284 results about "Stockout" 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 relates to the technical field of order management, and discloses a supply chain-oriented intelligent order management method, which comprises the steps of obtaining corresponding multi-modal data through an order demand flow, a production equipment state, logistics sensor dynamic information and an inventory topological graph; analyzing relevance between orders and equipment based on a space-time diagram convolutional network, and generating a capacity allocation scheme; calculating a logistics path planning scheme, predicting a stock stockout risk and generating a replenishment suggestion; if the high-priority order exists, inserting a productivity plan and adjusting an equipment process chain; if resource conflicts occur, dynamically allocating resources; if the path risk value exceeds the threshold value, standby path switching is triggered; adjusting weighting parameters through an adaptive federation algorithm, generating a global strategy and issuing the global strategy to the client; the client dynamically adjusts local configuration and uploads execution effect data in real time; and if abnormity is detected, triggering global strategy regeneration and updating the model through federated learning increment. According to the invention, efficient management of supply chain orders can be realized.

The invention discloses a warehouse cargo supply chain management method and system based on AI big data, and relates to the technical field of warehouse management, and the method comprises the steps: collecting and preprocessing warehouse multi-source data, and predicting the cargo delivery frequency based on an LSTM neural network; and a multi-target scheduling model combining pickup path time consumption and storage gravity center control is constructed based on a prediction result, a cargo task and AGV path planning are jointly modeled into an open path multi-starting-point asymmetric TSP problem, an FWA-GA hybrid optimization algorithm is used for model solving, and an optimal cargo allocation matrix and an optimal AGV scheduling path table are obtained. Therefore, integrated joint optimization of task allocation, path planning and goods allocation layout is achieved, a supply chain platform is further linked to complete stockout early warning and supplier intelligent selection, and multiple limitations of a traditional method in the aspects of prediction precision, scheduling coupling, structure control, supply collaboration and the like are effectively broken through.

The invention discloses an enterprise purchase collaborative management method and system based on a cloud platform, and relates to the technical field of cloud platform and supply chain management, and the method comprises the steps: collecting multi-factor demand prediction model data; predicting a purchase demand and performing optimization; and executing purchasing based on the demand prediction and optimization result. According to the enterprise purchase collaborative management method based on the cloud platform, purchase demand prediction is carried out through a multi-factor dynamic time sequence model, high precision and high adaptability of demand prediction are realized in combination with a dynamic weight adjustment mechanism and adaptive optimization of model parameters, a purchase demand trend chart and influence factor analysis can be provided, and the enterprise purchase collaborative management method based on the cloud platform can be applied to enterprise purchase collaborative management. According to the method and the system, the enterprise is helped to make a purchase plan in advance, the purchase cost, the inventory holding cost and the stockout risk are balanced by taking the minimization of the total purchase cost as the target through the demand-inventory joint optimization model, and the method and the system have better effects in the aspects of accuracy, controllability and stability of enterprise purchase.

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 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 the technical field of intelligent warehousing, and provides an inventory management method and system for intelligent warehousing in order to solve the technical problem of how to reduce the risk of inventory article overstock and stockout, and the method comprises the following steps: obtaining the historical data of the shipment amount of a warehouse; according to the shipment amount historical data, shipment change historical data of the warehouse is obtained, and the shipment change historical data comprises the shipment increment of the warehouse in each day in the historical time; a shipment quantity prediction model is trained according to the shipment quantity historical data and the shipment change historical data, and a loss function of the shipment quantity prediction model is defined according to the prediction difference between the shipment quantity historical data and the shipment change historical data; predicting the shipment prediction quantity of the warehouse in the next shipment period through the trained shipment quantity prediction model; obtaining the current stock amount and the safety stock proportion of the warehouse; and carrying out inventory management early warning on the warehouse according to the predicted shipment amount, the current inventory amount and the safe inventory proportion of the warehouse in the next shipment period.

