125 results about "Inventory cost" patented technology

The invention belongs to the intelligent information technology, and discloses an e-commerce platform operation system and method based on intelligent decision and dynamic optimization. The system comprises a plurality of modules of multi-source data acquisition and preprocessing, user portrait and demand prediction, intelligent commodity recommendation and the like. A recommendation algorithm fusing a knowledge graph and reinforcement learning and an inventory prediction and optimization algorithm based on dynamic time sequence analysis are innovatively adopted, and a reward function formula and an inventory cost function formula are arranged respectively. According to the method, data acquisition, user portrait construction, business operation and system optimization are realized according to collaborative operation of the system and the modules. The system can accurately recommend commodities, intelligently manage inventory, efficiently plan promotion activities, collaboratively optimize a supply chain, and improve the operation efficiency and commercial competitiveness of an e-commerce platform.

The invention discloses a material digital centralized management and control system based on the Internet of Things, which relates to the technical field of digital management and control and comprises a supply chain management unit, a loss management unit, a cost accounting unit, an inventory management unit, a dynamic adjustment unit and a user management unit. The system can accurately plan the purchase quantity and time, screen high-quality suppliers, guarantee the quality and stability of material supply from the source, deeply analyze the loss data in transportation, storage and processing, formulate a targeted strategy, reduce material loss, reduce waste, dynamically check the cost in real time, perform apportionment analysis according to different dimensions, and accurately control the cost. Through intelligent warehouse-in and warehouse-out management, regular inventory checking and intelligent inventory prediction, it is ensured that inventory data is accurate, inventory is reasonably planned, an inventory strategy is dynamically adjusted, inventory overstock or stockout is reduced, and inventory cost and service level are balanced.

The invention discloses an intelligent supply chain management system and method based on a cloud computing platform, and particularly relates to the technical field of supply chain management. According to the invention, the number of holidays, the number of special days, seasons, the number of sales promotion days, the sales promotion strength, the commodity prices, the commodity categories and the commodity brands are used as feature parameters of the commodity sales to construct the commodity sales demand prediction model, so that the accuracy of commodity sales demand prediction is effectively improved; according to the method, the commodity demand fluctuation coefficient is introduced during commodity inventory classification, product inventory classification is optimized, different replenishment strategies are made for different products, the purpose of differential management is achieved, the inventory cost is reduced, and meanwhile the market demand can be met; according to the method, the supply capacity of the cooperative suppliers is analyzed, the cooperative suppliers and the alternative schemes of the next cooperation period are intelligently screened when the supply capacity of the cooperative suppliers is lower than the expectation, and the operation efficiency of the supply chain can be guaranteed to the maximum extent.

The invention discloses an intelligent calculation method and system for concrete sales, and relates to the technical field of building and logistics management. By dynamically adjusting the production plan, accurately predicting the customer demand and optimizing the production capacity and the raw material ratio, the problem of excessive or insufficient production caused by large demand fluctuation in traditional production is solved, the inventory cost is reduced, the production efficiency is improved, the traffic condition is monitored in real time and the transportation route is dynamically optimized in the transportation link, and the transportation efficiency is improved. The transportation efficiency and the delivery punctuality are obviously improved, the transportation cost is reduced, and the concrete quality is ensured; in the production process, the raw material ratio and the concrete state are monitored in real time through a sensor and a machine learning algorithm, production parameters are dynamically adjusted, the quality fluctuation problem caused by inaccurate ratio or untimely monitoring in traditional production is solved, and the stability of product quality is ensured.

