182 results about "Stockout" patented technology

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention relates to an AI-based supply chain demand prediction and inventory optimization method and system, and the method comprises the following steps: calling a sales record from a supply chain, recognizing a periodic demand rule of a corresponding commodity, and predicting an estimated demand quantity in a future time period according to the periodic demand rule; carrying out supply and demand difference analysis by combining the current warehouse commodity stock margin, and judging the stock state; if the commodity stock surplus exists, making a corresponding stock digestion strategy; if a shortage quantification result appears, carrying out key degree evaluation and urgency sorting based on a shortage amount in combination with market factors, and generating a key commodity list and an out-of-stock urgency index; and finally, in combination with a preset commodity constraint condition, according to the key commodity list and the urgency index, an optimal replenishment scheme meeting resource and time limit is formulated, and the technical problem of how to realize conversion from passive response to active pre-judgment in a supply chain environment with large demand fluctuation and complex influence factors is solved.

The invention discloses an e-commerce supply chain intelligent scheduling optimization system and method based on big data, and relates to the field of intelligent logistics scheduling, and the system comprises a demand prediction module, a balance optimization module, a path optimization module, a collaborative replenishment module and a feedback driving module. According to the method, the deep learning model and the time sequence decomposition method are combined, and multi-level modeling is performed on the historical order data and the real-time sales data, so that supply chain imbalance caused by prediction deviation is avoided. A dynamic replenishment plan is generated based on the prediction result and the inventory early warning information, real-time matching of the inventory and the demand is achieved, the stockout rate and inventory redundancy are effectively reduced, and the storage resource utilization rate is improved. By establishing the multi-constraint path optimization model and comprehensively considering order distribution, vehicle load and real-time traffic data, the distribution path can be dynamically adjusted, the vehicle utilization rate and the distribution time efficiency are improved, and the energy consumption and the cost caused by empty driving and detour are reduced.

The invention discloses an optimization method and system for aviation equipment storage and a medium, and the method comprises the steps: collecting multi-dimensional dynamic data, carrying out the fusion cleaning and structural feature extraction, and generating a high-dimensional feature set; outputting a dynamic demand prediction value and uncertainty measurement by using a hybrid intelligent prediction model fusing time sequence prediction and ensemble learning; calculating an optimal inventory control parameter through a stochastic optimization model in combination with the aviation material key grade and the stockout loss cost; and generating an inventory operation instruction according to the optimal inventory control parameter and the real-time inventory state, and outputting a strategy report with confidence evaluation and key influence factor analysis. According to the method, multi-dimensional accurate prediction of the aerial material demand and dynamic optimization of the inventory strategy are realized, the inventory control precision and the resource utilization efficiency are remarkably improved, the stockout risk and the overstocked cost are effectively reduced, and the reliability and the economical efficiency of aviation equipment guarantee are enhanced.

The invention discloses a storage intelligent stock prediction and replenishment method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a stock cargo label set, extracting a stock cargo feature set and a storage operation feature set, integrating the stock cargo feature set and the storage operation feature set to obtain an integrated feature set, and then extracting a periodic correlation feature set; and extracting corresponding link feature enhancement information from preset link feature enhancement information, and refining the periodic association feature set to obtain a target feature set. And the target feature set is used to predict the stockout risk index of each to-be-replenished goods label, and a replenishment instruction is generated according to the stockout risk index, so that intelligent warehouse stock prediction and accurate replenishment are realized, and the warehouse management efficiency and benefits are improved.

The invention relates to the technical field of intelligent fabric decision, in particular to a fabric evaluation decision method, device and equipment and a storage medium. Performing attenuation analysis on the market performance data and the clothing demand data to obtain attenuation factors; calculating a preset reference time, an attenuation factor, the current sales data and the current time to obtain a demand attenuation coefficient; predicting the social media data and the e-commerce platform key event according to the demand attenuation coefficient, a preset demand prediction model and a preset association rule mining algorithm to obtain a stock quantity decision and an optimal stock quantity; generating a fabric evaluation decision according to the stock quantity decision and the optimal stock quantity; through generation of stock and fabric decisions, stock optimization, stockout and overstock reduction and agile response of a supply chain are realized, and the competitiveness and profit level of a clothing enterprise are remarkably improved.

