280 results about "Purchasing management" patented technology

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

The invention belongs to the technical field of supply chain management, and discloses an intelligent purchasing system and method of a supply chain. Comprising the steps of collecting demand prediction data and supplier performance data; carrying out deep mining on the demand prediction data, and predicting future purchase demands; generating a capability portrait based on the performance data of the suppliers, and performing quantitative evaluation on performance scores of different suppliers according to a pre-constructed evaluation system; historical order data is acquired, a supplier relation network topology is constructed in combination with the capability portraits, and a synergistic effect between suppliers is identified; fusing the performance score, the future purchase demand and the synergistic effect, and recommending an optimal supplier combination; acquiring purchase management data, and intelligently formulating a purchase strategy in combination with the supplier performance data of the optimal supplier combination; according to the invention, the purchase efficiency can be improved, and the flexibility and toughness of the supply chain are enhanced, so that the overall response speed, cost control capability and operation quality of the supply chain are effectively improved.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention relates to the technical field of medicine purchasing, and provides a hospital easy-to-expire medicine purchasing decision optimization method, which comprises the following steps of: firstly, extracting medicine inventory, consumption records and purchasing management parameters in a hospital information system, and constructing a complete inventory state data set; then, calling a demand prediction model constructed based on a time sequence and external factors, and outputting drug prediction demand values in a plurality of periods in the future; thirdly, constructing a multi-cycle purchase optimization model according to the current inventory and the prediction demand; then, dividing a planning period into rolling windows, and gradually generating an ordering scheme meeting conditions by using a heuristic greedy and local search algorithm; then, the scheme is pushed to a purchase decision-making interface, and a formal order is submitted and generated after the scheme is confirmed by a manager; after a supplier delivers goods and stores the goods, the system updates inventory information and records scrapping and shortage data; and finally, adjusting the model through a feedback mechanism so as to improve the accuracy of subsequent prediction and decision.

The invention discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention provides an enterprise order purchase management method and system, and relates to the technical field of purchase management. Performance state feedback information corresponding to a purchase order is acquired; performing multi-dimensional alignment analysis on the performance state feedback information, generating performance efficiency boundaries of different purchase material types, and constructing purchase constraint rules of order purchase in each execution stage according to the performance efficiency boundaries and the user sign-in data; performing anomaly detection on a keyword extraction strategy during purchase demand identification to obtain a matching deviation situation of the purchase demand in supply chain matching, performing hierarchical verification on resources of suppliers based on the matching deviation situation and delivery sign-in data, and generating supply recommendation rules of hierarchical suppliers; and generating a target order corresponding to the purchase demand in the supply chain according to the purchase constraint rule and the purchase recommendation rule. According to the application, the performance state can be dynamically monitored and suppliers can be recommended in a graded manner under the supply chain collaboration platform, so that the precision of supply chain matching in the purchasing process is improved.

The invention relates to the technical field of information, and provides a purchase management method based on an intelligent supply chain management platform, and the method comprises the steps: obtaining material demand data submitted by all departments, carrying out the standardization of the timestamp and demand of the material demand data, and forming standardized data containing an auxiliary classification dimension, a time dimension and a quantity dimension; generating an initial purchase plan table according to the real-time demand data and a preset purchase management rule, and grouping the purchase data in the initial purchase plan table according to a preset grouping rule to obtain a grouped purchase plan table; and obtaining response time prediction data, comparing the response time prediction data with a preset response time threshold value, if the response time prediction data is greater than the threshold value, optimizing the purchasing behavior by adjusting the priority and resource allocation, and determining an optimized purchasing strategy. The standardization, transparency and high efficiency of the purchasing process are realized, and the digital transformation and intelligent upgrading of enterprise purchasing are effectively promoted.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention discloses a material purchasing management system, and the system comprises a historical data summarization and analysis module which summarizes the historical data of material purchasing; the intelligent purchase plan drawing and demand prediction module draws a purchase plan according to historical data and real-time demands, predicts future demands, and constructs a seasonal change demand prediction model in combination with industry characteristics and seasonal factors; the arrival plan making and inventory management module is used for making an arrival plan; the purchase demand adjusting module is used for adjusting the purchase demand; the emergency demand plan processing module judges whether the temporary demand plan is an emergency demand plan or not, adjusts the purchase plan according to the emergency degree, provides an emergency purchase channel, adjusts the purchase plan according to the emergency demand, and feeds back an adjustment result to the seasonal change demand prediction model; and the sub-warehouse distribution plan making and real-time adjusting module is used for making sub-warehouse distribution plans according to different regions and the arrival plans.

