Intelligent purchasing management method and system

Abstract

The invention relates to the technical field of purchase management, in particular to an intelligent purchase management method and system, and the method comprises the steps: data collection, data processing and storage, demand prediction, supplier management, purchase plan generation, order tracking and management, data analysis and reporting and the like. The system is composed of a data acquisition module, a data processing module, a demand prediction module, a supplier management module, a purchase plan generation module, an order management module, a data analysis and report module and a user interface module. According to the invention, automation and intellectualization of the purchasing process can be realized, the purchasing cost is reduced, the supply chain management capability is enhanced, and the overall competitiveness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of procurement management, and in particular to an intelligent procurement management method and system. Background Art

[0002] Traditional procurement management methods usually rely on manual operations, and have problems such as low efficiency, opaque information, and unscientific decision-making. With the expansion of enterprise scale and the intensification of market competition, traditional procurement management methods can no longer meet the needs of modern enterprises. In recent years, the development of artificial intelligence, big data analysis, and cloud computing technologies has provided new solutions for procurement management. However, the existing intelligent procurement management system still lacks intelligent analysis and automated decision-making capabilities, and cannot fully meet the diversified and dynamic procurement needs of enterprises;

[0003] The problems can be summarized as follows: manual processing of purchase orders, supplier management and other processes are time-consuming and labor-intensive, affecting overall procurement efficiency;

[0004] Procurement-related data is stored in a decentralized manner, making it difficult to achieve unified management and analysis, resulting in insufficient basis for decision-making;

[0005] The existing system lacks the ability to deeply analyze procurement data and cannot realize intelligent functions such as demand forecasting and supply chain optimization;

[0006] Market demand and supply chain environment are changing rapidly, and traditional systems are unable to respond in real time, affecting corporate competitiveness.

[0007] Therefore, in view of the above-mentioned problems, this technical solution proposes an intelligent procurement management method and system. Summary of the invention

[0008] The purpose of the present invention is to provide an intelligent procurement management method and system to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: an intelligent procurement management method, comprising the following steps:

[0010] S1, data collection: collect procurement-related data through multiple channels, including internal procurement data, supplier information, and market data;

[0011] S2, data processing and storage: clean, organize and store the collected data and establish a unified data warehouse;

[0012] S3, demand forecasting: using machine learning algorithms to analyze historical procurement data and predict future procurement demand;

[0013] S4, supplier management: automatically evaluate and select the best suppliers based on their performance indicators and reputation information;

[0014] S5, procurement plan generation: Automatically generate procurement plans and orders based on demand forecasts and supplier evaluation results;

[0015] S6, Order Tracking and Management: Real-time tracking of order status, automatic handling of abnormal situations, and ensuring a smooth procurement process;

[0016] S7, Data Analysis and Reporting: Conduct in-depth analysis of procurement data, generate various reports, and assist in decision-making.

[0017] Preferably, S1 also includes collecting data from an enterprise resource planning system, a supplier database and a market situation website.

[0018] Preferably, the machine learning algorithm is a long short-term memory network (LSTM) model.

[0019] Preferably, supplier performance indicators include on-time delivery rate, product quality qualification rate and price competitiveness.

[0020] An intelligent procurement management system, comprising:

[0021] Data collection module: used to collect procurement-related data;

[0022] Data processing module: used to clean, organize and store the collected data;

[0023] Demand forecasting module: used to analyze historical procurement data and predict future demand;

[0024] Supplier management module: used to evaluate and select suppliers;

[0025] Procurement plan generation module: used to generate procurement plans and orders;

[0026] Order management module: used to track and manage order status;

[0027] Data analysis and reporting module: used to analyze procurement data and generate reports;

[0028] User interface module: used to provide user operation interface.

[0029] Preferably, the demand forecasting module uses a long short-term memory network (LSTM) model to perform demand forecasting.

[0030] Preferably, the supplier management module includes a performance indicator evaluation unit and a supplier selection unit, wherein the performance indicator evaluation unit is used to evaluate the supplier's delivery timeliness rate, product quality qualification rate and price competitiveness, and the supplier selection unit selects the best supplier based on the evaluation results.

