23 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.

To provide an information processor for improving the efficiency of an ordering task while optimizing stock.SOLUTION: In the information processing system 1, the information processing device receives an input of receipt and payment data including inventory data related to an inventory of a material, sales achievement data related to a sales achievement of the material, and contract remaining quantity data related to a contract remaining quantity of the material, acquires setting data including lead time data related to a lead time of the material, calculates a safety inventory quantity based on the sales achievement data and the lead time data, and predicts a demand quantity of the material based on the sales achievement data using a statistical prediction method. Whether or not to place an order for a material is determined on the basis of reception / payment data, lead time data, a safety stock quantity, and a demand quantity predicted by a demand quantity prediction part, and when it is determined to place the order for the material, an order quantity of the material is calculated, an order plan of the material is generated on the basis of the order quantity, order data related to the order of the material is generated on the basis of the order plan, and the order data is output.SELECTED DRAWING: Figure 1

A cloud-based configurator system and method for the automated design and engineering of electrical switchboards is disclosed. The system includes a web-based interface and a cloud-hosted configurator engine that accesses a database of pre-engineered copper bus components and current inventory data. The configurator automatically generates, evaluates, and selects optimal copper bus routing solutions between designated points using combinations and permutations of existing parts. The system prioritizes in-stock components to reduce manufacturing lead times and optimize cost-efficiency, while eliminating the need to design custom components for each new order. Additional features include dynamic pricing based on spot market data, CRM integration for pre-populating configuration fields, mobile compatibility, generation of AutoCAD-compatible technical drawings, and multi-vendor switchboard variant generation. An administrative dashboard supports configuration management, user analytics, inventory updates, and approval workflows, enabling faster manufacturing throughput, improved customer responsiveness, and increased profitability.

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 present invention relates to a method capable of actually estimating spare part consumption of a target device (brand and model) and monitoring the actual consumption of the brand and model over a user-defined time range. The invention includes a primary estimation function that provides, based on consumption history and processing, a prediction of parts to be consumed and its associated data over a time range relative to the start of operation of the equipment, which results in a possible average or maximum number to be consumed by type / identification; and an auxiliary consumption monitoring function providing a view / profile of part consumption of the given device over a time range relative or absolute to the start of operation. The functionality allows, among other information, to filter the consumption behavior of one or more pieces of equipment, and to compare the consumption profiles within the same plant or between groups of equipment of different plants. The manufacturer, the leading time of each consumed part, the manufacturer's part number, the material number, and optionally the historical collection price are provided together with the type / identification of each part consumed in the resulting portion of both the estimation function and the monitoring function.

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.

The embodiment of the invention discloses a production line task scheduling method and electronic equipment, and the method comprises the steps: taking a work station as a unit, and determining the replenishment advance of the work station; generating a pre-scheduling request according to the replenishment advance and submitting the pre-scheduling request to a transportation management system, wherein information in the pre-scheduling request comprises workstation information generating a scheduling demand and corresponding scheduling demand target information; and determining the work station generating the scheduling demand as a destination through the transportation management system, making a decision of a cache region or a target work station according to the scheduling demand target information, taking the cache region or the target work station as a departure place, generating a transportation task according to the departure place and the destination, and sending the transportation task to the transportation management system. And determining a target transporter, and allocating the transportation task to the target transporter for execution. According to the embodiment of the invention, the beat balancing scheme which is more flexible and more suitable for the real-time situation of the site can be provided when the variable complex environment of the production line site of mixed production is coped with.

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.

A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

To improve the efficiency of ordering work while optimizing stock.SOLUTION: Receiving, by an information processing apparatus, an input of receipt and payment data including inventory data related to an inventory of a material, sales achievement data related to a sales achievement of the material, and contract remaining quantity data related to a contract remaining quantity of the material, and acquiring setting data including lead time data related to a lead time of the material; A safety stock quantity is calculated based on sales result data and lead time data, a demand quantity of a material is predicted using a statistical prediction method based on the sales result data, whether or not to order the material is determined based on receipt and payment data, the lead time data, the safety stock quantity, and the demand quantity predicted by the demand quantity prediction part, an order quantity of the material is calculated when the order determination part determines to order the material, an order plan of the material is generated based on the order quantity, order data related to the order of the material is generated based on the order plan, and the order data is output.SELECTED DRAWING: Figure 1

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.

To provide a delivery date reply management device, a delivery date reply management method and a delivery date reply management program, allowing calculation of a scheduled delivery date in a lead time on the basis of a supplier and a delivery destination in cooperation with stock data from the supplier to reply a delivery date.SOLUTION: When the ordered commodity is a prepared commodity, a scheduled delivery date of the ordered commodity is calculated based on the order data, the supplier stock data, the calendar master, and the lead time master, order reception data in which the delivery destination, the ordered commodity, the ordered quantity, and the scheduled delivery date are set is acquired, and delivery date reply data in which the scheduled delivery date of the ordered commodity is set is notified to the delivery destination based on the order reception data.SELECTED DRAWING: Figure 1

The invention discloses a delivery time constraint-based curtain production dynamic scheduling optimization system and method, and relates to the technical field of production scheduling, a target task set is extracted from curtain to-be-produced tasks, and the target task set is a task set which is produced by the same equipment and has the same process; calculating a task complexity difference degree between the target task pairs, and determining a task production sequence based on the difference degree; calculating a compression decision value according to the production state information of the first two tasks in the production sequence, and judging whether to reduce a subsequent task switching gap or not; and if the compression decision value is greater than the preset threshold value, the switching gap can be properly compressed, so that the production rhythm is accelerated. According to the method, sufficient equipment adjustment can be ensured while delivery on time is ensured, the curtain production quality is ensured to be qualified, the equipment overload or product quality risk caused by blind compression is avoided, and the balance between dynamic scheduling optimization and stable operation of the equipment is ensured.

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.

To predict with high accuracy whether product shipments will meet deadlines. [Solution] The shipping delay prediction device includes an order processing means that inputs order data including the planned shipping date, delivery date, customer, product, quantity ordered, and order number, calculated using the shipping lead time obtained from the customer lead time master with the order date and delivery date-customer as keys, and based on the order data, for each product, it calculates the planned inventory quantity at the planned shipping date as current inventory - planned shipping quantity + planned incoming quantity, and calculates the planned shipping date using the manufacturing lead time obtained from the manufacturing lead time master with the current date + product as keys, and the planned inventory quantity

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.

A method includes capturing shipment milestone data of cost, insurance, and freight (CIF) shipments, recording the shipment milestone data within a distributed ledger, generating a training dataset using past shipment milestone data recorded within the distributed ledger, and generating a machine learning (ML) model configured with explainable artificial intelligence (XAI) based on the training dataset. The method also includes receiving information regarding a CIF shipment from a computing device, determining one or more relevant features from the information regarding the CIF shipment, the relevant features influencing prediction of a lead time for ownership transfer, and generating, using the ML model configured with XAI, a prediction of a lead time for ownership transfer with an explanation of the prediction for the CIF shipment based on the determined relevant features. The method may also include sending the prediction of the lead time for ownership transfer with the explanation to the computing device.

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.