11 results about "Material requirements planning" patented technology

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 discloses a flexible manufacturing-based DPA component matching method, which comprises the following steps of: establishing a DPA component requirement catalog, establishing a management database, and creating catalog entries for each component needing DPA detection; the method comprises the following steps: establishing DPA components, and creating a corresponding DPA component for each basic component needing to execute DPA detection in enterprise product bill of material structure management; the method comprises the following steps: establishing a corresponding relation between a DPA component requirement directory and a component, establishing a mapping relation between a DPA requirement directory and a DPA component, triggering to generate or call a corresponding DPA component demand according to the mapping relation, and realizing automatic conversion from a detection requirement to a specific execution material; and establishing a matched operation system, and integrating the system into a manufacturing execution system or a material demand planning module of an enterprise resource plan. According to the method, automatic identification of DPA detection requirements, intelligent matching of detection materials and accurate accounting of related cost can be realized.

Systems and methods are directed toward managing demand, such as for manufacturing in complex supply and / or demand driven environments, including aerospace-industry products and systems, environments with a multitude of stakeholders, and other sophisticated organizations. A production request may be provided with a component identifier for a top-level module component to be produced. Data associated with the component identifier may be determined. The data may include one or more additional component identifiers for sub-components which are to be consumed to produce the top-level module component. Supply allocations for the top-level module component and the sub-components may be generated based at least in part on the data. One or more action signals may be provided to fulfill the production request for the top-level module component according to the supply allocations.

Embodiments of the present application disclose a material demand planning method, equipment and medium. A period type table is created, wherein the period type table at least includes a period type name and a period span; based on the period type table, a period span related attribute is added in a preset independent demand to obtain a reference independent demand; and based on the period type table, a period span related attribute is added in a preset material dictionary table to obtain a reference material dictionary table; data in the reference independent demand is compared with data in the reference material dictionary table for processing, so as to determine a current material demand plan based on a comparison processing result; wherein the period span in the preset independent demand is related to material production or procurement days, and the period span in the preset material dictionary is related to material preparation time in advance. The above method reduces the workload of the developer.

The application provides a material demand planning method, system, device and medium, and relates to the technical field of material management. The method comprises the following steps: obtaining production consumption information, inventory information and in-transit logistics information of a target material; determining a basic demand quantity of the target material in a preset period and a material demand index according to the production consumption information and the inventory information of the target material; correcting the basic demand quantity according to the material demand index to obtain a target demand quantity of the target material; in the case that the target demand quantity is greater than 0, determining a delivery time corresponding to the target material according to the in-transit logistics information of the target material; and formulating a material demand plan of the target material by a pre-constructed material decision model according to the target demand quantity and the delivery time. The method can reduce the dependence on manual statistics, improve the accuracy and timeliness of the formulation of the material demand plan.

The application discloses a kind of main plan compilation system and method for solving customer order change.The main plan compilation method for solving customer order change is mainly realized in the main plan adjustment service based on local change strategy, the main plan local recompilation service based on reinforcement learning algorithm, capacity calculation service of system service layer.The system judges the numerical relationship between new demand capacity and residual capacity after receiving customer order change information, executes the main plan adjustment method based on local change strategy, generates new main plan compilation record according to the customer order increment allocation strategy based on multi-day residual capacity ratio and the non-urgent customer order reduction strategy, guarantees the premise of making full use of residual capacity, replaces, supplements original main plan, if still cannot meet the demand of customer order change by using local adjustment strategy, executes the main plan local recompilation method based on improved reinforcement learning algorithm, and recompiles part of main plan for the remaining days after change date.The application can quickly respond to customer order change, reduce large-scale adjustment of original main plan, and reduce the chain effect on material demand plan and other plans.

This invention discloses a production management method and system for single-phase prepaid smart meters, relating to the field of intelligent manufacturing. The method includes: collecting customer order data, inventory data, and supplier data; setting priority thresholds to determine the priority of pre-processed customer order numbers, defining nodes, edges, and attributes, and generating a production graph; constructing a dynamic production optimization model to obtain priority production batches through production batch optimization; allocating priority production batches using a linear programming algorithm to obtain production tasks for these batches, and analyzing these tasks to obtain an equipment requirement list; verifying the raw material requirements in the production tasks using material requirements planning (MRP) technology to obtain raw material warehousing information; and using a blockchain platform to compare the equipment requirement list and raw material warehousing information to generate a production completion report. This invention, by constructing a dynamic production optimization model, achieves intelligent sorting and optimization of production batches for single-phase prepaid smart meters.

The invention belongs to the technical field of nuclear power, and particularly relates to a group factory spare part purchasing and allocation decision-making method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring input data of spare part purchasing and allocation decisions of group factories; based on the input data, according to material demand plan types and batch type combinations of different spare parts, respectively determining the purchase quantity and the sharing quantity of each power plant; performing overall decision-making based on the purchase quantity and the sharing quantity of each power plant through the virtual power plant intelligent agent to obtain the total purchase quantity of the group plants; and performing local decision making through each entity power plant agent based on the total purchase quantity of the group plants to obtain a decision making scheme of each entity power plant. According to the method and the device, overall decision making is carried out through the virtual power plant intelligent agent, and then refined local decision optimization is carried out through each entity power plant intelligent agent on the basis, so that the accuracy of the obtained result is effectively improved.

Embodiments are directed to providing real-time forecasting for material requirements planning and inventory controls based on data from a variety of sources. More specifically, embodiments are directed to collecting data from sources including, but not limited to, manufacturing, inventory, and supplier data sources, and focusing on the most important data signals in near real-time, focusing on critical suppliers and inventory, and creating critical alerts while also enabling predictive analytics and machine learning to automate the processes and providing an ability to manage all the data.

This application relates to a method, system, device, and storage medium for calculating Material Requirements Planning (MRP). The method includes: loading basic data of an MRP task to be processed into the memory of a target computing node; dynamically calculating the balunable quantity of materials required for the MRP task to be processed using the memory and a balancing algorithm deployed on the target computing node; calculating the purchase quantity of materials required for the MRP task to be processed based on the balunable quantity; and calculating the material requirements plan corresponding to the MRP task to be processed based on the basic data, the purchase quantity, and the MRP algorithm deployed on the target computing node. The solution provided in this application can significantly improve the computational efficiency of material requirements planning.

A digital twin-based system for cell-based autonomous production consolidation in an enterprise resource planning (ERP) controlled discrete manufacturing environment, comprising: a module for generating a digital twin (101) configured to create and maintain a virtual representation of one or more physical production cells including the associated workstations, workflow structures, material dependencies and capacity constraints; a demand classification and cell grouping module (102) configured to receive production demand from an ERP system and classify the demand into cell-compatible demand clusters based on routing compatibility, product family attributes, and production cell capacities; a production consolidation engine (103) configured to generate consolidated planned orders at the cell level by aggregating the classified demand clusters and embedding cell-specific production intelligence into ERP planning objects; a real-time synchronization module (104) configured to collect operational data from manufacturing systems, including machine status, throughput rate, queue metrics and capacity utilization, and to update the digital twin models accordingly; a deviation detection module (105) configured to compare the real-time operating data with the parameters of the digital twin models to identify deviations that exceed predefined operating thresholds; and an autonomous recalibration engine (106) is configured such that, when a deviation is detected, the results of the material requirements planning at the level of the manufacturing cell are selectively regenerated and the production utilization is redistributed to one or more manufacturing plants (107).