The invention relates to the technical field of warehouse management, and discloses a warehouse management system and method based on dynamic inventory prediction. According to the invention, market, sales, supply chain and warehouse internal data are collected in real time, a dynamic inventory prediction model is constructed in combination with a long short-term memory (LSTM) network, the future inventory demand can be accurately predicted, and the prediction precision is significantly improved; an inventory optimization strategy is generated based on a multi-objective optimization algorithm, so that the inventory holding cost, the stockout cost and the transportation cost are effectively reduced; and intelligent warehouse operation instruction generation and real-time monitoring are realized through the execution control module, and the response capability of an enterprise to market fluctuation and emergencies is enhanced. According to the system and the method, the warehouse management efficiency is improved, the operation risk is reduced, and an efficient and reliable solution is provided for modern logistics and supply chain management.

The invention discloses an intelligent statistical analysis method and system for sales data of retail commodities, and relates to the technical field of statistical analysis. During operation of the system, data from different sales channels are collected and synchronized in real time, the collected data are preprocessed and then calculated to obtain a supply chain index SCI, and the SCI is used as a supply chain index; by analyzing historical sales data and a current inventory state, commodity sales dynamics are monitored in real time, inventory pressure is evaluated, potential stockout or excessive commodities are identified, the required replenishment amount is automatically calculated according to the predicted sales trend and inventory state, and the commodity replenishment amount is automatically calculated based on the historical sales data and external factors. A machine learning algorithm is used to predict future commodity demands, production, inventory and distribution plans are dynamically adjusted, a prediction model is automatically adjusted, key nodes in a supply chain are monitored in real time, early warning is carried out on abnormal conditions, an automatic feedback mechanism is triggered, and related departments are notified to take measures in time and find potential supply chain risks in time.

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 relates to a goods shelf inspection algorithm based on an image recognition technology and a big data mining technology, and belongs to the technical field of digital inspection, and the algorithm comprises the following operation steps: 1, carrying out the data collection of a display picture of a goods shelf and a picture of a commodity, and obtaining the commodity data based on the picture; 2, preprocessing the acquired data image, and performing a denoising algorithm by using median filtering and mean filtering to reduce noise interference in the image; and step three, target detection and commodity information identification are carried out. And 4, detecting stockout, placement and labels of the commodities. And a fifth step of performing sales data analysis and generating optimization suggestions. And 6, generating a report according to an abnormal detection result, a data analysis result and an optimization suggestion. The problems of high cost, low efficiency and subjectivity in the traditional manual inspection and the existing technical scheme are solved. And the shelf management level and the sales efficiency of the store are improved through data optimization.

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 relates to the technical field of inventory management, and discloses an inventory management system and method based on order data, and the system comprises a data collection unit, an order analysis unit, an inventory prediction unit, and an inventory management unit. According to the invention, commodities are accurately classified according to order data, a detailed basis is provided for inventory management, extensive management is avoided, and the accuracy is improved; inventory demands are predicted according to different commodity characteristics, multiple factors are combined with historical data, actual sales is close, overstock or stockout is reduced, and the turnover rate and the fund utilization rate are increased; the automatic process reduces manpower, monitors the inventory in real time and gives an early warning, replenishes goods in time, enables managers to concentrate on decision making, improves management efficiency, maintains the reasonable level of the inventory, and reduces cost; the inventory supplement signal contains key information and is quickly transmitted to suppliers, so that the suppliers can supply goods in time, collaboration and communication are enhanced, a stable and efficient supply chain is constructed, risks are reduced, and competitiveness is improved.

