221 results about "Inventory cost" patented technology

The invention provides an inventory optimization method and system based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining the multi-dimensional data of a commodity, constructing a dynamic convolution kernel through employing a graph neural network, constructing a soft clustering distribution matrix in combination with a multi-layer perceptron, and obtaining a long-term dependence feature fusing a space dimension and a time dimension. And fusing the short-term and long-term features, inputting the fused short-term and long-term features into a bidirectional long-short-term memory network to generate a sales prediction sequence, and calculating a prediction confidence interval to obtain a final sales prediction result. And calculating a safe inventory level based on a sales prediction result, generating a replenishment decision by using a dynamic planning model based on deep reinforcement learning in combination with an inventory cost function, and finally sending the decision to a supply chain execution system. The sales volume is accurately predicted through the deep learning model, the inventory management is optimized, the storage cost, the stockout cost and the overstock cost are reduced, and the supply chain efficiency is 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 belongs to the intelligent information technology, and discloses an e-commerce platform operation system and method based on intelligent decision and dynamic optimization. The system comprises a plurality of modules of multi-source data acquisition and preprocessing, user portrait and demand prediction, intelligent commodity recommendation and the like. A recommendation algorithm fusing a knowledge graph and reinforcement learning and an inventory prediction and optimization algorithm based on dynamic time sequence analysis are innovatively adopted, and a reward function formula and an inventory cost function formula are arranged respectively. According to the method, data acquisition, user portrait construction, business operation and system optimization are realized according to collaborative operation of the system and the modules. The system can accurately recommend commodities, intelligently manage inventory, efficiently plan promotion activities, collaboratively optimize a supply chain, and improve the operation efficiency and commercial competitiveness of an e-commerce platform.

A system (100) for autonomous inventory optimization and resilient supply chain management in the healthcare and medical device sector, comprising: (a) a data ingestion module configured to collect structured and unstructured data from a variety of sources, including hospital enterprise resource planning (ERP) systems, RFID scanners, electronic health records (EHR), supplier application programming interfaces (APIs), and Internet of Things (IoT) sensors; b) an artificial intelligence / machine learning (AI / ML) module configured to process the ingested data using deep learning and time series prediction models to predict item-level inventory requirements, with the models continuously trained with new data to improve prediction accuracy; c) an optimization module configured to apply operations research algorithms to generate inventory management strategies that minimize inventory costs, reduce expired inventory, and maintain predefined service levels; (d) a supply chain risk module configured to analyze external data sources, including news feeds, weather reports, and supplier performance metrics, to identify potential risks and generate mitigation strategies; and e) a user interface module configured to provide real-time dashboards, alerts and recommendations tailored to clinicians, procurement managers and supply chain analysts.

The invention discloses a multi-dimensional data statistics intelligent analysis and prediction system and method, and particularly relates to the technical field of data prediction. Historical sales data, consumer behavior data, market environment data and external dynamic data of a target analysis object are collected, an LSTM basic prediction model is constructed, consumer behavior change trend features and market environment dynamic features are further extracted, the features are fused with the LSTM model to generate a multi-modal prediction model, and the multi-modal prediction model is analyzed. Comprehensively analyzing the interaction influence of the multi-dimensional data by using a multi-input structure; according to the method, influence weights and contribution degrees of different characteristics are evaluated through sensitivity analysis and interaction analysis, model parameter weights are dynamically adjusted, an optimized prediction model is utilized to generate a multi-dimensional prediction result in a fixed time period, and inventory management and a promotion resource allocation strategy are optimized accordingly, so that long-term drift of consumption behaviors is effectively coped with, and the consumption performance is improved. The inventory cost and the resource waste are reduced, and the operation efficiency and the business decision accuracy are improved.

The invention discloses a material digital centralized management and control system based on the Internet of Things, which relates to the technical field of digital management and control and comprises a supply chain management unit, a loss management unit, a cost accounting unit, an inventory management unit, a dynamic adjustment unit and a user management unit. The system can accurately plan the purchase quantity and time, screen high-quality suppliers, guarantee the quality and stability of material supply from the source, deeply analyze the loss data in transportation, storage and processing, formulate a targeted strategy, reduce material loss, reduce waste, dynamically check the cost in real time, perform apportionment analysis according to different dimensions, and accurately control the cost. Through intelligent warehouse-in and warehouse-out management, regular inventory checking and intelligent inventory prediction, it is ensured that inventory data is accurate, inventory is reasonably planned, an inventory strategy is dynamically adjusted, inventory overstock or stockout is reduced, and inventory cost and service level are balanced.