The invention discloses a purchase efficiency optimization management method and system based on artificial intelligence. The method comprises the steps of data sub-item collection, data preprocessing, supply chain relation modeling, purchase strategy optimization and purchase efficiency optimization. The invention relates to the technical field of power fuel purchase management, in particular to a purchase efficiency optimization management method and system based on artificial intelligence, and adopts a comprehensive intelligent method combining cross-domain data fusion with supply chain modeling and purchase strategy optimization to realize real-time and dynamically optimized purchase efficiency optimization and management. A dynamic heterogeneous improved graph neural network method is adopted to carry out supply chain relation modeling, so that the state updating of the supply chain and the quantitative calculation of risk conduction are optimized, and the intelligent dimension of purchasing efficiency management is improved; and purchasing strategy optimization is carried out by adopting a multi-objective reward improved multi-layer strategy reinforcement learning method.

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.

The invention discloses a purchase management method and system based on business big data, and relates to the technical field of purchase prediction and supplier management, and the method comprises the steps: collecting historical purchase records of a business database, carrying out the preprocessing, generating a preprocessing data set, and constructing and training a purchase prediction model through a long short-term memory (LSTM) network algorithm; the purchase demand is predicted through the purchase prediction model, a demand prediction result is output, and the suppliers are graded through the demand prediction result to form graded suppliers; and generating a purchase suggestion list through the graded suppliers, and sending the purchase suggestion list to the purchase management terminal. According to the invention, the purchase prediction model based on the multilayer LSTM neural network is established, so that the long and short term characteristics of the purchase data are effectively captured and accurately predicted; and in combination with the purchase decision matrix and the differentiation processing strategy, the system automatically generates purchase suggestions and pushes the purchase suggestions to the management terminal through the encryption channel, so that the automation level and the operation efficiency of enterprise purchase management are effectively improved.

The invention is suitable for the technical field of enterprise purchase management, and provides an enterprise purchase service integrated management system and method, and the method comprises the steps: screening out all target purchase tasks which are not approved by a target enterprise auditor after a planned auditing time point of the target enterprise auditor is received, and determining a specific category and an initial priority index of each target purchase task, and extracting a historical purchase log and a historical application log of the target enterprise in a preset time period. According to the technical scheme, the priority index of the purchase task is dynamically optimized by combining the historical trend gradient and the use benefit deviation coefficient, and the actual demand and the potential delay risk of each task can be more accurately evaluated. The historical trend gradient reflects the difference between the time required for task completion and the estimated time, so that the possible delay trend is revealed; and the benefit deviation coefficient is used for comparing the actual application duration with the standard application duration to help judge whether the goods can be put into use in time or not.

The invention provides a BIM-based building carbon footprint quantification method and system, and the method comprises the steps: carrying out the coupling of the purchase management data of each building stage, the engineering management data of each building stage and the BIM data in a BIM model of a to-be-monitored building, and generating an engineering quantity list of each building stage; intelligently matching the engineering quantity list with a preset life cycle factor library to generate a carbon footprint list of each building stage; performing analysis based on the carbon footprint list, and generating carbon footprint analysis results of the building to be monitored in each building stage; and based on a carbon footprint analysis result, performing visualization processing, and displaying a visualization processing result in the BIM model. Therefore, the data collection efficiency and accuracy are improved, the carbon emission of the whole life cycle of the building can be subjected to refined and dynamic management, the influence of each stage and each factor of the building on the carbon emission can be comprehensively and accurately considered, and the low carbon of the whole life cycle of building design, purchase, construction and the like can be promoted.