The invention discloses an overseas warehouse operation intelligent wave order management method and system, and the method comprises the steps: constructing a commodity incidence matrix, mining a frequent item set, determining the commodity correlation degree, carrying out the centralized picking of high-correlation commodities, reducing the round-trip times, shortening the walking distance, improving the picking efficiency, planning a picking path in combination with the warehouse layout and inventory information, and achieving the intelligent wave order management of the overseas warehouse operation. The method comprises the following steps: preferentially selecting short-distance commodities in the same area, further reducing the picking time, allocating priorities according to order emergency degrees and customer levels, ensuring that important orders are processed in time, improving customer satisfaction, dynamically adjusting the sequence in the picking process, avoiding invalid walking caused by stockout, enhancing path flexibility and adaptability, and collecting warehouse-in and warehouse-out information in real time. Inventory is updated immediately, data are ensured to be accurate and timely, labor intensity and operation time of picking personnel are reduced, labor cost is reduced, and storage equipment loss and maintenance cost are 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 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 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 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 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 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 discloses a fresh food after-ripening characteristic and demand prediction transportation aging collaborative optimization method, and relates to the technical field of fresh food transportation, the method comprises the following steps: using a sensor to collect temperature and humidity, gas concentration and cell metabolite data in a fresh food after-ripening process; according to the invention, temperature, humidity, gas concentration and cell metabolite data of the fresh food are collected in real time by using a sensor in a transportation link, and deep fusion and analysis are carried out in combination with multi-source external information such as historical sales data, social media public opinions, weather disaster early warning and the like, so that precise control of the after-ripening state of the fresh food is realized; according to the method, deep learning and association rule mining technologies are applied, a dynamic decision model is constructed, and the transportation time efficiency and the distribution strategy are optimized in real time according to after-ripening characteristics and demand prediction, so that the fresh food loss can be effectively reduced, the product quality is ensured, the transportation resource utilization rate is improved, the logistics cost is reduced, the market demand is accurately matched, and stockout and overstock phenomena are reduced.

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 present invention relates to the technical field of business process management, specifically to an intelligent information management system for e-commerce, the system comprising: a transaction behavior monitoring module, an abnormality identification module, a transaction mode optimization module, an inventory optimization module, a risk monitoring module, and an intelligent response module. In the present invention, real-time monitoring and data analysis are used to track e-commerce transaction behaviors, which can instantly discover and screen transaction behaviors that deviate from the norm, improve transaction security and accuracy, use advanced data analysis technology, and effectively identify and manage abnormal transaction patterns through an isolation forest algorithm, which not only helps to prevent fraud and risks, but also optimizes transaction processes and rules, improves transaction efficiency, and can timely implement risk prevention and control measures through real-time monitoring and evaluation of inventory risks, maintain a dynamic balance between inventory and market demand, effectively avoid excessive inventory and out-of-stock situations, and improve the market competitiveness of enterprises.

The invention discloses a medical consumable intelligent warehouse based on the Internet of Things, and relates to the technical field of medical consumable management. An intelligent management system is adopted, the intelligent management system comprises a biological feature recognition unit, an access control unit, a material registration unit, a checking unit and an inventory statistics unit, and dynamic data of type, batch and quantity information of medical consumables in a warehouse are monitored in real time through biological feature recognition, material registration, checking and uploading to a cloud server; the system can accurately track the inventory and warehouse-in and warehouse-out conditions of operators and medical consumables, improves the management efficiency, guarantees the consistency of the input information and actual information of the medical consumables through layer-by-layer control, can improve the accuracy of inventory data, and improves the management efficiency. Therefore, the problems of supply chain chaos, product stockout or excessive stock and the like caused by wrong stock information can be avoided, intelligent management of warehouse management is realized, and on the premise of ensuring supply, expired scrapping of medical consumables is reduced as much as possible, and the medical cost is reduced to the maximum extent.

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 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.

Systems and methods for enabling unconstrained demand forecast based on accurate lost sales estimation using collective consumer behavior are disclosed. In some embodiments, a disclosed method includes: obtaining raw sales data of an item in a store for a past time period, wherein the item is offered for sale in the store; detecting at least one out-of-stock (OOS) time period within the past time period based on the raw sales data, wherein sale of the item is impacted by an OOS status of the item in the store in the at least one OOS time period; computing, based on a non-linear model, lost sales estimate of the item in the store for each of the at least one OOS time period; generating, based on the raw sales data and the lost sales estimate, recommended inventory for the item in the store for a future time period; and transmitting the recommended inventory to a computing device associated with the store for inventory arrangement in the future time period.