The invention provides a supply chain warehouse stock replenishment method, system and device and a readable storage medium. The method comprises the steps of obtaining supply chain data; constructing a deep learning model; inputting historical data of the current period to the model, and outputting a predicted value of the stock in the future N days; calculating a replenishment quantity by acquiring an actual stock amount in real time and combining the stock prediction value and a preset safety stock threshold value; and generating a replenishment order based on the replenishment quantity, creating a replenishment instruction based on the replenishment order, and transmitting the replenishment instruction to a supplier. According to the invention, the problem of inventory overstock or stockout caused by demand fluctuation, supply chain complexity and intense market competition in traditional inventory management is solved.

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 provides a cold chain supply chain meat inventory optimization regulation and control method and system, and the method comprises the steps: collecting multi-dimensional data to construct an impact factor data set, fusing a long and short-term memory network model, a gradient boosting tree algorithm model and an attention mechanism, and precisely predicting meat demands. Comparing the inventory, the threshold value and the predicted value, and generating an instruction according to a preset rule; and continuously optimizing the model through online learning, error evaluation and rolling prediction. According to the invention, the problem of overstock or stockout caused by inaccurate demand prediction and lack of a scientific regulation and control mechanism in the existing cold chain supply chain meat inventory management is solved.

The invention discloses a multi-target inventory optimization method based on dynamic demand prediction and an intelligent decision-making system, and the method comprises the steps: firstly collecting a cache library inventory state of a cache library system, including considering production order data and AGV transportation capability data, and carrying out the standardization processing of the cache library inventory state; inputting the data into an LSTM neural network model of a dynamic demand prediction module to predict raw and auxiliary material demands in a future time period; the replenishment amount and the replenishment period are solved based on an improved multi-objective optimization algorithm NSGA-II, and the optimization objectives are the lowest total cost, the lowest stockout rate and the lowest inventory fluctuation; issuing a replenishment instruction to the cache library system through the production management system, and scheduling the AGV to execute a transportation task; and the inventory state is monitored in real time in the production process, strategy adjustment or model retraining is triggered according to the fluctuation coefficient, and finally a zero-stockout, low-fluctuation and optimal-cost replenishment strategy is output, so that the method is suitable for a high-dynamic manufacturing environment.

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

The invention discloses a full life cycle management system for an enterprise product supply and demand order contract, relates to the technical field of enterprise supply and demand contract management, and is used for solving the problem of low efficiency of supply and demand performance collaboration. Through multi-source business event collection and unified coding time reference, records dispersed in a business system are integrated into an event stream sorted according to time, a supply and demand event chain and a life cycle unit corresponding to a contract are automatically identified, the performance state is visible and the abnormity is traceable, and the efficiency is improved. The performance terms and the settlement terms are analyzed into computable constraints, cooperative control of a performance task network and resources is driven, resource demand intervals are generated according to uncompleted tasks and constraints, key resource states are compared to identify resource conflicts, and processing control items such as production, delivery, purchase or term adjustment are generated, so that stockout and delay risks are reduced; and the performance stability and the resource utilization efficiency of the enterprise are 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 relates to the technical field of commodity purchase-sale management, in particular to a commodity purchase-sale-stock real-time management system. The commodity purchase-sell-stock real-time management system is provided. According to the invention, the environment sensing module collects dynamic data during the business period in real time, including the real-time stock of commodities, the remaining time of the shelf life of the commodities, the passenger flow volume of nearly 10 minutes and the like, thereby effectively solving the problem that the data update of a traditional purchase-sales-stock system is lagged, ensuring the real-time accuracy of stock and price information, and improving the real-time performance of the purchase-sales-stock system. Meanwhile, whether the commodities need to be replenished or not is judged in real time through a dynamic analysis module according to a preset safety inventory threshold value, the safety replenishment amount is calculated, the risk of stockout or excessive accumulation is reduced, the inventory turnover rate is increased, a price reduction sales promotion instruction is automatically triggered through the dynamic analysis module according to the real-time risk value of the commodities and the preset threshold value, and the price reduction sales promotion efficiency is improved. The price is quickly refreshed by using the electronic price tag, and the freshness change of the short-term preserved commodity is responded in time, so that the high loss rate is reduced, and the shopping experience of customers is improved.

The invention discloses a rule-based regional inventory balancing method. The method comprises the following steps: acquiring the current actual inventory of each warehouse; based on the sales prediction distribution of each warehouse in a future period, quantitatively judging the state of each warehouse to obtain a set of multi-cargo warehouses, out-of-stock warehouses and balanced warehouses; constructing a warehouse directed graph containing a supply-demand relationship based on the warehouse set; and finally, based on the distance between the warehouses, the transportation cost calculation factor and the unit profit of the target commodity, carrying out iterative optimization circulation on the warehouse directed graph, carrying out continuous cost comparison and scheme replacement until any new and better cargo transfer scheme cannot be found, and finally outputting a cargo transfer scheme with the optimal global cost. Therefore, through scientific quantitative calculation, automatic generation of the regional inventory balancing scheme is realized, the performability and scientificity of decision making are enhanced, and the economical efficiency of allocation behaviors is ensured.

