A method and system for precise replenishment and delivery based on real-time data.

Abstract

This invention discloses a real-time data-driven method and system for precise replenishment and delivery, belonging to the field of intelligent manufacturing and logistics distribution. The method includes the following steps: establishing communication connections between the delivery system and the production system and the logistics system respectively; calculating and configuring dynamic locations; in the delivery system, acquiring production data and logistics data from the production system and the logistics system respectively through the execution of predefined timed scripts; when the production system reaches the dynamic location, calculating the net demand based on the production data and logistics data; and using an adaptive order-grouping factor as a condition, assembling the net demand into a document and issuing it to the supplier to complete the delivery. This invention achieves dynamic and precise replenishment and delivery.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing and logistics distribution. Specifically, this invention relates to a method and system for precise replenishment and distribution based on real-time data. Background Technology

[0002] In large-scale assembly line production, the timeliness and accuracy of material replenishment and delivery directly determine the stability of production cycle time, inventory turnover rate, and production cost control. Currently, most existing delivery systems use MES (Manufacturing Execution System) and WMS (Warehouse Management System) to collaboratively manage materials. However, existing replenishment and delivery solutions still have many technical bottlenecks and are difficult to adapt to the needs of flexible, high-cycle production.

[0003] For example, traditional delivery systems often rely on manual triggering or static thresholds, which cannot adapt to the real-time demand for multiple varieties and small batches in a flexible manufacturing environment, leading to frequent inventory backlogs or material shortages.

[0004] Meanwhile, the existing delivery and replenishment schemes of the distribution system cannot match the production rhythm, resulting in premature or excessive production, which in turn leads to problems such as inventory backlog or inventory shortage, affecting production.

[0005] Therefore, this invention proposes a precise replenishment and delivery method and system based on real-time data. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of the prior art and proposes a real-time data-driven method and system for precise replenishment and delivery, in order to achieve the following objective: to realize dynamic and precise replenishment and delivery.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a precise replenishment and delivery method based on real-time data, the method comprising the following steps: Step S1: Establish communication connections between the distribution system and the production system and logistics system respectively; Step S2: Calculate and configure dynamic points; Step S3: In the delivery system, production data and logistics data are obtained from the production system and logistics system respectively by executing a predefined timed script; Step S4: When the production system advances to the dynamic point, calculate the net demand based on the production data and logistics data; Step S5: Using the adaptive group single factor as a condition, the net demand is grouped into a document and issued to the supplier to complete the delivery.

[0008] Preferably, in step S1, the communication connection between the delivery system and the production system and logistics system is realized based on the HTTP protocol.

[0009] Preferably, the HTTP protocol adopts a publish / subscribe mechanism, with the delivery system as the core node, publishing logistics data acquisition requests to the logistics system and production data acquisition requests to the production system; the production system and logistics system, as response nodes, push logistics data and production data to the delivery system in real time after receiving the requests.

[0010] Preferably, in step S2, the calculation of the dynamic point location is expressed by the following formula: Dynamic location = target workstation number - JPH * reported vacancy hours; JPH represents the production cycle time obtained from production data; shortage hour refers to the advance warning time reserved for material replenishment.

[0011] Preferably, in step S3, the predefined timing script is used to automatically call the communication interfaces of the production system and logistics system via API at a preset frequency to obtain production data and logistics data.

[0012] Preferably, the preset frequency is consistent with the production cycle of the production system.

[0013] Preferably, in step S4, the formula for calculating net demand is as follows: Net demand = Total demand for parts - In-transit inventory - Gate inventory - Storage area inventory - Line-side inventory; The total demand for parts refers to the sum of the demand for the same part from all workstations between the dynamic point and the target workstation.

[0014] Preferably, the method further includes: when the production system advances to the dynamic location, calculating the total demand based on the production data and logistics data; and using the adaptive grouping factor as a condition, forming the total demand into a document and issuing it to the supplier to complete the delivery; The formula for calculating the total demand is as follows: Total demand = Demand for vehicles that have not yet passed through checkpoints + Σ[(Dynamic checkpoint workstation number - 1) × Reduction quantity] - In-transit inventory - Checkpoint inventory - Storage area inventory - Lineside inventory; Among them, the reduction quantity refers to the number of parts required for each workstation; the demand for vehicles that have not yet passed the point indicates the total number of parts required for vehicles that have not yet passed the workstation for production and assembly; Σ[(dynamic point workstation number - 1) × reduction quantity] represents the total cumulative reduction of parts for all related workstations from the first workstation to the previous workstation of the current dynamic point workstation.

[0015] The present invention also provides a real-time data-driven precision replenishment and delivery system, using the above-described real-time data-driven precision replenishment and delivery method. The system includes a computer, which is used to execute a computer program constructed according to the above-described real-time data-driven precision replenishment and delivery method.

[0016] The technical effects of this invention are as follows: This invention adjusts the timing of replenishment demand triggering in real time by dynamically adjusting the location, and combines the calculation of net demand to accurately generate replenishment material demand, avoiding inventory backlog caused by premature or excessive pulling, while avoiding inventory shortages affecting production. This invention supports customizable timing scripts, customizable adaptive group single-factor programming, and cross-system compatibility of middleware protocols, making it adaptable to business scenarios in different industries and with different production cycles. This invention automates the entire process, replacing manual statistics and document processing, reducing human error, while real-time data interaction shortens the delivery response cycle and matches the production rhythm. Attached Figure Description

[0017] Figure 1 A flowchart of a real-time data-driven precision replenishment and delivery method is provided for an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. This is to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solutions of the present invention, and to facilitate its implementation. It should be noted that the terms "first," "second," etc., used in this application are only for the convenience of describing the technical solutions and to distinguish components; the corresponding component configurations may be the same or different, and are not intended to limit the scope of this application. To make the technical solutions of the present invention clearer, the present invention will be explained and illustrated through the following embodiments.

