97 results about "Inventory cost" patented technology

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 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 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 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 utility model relates to a semi-steel tire dual-purpose calendered large roller shutter fabric inventory screen display device, and belongs to the field of tire storage devices. Dual-purpose calendering large roller shutter cloth goods shelves are vertically arranged in parallel, the calendering large roller shutter cloth goods shelves are of a cylindrical structure, U-shaped LED screen supports are inserted into the top ends of the adjacent dual-purpose calendering large roller shutter cloth goods shelves, LED screen protection boxes are hung on the LED screen supports, LED screens are installed in the LED screen protection boxes, and the LED screens are connected with an EMS production management system through data lines. Arc-shaped shaft supporting plates are fixed to the positions, with the same height, between every two adjacent dual-purpose calendering large roller shutter cloth goods shelves. According to the utility model, on the basis of an EMS production management system, informatization management of the large roller shutter cloth inventory in the semi-steel tire dual-purpose calendaring process is realized, real-time monitoring and visualization of inventory data are realized, dynamic optimization of the inventory is realized, the inventory cost is reduced, accurate management of the inventory data is ensured, and manual operation errors are reduced.

A lock core device based on matching the cross-sectional shape of a multiple key and a corresponding lock key, the lock contains a keyway, and the outer contour of the keyway is configured as a first section contour and a second section contour after superimposition, and its' a union outer contour. By adapting to existing keys with various cross-sectional profiles, the lock cylinder does not need to be manufactured separately according to the cross-sectional profile of the key, thus effectively reducing its manufacturing and inventory costs; A method for configuring a lock ball slot so that, in response to the deviation of the position of the teeth of the keys with different cross-sectional profiles, the teeth of the keys with different cross-sectional profiles can correspond to the lock ball slot for unlocking, thus ensuring stability and effectiveness during unlocking.

The embodiment of the invention provides an industrial spare part service life intelligent identification system, which comprises: an industrial spare part service life intelligent identification system, which is characterized by comprising a composite detection terminal, an edge computing node and a cloud decision platform that are connected through a data bus; the composite detection terminal is provided with a multi-physical field sensor array for collecting spare part surface and internal defect data; the edge computing node performs noise reduction and feature extraction on the original detection signal to generate a standardized damage index; and the cloud decision platform receives the standardized damage index, establishes a dynamic life prediction model in combination with historical operation data, and outputs a digital certificate containing a residual life prediction value and a maintenance decision suggestion. The stock cost of spare parts can be reduced, and meanwhile, non-planned shutdown loss is avoided.

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.

The application provides a coal-fired power plant inventory management system and method based on a supply and demand relationship, relates to the technical field of coal-fired power plant inventory management, and the initial coal inventory, historical data information of coal consumption in each period in a historical database is called, an inventory and consumption model is constructed based on the historical data information, and a storage and consumption output relationship in the period is obtained; a coal inventory restriction condition is established according to the storage and consumption output relationship, and the coal inventory is generated according to the coal inventory restriction condition; the coal inventory is obtained, the total inventory cost of the coal-fired power plant inventory management system and the purchase time of the coal-fired power plant inventory management system are calculated, and the risk and loss of coal supply are effectively reduced.

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.

A method and system for generating procurement plans for chain pharmacies based on big data, relating to the field of procurement management, is disclosed. The method includes: first, collecting historical sales data, real-time inventory data, and external environmental data to construct a multidimensional database; then, standardizing the data and constructing a drug classification matrix using a data mining model; next, using causal analysis to identify key factors influencing drug demand and establishing a weight matrix of demand influencing factors; calculating procurement parameters such as safety stock levels, economic order quantities, procurement trigger points, and procurement cycles based on this weight matrix; and finally, combining a supplier evaluation system to generate a procurement plan including a list of drugs to be procured, procurement quantities, procurement time, and supplier selection. This method, through multidimensional data analysis and causal relationship mining, achieves scientific and precise procurement decisions, improves procurement efficiency, and reduces inventory costs.

