144 results about "Inventory level" patented technology

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and / or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

The invention relates to the technical field of storage intelligence, and discloses an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagent consumables, and the system comprises a consumption trend extraction module, a demand prediction module, a risk early warning module, an early warning trigger module, a supply feasibility evaluation module and an intelligent allocation module. Performing seasonal decomposition on the historical consumption record to obtain a consumption trend component; periodically superposing the consumption trend component to obtain a demand prediction result; based on the demand prediction result, performing inventory risk research and judgment on the current inventory to obtain an inventory early warning index; when the inventory early warning index exceeds a safety threshold value, inventory early warning information is generated; according to the consumable demand information, carrying out feasibility evaluation on inventory data and logistics constraint conditions of the supply point to obtain a feasible allocation point; generating a deployment scheme according to the safety inventory standard of the feasible deployment point; according to the invention, the perspectiveness and accuracy of early warning and allocation of the intelligent inventory of the inspection and quarantine reagent consumables can be improved.

The invention relates to the technical field of production management, and particularly discloses a bearing processing productivity optimization and production scheduling system, which comprises a process state sensing module, a dynamic bottleneck identification module, a self-adaptive buffer control module, a collaborative scheduling decision module and a productivity optimization execution module. Through cooperative work of the dynamic bottleneck identification module and the adaptive buffer control module, a static management mode of a traditional fixed buffer area is thoroughly changed. The system can sense the change of a production flow in real time, accurately identify a bottleneck, and dynamically adjust the target inventory level of a buffer area based on a fuzzy control algorithm, so that a buffer strategy can adaptively match a current production state. The problem that upstream and downstream processes wait for each other due to improper arrangement of a buffer area is solved, and passive blockage and material interruption between the processes are converted into active and flexible rhythm cooperation, so that systematic equipment waiting time is remarkably shortened, hidden capacity is released, and the equipment comprehensive utilization rate and output efficiency of a whole production line are improved.

The invention discloses an intelligent distribution and inventory monitoring system for surgical consumables. The intelligent distribution and inventory monitoring system comprises a data interface gateway, a central processing server and an intelligent consumable storage unit, operation schedules and unstructured text data in an HIS system, an ORIS system and an EMR system are integrated through a data interface gateway, an NLP analysis module in a central processing server is used for extracting requirements of'operation-special consumables', and a consumable consumption probability distribution model based on multi-dimensional features and a surgeon doctor preference coefficient are combined; using a dynamic target inventory calculation engine to generate a target inventory level that varies with time according to a formula; the inventory strategy module automatically generates an electronic replenishment instruction according to comparison with real-time inventory; the intelligent storage unit realizes accurate tracking of consumable delivery and warehousing through RFID (Radio Frequency Identification Device); and the closed-loop feedback module continuously optimizes model parameters by using actual consumption data. According to the system and the method, prospective inventory management based on future operation requirements can be realized, and the accuracy of consumable supply, the inventory turnover rate and the operation safety guarantee capability are remarkably improved.

The invention discloses an alloy smelting batching scheme generation method and system, particularly relates to the technical field of metal material production and supply chain collaborative optimization, and is used for solving the problems of uncontrollable execution, cost waste and quality risk caused by disconnection of static parameters and a dynamic supply chain in an existing batching scheme. The method comprises the following steps: acquiring target alloy component demand parameters, raw material inventory allowance and purchase cycle parameters; identifying a dynamic constraint key material with a supply interruption risk; establishing a dynamic constraint model containing an inventory safety buffer threshold based on the historical consumption volatility; candidate batching schemes are generated through multi-level optimization; verifying inventory performability and compatibility of alternative material components; sorting and outputting the effective schemes according to the cost and inventory suitability; through supply chain risk quantification and dynamic constraint modeling, the feasibility of a batching scheme is remarkably improved, and production halt loss and potential quality hazards caused by supply interruption or blind substitution are reduced.

