80 results about "Inventory level" patented technology

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 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.

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 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 relates to the technical field of supply chain intelligent optimization, in particular to an AI-based intelligent supply chain optimization management method and system, and the method comprises the steps: building a supply chain total-factor knowledge graph with a time attribute and a confidence attribute, accessing a multi-source heterogeneous supply chain operation data flow in real time, extracting a state updating event through analysis fusion, and carrying out the optimization of the supply chain total-factor knowledge graph. And driving the knowledge graph to dynamically evolve, updating the attribute value, and generating a real-time supply chain state mirror image. And the scheduling strategy circulation is simulated based on a mirror image, the inventory level and the performance time efficiency are predicted, potential anomalies are identified, cause paths are traced back, an optimization scheme is generated and decomposed into task lists to be distributed to execution entities, the task completion state is tracked, and a result is fed back to the data analysis and fusion step. According to the method, intelligent management and control and closed-loop adjustment of the whole process of the supply chain are realized through dynamic knowledge maps with attributes and abnormal traceability optimization.

A supply chain custody system for injectable medication administration provides secure tracking and verification of injection devices throughout the supply chain. The system includes modules for monitoring inventory levels and recording authenticated transfers between supply chain participants. A chain of custody module verifies proper handling during custody transitions and validates participant credentials using blockchain technology and geolocation verification. Temperature monitoring capabilities track environmental conditions during storage and transport. The system incorporates authentication mechanisms to verify device legitimacy and includes an authorization module that analyzes supply chain data to control distribution permissions and trigger automated actions for maintaining secure custody documentation.

This invention discloses a method and system for optimizing medical consignment inventory based on kit integrity constraints. The method includes: constructing a material dependency graph for surgical kits, representing each surgical kit as a graph structure containing nodes and edges; constructing a shortage risk propagation model based on historical borrowing and returning data, calculating the risk transmission coefficient of material shortages on kit availability, and forming a kit integrity probability assessment system; establishing an optimization model aimed at minimizing the probability of kit shortages under total safety stock budget constraints, dynamically allocating the safety stock level of each material within the kit; integrating surgical scheduling data with the inventory calculation model, and implementing differentiated inventory reservation and sharing strategies based on surgical certainty levels to achieve synergistic optimization of kit integrity and inventory costs. This effectively solves the problem that traditional single-material inventory calculation methods cannot guarantee kit integrity, significantly improving material availability and reducing redundant inventory costs.

An object of a system according to an embodiment is to quickly and flexibly respond to fluctuations in supply and demand and to propose and adjust an optimum inventory level.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand prediction data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an inventory level based on the analysis result obtained by the analysis unit. The adjustment unit adjusts the stock level proposed by the proposal unit in real time.SELECTED DRAWING: Figure 1

This invention relates to the field of logistics supply chain and discloses a digital and intelligent collaborative management system and method for logistics supply chain. The system includes collecting order submission time, delivery area information, warehouse inventory levels, vehicle location, and remaining vehicle loading space to form order demand change data; identifying the changing trends of order demand across different delivery areas based on the order demand change data, and constructing an order demand diffusion relationship structure; jointly calculating the inventory consumption of each delivery area based on the order demand diffusion relationship structure, and selecting a set of supply warehouses based on warehouse inventory levels; generating transportation task combinations for the supply warehouse set, combined with the current vehicle location and remaining vehicle loading space; monitoring changes in order demand along the demand propagation path based on the transportation task combinations, recalculating unexecuted transportation task combinations to form updated transportation task combinations. This invention has the advantages of improving operational efficiency and responsiveness.

A system for performing inventory management includes at least one processor; and at least one memory in electronic communication with the at least one processor. The at least one memory has instructions stored thereon which, when executed by the at least one processor, direct the at least one processor to communicate to at least one display device. The processor is operable to receive inventory data for a plurality of parts of a company with at least a first and second facility at separate site locations. The processor is operable to analyze the inventory data and identify a shortage or surplus of inventory of a part of the plurality of parts at the at least first and second facility relative to an optimal level of inventory that meets production demand and safety stock levels. The processor is further operable to match parts of the plurality of parts having surplus inventory at the at least first facility with parts of the plurality of parts having shortage inventory at the at least second facility wherein the processor outputs part information that is representative of the analyzed inventory data for the matched parts and communicates the part information to the display device. A method and a company are also disclosed.

The invention discloses a food material inventory management method and device based on time sequence data, equipment and a medium. The method comprises the steps of obtaining inventory time sequence data of food materials; determining a data change point of the inventory time sequence data; determining a preorder steady-state data interval and a postorder steady-state data interval of the data change point in the inventory time sequence data; determining a preorder steady-state data statistical value and a postorder steady-state data statistical value according to the preorder steady-state data interval and the postorder steady-state data interval respectively; according to the preorder steady-state data statistical value and the postorder steady-state data statistical value, determining the variable quantity of the food material inventory; according to the change quantity of the food material inventory, the food material inventory is updated, and automatic detection and quantification of the change of the food material inventory are realized, so that the actual consumption and supplement conditions of the food materials are accurately reflected in real time, a user is helped to accurately master the real-time inventory level of the food materials, and the conditions that the food materials are wasted due to expiration or normal use is influenced due to insufficient inventory are reduced; and convenience is brought to users.

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.

This invention discloses a smart warehouse inventory management method based on big data analysis, comprising the following steps: acquiring and cleaning multi-source warehouse data to form a standardized set of inventory event fragments; identifying valid demand and updating the quiescent state, generating a demand dormancy age sequence and an activity trigger propagation graph; estimating the wake-up risk based on the age sequence and propagation graph, generating a wake-up risk spectrum result; determining the released inventory objects and decoding demand impulses to generate demand impulse release packets; combining the release packets with inventory events to execute inventory envelope adjudication, generating inventory level results and inventory action results; updating the propagation graph and age sequence based on the level results, action results, and execution feedback to form a closed loop. This invention employs big data analysis and inventory event evolution modeling methods to achieve dynamic adjudication and closed-loop updating of smart warehouse inventory, possessing the advantages of accurate demand identification, fast decision response, and strong inventory risk control.

At least one processor may receive a plurality of review comments, identify at least a subset of the plurality of review comments representing at least one positive review, and identify at least one product indicated by the at least one positive review. The at least one processor may determine, from data produced by an inventory system, that the at least one product has a low inventory level. In response to the determining, the at least one processor may generate a message configured to automatically order the at least one product from a supplier system and send the message to the supplier system, thereby automatically ordering the at least one product.

A device may include a barrel in fluid communication with a needle. A device may include a piston comprising a plunger. A device may include a microprocessor in electronic communication with a wireless module. A device may include sensors for detecting injection parameters. A device may include a supply chain tracking system configured to: monitor distribution of a plurality of intelligent injection devices containing an injectable medication, verify an inventory of the injectable medication, track inventory levels, record usage data of the plurality of intelligent injection devices, transmit an update to the inventory based on the recorded usage; and forecast a future inventory level for the injectable medication.