219 results about "Inventory level" patented technology

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention relates to the technical field of supply chain management, in particular to a part supply chain full-process digital collaborative management system and method, and the method comprises the steps: evaluating supplier data through constructing a dynamic scoring model, and obtaining a supplier score; when the supplier score is lower than a predetermined threshold value, generating a multi-source purchase plan, and dynamically adjusting purchase distribution according to the supplier score; constructing a warehouse-in and warehouse-out part intelligent verification model to analyze warehouse-in and warehouse-out parts, generating an acceptance report and block chain record association, and triggering an alarm and freezing an order when a difference is found; a supply chain risk topological graph is constructed through the supplier fulfillment rate, the logistics data and the market demand fluctuation, and high-risk nodes are identified and early warned in real time; initiating emergency purchase to suppliers with high supplier scores based on high-risk nodes, and optimizing inventory scheduling based on geographic proximity; the inventory level is optimized in real time based on the production line state and the in-transit logistics data, and the safe inventory threshold is dynamically adjusted to cope with supply chain interruption.

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.

A method, computer program product, and computer system for a cloud-based medication tracking system. The system may include a plurality of intelligent injection devices across multiple healthcare facilities, each having: a barrel in fluid communication with a needle, a piston including a plunger, a microprocessor, a wireless communication module a supply chain tracking system configured to: monitor distribution of an injectable medication, verify availability of the injectable medication, track a global medication delivery pattern relating to the injectable medication, a machine learning system trained to: analyze medication usage data across healthcare entities, predict a supply chain event, optimize an inventory level, and improve a distribution datum.

The invention relates to the technical field of inventory optimization, in particular to an intelligent supply chain demand prediction and inventory optimization system, which comprises a supply chain dynamic demand prediction module for intelligently predicting the demand quantity of a supply chain, and an inventory intelligent optimization module for intelligently optimizing the inventory according to the demand prediction result of the supply chain. The prediction correction and optimization adjustment module is used for dynamically correcting demand prediction and intelligently optimizing and adjusting inventory; according to the invention, through establishment of a three-channel cross validation prediction and anomaly elimination mechanism, single-channel prediction anomaly can be resisted, the stability and accuracy of supply chain demand prediction are improved, and inventory risks caused by prediction errors are reduced; the safe inventory level is dynamically set according to different stages, so that the inventory strategy better conforms to the commodity market change, and the shortage rate and the unsalable inventory are reduced; the correction amplitude is dynamically adjusted through error fluctuation, excessive correction is avoided, and the self-learning and self-evolution ability of a long-term supply chain prediction system is improved.

The invention relates to the technical field of digital management, in particular to a data analysis system based on digital enterprise management. The system comprises a data acquisition module, a model establishment module, an inventory adjustment module and a risk assessment module. According to the invention, competitor data, market promotion data and raw material supply chain data are collected through the data acquisition module, a neural network model for predicting enterprise sales is established by using the model establishment module, and the inventory adjustment module calculates the order quantity of the product by using an economic order quantity model according to the predicted enterprise sales. The maximum inventory level of an enterprise is determined through a reorder point algorithm and safe inventory setting, the risk assessment module comprehensively analyzes various indexes by using an analytic hierarchy process to obtain a comprehensive risk assessment result, an inventory optimization strategy is formulated according to the assessment result, and the risk of the enterprise is optimized. The inventory turnover rate is improved, the customer satisfaction is improved, and the market competitiveness is enhanced.

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 relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

Dynamic storage location allocation and strategy adjustment system (100) for SAP Extended Warehouse Management (EWM) environments, comprising: a) a real-time warehouse data aggregation module configured to collect SKU attributes, inventory levels and bin location availability from SAP EWM; (b) a dynamic bin allocation engine that applies artificial intelligence algorithms to allocate optimal bin locations based on SKU characteristics, space utilization and operational parameters; c) an adaptive strategy adjustment module that dynamically modifies storage and retrieval strategies in response to real-time storage conditions; (d) a pattern recognition and forecasting module configured to analyze historical trends and predict inventory and demand fluctuations; (e) a warehouse resource optimisation module that aligns decisions on the allocation of storage space with the availability of labour and equipment; f) an SAP EWM integration module that communicates with SAP EWM via APIs or BAdIs to implement changes in real time; and g) a review and feedback learning module that tracks system decisions and results to continuously improve allocation and strategy logic.