The invention relates to the technical field of coal transportation and sales management, and discloses a coal transportation and sales management system based on deep learning. The system comprises a multi-source data acquisition and processing module which collects and preprocesses coal quality, a transportation state and market sales data; the coal demand prediction module predicts a market demand trend based on the preprocessed data; the inventory optimization scheduling module optimizes the inventory level according to the demand trend; the transportation task dynamic allocation module allocates transportation tasks in combination with the optimized inventory; the abnormity monitoring and analyzing module monitors transportation abnormity; the constraint evaluation and optimization module evaluates transportation resource constraints and optimizes task allocation; the real-time response adjustment module dynamically adjusts the transportation task according to the abnormality and constraint evaluation result; the life cycle prediction module predicts the aging trend of inventory equipment; the energy efficiency evaluation module evaluates the transportation energy efficiency; and the maintenance decision optimization module optimizes a maintenance strategy in combination with the aging trend and the energy efficiency evaluation result. The system realizes intelligent management of the whole coal transportation and sale process.

The invention discloses a WMS inventory optimization method and system based on digital twinning, and relates to the technical field of warehouse logistics management, and the method comprises the steps: collecting cargo inventory state data, refrigerated transport resource data and seasonal operation progress data from each cold chain warehouse management system, converting the data into different data formats through employing a standardized protocol, and carrying out the optimization of the data formats; the method comprises the following steps: acquiring a real-time inventory data set in a unified format, synchronizing the real-time inventory data set to a central processing node by adopting a distributed database mechanism according to the acquired real-time inventory data set, determining current cargo inventory levels and available refrigerated transport capacities of all cold-chain warehouse nodes, and if the cargo inventory level of a certain cold-chain warehouse node is determined to be lower than a preset threshold value, sending the cold-chain warehouse node to the central processing node; if yes, comparing the cargo inventory level and the transportation capacity available evaluation of adjacent warehouse nodes through an inventory level threshold; according to the WMS inventory optimization method and system based on digital twinning, efficient circulation of cold chain products such as goods is guaranteed.

The invention belongs to the technical field of intelligent manufacturing, and discloses a demand-driven material demand planning system based on deep reinforcement learning and an optimization method. The system comprises a demand module which is used for collecting, processing and outputting related demands and supplier environment data; the demand-driven material demand planning module is used for generating an inventory replenishment plan, a production plan and a material purchasing plan; the simulation module is used for constructing and operating a simulation model so as to output a simulation experiment statistical result; the deep reinforcement learning module interacts with the demand-driven material demand planning module and the simulation module, carries out training by utilizing a built-in algorithm library, and outputs an optimization parameter set of a demand-driven material demand plan; and the execution module is used for converting the plan information into an operation instruction and feeding back actual operation data and an execution result so as to make up for the deficiency of static parameter configuration of the traditional demand-driven material demand plan, realize collaborative optimization of inventory level and service rate and enhance the response capability of an enterprise to market demand change.

The invention relates to the technical field of computers, in particular to an internet fresh agricultural product distribution management system which comprises an order management module, a logistics management module, an inventory management module and a data analysis module. The order management module is used for processing customer orders, including order receiving, processing, tracking and state updating, so as to ensure efficient processing of the orders; the logistics management module is used for monitoring the temperature and humidity of the fresh agricultural products in the transportation process and planning a distribution route so as to ensure that the fresh agricultural products are delivered to customers in the shortest time; the inventory management module is used for monitoring the inventory level of the fresh agricultural products so as to ensure that enough inventory meets the order requirements; and the data analysis module is used for optimizing a purchasing strategy through data analysis. According to the invention, optimization of the fresh agricultural product distribution process is facilitated.

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 invention relates to a supply chain demand prediction and dynamic inventory optimization method based on big data, and relates to the technical field of inventory scheduling prediction, and the optimization steps are as follows: S1, collecting data, preprocessing the collected data, and uploading the preprocessed data to a data analysis module; s2, a data analysis module which performs grid division on different areas of the supply chain, analyzes supply demands of different grid division areas based on historical data, and judges factors which influence the demands of the supply chain of the divided areas; and S3, based on the obtained factors, obtaining the size degree of each influence factor existing in different divided areas in real time, and carrying out weighted comprehensive calculation to obtain an influence value. Complex multi-factor influences are presented in a quantitative form, the comprehensive effect of different factors on supply chain demands is scientifically measured, quantitative index support is provided for decision making, the warehouse stock amount is intelligently adjusted based on the influence value, and the stock level can be better matched with actual demands.

