9results about How to "Reduce inventory backlog" patented technology

This invention discloses a digital collaborative management system for the flexible textile production chain, relating to the field of textile production technology. The invention includes a data acquisition module, a data analysis module, a batch management module, a production module, and a database. First, based on sales information, return information, and remaining inventory of each flexible product, it determines whether each flexible product needs to be produced. The flexible products requiring production are referred to as "marked flexible products." Second, the raw materials required for each marked flexible product are acquired. The requirements of the raw materials for each marked flexible product on the production process and the mutual influence between the raw materials required for each marked flexible product are analyzed. Then, based on the analysis results, production batches for each marked flexible product are set, and finally, production is carried out. This reduces the frequency of inventory backlog and ensures the quality and production rate of flexible products.

The invention discloses a cigarette material demand prediction and inventory optimization method based on a time sequence model. The method comprises the following steps: acquiring cigarette production multi-source data, and constructing a standardized data set in combination with derived feature data calculated based on the cigarette production multi-source data; inputting the standardized data set into a pre-trained time sequence model, and outputting probabilistic demand prediction results respectively corresponding to the plurality of quantiles; calculating a relative confidence interval width according to a probabilistic demand prediction result; generating a matched target inventory value based on a comparison result of the relative confidence interval width and a preset confidence threshold; according to the target inventory numerical value and the current inventory data in the cigarette production multi-source data, the replenishment amount of the cigarette materials is calculated, an inventory optimization instruction is generated to optimize the inventory, the balance between the inventory and the risk is achieved, the cigarette production continuity is guaranteed, the inventory overstock and the storage cost are reduced, and the cigarette production efficiency is improved. And the lean and intelligent level of tobacco material inventory management is improved.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

The utility model discloses a water tank empty real exchange goods shelf device relates to the technical field of logistics operation, wherein, water tank empty real exchange goods shelf device includes frame, conveying mechanism, first push mechanism, second push mechanism and elevating system, and conveying mechanism includes first conveying assembly and second conveying assembly that first conveying assembly and second conveying assembly are spaced apart on the frame along the vertical direction, and elevating system is used for moving material from the discharge end of second conveying part to the feed end of third conveying part. The utility model discloses the technical scheme through adopting four oblique settings conveying part, forms the continuous goods box circulation path, and effectively simplifies the conveying flow, and improves work efficiency, through adopting first push mechanism and second push mechanism, can realize the accurate transfer of goods box between different levels, through adopting elevating system, realizes the automation exchange of empty box and real box, ensures that empty box can be in time back to logistics system and continue using, guarantees the stability and continuity of production rhythm.

This application discloses a warehouse logistics sniffing method, device, and medium based on big data analysis, relating to the field of logistics technology. The warehouse logistics sniffing method includes: determining a global adjacency matrix of goods based on the local adjacency matrix of goods in all warehouses; determining global multidimensional attribute features of each good based on the local multidimensional attribute features of each good in all warehouses; constructing a probabilistic graphical model using the global adjacency matrix and global multidimensional attribute features as observation data; inferring the probability distribution of sales type of each good through the probabilistic graphical model; constructing a product association graph based on the global adjacency matrix and the probability distribution of sales type of each good; each node in the product association graph carries the local multidimensional attribute features of the good in each warehouse and the historical sales sequence; inputting the product association graph into a time-series graph convolutional network to output the estimated demand of each good in each warehouse; and completing the inventory allocation of each warehouse based on the estimated demand.

The application discloses a coal type sales planning and sales time collaborative control method and system based on a coal enterprise, and relates to the technical field of coal sales management.The application integrates multi-source data through a data fusion algorithm and other technical means, solves the problems of data dispersion and different formats, provides high-quality data support for subsequent analysis, accurately predicts sales trends and price fluctuations by using a long short-term memory neural network and a regression analysis model, helps enterprises optimize sales strategies, reduces inventory backlog risks, dynamically adjusts sales tasks by means of linear programming and reinforcement learning algorithms in combination with real-time data, improves market response speed and sales efficiency, and effectively improves the scientific nature and flexibility of coal enterprise sales management and enhances market competitiveness.

The present application relates to the clothing e-commerce technical field, specifically to the intelligent product selection and inventory prediction system based on clothing e-commerce platform.The present application extracts the popular color data, version data and fabric data in the clothing text and image of social media, and matches and analyzes the version parameters and fabric parameters of the clothing on sale on the platform, obtains the matching degree of each clothing on sale and the current popular trend, and provides a quantifiable screening basis for product selection.The present application selects target clothing according to the matching degree, inputs the version parameters, fabric parameters and historical sales of the target clothing into a multi-period sales prediction model, and outputs the predicted sales of each target clothing in different sales periods, realizes the association of product selection and periodical sales prediction based on the popular trend matching degree, is beneficial to realize sales-oriented inventory, inventory-oriented replenishment, reduces inventory backlog and shortage risk.

The present application relates to the technical field of material management in production and manufacturing, and particularly relates to an intelligent matching and inventory management system for automobile wiring harness production materials. Through the collaborative operation of a work order analysis module, a replenishment triggering module, a distribution scheduling module and an execution feedback module, the present application realizes intelligent matching and dynamic inventory management of automobile wiring harness production materials. The system first accurately calculates material demand and readiness based on production work orders and real-time inventory, generates executable work order queues and predicts consumption benchmarks; then, through dynamic replenishment points, it intelligently triggers replenishment and prioritizes replenishment instructions in combination with multi-dimensional factors; finally, through execution feedback, a management closed loop is formed.