The invention relates to the technical field of data fusion, and discloses an inventory prediction system based on multi-source data fusion. An inventory data acquisition module of the system is responsible for acquiring multi-dimensional dynamic data including a real-time sales fluctuation sequence, a storage turnover period record, a logistics transportation delay index and the like in a supply chain link; the feature fusion processing module extracts sales trend, storage efficiency and logistics stability features according to the multi-dimensional dynamic data, and generates a fusion feature matrix according to time axis alignment; the inventory state modeling module constructs a dynamic inventory state model capable of reflecting the incidence relation between the inventory consumption rate and the replenishment delay by using the fusion feature matrix; the anomaly detection module carries out deviation degree analysis on an inventory change curve output by the dynamic inventory state model, identifies abnormal fluctuation nodes and marks corresponding supply chain links; and the prediction result generation module outputs an inventory surplus early warning value and stockout risk probability distribution in a future period based on the corrected dynamic inventory state model.

The invention relates to the technical field of logistics management systems, and particularly discloses an artificial intelligence-based supply chain logistics information management system, which comprises a data acquisition module, a demand analysis module, a logistics decision module, a dynamic sensing module and a supply chain monitoring module. Through multi-source data acquisition and standardized preprocessing, the problem of traditional system data is solved, and a foundation is laid for analysis; on the basis of an improved LSTM model and a dynamic threshold value, demands are accurately predicted, risks are early warned, and inventory and stockout losses are reduced; a hybrid algorithm is adopted to collaboratively optimize paths, assess inventory and dynamically adjust environmental interference, and balance logistics efficiency and cost; a toughness index is generated by means of an Attention-LSTM model, and risk quantitative traceability and rapid disposal are realized in combination with hierarchical response and visualization; the modules are in real-time cooperative closed loop, cooperate with parameter self-optimization and threshold updating, adapt to industry and environment changes, and provide stable and intelligent support for supply chain management.

The invention belongs to the technical field of medical consumable supply management, and particularly relates to a medical consumable supply management system and method and an adaptive intelligent cabinet. Through real-time dynamic adjustment and closed-loop management, the accuracy and efficiency of medical consumable supply are remarkably improved, the risks of stock overstock and stockout are effectively reduced, resource allocation is optimized, the continuity and stability of medical services are ensured, and particularly, through the combination of real-time data collection and intelligent prediction, the real-time dynamic adjustment and closed-loop management are achieved. Consumable demand and supply among medical nodes are accurately matched, inventory dynamic balance is realized, operation cost is reduced, response speed is improved, medical safety is guaranteed, in addition, according to actual consumption conditions of different medical nodes, replenishment strategies are flexibly adjusted, supply chain management is further optimized, and supply efficiency is improved. The timeliness and accuracy of consumable supply of each node are ensured, and the overall medical service quality is improved.

The invention relates to the technical field of purchase demand prediction, and discloses a part purchase demand prediction method and system based on machine learning, and the method comprises the steps: obtaining modular product basic data, and generating a BOM graph structure; calculating prior probability distribution through a Bayesian inference algorithm; executing an adaptive probability pruning algorithm to solve the problem of combinatorial explosion; quantizing uncertainty by using a Bayesian deep learning network; calculating a differentiated safety inventory coefficient based on the value-at-risk model; executing an importance sampling algorithm to carry out Monte Carlo simulation on high-risk low-frequency configuration, and verifying a demand coverage rate in an extreme scene; an incremental learning mechanism is utilized to update probability distribution and a pruning threshold according to the new order data, and a self-adaptive optimization demand prediction result is output; according to the method, the inventory cost is remarkably reduced, the stockout risk is reduced, and the balance between the calculation efficiency and the prediction accuracy is realized.