[0031] Preferably, the order management module includes an order tracking unit and an exception handling unit, wherein the order tracking unit is used to track the order status in real time, and the exception handling unit is used to automatically handle order exceptions.

[0032] Preferably, the data analysis and reporting module includes a procurement cost analysis unit, a supplier performance reporting unit and a demand trend analysis unit.

[0033] Preferably, the user interface module includes a dashboard, a data visualization interface and an operation interface, wherein the dashboard is used to display real-time data of key procurement indicators, the data visualization interface is used to graphically display procurement data, and the operation interface is used to support users in viewing, modifying and approving procurement plans.

[0034] Compared with the prior art, the beneficial effects of the present invention are: by integrating artificial intelligence and big data technology, the automation and intelligence of the procurement process are realized:

[0035] Improve efficiency: Automate the procurement process, reduce manual operations, and improve overall procurement efficiency.

[0036] Reduce costs: Reduce procurement costs through intelligent analysis and optimized supplier selection.

[0037] Enhance decision-making capabilities: Based on in-depth analysis of big data, provide scientific decision-making basis and optimize procurement strategies.

[0038] Improve supply chain management: track order status in real time, handle exceptions promptly, and ensure the stability and reliability of the supply chain.

[0039] Adapt to dynamic environment: quickly respond to market changes, adjust procurement plans, and maintain the company's competitive advantage. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of an intelligent procurement management method.

[0041] Figure 2 This is a framework diagram of an intelligent procurement management system. DETAILED DESCRIPTION

[0042] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0043] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0044] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0045] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0046] Embodiment 1,

[0047] See also Figure 1 , an intelligent procurement management method, comprising the following steps:

[0048] S1, data collection: collect procurement-related data through multiple channels, including internal procurement data, supplier information, market data, etc.;

[0049] Among them, internal procurement data includes historical purchase orders, purchase contracts, inventory levels, financial statements, etc. For example, a manufacturing company's monthly raw material procurement records for the past three years, including material name, specification, quantity, price, supplier and other information;

[0050] Supplier information includes supplier qualification certification, historical delivery records, product quality inspection reports, price change trends, etc. For example, an electronics company cooperates with multiple resistor suppliers and regularly obtains data such as the on-time delivery rate and product qualification rate of each supplier through the system.

[0051] Market data include raw material market price index, industry development trends, economic indicators, etc. For example, a chemical company cooperates with a third-party data provider to obtain real-time polyurethane market price index and related industry analysis reports;

[0052] Furthermore, it should be noted that the data collection methods include: API interface collection, data crawling technology, and real-time streaming data processing;

[0053] API interface collection uses the API interface provided by the enterprise's internal ERP system to automatically collect purchase order data. For example, the system extracts the purchase order information of the last six months from SAP ERP through the API and automatically imports it into the intelligent procurement management system;

[0054] Data crawling technology: Through web crawler technology, the latest product price and inventory information is crawled from the supplier's official website or third-party market website. For example, the system regularly crawls the quotation information of various resistor suppliers on the Alibaba platform and updates the market data.

[0055] Real-time streaming data processing uses IoT devices to collect real-time inventory and transportation status data. For example, a logistics company installs GPS and temperature and humidity sensors on transportation vehicles to upload the location and status information of goods during transportation to the procurement management system in real time;

[0056] Furthermore, data collection also includes data collection from enterprise resource planning systems, supplier databases and market information websites.

[0057] S2, data processing and storage: cleaning, sorting and storing the collected data and establishing a unified data warehouse;

[0058] Among them, data cleaning includes removing duplicate data, filling missing values, and correcting erroneous data;

[0059] Remove duplicate data: During the data collection process, there may be duplicate purchase order records. The system automatically removes duplicates by comparing key fields such as order number and supplier information. For example, during the data collection process, a company found that the same order was recorded twice in the ERP system, and the system automatically deleted one of the duplicate records;

[0060] Filling missing values: For some missing purchase data, such as some orders that lack delivery dates, the system uses an interpolation method based on historical data to fill in the missing values. For example, an electronics company found that some orders lacked an estimated delivery date. The system automatically filled in the missing date information by analyzing the supplier's historical delivery patterns.

