation rule can be implemented by the following steps:

[0089] Determine the elastic matching path corresponding to the procurement demand in the supply chain according to the procurement constraint rule and the supply recommendation rule;

[0090] Generate a candidate order set that matches the demand through the elastic matching path combined with the real-time supply chain disturbance parameters;

[0091] Determine the target order corresponding to the procurement demand in the supply chain according to the candidate order set.

[0092] In specific implementation, first, parse the procurement contract and the enterprise procurement strategy, extract the procurement constraint conditions, such as price ceiling / floor, delivery time window, quality standards (ISO certification, etc.), and supplier rating requirements. Then, use a rule engine to automatically parse these rules and store them in the procurement decision-making system. Based on historical procurement data, use a machine learning model to predict the matching degree between suppliers and current demands, and combine a scoring mechanism to calculate the recommended weights of different suppliers. Use linear programming to generate a matching path for procurement demands, and take this matching path as the elastic matching path corresponding to the procurement demands in the supply chain. Then, obtain the real-time supply chain status through IoT sensors, ERP systems, supplier platforms, etc., such as changes in supplier inventory (if the inventory is insufficient, alternative suppliers need to be found), logistics tracking information (whether the transportation is delayed), fluctuations in raw material market prices (if the price exceeds the limit, the supplier needs to be adjusted), and changes in production plans (changes in supplier production capacity); use Bayesian networks or Markov decision processes to analyze the impact of different disturbance factors on procurement matching and make predictions. Combine the elastic matching path and real-time supply chain disturbance information to calculate whether each supplier can still meet the procurement demands, and use a multi-objective optimization algorithm to generate multiple candidate order plans, and sort them according to criteria such as the lowest cost, shortest delivery time, and highest quality score. Take the sorted candidate order plans as the candidate order set for demand matching. Finally, score the candidate orders according to the comprehensive scoring model. Among them, the price weight (W1): the lower the price, the higher the score; the delivery time weight (W2): the shorter the delivery time, the higher the score; the quality scoring weight (W3): the better the historical delivery quality of the supplier, the higher the score; the supply chain stability weight (W4): the order less affected by disturbances gets a higher score. S = W1×price score + W2×delivery time score + W3×quality score + W4×supply chain stability, where S represents the target order score. Then set a threshold, for example: give priority to orders with a total order score > 80. If there are multiple orders with similar scores, give priority to orders with higher supply chain stability. Use an intelligent decision-making algorithm to automatically optimize the selection of target orders in a dynamic environment, and send the finally determined target order to the ERP system to execute the procurement process and monitor the order execution situation. If an exception occurs (such as the supplier cancelling the order), then return to the candidate order set for re-matching.

[0093] It should be noted that in this application, the elastic matching path means that in the procurement process, a certain degree of flexible adjustment is allowed to adapt to the real-time changes in the supply chain and optimize the matching plan for procurement demands; the real-time supply chain disturbance parameters refer to the factors affecting procurement matching during the operation of the supply chain, such as inventory fluctuations, transportation delays, raw material shortages, and market price changes; the candidate order set refers to the set of alternative orders that meet the procurement demands under different supply chain disturbance situations.

[0094] It can be seen that in this application, the intelligent level of the procurement process and the closed-loop optimization ability of the performance management can be improved; among them, the real-time monitoring of the supply chain performance process is realized, the accuracy and timeliness of data are guaranteed, the visualization ability of the procurement management for the supplier shipment, logistics distribution and user signature links is improved, and the performance risk caused by information lag is reduced; through multi-dimensional analysis of the performance data, the performance efficiency boundaries of different procurement materials are formed, and based on this, the constraint rules in the procurement execution stage are constructed, so that the procurement can accurately identify the matching degree between the procurement demand and the supplier performance ability through keyword extraction anomaly detection and matching deviation analysis, optimize the supplier grading strategy, improve the scientificity of supplier resource management, and reduce the procurement failure rate caused by supply chain matching deviation. The process is more accurate and standardized, the executability of the procurement plan is improved, and the probability of performance anomalies is reduced; combined with intelligent procurement constraints and supplier recommendation rules, automatic optimization configuration of orders is realized, the matching accuracy of procurement demand and supply chain resources is improved, the procurement cycle is optimized, and the performance success rate is improved, thereby reducing procurement costs and improving the overall efficiency of the supply chain.

[0095] In summary, the technical solution adopted in this application can dynamically monitor the performance status and recommend supplier grading under the supply chain collaboration platform to improve the accuracy of supply chain matching in the procurement process.

[0096] Embodiment 2, this application provides an enterprise order procurement management system, referring to Figure 4 As shown in the figure, which is a module structure diagram of the enterprise order procurement management system according to this embodiment of this application, the procurement management system includes:

[0097] A data acquisition module 100, configured to collect the shipment data of the supplier and the user signature data from the supply chain collaboration platform, and obtain the performance status feedback information corresponding to the procurement order in real time;

[0098] An information analysis module 200, configured to perform multi-dimensional alignment analysis on the performance status feedback information in the order procurement management, generate the performance efficiency boundaries of different procurement material types, and construct procurement constraint rules for the order procurement in each execution stage according to the performance efficiency boundaries and the user signature data;

[0099] A grading verification module 300, configured to perform anomaly detection on the keyword extraction strategy during procurement demand identification, obtain the matching deviation trend of the procurement demand in the supply chain matching, and perform grading verification on the resources of the supplier based on the matching deviation trend and the shipment data, and generate supply recommendation rules for the graded suppliers;

[0100] An order generation module 400, configured to generate a target order corresponding to the procurement demand in the supply chain according to the procurement constraint rules and the supply recommendation rules.