To provide an inventory estimation method that can estimate inventory levels for a wide variety of parts based on a common logic, thereby simplifying registration and management tasks. [Solution] The inventory quantity estimation method involves performing a 3D scan on multiple parts boxes (S11), calculating the three-dimensional shape of the multiple parts boxes using a calculation device based on the results of the 3D scan (S12), calculating the surplus volume, which is the empty volume of the multiple parts boxes, using a calculation device based on the three-dimensional shape of the multiple parts boxes (S13), and if there is a parts box whose calculated surplus volume is greater than a predetermined threshold, the existence of that part box is notified to the worker (S14, S15).

The invention discloses a lottery logistics accurate inventory and distribution optimization method. The method comprises the following steps: S1, data acquisition and input; s2, inventory management and analysis: predicting the sales volume of each lottery ticket type in a specified period in the future based on historical sales data, external influence factors and a real-time inventory state, and dynamically adjusting the safety inventory level and replenishment threshold value of each warehouse / sales point according to the sales volume; s3, distribution path optimization: based on the input related information, automatically calculating a vehicle distribution path set which meets the constraint condition and has the minimum total cost; and S4, delivery execution and feedback. According to the lottery logistics precise inventory and distribution optimization method disclosed by the invention, the problems of inaccurate inventory, large demand prediction deviation, low distribution efficiency and the like in traditional lottery logistics management are solved, precise management of lottery logistics is realized, the operation cost is reduced, and the service quality is improved.

The invention relates to a data analysis method based on a supply chain, in particular to the technical field of business management, and the method comprises the following steps: S1, collecting supply chain data from an internal system and an external source, the internal data comprising a production plan, a production schedule, an inventory level, a purchase order and a sales record, the external data comprises logistics transportation information, market demand indexes, consumer behavior characteristics, meteorological monitoring data, road traffic flow and policy and regulation change information; according to the invention, by establishing a multi-source data access channel, data of an ERP system, an MES system, a WMS system, an external data platform and an Internet of Things device are uniformly collected, and historical data and real-time data are combined to construct a multi-dimensional time sequence database, so that complete acquisition and structured storage of supply chain global information are realized; and the comprehensiveness and accuracy of data input are ensured.

The invention provides a full-process intelligent control method and system for a stock yard, and the method comprises the steps: decomposing a task according to a stock yard feeding plan, starting a process, and completing an unloading task of the stock yard; the material level of a blast furnace bin is scanned in real time, according to the priority of the bin level from low to high, a feeding request is automatically triggered to start a blast furnace feeding process, a material requiring request sent by sintering and pelletizing is scanned, a corresponding feeding process is started, and a raw material feeding task is completed; according to the blending and stacking plan, automatic batching is carried out according to the formula model of the blended ore, and a blending and stacking task is executed; in the process, the stock of the stockyard is updated in real time, and the flow path is automatically optimized according to the stock and participates in intelligent control.

Disclosed are systems and methods for determining swap and replenishment opportunities in a pick area of a storage facility. A method can include receiving, by a computer system, current stock levels for bays in the pick area, identifying a subset of the bays as having a replenishment or swap opportunity based on determining that the current stock levels for the subset of the bays is less than a threshold stock level, and determining, for each bay in the subset of the bays, whether the bay has a swap opportunity or a replenishment opportunity. The method can also include determining whether each bay is a static bay based on pick area build information, which can include a pick area layout, current SKUs in the pick area, date of layout build, static location assignments for the bays, and dynamic location assignments for the bays.

The present disclosure relates to determining acceptability of a remedy for a supply plan deviation with a machine learning model. A system for analyzing a supplier communication regarding a deviation from a supply plan is described. The system can determine a severity of the deviation and determine an impact on a supply chain or inventory level caused by the deviation. A remedy in the supplier communication can be identified, and the system can determine whether the remedy is acceptable to address the deviation. The analysis of the supply plan deviation, the severity of the deviation, the acceptability of the remedy, and / or other factors can be used to generate a supplier score.