The invention discloses a supply chain deployment system and method based on big data, and particularly relates to the technical field of supply chain deployment, and the system comprises a data collection module which is used for collecting the data of each link of a supply chain; a data storage and processing module; the demand prediction module is used for carrying out market demand prediction by using a time sequence analysis model and a regression analysis model; the intelligent allocation module is used for formulating an optimal supply chain allocation scheme by using an optimization algorithm; the logistics optimization module is used for carrying out optimization scheduling on logistics transportation routes and vehicles; the monitoring and feedback module is used for monitoring the operation state of each link of the supply chain in real time; according to the supply chain deployment system and method based on big data, the demand prediction accuracy is improved, through big data analysis and an advanced prediction algorithm, multiple influence factors are fully considered, the market demand can be predicted more accurately, the inventory overstock and stockout phenomena are reduced, and the inventory cost and opportunity cost of an enterprise are reduced.

The invention relates to the technical field of e-commerce, and relates to a digital shopping mall management SaaS system, and the system comprises the steps: collecting shopping mall call and network events, and generating an embedded vector through optical reserve wavelength division multiplexing nonlinear mapping; under the constraint of the causal structure in the last round, the de-noising diffusion model generates twinborn events, an updated causal graph is formed through incremental Bayesian information criterion learning, and a causal feature tensor is obtained through random walk embedding; a reinforcement learning framework formed by the neural morphological execution network and the GPU evaluation network fuses tensors with inventory, price and promotion business data into a value state, and outputs a price adjustment-discount-replenishment strategy; the strategy is compiled into a WebAssembly micro contract, and the components are subjected to hot replacement without shutdown through a BPF sandbox; generating a zero-knowledge proof for the micro contract and storing the proof on the side chain of the block chain; in the gray stage, profit, inventory and cost feedback is collected and written back to a data link to complete self-adaptive closed loop; according to the invention, millisecond-level strategy iteration, second-level security release and extreme scene robust operation are realized.

The invention provides an intelligent unmanned library scheduling system for spare parts. The intelligent unmanned library scheduling system comprises an information perception module, a demand prediction analysis module, an intelligent scheduling core and an unmanned library execution monitoring module. The system obtains spare part information in all directions, predicts future demands, generates scheduling strategies, controls unmanned equipment to complete carrying and storage operations, monitors equipment states and environmental parameters, and realizes intelligent scheduling of multi-objective optimization. According to the invention, the efficiency and accuracy of spare part management can be improved, the inventory cost is reduced, the efficient operation of equipment and the stability of environmental parameters are ensured, and the response speed and service quality of the whole supply chain are optimized.

The invention provides a maintenance spare part prediction system based on a big language model plug-in knowledge base, and the system comprises the steps: carrying out the information data extraction and preprocessing of a historical work order, and obtaining a preprocessing text; performing semantic vector conversion and association binding processing on the preprocessed text to obtain a vector database; extracting attribute information from the new work order, and performing semantic vector conversion on the attribute information to obtain a query vector; retrieving a work order related to the query vector from a vector database; semantic reasoning is carried out on a new work order and a related work order, a maintenance spare part prediction result is generated, natural language fault description and equipment model information in a maintenance record are fully utilized, natural language information is fully understood in the face of complexity and diversity, the spare part prediction accuracy and practicability are improved, reasonable configuration of maintenance resources is achieved, and the maintenance efficiency is improved. Spare part inventory cost and inventory waste are reduced, and maintenance efficiency is improved.

The invention relates to a material replenishment method and system based on dynamic inventory prediction. The method comprises the following steps: firstly, acquiring a historical inventory data set of multiple types of materials comprehensively covering warehouse-in, warehouse-out and inventory allowance records; secondly, in the inventory feature extraction link, material consumption rules and storage state features are innovatively extracted from time and space dimensions respectively; moreover, the dynamic inventory prediction model carries out combined processing on the two types of features, the limitation of a traditional prediction method is broken through, the complex association of materials in time and space can be fully considered, and the accuracy and reliability of inventory change prediction in a target time period are greatly improved. And finally, a supply trigger condition and a supply amount adjustment strategy are generated based on an accurate prediction result, and are fed back to a supply execution system, so that dynamic and accurate supply operation is realized, the inventory overstock or stockout phenomenon is effectively avoided, the inventory management efficiency is remarkably improved, the inventory cost is reduced, and the whole material inventory management process is comprehensively optimized.