[0061] Correcting erroneous data: By setting reasonable verification rules, errors in data can be automatically corrected. For example, if a manufacturing company finds that the unit price of a batch of raw materials is abnormally high in the purchase record, the system will automatically mark the data and notify the purchaser to verify and correct it.

[0062] Data collation includes unified data format and data standardization;

[0063] Unified data format: Different data sources may have different data formats. The system converts them into a unified format through the data conversion module to facilitate subsequent processing and analysis;

[0064] Data standardization: Standardize the purchase quantities in different units. For example, if a company purchases steel in different measurement units (such as tons and kilograms), the system will convert them into kilograms as the standard unit to ensure data consistency.

[0065] Data storage includes the establishment of a unified data warehouse, data security and privacy protection;

[0066] Establish a unified data warehouse: Use distributed database technology to centrally store processed data in a unified data warehouse, supporting large-scale data storage and rapid retrieval. For example, a chemical company uses Hadoop Distributed File System (HDFS) to establish a data warehouse to store all procurement-related data and support rapid query and analysis;

[0067] Data security and privacy protection: Encrypted storage and permission management ensure data security and privacy. For example, the system encrypts all stored procurement data with AES-256, and only authorized procurement managers and data analysts can access and operate relevant data.

[0068] S3, demand forecasting: using machine learning algorithms to analyze historical procurement data and predict future procurement demand;

[0069] Among them, feature selection prediction;

[0070] Seasonal factors: The fabric purchasing demand of a certain clothing company has obvious seasonality, and the purchase volume increases significantly in spring and autumn. The system analyzes historical data to identify the impact of seasonal changes on purchasing demand.

[0071] Promotional activities: During a specific promotional period (such as "Double Eleven"), the purchasing demand of a retail enterprise increases significantly. The system uses the promotional activity date as a feature and incorporates it into the demand forecasting model.

[0072] Market trends: Based on market trend reports, an electronics company predicts that the release of new products will drive demand for related components. The system adjusts the input features of the demand forecasting model by integrating external market trend data.

[0073] Model training prediction;

[0074] Model selection: LSTM (Long Short-Term Memory Network) model is selected for demand forecasting because of its excellent performance in processing time series data.

[0075] Training process: The system uses the monthly purchase data of the past three years to train the LSTM model, adjusting the number of hidden layers and learning rate of the model to improve prediction accuracy.

[0076] Model validation: Through the cross-validation method, the prediction performance of the model in different time periods is evaluated to ensure the robustness and generalization ability of the model.

[0077] Demand application forecasting;

[0078] Forecast results: A chemical company uses the trained LSTM model to predict the raw material procurement demand in the next six months and generate detailed forecast reports and visualization charts.

[0079] Decision support: Based on the demand forecast results, the purchasing department formulates corresponding purchasing plans to ensure that the inventory level matches the forecast demand and avoid inventory backlogs or shortages.

[0080] S4, supplier management: automatically evaluate and select the best suppliers based on supplier performance indicators, credibility and other information;

[0081] Among them, supplier performance indicators include on-time delivery rate: a certain automobile manufacturer evaluates the on-time delivery rate of its suppliers and sets a target of more than 95%. The system automatically calculates the on-time delivery rate of each supplier in the past year and generates a performance report.

[0082] Product quality pass rate: An electronics company conducts quality inspections on resistor products provided by suppliers and records the pass rate of each batch. The system evaluates the supplier's product quality performance based on the quality inspection results.

[0083] Price competitiveness: The system evaluates whether the price offered by suppliers is competitive by comparing market data. For example, if a chemical company finds that the price of raw materials from a supplier is higher than the market average, the system will automatically reduce the supplier's score weight;

[0084] Supplier classification management

[0085] Preferred suppliers: Based on comprehensive evaluation results, a food company classifies suppliers with high on-time delivery rate, excellent product quality and competitive prices as preferred suppliers, and gives priority to allocating purchase orders to them.