This utility model relates to a splicing car sill, comprising a first material and a second material. A first contact edge is integrally formed on one vertical surface of the first material, and a second contact edge is integrally formed on one vertical surface of the second material. This utility model, through the splicing design of the first and second materials, achieves rapid adaptation of sills of different specifications, significantly reducing material types and inventory costs. The insertion and matching of the guide post and countersunk hole ensures precise splicing alignment, eliminating manual adjustment errors and improving assembly efficiency. The combined design of the first and second sliding grooves is compatible with center-split, center-split double-fold, and side-opening double-fold sill supports, offering strong versatility. The T-shaped limiting part of the auxiliary connecting component works in conjunction with the connecting plate to significantly improve the shear resistance of the splicing node. The connecting plate is fixed by the sliding T-shaped limiting part and can be disassembled without special tools, facilitating later maintenance or component replacement.

The invention provides a ship body large-assembly goods store design and planning method, a medium and a terminal. The method specifically comprises the steps of obtaining a production plan and making a demand plan based on the production plan; analyzing the demand plan to obtain a stable period and demand days of the large assemblage; designing goods stores based on the stable period and the number of required days of the large assemblage; and obtaining a plan advance period and a plan period, and formulating a store use rule. Through the method, the inventory level can be remarkably reduced, the inventory cost is reduced, and the circulation efficiency is improved.

The invention relates to the technical field of data processing, and discloses a Tekla-based steel structure plant profile blanking optimization method and system. The method comprises the following steps: extracting component parameters based on Tekla, and comparing the component parameters with a standard database to distinguish a rolled piece and a welded piece; the welding part is disassembled, and the list is corrected according to the plate thickness; counting the inventory to generate an available resource list; determining the demand quantity by adopting a multi-stage matching strategy aiming at the rolled piece; a thin plate and thick plate cutting plan is generated through plate cutting optimization according to the welding parts, variable cross-section webs are processed through pairwise complementary splicing, and the demand quantity of coiled materials and steel plates is determined; and constructing a dynamic optimization model fusing inventory and fund constraints, and outputting an optimal scheme by using the fund limit constraints. The comprehensive utilization rate of materials is increased, supply of main body components is guaranteed preferentially, the purchase and inventory cost is reduced, and scientific decision support is provided for discharging of the steel structure factory building.

The invention discloses a hierarchical clustering Q learning-based ship equipment spare part demand prediction method. The method comprises the following steps of 1) obtaining historical demand data of ship equipment spare parts; 2) preprocessing the collected data set, and identifying and processing missing values and abnormal values in the data; 3) dividing the data sets into a training data set and a test data set, and respectively performing clustering operation on the two data sets; 4) constructing a spare part prediction model; 5) training a spare part prediction model of Q learning by using the training data set; and 6) based on to-be-predicted ship spare part demand data, using the trained model to carry out subsequent ship spare part demand data prediction. According to the hierarchical clustering Q learning-based spare part demand prediction method provided by the invention, the optimal prediction model can be automatically selected according to different spare part feature distribution, the prediction error of the spare part demand is reduced, and the purpose of reducing the spare part inventory cost is achieved.

The application discloses a retail cabinet inventory path decision optimization method and equipment, comprising the following steps: (1) establishing an inventory path optimization model of a retail cabinet, wherein the inventory path optimization model takes the sum of inventory cost, shortage cost and vehicle path cost in a current planning period as a target function, and takes replenishment constraints, maximum inventory level constraints, vehicle capacity constraints, retail cabinet flow balance constraints, vehicle closed loop constraints and elimination sub-loop constraints as constraint conditions; (2) obtaining inventory path related information; (3) according to the inventory path related information, a tabu search method is used to solve the inventory path optimization model, and optimized inventory decisions and path decisions are obtained. The application simultaneously considers replenishment decisions and path planning decisions when making decisions, and is more efficient.