The invention relates to a CNC machining material unified management system based on intelligent early warning and multi-dimensional fusion, and belongs to the technical field of intelligent manufacturing and industrial informatization. The invention aims to solve the technical challenges in the existing CNC processing material management. The system adopts a three-layer architecture, comprises a data layer, a business logic layer and an application layer, and realizes the functions of unified modeling, classified management, batch tracking, inventory early warning and the like of material data. Through the intelligent tool breakage prediction module, the two-stage position management module, the intelligent inventory prediction module and the like, the system can automatically identify tool breakage abnormity, optimize the storage position, predict the inventory depletion date and generate intelligent replenishment suggestions. The system improves the management efficiency, reduces the inventory cost and the stockout risk, is suitable for the fields of intelligent manufacturing and industrial informatization, and meets the requirements of digital transformation of enterprises.

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

The invention discloses an inventory-related refinery plan optimization multi-objective evaluation method. A refinery plan optimization objective is Max Z = sales amount-purchase cost-inventory cost, and the inventory cost = storage cost-asset value fluctuation + sales loss. The inventory cost model is more accurate, the part exceeding the upper limit of the inventory is depicted by using the storage cost, and the part below the lower limit of the inventory is depicted by using the sales loss. According to the scheme, crude oil price change factors are considered, crude oil is regarded as assets, asset value fluctuation is increased, and surplus inventory can bring benefits to enterprises.

The manual quick changing device comprises a flange shaft, a first flange and a second flange, the first flange and the second flange are connected to the two ends of the flange shaft respectively, the second flange is connected with a transverse rod, and a fixing base is fixedly connected to the upper side of the transverse rod and used for being connected with an end picking-up device; a connecting seat in butt joint with the fixing seat is arranged at the connecting end of the tooling, a guide pin hole is formed in the connecting seat, a guide pin is arranged on the positioning seat in a matched mode, and the fixing seat and the connecting seat are locked through a lock catch mechanism after being positioned in a guiding mode. According to the manual quick replacement device for the unstacking tooling, quick disassembly and replacement of the tooling are achieved, the replacement time of the tooling is shortened, the waiting time of a production line is shortened, and the efficiency of the production line is improved while the inventory cost of spare parts of the tooling of multiple specifications is reduced.

The invention relates to the technical field of computer application technology and Internet of Things, in particular to an intelligent food sales inventory management system based on Internet of Things. The system comprises an Internet of Things sensor network, a data acquisition module, a data processing module and a decision control module. The Internet of Things sensor network is provided with a temperature sensor, a humidity sensor, a weight sensor and an RFID tag, and is used for collecting food stock environment parameters and stock data in real time. The data acquisition module is connected with a sensor network and an external data source, and integrates and preprocesses sales data, weather forecast data and holiday event data. And the data processing module adopts a long-short-term memory network model to carry out sales prediction, and adopts a support vector machine model to evaluate the food deterioration risk. Real-time monitoring, accurate prediction and dynamic optimization of food inventory are realized through multi-source data fusion and machine learning, food waste and inventory cost are effectively reduced, and operation efficiency is improved.

The invention relates to a material reserve management method and system, and belongs to the technical field of electric power material management. The method comprises the steps of data collection and analysis, material classification and warehouse classification, material distribution through an optimization model, obtaining of information of objects to be stored, material storage and label generation, sending of a goods taking request, goods taking and transportation, delivery and information updating. According to the invention, the efficiency of material management is improved, the inventory cost is reduced, the reliability of a power system is enhanced, the optimal utilization of resources is promoted, and scientific data support is provided for material management and allocation.