[0086] Qualified Suppliers: A pharmaceutical company classifies suppliers with stable performance but not meeting the preferred standards as qualified suppliers, maintains cooperative relationships, and develops improvement plans.

[0087] Suppliers to be improved: A clothing company classified suppliers with delayed delivery and unstable product quality as suppliers to be improved, requiring them to improve their performance within a certain period of time, otherwise they would be considered for elimination.

[0088] Supplier selection algorithm

[0089] Application of multi-criteria decision-making algorithm: An electronics company uses the TOPS IS algorithm to score each supplier based on indicators such as delivery timeliness, product quality qualification rate, and price competitiveness, and generates a comprehensive ranking.

[0090] Weight setting: Set the weights of different indicators according to the enterprise strategy and procurement needs. For example, a manufacturing enterprise sets the weight of on-time delivery rate to 40%, product quality qualification rate to 35%, and price competitiveness to 25%.

[0091] Supplier Relationship Management

[0092] Communication and feedback mechanism: A chemical company regularly sends performance feedback reports to suppliers through the system, organizes supplier meetings, discusses performance improvement measures, and improves the quality of cooperation.

[0093] Long-term cooperation incentives: For suppliers with outstanding performance, a food company automatically identifies them through the system and grants them long-term cooperation contracts and rewards to enhance their enthusiasm for cooperation.

[0094] S5, procurement plan generation: Automatically generate procurement plans and orders based on demand forecasts and supplier evaluation results;

[0095] Demand matching: matching demand with supply capacity: an electronics company predicts that the demand for resistors in the next six months will be 1 million pieces. The system allocates purchase orders reasonably according to the supply capacity of each supplier. For example, if the supply capacity of supplier A is 600,000 pieces and the supply capacity of supplier B is 400,000 pieces, the system will place orders of corresponding quantities to the two suppliers respectively.

[0096] Order generation rules: Minimum order quantity: A manufacturing company sets the minimum order quantity for raw materials to 1,000 pieces. When the system generates a purchase order, it automatically ensures that the quantity of each order is not less than this threshold.

[0097] Procurement cycle: A clothing company sets the procurement cycle to once a month. The system automatically generates monthly purchase orders based on demand forecasts and inventory levels to avoid increased transportation costs caused by frequent orders.

[0098] Automation of approval process: Automatic submission for approval: After a chemical company generates a purchase order, the system automatically submits the order information to the purchasing manager for approval. The purchasing manager performs approval operations through the system interface without having to fill out paper forms manually.

[0099] Multi-level approval process: For large purchase orders, the company has set up a multi-level approval process. The system automatically determines whether further senior management approval is required based on the order amount to ensure the rationality and compliance of procurement decisions.

[0100] Order optimization: Reduce the number of purchases: Through system optimization algorithms, enterprises can combine multiple small orders into one large order, reduce the number of purchases, and reduce transportation and management costs.

[0101] Transportation cost optimization: The system optimizes the transportation routes and methods of orders based on the supplier's geographical location and transportation methods. For example, a manufacturing company consolidates orders from the same region and reduces overall transportation costs through centralized transportation.

[0102] Manual adjustment and optimization: Special demand processing: Based on the procurement plan generated by the system, the company finds that the new product to be released requires parts with special specifications. The procurement manager manually adjusts the order through the system to increase the purchase quantity of the relevant parts;

[0103] S6, Order Tracking and Management: Real-time tracking of order status, automatic handling of abnormal situations, and ensuring a smooth procurement process;

[0104] Order Status Tracking

[0105] Real-time monitoring: The enterprise monitors the status of purchase orders in real time through the system, and can view all links in real time, from order generation, supplier confirmation, transportation, arrival, etc. For example, the purchasing manager views the raw material order with order number PO12345 through the system, which is currently in the transportation stage and is expected to arrive at the warehouse in two days.

[0106] Automatic reminder: The system automatically sends reminder notifications based on the order status. For example, the company sets the system to automatically remind relevant personnel to prepare for receiving the order two days before the scheduled delivery date.