The invention discloses an intelligent construction site management method and system, electronic equipment and a storage medium, and relates to the technical field of construction site management. The method comprises the steps of obtaining material information of a target construction site; generating a material consumption thermodynamic diagram of the target construction site according to the material information, and dividing the target construction site into a plurality of material use frequency areas based on the material consumption density in the material consumption thermodynamic diagram; calculating a material consumption rate corresponding to each material use frequency area, and determining an inventory early warning threshold value corresponding to each use frequency area based on each material consumption rate; obtaining the current material inventory of each material use frequency area; and when the target use frequency area with the material inventory smaller than the corresponding inventory early warning threshold exists, determining the corresponding material purchase quantity according to the material consumption rate and the material inventory of the target use frequency area, and generating a purchase management report of the target construction site based on each material purchase quantity. By implementing the technical scheme provided by the invention, the construction site management efficiency can be improved.

The invention relates to procurement management, in particular to a bidding procurement management method based on a large model, and the method comprises the steps: enabling a purchaser to clarify a procurement demand, and inputting the procurement demand into the large model; the large model automatically collects market data related to purchase demands, analyzes the collected market data, and provides a market quotation report for a purchaser; the purchaser formulates a bidding rule according to the purchase demand and the market quotation report, inputs the bidding rule into the large model, and performs evaluation and optimization by using the large model; the large model generates a purchase announcement according to a purchase demand and a bidding rule, and issues the purchase announcement through multiple channels; the suppliers apply for participating in bidding through the purchase management system, the large model audits the suppliers and issues bidding participation vouchers to the suppliers according to the auditing result; according to the technical scheme provided by the invention, the defects that the bidding purchasing efficiency is low and the bidding process is not fair and transparent enough in the prior art can be effectively overcome.

The invention discloses a purchase full life cycle management method based on digital twinning, which relates to the field of purchase management, and comprises the following steps: firstly, analyzing purchase demands, analyzing records from different processes, and establishing digital twinning bodies in a demand analysis stage; according to the method, the overall purchase information is updated in real time by configuring a list and establishing the digital twinborn body, a target is provided for purchase, products introduced by market data mining and prediction and suppliers thereof are scientifically matched with the digital twinborn body, a plurality of appropriate suppliers are selected, and a transaction stage digital twinborn model is established, so that the transaction efficiency is improved. Product orders are distributed among a plurality of suppliers, formed sub-order data are fed back to a management module in a supplier selection management system, assessment, evaluation and rating of the suppliers are carried out, a circulation route of supplier selection and management is formed, feedback among all stages of purchasing is synchronized in time, the association degree of all stages of purchasing is enhanced, and the purchasing efficiency is improved. The purchasing quality is ensured and the purchasing efficiency is improved.

The invention provides a multi-field intelligent supply chain decision system and method based on AI decision, and the system comprises an identification analysis module which is used for carrying out the identification analysis of an identification carried by a material when the material flows in different supply chain links, and obtaining the identification analysis data; and the purchase management module, the logistics management module, the inventory management module and the production plan management module are used for performing purchase management, logistics management, inventory management and production plan management on the supply chain based on the AI decision and according to the identification analysis data. According to the invention, based on the AI decision, the data is analyzed according to the identifier, and intelligent decision and optimization management are realized, so that the efficiency and transparency of the whole supply chain are improved, and the production takt of the supply chain is optimized.

The invention relates to the technical field of bid procurement, in particular to a bid procurement management platform based on artificial intelligence and a processing method. Comprising a multi-source heterogeneous data acquisition module which is configured to be in real-time butt joint with an enterprise ERP system, a supplier database and a third-party credit investigation platform through an API interface; a dynamic weight distribution unit and a time sequence feature extraction unit are arranged in the feature engineering processing module, and the feature engineering processing module is used for carrying out dimension reduction on the historical transaction data of the suppliers; the deep learning matching engine comprises a double-tower neural network architecture based on an attention mechanism and is used for establishing dynamic association between a purchase demand vector and a supplier capability vector; the intelligent decision matrix generation module is used for integrating a fuzzy comprehensive evaluation algorithm and a multi-objective optimization algorithm and outputting a three-dimensional decision space for comprehensive evaluation of suppliers; according to the scheme, the problem that cross-system data formats are incompatible in a traditional technical scheme can be solved.