The invention provides a spare part optimization method, device and equipment for key parts of a centrifugal compressor and a medium. The method comprises the steps that use information of the key parts of a key part standby system of the centrifugal compressor in the current use state and spare part information of the key parts in the current standby state are obtained; when the number of the spare parts is smaller than a preset value, using information of the key parts in the current using state and spare part information of the key parts in the current standby state are input into a spare part optimization model, and the spare part optimization model outputs spare part purchasing conditions of the key parts of the key part standby system; the spare part optimization model comprises an average cost rate calculation model, a reliability evaluation model and constraint conditions of the key part standby system. On the premise of ensuring the reliability requirement of the system, the ordering quantity and ordering time interval of the spare parts are optimized, and the inventory cost and the management cost are reduced.

The invention provides a real-time monitoring and risk assessment method and system for a product registration rate in a satellite general assembly process, and the method comprises the steps: building a satellite general assembly process flow in advance, and determining the demand type, number and time node of each process for a product; actual arrival data, production progress data and supply chain state data of products in the satellite final assembly process are collected in real time; according to actual arrival data and demands, calculating a product neat rate at the current moment, and dynamically updating a neat rate evaluation result; analyzing the potential risk based on a risk prediction algorithm to obtain a risk level; and generating early warning information according to the risk level, and formulating an optimal scheduling strategy to adjust a production plan and supply chain resource allocation. According to the method, efficient production organization and supply chain collaboration can be achieved, the production efficiency can be remarkably improved, the inventory cost is reduced, and the risk of production delay caused by irregular products is reduced.

Example implementations described herein involve systems and methods for bound enhanced reinforcement learning systems for distribution supply chain management which can include initializing a replay buffer, a first state-action value function network having first random weights, and a second state-action value function network having second random weights; determining an action corresponding to an inventory ordering quantity at one or more facility in a multi-echelon supply chain network based on an (epsilon) ϵ-greedy exploration policy; executing the action in a simulated environment, and storing transition results in the replay buffer; calculating an upper bound and a lower bound of the optimal inventory costs; incorporating the upper bound and the lower bound with at least hyper-parameters T1, τ2 in updating at least one of the first or the second state-action value function networks; and performing a gradient descent on the first state-action value function network based on the upper or the lower bound.

The invention discloses an aluminum plate strip foil processing enterprise secondary aluminum resource use profit accurate accounting method, and the method specifically comprises the following steps: S1, a single-batch secondary aluminum profit accounting method is used for calculating the independent profit generated in the use process of secondary aluminum of a specific purchase batch; s2, a monthly secondary aluminum profit accounting method used for calculating the total profit generated by monthly secondary aluminum use of an enterprise; and S3, a cross-period inventory cost rolling mechanism which is used for solving the inventory cost accounting problem during cross-month use of the secondary aluminum purchase batch and ensuring that the historical purchase cost continuously affects subsequent monthly profit calculation. The beneficial effects of the invention are that: accurate traceability: profit independent accounting of secondary aluminum of a specific purchase batch is realized; dynamic response: the influence of market fluctuation on profit is reflected in real time through an SMM price linkage mechanism; cost continuity: inventory cost rolling calculation eliminates inter-period profit distortion; and decision support: quantifying economic benefits of different secondary aluminum suppliers / purchase time points, and guiding purchase strategy optimization.

The application relates to the technical field of data processing, and discloses a steel structure plant profile blanking optimization method and system based on Tekla. The method comprises the following steps: extracting component parameters based on Tekla, comparing with a standard database to distinguish rolled pieces and welded pieces; disassembling the welded pieces and correcting a list according to plate thickness; counting inventory to generate an available resource list; adopting a multi-level matching strategy to determine the demand for the rolled pieces; adopting plate cutting optimization to generate thin plate and thick plate cutting plans for the welded pieces, and processing variable cross-section webs through pair-by-pair complementary splicing, so as to determine the demand for coiled materials and steel plates; and constructing a dynamic optimization model integrating inventory and fund constraints, so as to output an optimal scheme under the fund limit constraint. The application improves the comprehensive utilization rate of materials, preferentially guarantees the supply of main components, reduces procurement and inventory costs, and provides scientific decision support for steel structure plant blanking.