The invention provides a multi-node time sequence data fusion-oriented replenishment demand dynamic prediction method and system, and relates to the technical field of data synchronization, and the method comprises the steps: deploying a plurality of lightweight store agents at the background of a plurality of store cashier terminals; according to a commodity sales volume accumulation calculation result, performing virtual inventory updating of the plurality of chain stores to obtain a plurality of groups of real-time commodity inventory data; carrying out inventory demand prediction according to the multiple groups of time sequence commodity inventory data and the multiple groups of real-time commodity inventory data to obtain multiple groups of commodity replenishment time and multiple groups of commodity replenishment prediction quantity; performing supply chain dynamic fitting, and outputting a replenishment distribution route; and the supply chain end responds to the replenishment delivery route to perform dynamic replenishment of the plurality of chain stores. The technical problems that in the prior art, sales and inventory of chain stores are mutually independent, a unified real-time data synchronization mechanism is lacked, dynamic replenishment optimization is difficult to carry out according to the sales and inventory data of the whole chain, and inventory overstock or frequent occurrence of stockout is caused are solved.

The invention provides an e-commerce integrated order management system, and relates to the technical field of e-commerce, and the system comprises an order processing module which is used for receiving, splitting, merging and updating order states, automatically optimizing order circulation, improving the order processing efficiency, guaranteeing the real-time synchronization of the order states, and reducing the processing delay. In the invention, the data analysis module analyzes user behavior data (such as browsing, purchasing and returning) and market sales data in real time and predicts future commodity demands and sales trends by using the prediction model, and the system can automatically provide a real-time report for an operation team based on the data, so that the user experience is improved. The module helps to adjust the inventory replenishment cycle, sales promotion activities and commodity shelving plans, and has the core advantages that market changes are identified through big data analysis, response measures are automatically generated, the inventory management precision is improved, and the inventory overstock and stockout conditions are reduced.

A method includes: accessing a first image captured at a first time; deriving a first in-stock condition of the slot at the first time based on product units of a product type occupying a slot, depicted in the first image, at the first time based on features detected in the first image; accessing a second image captured at a second, later time; deriving a second out-of-stock condition of the slot at the second time based on features detected in the second image; accessing a back-of-store inventory status of the product type at the second time; and triggering an increase in quantity of facings of the product type at the slot based on a) the first in-stock condition at the slot at the first time, b) the first out-of-stock condition at the slot at the second time, and c) presence of back-of-store inventory of the product type at the second.

The invention provides a medical material multistage warehouse intelligent management system and method based on demand prediction, and the system comprises an intelligent recognition module which is used for achieving the quick warehouse-in and warehouse-out operation of medical materials; the stockout pre-taking module is used for receiving and recording the request application when the inventory is insufficient; the demand prediction module is used for adopting a prediction model of multi-granularity time decomposition; the dynamic priority adjustment module is used for constructing an evaluation system; the multi-target optimization distribution module is used for carrying out material distribution; the multi-stage warehouse collaborative scheduling module is used for monitoring the inventory difference of the multi-stage warehouses in real time; the real-time monitoring and early warning module is used for monitoring the storehouse in real time; and the intelligent analysis and optimization module is used for generating a visual report. The intelligent level and the operation efficiency of medical material management are comprehensively improved. According to the system, the group fairness and the resource utilization efficiency can be considered while the quick response of the emergency medical demand is ensured, and the stockout risk and the operation cost are remarkably reduced.

Disclosed in the present disclosure are a system, method and apparatus for inventorying commodities on a shelf. In the system, millimeter wave radar terminal devices are each used for: upon receiving a signal transmission instruction, transmitting a millimeter wave signal, collecting an echo signal, calculating the angle and distance of the echo signal, and transmitting the angle and distance of the echo signal to a cloud server by means of a wireless base station communication device; and the cloud server is used for: sending the signal transmission instruction to different millimeter wave radar terminal devices by means of the wireless base station communication device; upon receiving the angle and distance of the echo signal, acquiring space information of a vacant position of a shelf grid where each millimeter wave radar terminal device is located; on the basis of appearance and size information of commodities stored in the shelf grid where each millimeter wave radar terminal device is located, calculating space information needing to be occupied by each commodity; and calculating the number of actual commodities and the number of out-of-stock commodities on the basis of the space information of the vacant position, the space information of the shelf grid, and the space information needing to be occupied by each commodity. The present invention can realize timely and accurate inventory of commodities on the shelf.

The invention discloses an ERP intelligent management system and method based on big data. The method comprises the following steps: firstly, arranging historical sales volume data and historical price data of multiple days according to a time dimension, and then performing local time sequence feature analysis to obtain a sequence of historical sales volume local time sequence feature vectors and a sequence of historical price local time sequence feature vectors; performing fusion processing on each group of corresponding historical sales volume local time sequence feature vectors and historical price local time sequence feature vectors to obtain a sequence of historical sales volume-historical price local time sequence fusion feature vectors; and determining a short-time yield demand prediction value of the to-be-predicted commodity based on historical sales-historical price time sequence fusion features obtained by sequence cascade of the historical sales-historical price local time sequence fusion feature vectors, and judging whether loss will be caused or not. In this way, the situation of commodity inventory excess or stockout can be avoided, and the production efficiency and the customer satisfaction degree are improved.