The invention relates to a supply chain distribution network multi-level inventory cost optimization method, which belongs to the field of supply chain inventory, and comprises the following steps: obtaining initial information of a full-process supply chain distribution network multi-level inventory cost optimization problem; establishing a multi-level inventory cost model based on the initialization information, and analyzing multi-level inventory cost components of the cost model; determining an objective function of a multi-level inventory cost optimization problem of the whole-process supply chain distribution network based on the cost component; designing and improving a near-end strategy optimization algorithm; and performing optimization and adaptive adjustment on core parameters of the improved near-end strategy optimization algorithm to obtain an optimal multi-level inventory cost control strategy. The method can adapt to the influence of various factors of a supply chain environment, can avoid emergencies such as extreme and abnormal conditions, adopts a time difference error mode to estimate the optimal base line, increase entropy rewards, improve the convergence speed and the return limit, ensures the stability of strategy updating, and achieves the target of long-term cost reduction and efficiency increase of the supply chain.

The invention discloses an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention discloses an intelligent supply chain management system and method based on a cloud computing platform, and particularly relates to the technical field of supply chain management. According to the invention, the number of holidays, the number of special days, seasons, the number of sales promotion days, the sales promotion strength, the commodity prices, the commodity categories and the commodity brands are used as feature parameters of the commodity sales to construct the commodity sales demand prediction model, so that the accuracy of commodity sales demand prediction is effectively improved; according to the method, the commodity demand fluctuation coefficient is introduced during commodity inventory classification, product inventory classification is optimized, different replenishment strategies are made for different products, the purpose of differential management is achieved, the inventory cost is reduced, and meanwhile the market demand can be met; according to the method, the supply capacity of the cooperative suppliers is analyzed, the cooperative suppliers and the alternative schemes of the next cooperation period are intelligently screened when the supply capacity of the cooperative suppliers is lower than the expectation, and the operation efficiency of the supply chain can be guaranteed to the maximum extent.

The invention discloses an intelligent logistics supply chain management system, and the system comprises a block chain information sharing platform which is responsible for building an information sharing platform based on a block chain, inviting all participants on the supply chain to join, and storing and transmitting the data of each link of the supply chain through the distributed account book technology of the block chain; the product information tracing system is responsible for distributing a unique block chain ID for each fresh food and recording the circulation process of the fresh food in a supply chain; and the collaborative management platform is responsible for integrating and uniformly managing data of purchasing, warehousing, transportation and sales, predicting future sales trend and inventory demand change by using a random forest according to historical sales data and market trend information, and optimizing an inventory level, a reorder strategy, a sales strategy and a promotion activity plan in combination with a genetic algorithm, so as to achieve the purpose of sales promotion. The inventory cost is minimized, the sales demand is met, and the block chain information sharing platform and the collaborative management platform carry out data synchronization.

The invention discloses an intelligent order flexible processing production scheduling management method, and relates to the technical field of order management. The method comprises the steps of obtaining demand data from a baking order management system and performing priority classification on orders to ensure timely delivery of important orders and improve customer satisfaction. The future consumption of the baking raw materials is predicted through a time sequence prediction algorithm, the inventory cost is reduced, and the production continuity is guaranteed. And configuration of production equipment, workers and raw materials is optimized through mixed integer linear programming, reasonable utilization of resources is achieved, and the production efficiency is improved. The production progress and the variation of raw material consumption are controlled and monitored through the statistical process, abnormal conditions are recognized in time, and stable product quality is ensured. And flexible processing scheduling adjustment is carried out according to the production abnormity index, and the adaptability of the production system is enhanced. In conclusion, according to the method, the intelligent level of production management is effectively improved, the production process is optimized, and powerful support is provided for enterprises in the market with fierce competition.