The application discloses a kind of concrete sales intelligent computing method and system, it is related to building and logistics management technical field.The application is accurate to predict customer demand and optimize production quantity and raw material proportioning by dynamically adjusting production plan, the problem that production surplus or deficiency caused by large demand fluctuation in traditional production is solved, inventory cost is reduced and production efficiency is improved, in transportation link, real-time monitoring traffic condition and dynamically optimizing transportation route, transportation efficiency and delivery punctuality are significantly improved, transportation cost is reduced and concrete quality is ensured;In production process, raw material proportioning and concrete state are monitored in real time using sensor and machine learning algorithm, production parameters are dynamically adjusted, the quality fluctuation problem caused by inaccurate proportioning or not timely monitoring in traditional production is solved, and the stability of product quality is ensured.

The utility model provides an adjustable condenser support belongs to condenser support technical field, include: support mechanism, the outside of support mechanism is equipped with adjusting mechanism, the surface fixedly connected with two connecting mechanisms outside adjusting mechanism, the surface of connecting mechanism is equipped with the mounting seat, support mechanism includes two first support plate, the surface of first support plate is equipped with second support plate, the surface of first support plate is equipped with two second clamping groove, the surface of second support plate is equipped with two second clamping groove, the second clamping groove is adapted, this scheme passes through support mechanism and hinged adjusting mechanism, realizes the stepless regulation of height and span, thereby has increased the practicality of this support, has reduced the inventory cost, passes through the clamping structure of the sliding support seat and mounting seat of connecting mechanism, realizes the independent disassembly of condenser, promotes the maintenance efficiency, and avoids the support structure fatigue caused by the frequent operation.

This invention relates to the field of inventory cost analysis, addressing the problems of lagging information updates and lack of predictive warnings in cost accounting, which prevent the automatic triggering of targeted inventory optimization strategies. Specifically, it is a rolling analysis method for spot inventory costs with full-process tracking, including a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost prediction statistics module, and an inventory optimization decision-making module. Based on complete inventory cost accounting, this invention endows cost management with real-time and forward-looking capabilities through rolling analysis and dynamic prediction. Ultimately, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it achieves an intelligent closed loop from data to action, transforming inventory cost management from traditional recording into a core decision-making tool for profit protection and risk prevention, effectively improving the enterprise's inventory operation efficiency and overall profitability.

The application discloses a spare part prediction method based on a PSO training quantile neural network, combines a quantile regression neural network (QRNN) with a recurrent neural network (RNN) to construct a spare part demand prediction model, can more comprehensively capture the conditional distribution characteristics of the spare part demand through quantile regression analysis, and further provides deep insight into demand fluctuation, meanwhile, the introduction of the RNN enables the model to effectively process the time sequence dependency in time sequence data, so that the influence of past demand on current demand is considered in the prediction process, the efficiency of spare part management is effectively improved, the inventory cost is reduced, the quality and satisfaction of customer service are improved, important economic benefits and competitive advantages are brought to the manufacturing and service industries, therefore, the application has wide application prospects and can play an important role in various electronic products and after-sales services.

This invention discloses a real-time assessment method for in-transit inventory costs based on process-oriented tracking and statistics, belonging to the field of logistics cost accounting technology. It aims to solve problems such as lagging in-transit inventory cost accounting, reliance on manual labor, inability to dynamically calculate, and difficulties in full-chain traceability. This invention constitutes a complete solution through layered creation of tracking pools, automatic aggregation based on logistics identifiers, real-time rolling calculation triggered by events, and full-chain aggregation presentation centered on contract numbers. On the one hand, it achieves real-time, automatic, and accurate accounting of in-transit inventory costs through automatic aggregation and rolling calculation of costs throughout the entire process from order to warehousing. On the other hand, through dynamic updates and deep traceability, as well as strengthening the correlation query between contract numbers and accounts payable and business documents, it provides enterprises with unprecedented cost transparency and control capabilities, fundamentally overcoming the core defects of existing technologies.