The application provides a kind of intelligent delivery and replenishment management method and system based on multi-dimensional inventory early warning, belong to goods management technical field, this method includes: obtaining the global demand data and full-link supply data of inventory object, and determining demand fluctuation intensity sequence and supply interruption frequency sequence;The demand fluctuation intensity sequence and supply interruption frequency sequence are cross recursive analysis, determine the mismatch risk evolution path between the goods demand and goods supply;According to the mismatch risk evolution path, the adaptive adjustment coefficient when safety stock is determined, and the safety stock threshold curve that evolves smoothly with time is obtained;When the inventory level of inventory object is lower than safety stock threshold curve, generate out-of-stock risk early warning, when inventory level is higher than safety stock threshold curve, generate backlog risk early warning.The technical scheme provided by the application can realize the adaptive management of delivery and replenishment under the nonlinear coupling effect of demand fluctuation and supply interruption.

The application discloses a virtual commodity intelligent shelving and management system for a financial institution credit mall, relates to the technical field of virtual commodity intelligent shelving and management, and is aimed at the sudden shortage of a single product in a combination product, automatically screens the best substitute based on a dynamically constructed equity relation graph, and performs real-time rule conflict detection through graph weight fusion, so that the substitute scheme can meet the user preference and the cost limit of the financial institution at the same time, compared with the existing scheme that needs manual checking of the inventory and rules, the system can compress the processing time limit from hours to seconds, and completely avoid customer complaints caused by manual errors; meanwhile, a double protection mechanism through an intelligent compliance fuse is introduced, the matching degree of a user risk label and a KYC strategy is verified in the commodity shelving stage, and high-risk exchange is intercepted.

The invention provides a replenishment data processing method and device, electronic equipment and a storage medium. The method comprises the steps of displaying a historical replenishment record query page, and displaying at least one historical replenishment record in the historical replenishment record query page; in response to selection triggering of the target historical replenishment record, displaying a replenishment detail page, and displaying replenishment data of a target out-of-stock commodity corresponding to the target historical replenishment record in the replenishment detail page; receiving a tracing operation of tracing the replenishment data on the replenishment detail page, and displaying a replenishment decision information prompt page corresponding to the replenishment data; and displaying the plurality of first sorting results of the replenishment source storage locations and the available inventory information of each replenishment source storage location in a replenishment decision information prompt page, and displaying the plurality of second sorting results of the replenishment destination storage locations and the to-be-replenished information of each replenishment destination storage location in the replenishment decision information prompt page. According to the embodiment of the invention, the efficiency of tracing the abnormality reason of the replenishment data can be improved.

The application provides a commodity out-of-stock scheduling method and device, equipment and medium, belonging to the technical field of logistics scheduling and retail management, which comprises the following steps: receiving historical sales time series data and real-time inventory data uploaded by edge computing nodes of each vending machine in a target area; for each target vending machine, performing a first target operation to realize a restocking operation of the target vending machine. The commodity out-of-stock scheduling method, device, equipment and medium provided by the application can improve the commodity scheduling efficiency and accuracy of the vending machine.

To provide a store visit time guidance system and program capable of notifying a customer of an appropriate store visit time according to commodities to be purchased.SOLUTION: A store visit time guidance system includes a customer terminal (terminal device) and a store server (server device). The customer terminal includes: a store visit information output unit which outputs, to the store server, information specifying the name of a store to be visited, the time at which a customer desires to visit the store, and a commodity to be purchased; and a store visit recommended date acquisition unit which acquires a store visit recommended date, from the store server. The store server includes: a store visit information acquisition unit which acquires, from the customer terminal, information specifying the name of the store, the date at which the customer desires to visit the store, and the commodity to be purchased; a sold-out time estimation unit which calculates estimated sold-out times based on estimated trends of the quantity of stock of the commodity to be purchased; a store visit recommended date calculation unit which calculates a store visit recommended date, based on the desired visit date and the earliest estimated sold-out time of the estimated sold-out times of the commodity to be purchased; and a store visit recommended date output unit which outputs the store visit recommended date to the customer terminal.SELECTED DRAWING: Figure 12