The invention provides a medical consumable refined management method and system, and the method comprises the steps: a medical consumable database establishment step: carrying out the warehousing management of medical consumables, and establishing a database containing the information of various medical consumables; dynamic management of the medical consumables: in the use process of the medical consumables, obtaining ex-warehouse and use information of the medical consumables, linking with the database, and dynamically updating the database in real time; a first data analysis step: on the basis of the database information and the dynamic update information, using a big data model to predict the use cycles of various consumables, and realizing refinement of consumable management and control; and a purchase strategy output step: based on the period predicted by the big data model, setting the upper limit and the lower limit of the inventory of various consumables, and when a certain medical consumable exceeds the lower limit, outputting a purchase plan of the medical consumables to provide reference for the purchase behavior of purchasers. The inventory of the medical institution is reduced, and the inventory cost of the medical institution is effectively reduced.

An intelligent warehouse management method based on the Internet of Things and artificial intelligence comprises the steps that 1) intelligent inventory management is carried out, and each commodity which is put in storage and put out of storage is automatically identified and tracked by utilizing a cargo container label and an image identification technology; the artificial intelligence technology is combined with big data analysis, future inventory demands are predicted, the inventory level is automatically adjusted, and the inventory cost and the stockout risk are reduced; 2) an automatic operation process: monitoring the equipment state in real time through the Internet of Things technology, ensuring the efficient operation of the equipment, and reducing the downtime; 3) performing real-time data analysis and decision support, and collecting various data generated in the storage process, including inventory data, order data and equipment operation data; deep learning and analysis are performed on storage data by using an artificial intelligence technology, intelligent decisions such as inventory prediction and path planning are realized, and storage management and logistics distribution processes are optimized; and 4) cross-system collaboration and information interconnection of a plurality of systems such as data sharing, storage, transportation, sales and the like are carried out to form end-to-end supply chain collaboration.

The invention discloses a retail cabinet inventory path decision optimization method and equipment, and the method comprises the following steps: (1), building an inventory path optimization model of a retail cabinet, and enabling the inventory path optimization model to take the minimum sum of the inventory cost, the stockout cost and the vehicle path cost at the current planning period as an objective function, taking a replenishment constraint, a maximum inventory level constraint, a vehicle capacity constraint, a retail cabinet flow balance constraint, a vehicle closed-loop constraint and an elimination sub-loop constraint as constraint conditions; (2) obtaining inventory path related information; and (3) solving the inventory path optimization model by adopting a tabu search method according to the inventory path related information to obtain an optimized inventory decision and path decision. According to the invention, the replenishment decision and the path planning decision are considered at the same time during decision making, and the efficiency is higher.

The invention relates to the technical field of e-commerce replenishment, in particular to a cross-border e-commerce replenishment management system based on big data, which comprises a data interface connection module, a sales data analysis and prediction module, an inventory monitoring and replenishment decision module and a sales prediction and adjustment module, the inventory monitoring and replenishment decision-making module judges the replenishment date of the commodity A by sensing the inventory number of the commodity A in the cross-border e-commerce system, predicts the change trend of the daily sales volume of the commodity A in the current time interval through the commodity sales volume prediction unit, and calculates the replenishment number of the commodity A. Accurate inventory management and replenishment decision-making are realized. According to real-time inventory and accurate sales volume prediction, replenishment can be performed at a proper time, and a reasonable replenishment quantity can be determined. The problems of stock-out in the peak season and inventory overstock in the slack season caused by inaccurate experience judgment in a traditional method are avoided, the inventory cost is reduced, and the operation efficiency and customer satisfaction of enterprises are improved.

The invention discloses an intelligent calculation method and system for concrete sales, and relates to the technical field of building and logistics management. By dynamically adjusting the production plan, accurately predicting the customer demand and optimizing the production capacity and the raw material ratio, the problem of excessive or insufficient production caused by large demand fluctuation in traditional production is solved, the inventory cost is reduced, the production efficiency is improved, the traffic condition is monitored in real time and the transportation route is dynamically optimized in the transportation link, and the transportation efficiency is improved. The transportation efficiency and the delivery punctuality are obviously improved, the transportation cost is reduced, and the concrete quality is ensured; in the production process, the raw material ratio and the concrete state are monitored in real time through a sensor and a machine learning algorithm, production parameters are dynamically adjusted, the quality fluctuation problem caused by inaccurate ratio or untimely monitoring in traditional production is solved, and the stability of product quality is ensured.