The embodiment of the invention provides an order processing method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-processed order, executing the order through employing all finished products when the inventory of the finished products is insufficient, and calculating a first demand quantity; determining whether to produce or not according to whether the semi-finished product stock meets the first demand quantity, and calculating a second demand quantity when the semi-finished product stock is insufficient; and finally using the raw material to produce the second demanded material to complete the order. By sequentially utilizing the finished product stock, the semi-finished product stock and the raw materials and responding to the order demand step by step, order execution can still be effectively promoted when the finished product stock or the semi-finished product stock is insufficient to meet all the demands, the utilization rate of stock resources and the order processing efficiency are remarkably improved, the production cycle is shortened, and the stock cost is reduced. And the adaptability of the system to complex order demands is enhanced.

This disclosure provides a method, apparatus, storage medium, and electronic device for supply chain replenishment quantity forecasting. The method includes: constructing a first feature vector based on the inventory status on the replenishment decision date; inputting the first feature vector into a prediction model, and determining that the output value of the prediction model is the first replenishment quantity corresponding to the current replenishment decision date, wherein the prediction model is used to characterize the correlation between the feature vector and the replenishment quantity. This disclosure obtains a prediction model for characterizing the relationship between the feature vector and the replenishment quantity by training a preset model, uses the prediction model to perform end-to-end replenishment quantity forecasting based on the inventory status on the current replenishment decision date, and uses the predicted replenishment quantity to assist supply chain inventory management decisions, achieving the lowest inventory cost while ensuring service levels.

This invention, entitled "A Distributed Hybrid Interleaved Batch Scheduling Method and System for Parallel Production Lines," belongs to the field of production line scheduling. It addresses the problem of minimizing inventory and stockout costs. The method includes: complete delivery based on vehicle model bill of materials; each production line comprises multiple sequentially connected stages, with the same set of dies only allowed on one production line at any given time; during the stamping stage, only one production line can utilize the stamping die to process the corresponding sub-batch; a consistent sub-batch partitioning strategy is adopted for the same batch, ensuring that the quantity and corresponding dimensions of each sub-batch remain unchanged in subsequent processing stages after partitioning; the dimensions between different sub-batches can be determined by the selected sub-batch partitioning rules; alternating processing of sub-batches from different batches is allowed on the same production line; each sub-batch can only be processed by one production line in each stage; the scheduling objective is to minimize the total sum of inventory and stockout costs while achieving complete delivery.

The invention discloses an IGS valve runner adaptive flow changing method based on a sandwich structure, which comprises the steps of material selection, stock comparison, switching block selection and the like, and aims to solve the problems of stock troubles and installation adaptation caused by unsuitability and incompatibility of an existing gas runner piece and a valve runner. Compared with the prior art, the flow channel effectively overcomes the existing defects, can efficiently adapt to stock differences, achieves the effect of reducing the stock cost, has the technical characteristics of compact and compatible structure, reliable flow channel design and simple processing technology, effectively improves the design and production flexibility, and is suitable for wide popularization.

The invention discloses a project cost accurate accounting and control method fused with big data analysis, and belongs to the technical field of big data analysis, and the method comprises the steps: S10, obtaining the cost data of a preset enterprise related project, the cost data comprises the direct material cost, the direct labor cost, the manufacturing cost, the indirect cost and other costs, and carrying out the calculation of the cost data; generating a database based on the cost data; and step S20, preprocessing the cost data in the database, wherein the preprocessing comprises cleaning and standardization processing of the cost data, and classification and labeling of the cost data. By fusing the big data analysis technology, accurate accounting and control of the project cost are realized, the inventory cost and capital occupation are reduced, the production efficiency and resource utilization are improved, the market adaptability and competitiveness are enhanced, the scientificity and flexibility of decision making are improved, and the comprehensive benefits of enterprises are improved.