The invention discloses an engineering project purchase management system based on a bim technology, relates to the technical field of engineering management, and solves the technical problems that a finally purchased product does not accord with an expectation and the engineering progress is affected due to the fact that BIM and purchase business are fused and goods and evaluation of suppliers are not considered in the prior art. The method comprises the steps of obtaining a design drawing and parameters of an engineering project, and constructing a BIM model according to the design drawing and the parameters; component information is extracted from the BIM model, and a purchase list is generated according to the component information; screening a plurality of suppliers according to the purchase list, obtaining supply information of the plurality of suppliers, and calculating evaluation coefficients of the plurality of suppliers; screening a final supplier from the suppliers based on the evaluation coefficient screen; the delivery information of the final supplier is obtained, the delivery of the product is detected, and when the product is not delivered within the specified time limit, a new round of purchase is carried out on the product in time; the suppliers can be evaluated, the optimal supplier is selected, and the project progress is prevented from being affected.

The invention belongs to the technical field of IT type project cost financial management, and discloses an IT type project cost financial independent accounting system, which comprises a project management module, a receivable management module, a payable management module, a purchase management module, a financial system data exchange module, a human resource data exchange module and an early warning management module. According to the system, integrated management is adopted, project cost is monitored in real time and dynamically adjusted, the system collects and updates cost data in real time, rapid adjustment is carried out once deviation is found, it is ensured that the cost is controlled within a prediction range, project management flexibility and response speed are improved, cost hyper-branched risks are reduced, and meanwhile through automatic data processing and accounting processes, the project management efficiency is improved. The cost accounting accuracy and efficiency are remarkably improved, personal errors are avoided, manual calculation is simplified, a project manager can efficiently master the cost condition, powerful data support is provided for project decision making, and the scientificity and effectiveness of project management are ensured.

In a purchase management system using a self-checkout register, pieces of information, which are respectively registered by individual terminal devices, are shared among a plurality of customers. A terminal device capable of acquiring item identification information of each item to be purchased first acquires a cooperation ID. Next, the terminal device shares the information in real time with a cooperative terminal device that is another terminal device and includes the same cooperation ID.

The invention belongs to the technical field of college purchase risk management, and discloses a college purchase risk management and fund early warning system in a multi-supplier environment. The invention provides a college purchase risk management and fund early warning system in a multi-supplier environment. The system comprises a supplier management module, a purchase plan scientific making module, a fund early warning and monitoring module and a data statistical analysis and decision support module. According to the invention, a set of integrated purchase risk management and fund early warning system is constructed, the overall efficiency of college purchase management is improved through a digital whole process, risks occurring in the purchase process can be effectively identified and managed, and unnecessary expenditure is reduced through accurate fund management; therefore, high efficiency, transparency and compliance of college purchasing activities are ensured.

The invention provides an enterprise finance and taxation service method, system and device and a medium, and relates to the technical field of finance and taxation service, and the method comprises the steps: displaying an enterprise finance and taxation service main interface which comprises a commodity tagging control, an invoice issuing control and an invoice management control; in response to a trigger instruction for the commodity tagging control, displaying a commodity tagging sub-interface, adding commodity basic information in the commodity tagging sub-interface, and generating a commodity code based on keyword matching tax classification code library; in response to a trigger instruction for the invoicing control, displaying an invoicing sub-interface, adding a commodity and related information thereof, and calculating and generating an invoice in combination with a commodity code; and in response to a trigger instruction for the invoice management control, displaying the invoice management sub-interface to classify the invoices into output or input invoices, performing output and input management, and adding a circulation state mark. Through commodity tagging and invoice classification management, the enterprise finance and taxation processing efficiency is effectively improved, and the manual error rate is reduced.

The invention relates to the technical field of material management, and particularly discloses a material management system which is used for solving the problems that an existing material management system has limitations, including budget depends on experience, and scientific prediction and dynamic adjustment capabilities are lacked. First basic information and project member information of a project are managed through a project management module, and first budget information of the project is managed based on a budget management module; the first budget information comprises project budget, actual cost, budget project amount and supply unit price; a purchase management module is used for managing material application and purchase application of a project, a stock management module is used for managing material entry, ex-warehouse orders, warehousing orders and allocation orders, and a dynamic budget adjustment unit is used for dynamically adjusting project budget according to actual project amount and supply unit price. The limitation in traditional material management can be effectively solved, synchronous optimization of budget and actual construction is realized, and the material management efficiency and benefit of the construction project are comprehensively improved.