The present invention discloses a multimodal transport inventory resonance management method, medium and equipment based on dynamic rules. The method includes: classifying and collecting raw data and preprocessing it into standardized intermodal transport data; constructing a transport time prediction model based on the preprocessed second transport state parameter to extract dynamic transport characteristics, and performing spectral analysis on the second inventory level parameter to obtain inventory cycle fluctuation characteristics; extracting the resonance coupling characteristics of the second transport state parameter and the second inventory level parameter through time-frequency correlation analysis and establishing a feature coding library; constructing a mapping relationship network between transport mode switching rules and inventory adjustment strategies based on the above characteristics; establishing a resonance control parameter conversion relationship between a baseline transport scenario and a target transport scenario; and finally constructing a multidimensional resonance management knowledge base. The present invention realizes the coordinated control of transport delays and inventory fluctuations under multimodal transport, effectively suppresses the supply chain resonance effect, and improves the system's responsiveness and stability.

The invention relates to the technical field of warehouse management, in particular to an AGV scheduling and warehouse stock decision-making system and method based on data analysis. Comprising an inventory management module used for obtaining storage data, sales data and to-be-warehoused data, the storage data comprises real-time inventory data, and the storage quantity and the storage amount of each type of goods, the sales data comprises the sales amount of each type of goods, and the to-be-warehoused data comprises to-be-warehoused goods and the to-be-warehoused quantity; the area analysis module is used for analyzing the occupancy rate of each current inventory area according to the real-time inventory data, and the inventory area with the occupancy rate lower than a preset occupancy threshold value is marked as a low-occupancy area mark; and the turnover rate analysis module is used for calculating the stock turnover rate warehousing grade of the goods in the preset period according to the storage amount and the sales amount of the goods in the preset period, and is used for determining the warehousing grade of the goods to be warehoused according to the to-be-warehoused quantity and the storage amount turnover rate of the goods to be warehoused.

A vehicle 100 has items stored in containers 104, such as a van having plumbing tools and parts in bins on shelves. An inventory system measures stock level data for each item and wirelessly transmits the stock level data to an inventory engine. In response to a change in an item’s stock level and at least one other factor (such as the time of day, the day of the week, or the vehicle’s location), the inventory engine communicates with a user device to warn the user of possible theft. In response to a reply from the user device, the inventory engine may alert the police or other authorities. Alternatively, the inventory engine may alert the police or other authorities if there is no reply from the user device. The vehicle may have a camera to capture images in response to possible theft being indicated by the change in stock level and the other factor.

The invention relates to the technical field of catering management, and discloses a canteen food material purchase-sale-stock intelligent management system, which comprises a food material data acquisition module, a demand prediction module, a purchase plan generation module, a stock monitoring module, a risk assessment module, an intelligent report module, a data synchronization module, a user interaction module and a performance optimization module. The food material data acquisition module acquires inventory data of canteen food materials in real time; analyzing a historical sales mode and external factors by using a demand prediction module; according to the predicted demand data, combining supplier evaluation to generate purchase plan data; the inventory monitoring module dynamically updates the inventory level; the risk assessment module calculates out-of-stock probability and excess inventory risk; the food material state data, the prediction demand data, the purchase plan data, the inventory early warning signal and the risk level index are integrated, and the intelligent report module generates a visual comprehensive management report and pushes the visual comprehensive management report to the user terminal. The economical efficiency and stability of canteen operation are enhanced.

This application relates to the field of database operation and maintenance technology, specifically to an automated operation and maintenance method for audit system databases. This method includes: periodically collecting inventory levels and thresholds at each level of an LSM-Tree, and calculating the level capacity occupancy ratio; using eBPF technology to statistically analyze background I / O latency and traffic within a sliding window, and calculating the unit latency for background disk writes; performing recursive calculations in reverse order using the level capacity occupancy ratio to obtain the end-to-end logical write multiple; analyzing the coupling relationship between the level capacity occupancy ratios of adjacent levels, determining the multi-level concurrent congestion coefficient, obtaining the estimated unit data occupancy time, and inversely deriving the allowed front-end write rate; and combining this with a token bucket algorithm to smoothly limit the write requests of the front-end audit system. The goal of this application is to achieve early detection of backlog trends within the LSM-Tree and to automatically perform predictive traffic control to optimize the operation and maintenance management of the audit system database.

This specification provides an embodiment of a method for outbound goods processing. The method involves acquiring outbound orders to be processed in a target warehouse; determining the inventory information of each item in the outbound order; configuring picking tasks based on the inventory information, whereby the picking tasks are configured based on unit carrier capacity; sending the picking tasks to picking objects to enable them to pick items and obtain containers ready for outbound processing; then performing a verification task via an RFID channel machine; and finally, responding to the execution of the verification task, outbound operations are performed on the containers that have passed the verification task. This achieves a batch outbound process based on an RFID channel machine. Because it uses unit carrier capacity to batch pick items with different inventory levels, and employs a piecing operation to collect scattered items, it reduces the picking process path. Furthermore, the RFID channel machine performs batch verification of the collected items, ensuring the accuracy of the picked outbound goods.