[0107] Exception handling

[0108] Delayed delivery processing: The company's purchase order PO67890 is delayed due to a supplier production line failure. The system automatically identifies the order delay status, triggers the exception handling process, and notifies the purchasing manager to communicate with the supplier to adjust the delivery plan or find an alternative supplier.

[0109] Quality issue handling: If the raw materials received by the company have quality problems, the system will automatically record the abnormal situation and notify the supplier to make quality rectification. At the same time, it will adjust the relevant procurement strategy to avoid similar problems from happening again.

[0110] Performance Feedback

[0111] Order execution feedback: After the enterprise completes the order, the system automatically collects order execution data, such as actual delivery time, quality inspection results, etc., and feeds it back to the supplier management module as a basis for supplier performance evaluation.

[0112] Continuous optimization: Based on order execution feedback, the system analyzes supplier performance trends and automatically adjusts supplier scoring weights to ensure continuous optimization of procurement decisions. For example, if a supplier's on-time delivery rate has improved month by month over the past six months, the system will automatically increase its priority in supplier selection.

[0113] Historical order query

[0114] Order retrospective analysis: Enterprises need to analyze the execution status of purchase orders in the past year. The system provides a historical order query function that supports retrieval and analysis based on multiple dimensions such as time, supplier, and product category.

[0115] Decision support: Through the query and analysis of historical order data, purchasing managers can identify the performance fluctuations of certain suppliers within a specific time period and adjust future purchasing strategies.

[0116] S7, Data Analysis and Reporting: Conduct in-depth analysis of procurement data, generate various reports, and assist in decision-making;

[0117] Procurement cost analysis

[0118] Analysis by category: Through the procurement cost analysis report generated by the system, the company found that the procurement cost of a certain type of raw materials showed an upward trend in the past year, identified the main reasons for the cost increase (such as market price increase, supplier price increase, etc.), and provided a basis for adjusting the procurement strategy.

[0119] Analysis by supplier: When a company analyzes the purchase costs of different suppliers and finds that the price of a certain supplier is higher than the market average, it decides to reduce the purchase costs through negotiation or by looking for alternative suppliers.

[0120] Supplier Performance Report

[0121] Performance score: The supplier performance report generated by the enterprise system shows that Supplier A's on-time delivery rate is 98%, the product quality pass rate is 99%, and the overall score is 95 points, which is an excellent performance.

[0122] Performance trend analysis: The system analyzes the performance trend of supplier B in the past six months and finds that its on-time delivery rate has been declining month by month. The system recommends that the purchasing department pay attention and take measures.

[0123] Demand trend analysis

[0124] Long-term trend forecast: Through systematic demand trend analysis, the company predicts that procurement demand will increase year by year in the next two years. It is recommended that the procurement department make arrangements in advance to ensure the stability of the supply chain.

[0125] Regional demand analysis: The company analyzed the changes in purchasing demand in different regions and found that demand in the north was growing faster, while demand in the south was relatively stable. It adjusted the regional purchasing strategy and optimized inventory distribution.

[0126] Abnormal situation analysis

[0127] Order anomaly analysis: The company identified the delivery delay of a batch of orders through the system. After analyzing the cause, it was found that it was a failure in the supplier's production line. The system recommended establishing a backup supplier to deal with similar emergencies.

[0128] Quality anomaly analysis: The company discovered that a product provided by a supplier had quality problems in a specific batch. System analysis found that the problem was related to insufficient maintenance of the supplier's production equipment. It is recommended to strengthen the supplier's equipment management and supervision.

[0129] Custom Reports

[0130] Customized requirements: Management needs to understand the procurement cost distribution of different product lines. The system supports user-defined report templates to generate highly targeted procurement cost distribution charts to help management make accurate decisions.

[0131] Dynamic report generation: The system allows users to adjust the content and format of reports based on real-time needs. For example, a purchasing manager needs a monthly purchasing performance report. The system automatically generates and exports the corresponding report based on the parameters set by the user.

