15 results about "Lead time" patented technology

A computer-implemented system (100) for real-time detection of supply chain disruptions and forecasting of the financial impact in a computer-driven ERP environment, wherein the system (100) comprises: a data acquisition interface (1) configured to continuously capture structured and unstructured operational data from one or more ERP modules, supply chain execution systems, and enterprise event streams; a data management and harmonization engine (2) configured to validate, normalize, deduplicate and semantically map the captured data into a unified, time-aligned canonical enterprise schema; a disturbance signal fusion and anomaly detection engine (3) configured to fuse signals from multiple sources and detect disturbance events using one or more machine learning models to output a disturbance value, event class and a list of affected nodes; a supply chain dependency graph and a digital twin builder (4) configured to create and update a dynamic dependency graph representing suppliers, facilities, transportation routes, SKUs, orders, contracts and lead times, and propagate disruption effects across the graph; a financial impact forecasting engine (5) configured to estimate the real-time and forward-looking effects of the disruption on one or more financial measures, including sales, margin, working capital, cash flow, service level penalties and inventory holding costs, and furthermore the effects on the cost of goods sold (COGS) and / or the landed costs using a Kl-based forecast with a range of uncertainty; a module for coordinating corrective actions and automating workflows (6) configured to generate ranked corrective actions and initiate ERP workflow steps, including reordering, reassignment, alternative sources of supply, production rescheduling and logistics diversions, wherein the ranked corrective actions include recommendations to resolve the disruption by using available stock, reassigning low-priority shipments or diverting shipments that can be delivered later within a lead time window; an explainability, audit trail and compliance logging module (7) configured to record model inputs, feature assignments, decision justifications, scenario assumptions and action results as an immutable audit trail; and a visualization, alerting and collaboration interface (8) configured to output real-time alerts, dashboards and scenario comparisons to authorized users and downstream systems via APIs and role-based access controls, wherein the interface (8) generates a quick report view that displays early disruption signals and impact on manufacturing costs, delivers notifications to mobile devices, triggers a special urgent notification if the disruption affects a high-priority trading partner, and accepts user responses on mobile devices that automatically and without delay trigger corrective actions or updates in the ERP system.

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, 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 inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost. The optimization algorithm can comprise constraint conditions.

This application provides a task management method and a middleware scheduling system. The method is applied to a middleware scheduling system, which includes multiple production units and multiple transportation units. The transportation units transport materials to the production units. The method includes: acquiring scheduling time parameters, which indicate the lead time between a first moment and a second moment, where the first moment is the time to schedule the transportation unit and the second moment is the time to use the transportation unit; and scheduling the transportation unit according to the scheduling time parameters. The technical solution of this application can improve the production line efficiency of the system.

This application relates to the field of manufacturing scheduling technology, specifically disclosing a production scheduling method, apparatus, and storage medium based on a hybrid production mode. This application determines the safety stock levels of finished and semi-finished products based on inventory parameters such as the first average replenishment lead time for finished products, the second average replenishment lead time for semi-finished products, the average demand rate and standard deviation for each order type within a preset time period, the normal quantile corresponding to the target service level, the proportion of MTO orders, the response time of MTO orders, the MTO additional inventory coefficient, the correlation coefficient between different demand sources, the semi-finished product conversion rate, the inventory reduction rate, and the lower limit ratio of finished product inventory. This allows for the scheduling of production plans for different types of orders from customers using their respective corresponding production modes to ensure optimal delivery speed and ultimately fulfill order requirements, while simultaneously reducing the risk of inventory backlog for finished and semi-finished products.

Provided is a method for generating a lead time prediction model including: receiving input data from a user; identifying a final part corresponding to the input data; identifying one or more components that constitute the final part; classifying the identified one or more components into in-house production parts, which are produced in-house, and ordered parts, which are procured from suppliers; determining a lead time for the in-house production parts; obtaining first data including at least price data and historical lead time data, for each of the ordered parts; generating a model for generating a predicted lead time for at least one of the final part and the ordered parts, based on the obtained first data; and optimizing the model based on at least one of fixed costs, inventory costs, ordering costs, backlog costs, and demand loss costs.

This enables the securing of inventory to accommodate orders with long lead times. [Solution] The delivery date determination unit determines that an order is a short-term delivery order if the delivery date of the ordered commercial item is less than the set period set in the delivery date determination master table, and determines that an order is a long-term delivery order if the delivery date of the ordered commercial item is equal to or longer than the set period set in the delivery date determination master table. When the order data generation unit determines that an order is a long-term delivery order, it generates order data that includes an order quantity equal to or greater than the ordered quantity for purchasing the commercial item. When the commercial item for a long-term delivery order is purchased based on the order data, the inventory adjustment unit does not add the number of purchases corresponding to the ordered quantity to the inventory quantity shown in the inventory data, thereby increasing the physical inventory quantity due to the purchase without increasing the inventory quantity in the inventory data, and thus ensuring inventory for long-term delivery orders.