The invention relates to a material management method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of data processing. The method comprises the steps of training based on a historical data set of a to-be-managed warehouse to obtain a material management model; the historical data set comprises at least two groups of historical material demand prediction results and historical inventory costs corresponding to the historical material demand prediction results; the objective function of the material management model is the minimum value of the historical inventory cost; obtaining a current material demand prediction result and a current inventory cost of the to-be-managed warehouse; the current material demand prediction result and the current inventory cost are input to a material management model, an output target inventory strategy is obtained, and the target inventory strategy is used for material management of the to-be-managed warehouse; the target inventory strategy comprises an ordering point, an ordering batch and a material quantity threshold value of the next ordering period corresponding to each material. By adopting the method, the management efficiency of the material inventory can be improved.

The invention discloses a multi-target integrated production plan scheduling method based on improved NSGA-II, which considers multiple constraints such as materials, productivity and inventory on the basis of the existing production plan, and constructs a multi-target integrated production plan scheduling method based on the multiple constraints by taking the materials, the productivity and the inventory as the constraints. According to the method, an integrated production plan model with minimization of tardiness cost, minimization of inventory cost and maximization of balanced production as targets is established, an improved non-dominated sorting genetic algorithm is designed to effectively solve a multi-target optimization problem, the accuracy of a production plan is improved, and compared with an original solving method, the method has the advantages that the cost is reduced, and the production efficiency is improved. The improved NSGA-II algorithm has the advantages of being high in solving precision, high in convergence speed and the like, and is very suitable for solving the problem of complex function optimization under the multi-constraint condition.

The invention discloses an efficient logistics storage scheduling system, and relates to the technical field of logistics storage scheduling, and the technical scheme is that the system comprises a storage scheduling management module, the connection end of the storage scheduling management module is provided with a data analysis optimization module, and the storage scheduling management module comprises an automatic scheduling module and a storage safety and stability guarantee module. The beneficial effects of the system are that through the automatic warehousing operation module, the links of manual operation are reduced, rapid and accurate cargo processing is realized, the overall logistics efficiency is significantly improved, the transportation route can be intelligently optimized according to the real-time order quantity and the transportation demand, and the transportation efficiency is improved. According to the method and the system, the waiting time and the empty driving rate are reduced, the logistics efficiency is further improved, the manpower demand is reduced through automatic and intelligent storage scheduling, the labor cost is reduced, and through real-time inventory state monitoring and accurate demand prediction, inventory overstock and excessive replenishment can be reduced, and the inventory cost is reduced.

The invention discloses a supply chain deployment system and method based on big data, and particularly relates to the technical field of supply chain deployment, and the system comprises a data collection module which is used for collecting the data of each link of a supply chain; a data storage and processing module; the demand prediction module is used for carrying out market demand prediction by using a time sequence analysis model and a regression analysis model; the intelligent allocation module is used for formulating an optimal supply chain allocation scheme by using an optimization algorithm; the logistics optimization module is used for carrying out optimization scheduling on logistics transportation routes and vehicles; the monitoring and feedback module is used for monitoring the operation state of each link of the supply chain in real time; according to the supply chain deployment system and method based on big data, the demand prediction accuracy is improved, through big data analysis and an advanced prediction algorithm, multiple influence factors are fully considered, the market demand can be predicted more accurately, the inventory overstock and stockout phenomena are reduced, and the inventory cost and opportunity cost of an enterprise are reduced.

The invention discloses a new tea drink sales volume prediction method and system based on a graph neural network, and the method comprises the steps: building an algorithm model trained based on spatial features and time sequence features through employing the graph neural network; according to the invention, store sales volume prediction can be carried out in different festivals and holidays, sales promotion activities, weather conditions, store business districts and consumer groups. By constructing a graph network topology structure based on stores, modeling is performed on store information data by adopting an undirected weighted graph, and the influence of surrounding stores on the current store is fully considered, so that the sales volume of each store is predicted; the problem that a conventional method is inaccurate in prediction due to the industry characteristics of short commodity life cycle, old product degradation, high commodity renewal speed and the like in the new tea beverage industry is solved, the number of stockout times and the inventory cost of the commodities are reduced, and therefore the store income is increased.