[0132] Data Visualization Examples

[0133] Chart display: Enterprises use the line chart provided by the system to display the changing trend of procurement costs of different raw materials in the past year and intuitively present price fluctuations.

[0134] Embodiment 2:

[0135] See also Figure 2 In this embodiment, an intelligent purchasing management system is provided, which includes:

[0136] Data collection module: responsible for collecting procurement-related data from multiple channels such as internal ERP system, supplier database, market information website, etc.

[0137] Data processing module: cleans, organizes and stores the collected data, and uses big data technology to establish a unified data warehouse.

[0138] Demand forecasting module: Use machine learning algorithms (such as time series analysis, regression analysis, etc.) to analyze historical procurement data and predict future procurement needs.

[0139] Supplier management module: automatically evaluates and selects the best supplier based on the supplier's performance indicators (such as on-time delivery rate, quality qualification rate, etc.) and credibility.

[0140] Procurement plan generation module: Based on demand forecast and supplier evaluation results, it automatically generates procurement plans and orders and submits them for approval process.

[0141] Order management module: real-time tracking of order status, automatic handling of abnormal situations (such as delayed delivery, quality issues, etc.), to ensure a smooth procurement process.

[0142] Data analysis and reporting module: conduct in-depth analysis of procurement data, generate various reports (such as procurement cost analysis, supplier performance report, etc.), and assist management decision-making.

[0143] User interface module: provides a friendly user interface to support managers to view and operate various functional modules.

[0144] As a preferred embodiment of the present invention, the demand forecasting module adopts a time series forecasting model based on machine learning.

[0145] The specific steps are as follows:

[0146] Data preparation: Collect monthly procurement data for the past three years, including procurement quantity, amount, category, and other information.

[0147] Feature engineering: Extract relevant features that affect purchasing demand, such as seasonal factors, promotional activities, market trends, etc.

[0148] Model training: Select a suitable time series model (such as ARIMA, LSTM, etc.) and use historical data for training.

[0149] Model validation: Evaluate the prediction accuracy of the model through methods such as cross-validation and select the optimal model.

[0150] Demand forecasting: Use the trained model to predict procurement demand for the next six months and generate a forecast report.

[0151] As a preferred embodiment of the present invention, the supplier management module evaluates and selects suppliers through the following steps:

[0152] Indicator setting: Determine key indicators for supplier evaluation, including delivery timeliness, product quality, price competitiveness, service response speed, etc.

[0153] Data collection: Obtain relevant data from ERP system and supplier feedback.

[0154] Scoring mechanism: Set weights for each indicator and score suppliers based on their actual performance.

[0155] Comprehensive evaluation: Calculate the comprehensive score of each supplier and generate a supplier ranking.

[0156] Select the best suppliers: Select the suppliers with the highest scores as partners based on the comprehensive scores.

[0157] As a preferred embodiment of the present invention, the procurement plan generation module automatically generates a procurement plan and an order according to the demand forecast and the supplier evaluation results. The specific steps are as follows:

[0158] Demand matching: Matching forecasted purchasing needs with suppliers’ supply capabilities.

[0159] Order generation: Automatically generate a purchase order based on the matching results, including information such as category, quantity, price, delivery time, etc.

[0160] Approval process: The generated order will be submitted to the relevant management personnel for approval, and will be automatically sent to the supplier after approval.

[0161] Order tracking: Track order status in real time, monitor supplier's delivery progress, and handle abnormal situations promptly.

[0162] As a preferred embodiment of the present invention, the data analysis and reporting module provides multi-dimensional data analysis functions to support management to conduct comprehensive procurement analysis. The specific functions include:

[0163] Procurement cost analysis: Analyze procurement costs of different categories and suppliers to identify cost saving opportunities.

[0164] Supplier Performance Report: Evaluate supplier performance and identify quality suppliers and those that require improvement.

[0165] Demand trend analysis: Analyze the changing trend of procurement demand and assist in formulating long-term procurement strategies.

[0166] Abnormal situation analysis: Identify abnormal situations in the procurement process (such as delayed delivery, quality issues, etc.) and provide improvement suggestions.