The invention relates to the field of order data processing, in particular to a step-by-step MRP method, system and device and a medium, and the method comprises the steps: obtaining order information, and calculating the net demand number of a production piece through combining a product BOM structure, the available stock amount and the safe stock amount; based on the net demand quantity and the production resource information, making a production plan through an APS system, and generating starting and finishing time of the production piece; formulating a workshop operation plan through workshop scheduling operation based on the production plan; on the basis of the starting and completion time and the material supply advance period of the production piece, the demand time and the demand quantity of purchasing pieces are calculated, and a purchasing plan is made; and formulating a supplier delivery plan based on the purchase plan and the workshop operation plan. According to the characteristics of multi-variety and small-batch orders in the discrete manufacturing industry, production pieces and purchasing pieces are processed in a layered mode through the step-by-step process, the complex product structure and the multi-level part supply relation are effectively dealt with, and the responsiveness of enterprises to diversified orders is improved.

One aspect of the present disclosure is a price determination system that determines dispatch costs indicating the price of dispatching personnel to be charged to a customer, the price determination system comprising: an acquisition unit that acquires the customer's desired time and number of personnel for dispatching personnel and the order time when the customer places an order for dispatching personnel; a period calculation unit that calculates a lead time indicating the period from the order time to the desired time; a coefficient determination unit that determines a first coefficient, which is a coefficient for increasing or decreasing the dispatch cost based on the desired time, and determines a second coefficient, which is a coefficient for increasing or decreasing the dispatch cost depending on the length of the lead time, based on the lead time; a memory unit that stores a basic price that serves as the basis for the dispatch cost; and a price determination unit that determines the dispatch cost based on the desired number of personnel, the first coefficient, the second coefficient, and the basic price.

The embodiment of the invention discloses an intelligent material management method and system, electronic equipment and a storage medium. The intelligent material management method comprises the steps that demand quantity historical information, delivery date historical information, in-transit and inventory real-time information and procurement advance period of a target material are acquired; obtaining the current predicted demand quantity based on the demand quantity historical information of the target material; on the basis of the demand quantity historical information, the delivery date historical information and the current predicted demand quantity, current safety stock is obtained; based on the in-transit and inventory real-time information, the procurement advance period, the current predicted demand quantity and the current safe inventory, determining a replenishment triggering inventory quantity; when the stock remaining amount of the target material is smaller than or equal to the replenishment triggering stock amount, the target replenishment amount is obtained based on the current predicted demand amount and the in-transit and stock real-time information; and replenishment is performed based on the target replenishment amount. The contradiction between the flow fund occupation amount and the material supply stability is balanced through data driving in cooperation with a prediction algorithm and a business algorithm, and the fund utilization efficiency is improved.

The manufacturing panning system includes an arithmetic circuit accessible to data on a manufacturing plan for manufacturing a plurality of different products by a manufacturing facility performing a plurality of processes. The arithmetic circuit is configured to: for a shortage product among the plurality of different products of which a predicted quantity of a finished product by a due date is less than a target quantity, output, a first modification proposal to change the manufacturing plan so as to increase a production quantity of the shortage product in a first process performed first among the plurality of processes when remaining time until the due date is equal to or longer than manufacturing lead time; and output, a second modification proposal to change the manufacturing plan so as to prioritize manufacturing the shortage product in a second process performed after the first process among the plurality of processes.

The present invention provides a novel 3D printing process for metal printers that aims to reduce the process lead time while using compact equipment and conserving resources. The process includes feedstock preparation, printing, debinding, sintering, and heat treatment. By integrating debinding, sintering, and heat treatment into a single cycle, the process significantly reduces the lead time by hours, making it a faster and more efficient method for producing 3D metal parts. The novel feedstock mixture, including but not limited to metallic powder, reinforcement, and a binder system with lubricating agents, allows for the successful molten material extrusion hence the printing of complex parts. The integration of debinding, sintering, and heat treatment into a single cycle significantly reduces the process lead.

The invention discloses a material demand calculation method, system and equipment for dynamic quota correction and a storage medium in the field of resource planning and supply chain management, and aims to solve the problem that the quota in the traditional MRP is fixed and is difficult to adapt to the dynamic change of a supply chain and a complex business scene. The method comprises the following steps: decomposing material requirements layer by layer based on independent material requirements, BOM (Bill of Materials) and set group information; using a sequential BOM model to combine with the advance period expansion demand and determining the rough demand of each period; calculating a net demand and carrying out batch adjustment to form a suggested purchase quantity; and dynamically adjusting the quota proportion according to the historical performance of the suppliers, and distributing the quota proportion to each supplier in combination with constraints such as customer appointment. The method is applied to the engineering machinery manufacturing industry, quota dynamic adaptation, plan closed-loop optimization and complex scene automatic processing can be achieved, and supply chain toughness and inventory turnover and plan accuracy are improved.