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 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, and / 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 in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

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 discloses an intelligent inventory control system based on a real-time data analysis and automation technology, and relates to the technical field of inventory control, and the system comprises an inventory tracking module which tracks the inventory in real time, updates the inventory data, and records the basic information of the inventory materials through the radio frequency identification technology and the bar code scanning technology; the inventory demand prediction module constructs and trains an inventory demand prediction model based on historical data of a certain type of people, uses the model to determine the material type of the current needed inventory, predicts the material inventory quantity based on customer demands, outputs a prediction result, dynamically adjusts the safety inventory level based on the prediction result, and formulates a replenishment plan. Abnormal detection and early warning are carried out; and the inventory decision optimization module is used for comprehensively analyzing historical data and real-time inventory data of a certain type of people, filtering and controlling the inventory data according to an analysis result, and optimizing inventory management. According to the invention, inventory management decisions can be optimized, and risks of inventory overstock and shortage can be 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 discloses a medical consumable demand prediction method based on multi-modal deep learning, and the method comprises the steps: obtaining multi-modal medical data, which comprises text data and time series data; the text data comprises electronic medical records, operation records and medicine instructions; the time sequence data comprises historical consumable usage amount, inventory level, purchase record, department type, operation frequency and timestamp; performing semantic feature extraction on the text data through a text encoder to generate a text feature vector; performing multi-scale feature extraction on the time sequence data through a time sequence encoder to generate a time sequence feature vector; inputting the text feature vector and the time sequence feature vector into a cross-modal attention module, and generating a fusion feature vector through a cross attention mechanism; and obtaining a medical consumable demand prediction result based on the fusion feature vector. According to the method, multi-modal data are fused, the prediction precision and efficiency are improved, and resource scheduling is optimized.

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 invention discloses a civil aviation aviation material inventory guarantee management and control method and system and electronic equipment, and relates to the field of aviation material inventory management, and the method comprises the steps: calculating aviation material demands according to calendar time, use time and use cycle based on routine maintenance work in a future time period T, and carrying out the comprehensive calculation to obtain the demand number of aviation materials i; in the time period T, the installed parts corresponding to the aerial materials are divided into two types which belong to the MEL list and do not belong to the MEL list, and non-routine work demand calculation is carried out; supplementarily calculating the aerial material demand of associated replacement, and comprehensively calculating to obtain the aerial material demand of non-routine work; and calculating the aerial material demand quantity on the basis of the overlay fleet scale. According to the method, accurate control, scientific configuration and dynamic adjustment of various types of general aviation material inventory quantities are realized, the general aviation enterprise aviation material inventory structure and safety inventory level are further optimized, the aviation material inventory guarantee timeliness rate is improved, and the operation cost is saved.

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.

An automatic inventory system for a storage facility is disclosed, the storage facility containing a store adapted to store a plurality of each of a plurality of items in containers. The inventory system is arranged to measure stock level data for each item stored in the containers and transmit the stock level data for each container to an inventory engine. The containers comprise an elongate container such as a pipe tube mounted in or on the storage facility, the elongate container being attached to the storage facility via at least two weight sensing devices and being arranged to transmit the weight data from the at least two sensing devices to an inventory engine. The inventory engine is arranged to identify the items within the elongate container from the weight data. Also disclosed are a reel for storing a wound item (e.g. a cable reel) and measuring an amount of wound item on the reel and a portable item store that can be removed from the storage facility.

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.

The invention relates to the technical field of digital production systems, in particular to a raw material inventory management method based on an MES system, and the method comprises the steps: obtaining raw material demands and production data during production based on the MES system, and calculating the equipment fault probability, the order insertion probability, the supply chain delay probability and the environmental risk probability according to the production data; and obtaining a raw material demand fluctuation coefficient according to the probability, and finally performing inventory management according to the raw material demand and the fluctuation coefficient. According to the invention, by constructing the multi-dimensional risk prediction mode and adjusting the inventory, raw material demand transaction can be pre-judged in advance, the superimposed influence of emergency order insertion and conventional production on raw material consumption is effectively balanced, the supply guarantee rate is also considered, the scientificity and adaptability of inventory management are significantly improved, and the method is suitable for large-scale popularization and application. Self-adaptive optimization of the inventory level in a complex production environment is realized, the method has double values of cost control and supply chain toughness improvement, and the problem of frequent occurrence of stockout and overstock conditions in an existing inventory management method is solved.

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.