[0101] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 a means for implementing the functions specified in one block or multiple blocks.

[0102] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0103] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. An enterprise order procurement management method, characterized in that, The procurement management method includes the following steps: Collect the shipment data of suppliers and the user signature data from the supply chain collaboration platform, and obtain the feedback information on the performance status corresponding to the procurement order in real time; In the order procurement management, conduct multi-dimensional alignment analysis on the performance status feedback information to generate the performance efficiency boundaries of different procurement material types, and construct procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundaries and the user signature data; Among them, constructing procurement constraint rules for order procurement at each execution stage according to the performance efficiency boundaries and the user signature data specifically includes: Determine the dynamic correlation deviation during order procurement according to the performance efficiency boundaries and the user signature data; Generate the performance risk characteristics of different procurement stages according to the dynamic correlation deviation; Determine the procurement constraint rules for order procurement at each execution stage from the performance risk characteristics; The procurement constraint rule refers to the control conditions for successful performance in different procurement execution stages; Conduct anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of procurement demand in supply chain matching, and based on the matching deviation trend and the shipment data, conduct hierarchical verification on the resources of suppliers to generate supply recommendation rules for hierarchical suppliers; Generate the target order corresponding to the procurement demand in the supply chain according to the procurement constraint rules and the supply recommendation rules.

2. The enterprise order procurement management method according to claim 1, wherein, In the order procurement management, conducting multi-dimensional alignment analysis on the performance status feedback information to generate the performance efficiency boundaries of different procurement material types specifically includes: Determine the supply response intensity of the supplier during order supply according to the performance status feedback information; Extract multi-dimensional features of the supply response intensity to obtain the dynamic coupling information of different procurement material types; Construct the performance efficiency gradient of different procurement material types according to the dynamic coupling information; Determine the performance efficiency boundaries of different procurement material types from the performance efficiency gradient.

3. The enterprise order procurement management method according to claim 1, characterized in that Conduct anomaly detection on the keyword extraction strategy during procurement demand identification to obtain the matching deviation trend of procurement demand in supply chain matching specifically includes: Obtain the keyword extraction strategy during procurement demand identification; Extract the matching evolution path of procurement demand in supply chain matching from the keyword extraction strategy; Determine the deviation level map of procurement demand in supply chain matching according to the matching evolution path; Obtain the response ability characteristics of supply chain nodes; Determine the matching deviation trend of procurement demand in supply chain matching according to the deviation level map and the response ability characteristics.

4. The enterprise order procurement management method according to claim 1, characterized in that, The matching deviation trend refers to the matching deviation status of procurement demand in each link of the supply chain.

5. The enterprise order procurement management method according to claim 1, characterized in that, Based on the matching deviation trend and the shipment data, conduct hierarchical verification on the resources of suppliers to generate supply recommendation rules for hierarchical suppliers specifically includes: Based on the matching deviation trend and the shipment data, extract the collaborative supply parameters during the resource matching of hierarchical suppliers; Determine the quality fluctuation compensation amount of the supplier during cross-cycle delivery according to the collaborative supply parameters; Determine the resource allocation confidence interval of hierarchical suppliers through the quality fluctuation compensation amount; Determine the corrected scheduling level of the hierarchical supplier according to the confidence interval of the resource adaptation configuration.

6. The enterprise order procurement management method according to claim 5, characterized in that The corrected scheduling level refers to the scheduling strategy for adjusting the delivery plan of the supplier at each node in the supply chain.

7. The enterprise order procurement management method according to claim 1, characterized in that, Generating the target order corresponding to the procurement demand in the supply chain according to the procurement constraint rule and the supply recommendation rule specifically includes: Determine the flexible matching path corresponding to the procurement demand in the supply chain according to the procurement constraint rule and the supply recommendation rule; Generate a candidate order set that matches the demand through the flexible matching path combined with the real-time supply chain perturbation parameters; Determine the target order corresponding to the procurement demand in the supply chain according to the candidate order set.

8. An enterprise order procurement management system for implementing an enterprise order procurement management method according to any one of claims 1 to 7, characterized in that, The procurement management system includes: A data acquisition module, which is used to collect the shipment data of the supplier and the user's signature data from the supply chain collaboration platform, and to obtain the feedback information on the performance status corresponding to the procurement order in real time; An information analysis module, which is used to perform multi-dimensional alignment analysis on the performance status feedback information in the order procurement management, generate the performance efficiency boundary of different procurement material types, and construct the procurement constraint rules for the order procurement at each execution stage according to the performance efficiency boundary and the user's signature data; A hierarchical verification module, which is used to perform anomaly detection on the keyword extraction strategy during the procurement demand identification, obtain the matching deviation trend of the procurement demand in the supply chain matching, and perform hierarchical verification on the resources of the supplier based on the matching deviation trend and the shipment data to generate the supply recommendation rules for the hierarchical supplier; An order generation module, which is used to generate the target order corresponding to the procurement demand in the supply chain according to the procurement constraint rule and the supply recommendation rule.

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