[0167] As a preferred embodiment of the present invention, the user interface module is designed to be concise and intuitive, and supports use by multiple roles (such as purchasing managers, financial personnel, supply chain managers, etc.). The specific functions include:

[0168] Dashboard: Displays real-time data of key procurement indicators, such as total procurement amount, ranking of major suppliers, order status, etc.

[0169] Data visualization: Provides charts and reports to help users intuitively understand procurement data and analysis results.

[0170] Operation interface: supports users to view, modify and approve procurement plans, manage suppliers, track orders, and perform other operations.

[0171] Notification system: Send important reminders and notifications to users in a timely manner, such as order approval requests, abnormal order reminders, etc.

[0172] Through the above technical solutions and embodiments, the present invention can effectively solve the problems of low efficiency, high cost, delayed response, etc. in traditional procurement management. The system integrates artificial intelligence and big data technology to realize the automation and intelligence of the procurement process, significantly improve the efficiency and accuracy of procurement management, optimize supply chain management, and help enterprises maintain their advantages in a highly competitive market environment.

[0173] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. An intelligent procurement management method, characterized in that: The following steps are involved: S1, data collection: collect procurement-related data through multiple channels, including internal procurement data, supplier information, and market data; S2, data processing and storage: clean, organize and store the collected data and establish a unified data warehouse; S3, demand forecasting: using machine learning algorithms to analyze historical procurement data and predict future procurement demand; S4, supplier management: automatically evaluate and select the best suppliers based on their performance indicators and reputation information; S5, procurement plan generation: Automatically generate procurement plans and orders based on demand forecasts and supplier evaluation results; S6, Order Tracking and Management: Real-time tracking of order status, automatic handling of abnormal situations, and ensuring a smooth procurement process; S7, Data Analysis and Reporting: Conduct in-depth analysis of procurement data, generate various reports, and assist in decision-making.

2. The intelligent procurement management method according to claim 1, characterized in that: The S1 also includes collecting data from an enterprise resource planning system, a supplier database and a market situation website.

3. The intelligent procurement management method according to claim 1, characterized in that: In S3, the machine learning algorithm is a long short-term memory network (LSTM) model.

4. The intelligent procurement management method according to claim 1, characterized in that: In S4, the supplier performance indicators include on-time delivery rate, product quality qualification rate and price competitiveness.

5. An intelligent procurement management system, applied to the intelligent procurement management method according to any one of claims 1 to 4, characterized in that: The system comprises: Data collection module: used to collect procurement-related data; Data processing module: used to clean, organize and store the collected data; Demand forecasting module: used to analyze historical procurement data and predict future demand; Supplier management module: used to evaluate and select suppliers; Procurement plan generation module: used to generate procurement plans and orders; Order management module: used to track and manage order status; Data analysis and reporting module: used to analyze procurement data and generate reports; User interface module: used to provide user operation interface.

6. The intelligent purchasing management system according to claim 5, characterized in that: The demand forecasting module uses a long short-term memory network (LSTM) model to perform demand forecasting.

7. The intelligent purchasing management system according to claim 5, characterized in that: The supplier management module includes a performance indicator evaluation unit and a supplier selection unit. The performance indicator evaluation unit is used to evaluate the supplier's delivery timeliness rate, product quality qualification rate and price competitiveness. The supplier selection unit selects the best supplier according to the evaluation result.

8. The intelligent purchasing management system according to claim 5, characterized in that: The order management module includes an order tracking unit and an exception handling unit. The order tracking unit is used to track the order status in real time, and the exception handling unit is used to automatically handle order exceptions.

9. The intelligent purchasing management system according to claim 5, characterized in that: The data analysis and reporting module includes a procurement cost analysis unit, a supplier performance reporting unit and a demand trend analysis unit.

10. The intelligent purchasing management system according to claim 5, characterized in that: The user interface module includes a dashboard, a data visualization interface and an operation interface. The dashboard is used to display real-time data of key procurement indicators, the data visualization interface is used to graphically display procurement data, and the operation interface is used to support users in viewing, modifying and approving procurement plans.

Images