The invention discloses a smart retail purchase management method and system, and the method comprises the steps: obtaining a multi-supplier order / quoted price and a store demand, carrying out the adaptive analysis, cleaning and verification, and generating a standardized purchase and suggestion list; evaluating risks based on rules and models and automatically selecting an approval link; scanning and collecting bar codes / serial numbers / batches / quantity when goods arrive, and storing according to unique association of suppliers, order numbers, codes and batches; after warehousing, carrying out consistency comparison, abnormity early warning, freezing and closed-loop processing on orders, warehousing, invoices and payment; and in the settlement stage, convergent transaction, bank and billing account checking are carried out, and results are written back to supplier and inventory parameters to drive subsequent suggestion and approval threshold adaptive optimization. According to the technical scheme, the data accuracy and compliance are remarkably improved, the approval and checkout cycle is shortened, and the purchase cost and the operation risk are reduced.

The invention relates to the technical field of supplier full life cycle technologies, and provides a supplier full life cycle management method based on multi-dimensional evaluation, which comprises the steps of demand source searching, supplier classification and selection, supplier relationship management, contract management, purchase management, order execution, invoice reconciliation and expenditure analysis. According to the method, a whole-process closed-loop management system from demand source searching to expenditure analysis is established, so that information barriers among links are broken, and seamless circulation and sharing of data are realized; a standardized evaluation model and a classification system are adopted, traditional supplier decision-making depending on subjective experience is converted into scientific decision-making based on quantitative data, and the management efficiency and decision-making accuracy are remarkably improved. By establishing a contract performance early warning mechanism and an abnormal order grading processing flow, full-flow dynamic monitoring of contract risks and execution abnormities is realized; based on the risk assessment matrix and the change influence analysis model, various potential risks can be identified and responded in time.

The invention discloses a material purchasing data processing method and system based on safety risk identification and prediction. The method comprises the following steps: material purchasing data acquisition and preprocessing; material purchasing data feature extraction and optimization; constructing a multi-task learning model; training and optimizing a multi-task learning model; material purchasing data security risk identification and dynamic evaluation; according to the method, multiple risks in material purchasing are predicted based on the multi-task learning model, multi-task learning is optimized through the self-adaptive weighted loss function, the coverage range of risk identification is enlarged, the prediction precision is improved, the accuracy and the real-time performance of material purchasing risk prediction are remarkably improved, and the risk prediction efficiency is improved. And real-time early warning and business strategy suggestions are provided, so that the intelligent level of material purchasing management is improved, the prevention and control capability of enterprises on material purchasing risks is enhanced, and the uncertainty of supply chain operation is reduced.

The invention relates to the technical field of material purchasing management, and particularly discloses a material purchasing process optimization method and system based on deep learning, and the method comprises the steps: firstly constructing a unified data set containing time sequence features and static features through multi-source data collection; secondly, time sequence features are decomposed into trend, period and fluctuation components through the multi-scale decomposition technology, the feature contribution degree is dynamically adjusted in combination with static features, and accurate demand prediction is achieved; establishing a multi-dimensional evaluation matrix to quantify the out-of-stock influence, and dividing the purchase emergency degree into three levels through a decision tree algorithm in combination with a supplier capability evaluation system; constructing a task association network, calculating priority scores through feature propagation and deep fusion, and forming an optimized task sequence; and finally, based on historical approval data, recommending an optimal approval path through multi-dimensional similarity matching, and realizing continuous updating of approval features. According to the invention, intelligent optimization of the whole purchasing process is realized.

The invention relates to the field of purchase management, in particular to a raw material purchase management system based on a one-stop flexible supply chain, and the system achieves the butt joint of upstream and downstream industrial chains through the one-stop flexible supply chain, enables manufacturers to timely know the supply information of upstream manufacturers, also can know the demand information of downstream manufacturers, and improves the efficiency of purchase management. According to the method, the production speed and the raw material supply are allocated, the production speed capable of meeting the delivery time is obtained by analyzing the goods requirements of downstream manufacturers, the logistics transportation time and the raw material supply are combined, and the raw material inlet and outlet behaviors in the production process of the manufacturers are analyzed in a dynamic model analysis mode; therefore, the stock of a manufacturer can be effectively controlled, the capability of the manufacturer to deal with production fluctuation is ensured, and the influence on the manufacturer caused by market change due to excessive raw material overstock is avoided.