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost. The optimization algorithm can comprise constraint conditions.

The system is an inventory replenishment solution based on artificial intelligence (AI), and aims to optimize the inventory management process of an enterprise through automatic and intelligent technical means. The system integrates a plurality of data sources, utilizes an advanced AI model to carry out prediction and decision making, provides an accurate inventory replenishment strategy for enterprises, ensures efficient inventory management, and reduces risks of stockout and excessive inventory. The system is a key component in modern supply chain management, and aims to automatically adjust the inventory level by means of intelligent analysis of historical sales data, market demand prediction, supply chain efficiency optimization and the like so as to reduce inventory overstock, avoid stockout conditions and improve the overall operation efficiency. According to the system, through intelligent inventory management and replenishment decision, the inventory management efficiency is greatly improved, the inventory cost is reduced, and the customer satisfaction is improved. The method is suitable for enterprises of various scales, and is especially suitable for industries with complex supply chains and large demand fluctuation, such as retail, manufacturing and electronic commerce.

We provide server equipment, inventory management equipment, and programs to improve the maintenance of appropriate inventory levels of pharmaceuticals in medical institutions. [Solution] In the inventory management system 10, the server device 12 is capable of communicating with the client device 11. The communication interface 121 receives inventory lot information ST from the client device 11, which specifies the quantity and date of receipt of pharmaceuticals recorded in the medical institution's inventory. The storage 123 stores the inventory lot information ST. When the communication interface 121 receives outbound information DL from the client device 11, which specifies the outbound quantity of pharmaceuticals to be excluded from inventory, and inventory lot information ST specifying the expiration date of the pharmaceuticals is stored in the storage 123, the processor 122 subtracts the outbound quantity from the quantity specified by the inventory lot information ST that specifies the earlier expiration date first.

This application provides a method, system, equipment, medium, and product for optimizing crude oil supply chain scheduling, relating to the field of petroleum industry chain optimization technology. The method involves: determining a target operations operator based on current state information; determining the target inventory level and target inventory penalty function for each node in the crude oil supply chain based on current inventory levels and the target operations operator; determining the objective function of a mathematical programming model using a preset oil transportation cost function and the target inventory penalty function; determining a production scheduling plan for the day based on current inventory levels and the objective function; executing the production scheduling plan for the day to obtain daily cost information; determining a reward function for the day based on the daily cost information; obtaining the daily reward function for each day within a preset time period; accumulating the daily reward functions within the preset time period to obtain the accumulated reward function; if the accumulated reward function value is not the maximum value, using a backpropagation algorithm to adjust the parameters to obtain an adjusted reinforcement learning layer.

Examples relate to managing node inventory levels are disclosed. An example may involve: obtaining historical data of items at a node in a network for a past time period; generating a supply chain lead time variability for an item at the node based on the historical data; generating a demand variability for the item at the node based on the historical data and the supply chain lead time variability; generating, based on the supply chain lead time variability and the demand variability, a recommended inventory level of the item in the node for a future time period; and transmitting the recommended inventory level to a computing device associated with the node for inventory arrangement in the future time period.

The invention discloses a supply chain intelligent scheduling method and system based on big data analysis, and relates to the field of food supply chain management, and the method comprises the steps: collecting and preprocessing supply chain node operation data, calculating the discrete degree and change rate of standardized operation data, constructing a fluctuation feature matrix, and generating an operation trend graph; a self-learning early warning mechanism is established, a final risk early warning value is output after early warning accuracy correction is combined, a node risk score is calculated, an initial allocation scheme is generated in combination with an inventory level, and a final resource allocation scheme is formed after node processing capacity matching analysis; and issuing a deployment scheme by adopting an identity recognition and data encryption mode, tracking an execution condition, and collecting feedback data to realize risk early warning and elastic regulation and control. According to the invention, supply chain node risks can be effectively predicted, resource optimization configuration is realized, and supply chain operation efficiency and stability are improved.