The embodiment of the invention provides an intelligent commodity selection method and system for commodity racking in a point exchange shopping mall based on bank users. According to the embodiment of the invention, the method comprises the steps: constructing a multi-dimensional user preference map through employing the behavior track of a bank user, and generating a dynamic prediction result according to the multi-dimensional user preference map; on the basis of the dynamic prediction result, context-aware calculation is fused, environment variables are calculated, and a commodity recommendation list is generated for each user in a customized mode; utilizing the commodity recommendation list of all bank users to generate an initial commodity selection strategy; implementing shelving according to a commodity shelving scheme in the initial commodity selection strategy, collecting user interaction data after the commodities are shelved, establishing a rapid feedback channel, optimizing the initial commodity selection strategy, and generating an optimal commodity selection strategy; according to the technical scheme provided by the embodiment of the invention, the accuracy of personalized recommendation is improved, and a bank is helped to better cope with market changes, so that the exchange rate and the user satisfaction are improved, the inventory cost is effectively controlled, and the supply chain risk is managed.

The invention relates to the technical field of inventory optimization, and discloses a dynamic inventory optimization method, which is used for dynamically determining an industrial optimal inventory. Through real-time data collection and preprocessing, accurate demand prediction and a dynamic replenishment strategy, an enterprise can quickly respond to market changes, and the risks of stock overstock and stockout are effectively avoided. Meanwhile, the inventory cost is further reduced through the dynamic safe inventory strategy and economic order quantity optimization calculation, and the fund utilization efficiency is improved. In addition, the method further pays attention to inventory minimization and cost optimization, and effective control of the total cost is achieved by optimizing a supply chain and formulating an inventory minimization strategy. And the accuracy of inventory data is ensured through periodic inventory and difference analysis, and the transparency and credibility of inventory management are improved.

The invention relates to the technical field of e-commerce, and relates to a digital shopping mall management SaaS system, and the system comprises the steps: collecting shopping mall call and network events, and generating an embedded vector through optical reserve wavelength division multiplexing nonlinear mapping; under the constraint of the causal structure in the last round, the de-noising diffusion model generates twinborn events, an updated causal graph is formed through incremental Bayesian information criterion learning, and a causal feature tensor is obtained through random walk embedding; a reinforcement learning framework formed by the neural morphological execution network and the GPU evaluation network fuses tensors with inventory, price and promotion business data into a value state, and outputs a price adjustment-discount-replenishment strategy; the strategy is compiled into a WebAssembly micro contract, and the components are subjected to hot replacement without shutdown through a BPF sandbox; generating a zero-knowledge proof for the micro contract and storing the proof on the side chain of the block chain; in the gray stage, profit, inventory and cost feedback is collected and written back to a data link to complete self-adaptive closed loop; according to the invention, millisecond-level strategy iteration, second-level security release and extreme scene robust operation are realized.

The invention provides an intelligent unmanned library scheduling system for spare parts. The intelligent unmanned library scheduling system comprises an information perception module, a demand prediction analysis module, an intelligent scheduling core and an unmanned library execution monitoring module. The system obtains spare part information in all directions, predicts future demands, generates scheduling strategies, controls unmanned equipment to complete carrying and storage operations, monitors equipment states and environmental parameters, and realizes intelligent scheduling of multi-objective optimization. According to the invention, the efficiency and accuracy of spare part management can be improved, the inventory cost is reduced, the efficient operation of equipment and the stability of environmental parameters are ensured, and the response speed and service quality of the whole supply chain are optimized.

The invention provides a maintenance spare part prediction system based on a big language model plug-in knowledge base, and the system comprises the steps: carrying out the information data extraction and preprocessing of a historical work order, and obtaining a preprocessing text; performing semantic vector conversion and association binding processing on the preprocessed text to obtain a vector database; extracting attribute information from the new work order, and performing semantic vector conversion on the attribute information to obtain a query vector; retrieving a work order related to the query vector from a vector database; semantic reasoning is carried out on a new work order and a related work order, a maintenance spare part prediction result is generated, natural language fault description and equipment model information in a maintenance record are fully utilized, natural language information is fully understood in the face of complexity and diversity, the spare part prediction accuracy and practicability are improved, reasonable configuration of maintenance resources is achieved, and the maintenance efficiency is improved. Spare part inventory cost and inventory waste are reduced, and maintenance efficiency is improved.

The invention relates to a commodity plan management system supporting a fourth dimension, which is applied to the technical field of e-commerce, and comprises the steps: obtaining the historical sales data of a commodity in a time dimension, a space dimension, a product dimension and a medium dimension required by the subsequent commodity sales prediction through a data integration module, a sales prediction module is used for predicting sales volume in three dimensions of traditional time, space and products, a fourth dimension, namely a medium dimension, can be added on the basis of the three dimensions of the traditional time, space and products to predict the sales volume of the commodities, and a prediction mode is determined according to whether the commodities contain medium attributes or not. Compared with the prior art, the accuracy of commodity sales volume prediction under the condition that commodities have medium attributes is improved; a commodity sales plan is generated through the commodity planning module according to the predicted sales data, a store is helped to reasonably plan the inventory level, the inventory cost and the storage cost are reduced, and waste caused by commodity overstock is reduced.

The invention relates to the field of material purchase optimization, and discloses an intelligent manufacturing enterprise multi-material joint purchase optimization method and system, which is used for solving the problem that real-time adjustment cannot be carried out during material joint purchase, and comprises the steps of obtaining historical purchase related data, carrying out prediction by using a time sequence method, obtaining the demand quantity of each material, and carrying out multi-material joint purchase optimization. Screening suppliers according to the material demands, obtaining data of the suppliers, obtaining a priority selection index according to the data of the suppliers through an optimal sequence graph method, selecting an initial supplier according to the priority selection index, obtaining the purchasing cost, the transportation cost and the inventory cost of the initial supplier, and obtaining an optimal purchasing scheme by using a mathematical optimization model. According to the method and the system, combined purchasing is carried out, historical purchasing related data and supplier data are continuously acquired in real time, and real-time purchasing adjustment is carried out, so that the accuracy of material combined purchasing is effectively improved, and the production efficiency of enterprises is improved.

The invention relates to a material replenishment method and system based on dynamic inventory prediction. The method comprises the following steps: firstly, acquiring a historical inventory data set of multiple types of materials comprehensively covering warehouse-in, warehouse-out and inventory allowance records; secondly, in the inventory feature extraction link, material consumption rules and storage state features are innovatively extracted from time and space dimensions respectively; moreover, the dynamic inventory prediction model carries out combined processing on the two types of features, the limitation of a traditional prediction method is broken through, the complex association of materials in time and space can be fully considered, and the accuracy and reliability of inventory change prediction in a target time period are greatly improved. And finally, a supply trigger condition and a supply amount adjustment strategy are generated based on an accurate prediction result, and are fed back to a supply execution system, so that dynamic and accurate supply operation is realized, the inventory overstock or stockout phenomenon is effectively avoided, the inventory management efficiency is remarkably improved, the inventory cost is reduced, and the whole material inventory management process is comprehensively optimized.

The invention discloses an inventory management optimization method for preventing fast moving consumer goods shortage in a retail supply chain, and belongs to the field of inventory management optimization, and the method comprises the following steps: S1, building a fast moving consumer goods replenishment optimization model by taking the minimization of replenishment cost as a target function; s2, introducing a deep learning model combining reinforcement learning and a Q network, modeling the decision process of the fast moving consumer goods into a Markov decision process, training the deep learning model by using a deep Q network algorithm, and solving the fast moving consumer goods replenishment optimization model by using the deep learning model to obtain an optimal replenishment strategy; and S3, executing the optimal replenishment strategy to replenish the fast moving consumer goods. According to the invention, the replenishment decision can be optimized, the overall inventory cost can be reduced, the occurrence rate of inventory shortage can be significantly reduced, the replenishment strategy can be dynamically adjusted according to the actual situation through online learning and real-time monitoring, and the real-time performance and effectiveness of the replenishment decision can be ensured.