[0019] This invention provides a real-time data-driven method for precise replenishment and delivery. It uses dynamic locations as the core trigger point, achieves cross-system data interaction through middleware protocols, combines timed scripts to drive data collection and subsequent demand calculations, and ultimately completes the replenishment and delivery loop through adaptive order grouping. The overall process is executed in an orderly manner according to the following steps, with each stage deeply integrating core technical points to ensure accuracy and real-time performance. Figure 1 As shown, the method includes the following steps: Step S1: Establish communication connections between the distribution system and the production system (e.g., MES, Manufacturing Execution System) and the logistics system (e.g., WMS, Warehouse Management System); Step S2: Calculate and configure dynamic points; Step S3: In the delivery system, production data and logistics data are obtained from the production system and logistics system respectively by executing a predefined timed script; Step S4: When the production system advances to the dynamic point, calculate the net demand based on the production data and logistics data; Step S5: Using the adaptive group single factor as a condition, the net demand is grouped into a document and issued to the supplier to complete the delivery.

[0020] Referring to step S1, this embodiment uses the HTTP protocol to establish communication connections between the delivery system, production system, and logistics system. The HTTP protocol has the following advantages: Real-time performance: Fast response, ensuring data synchronization; Reliability: Ensure message delivery and prevent data loss; Scalability: Supports multi-system integration and is compatible with MES / WMS standard interfaces.

[0021] The HTTP protocol uses a publish / subscribe mechanism. The delivery system acts as the core node, publishing logistics data acquisition requests to the logistics system and production data acquisition requests to the production system. The production system and logistics system act as response nodes, pushing logistics data and production data to the delivery system in real time after receiving the requests.

[0022] Before the distribution system officially goes into operation, this embodiment requires its configuration. Referring to step S2, this embodiment calculates and configures dynamic points to support subsequent processes. Dynamic points refer to trigger stations used to control the materials needed for production. When the production system reaches a dynamic point, it means replenishment is required. By adjusting the trigger timing in real time using dynamic points, inventory backlog caused by premature or excessive pulling can be avoided, while also preventing inventory shortages from affecting production. Dynamic points are not fixed but calculated based on JPH (i.e., production takt time).

[0023] The calculation of dynamic points is expressed by the following formula: Dynamic location = target workstation number - JPH * reported vacancy hours; Here, JPH represents the production cycle time obtained from production data; shortage hour refers to the advance warning time reserved for material replenishment. In actual execution, the dynamic points will continuously update the triggering stations according to the real-time production cycle time to ensure that the dynamic points match the production progress. When JPH increases, the dynamic points decrease, that is, the triggering stations are brought forward to avoid material shortages; when JPH decreases, the dynamic points increase, that is, the triggering stations are delayed to avoid material backlogs.

[0024] For example, if JPH=60 (60 units produced per hour), shortage hour=2 hours, target workstation number=200, then dynamic points=200-60×2=80. That is, when production progresses to workstation 80, material demand is triggered, and 2 hours are reserved for supplier delivery to ensure that the materials arrive on time at workstation 200.

[0025] Referring to step S3, after the delivery system starts operating, it acquires production data and logistics data from the production system and logistics system respectively through the execution of predefined timed scripts. The predefined timed scripts are used to automatically call the communication interfaces of the production system and logistics system via API at a preset frequency to acquire production and logistics data; for example, executing a data acquisition task once per second / minute / hour to adapt to different production rhythms.

[0026] In this embodiment, the preset frequency is set to match the production cycle of the production system, and data delay is avoided by synchronizing data according to the production cycle.

[0027] By acquiring real-time production data, the current production progress of the production system can be determined, including which workstation the production system has reached. When the production system reaches a pre-configured dynamic location in the distribution system, the distribution system calculates the net demand based on the production and logistics data to prepare for replenishment.

[0028] The formula for calculating net demand is as follows: Net demand = Total demand for parts - In-transit inventory - Gate inventory - Storage area inventory - Line-side inventory; The total demand for parts refers to the sum of the demand for the same part from all workstations between the dynamic point and the target workstation. In-transit inventory: includes the quantity of goods in the following statuses: "new demand not yet organized, published, received, confirmed, responded to, shipped at the checkpoint, partially shipped at the checkpoint"; Level crossing inventory: Includes the quantity of goods in the "partially received" or "received but not yet shelved" status; Storage area inventory: The quantity of goods in the "listed but not online" status; Lineside inventory: The quantity of goods that are "online but not consumed".

[0029] After obtaining the net demand, refer to step S5. In this embodiment, the net demand is grouped into a single document and issued to the supplier for delivery, using adaptive grouping factors as a condition. Grouping refers to combining the demands generated by the delivery system into a single document. Adaptive grouping factors mean that the delivery system pre-sets grouping factors (such as supplier, origin, destination, required delivery time, etc.). When a demand is generated, it automatically groups demands based on their shared factors. For example, multiple demands can be grouped into one document based on "same origin + same destination"; if multiple demands have different origins and destinations, multiple documents are created.

[0030] For example, the delivery system generates 10 parts requirements. For requirements 1-4, the shipping location is "Warehouse A", the receiving location is "Production Line 1", the supplier is "Company A", and the delivery time is "before 14:00 on the same day". For requirements 5-8, the shipping location is "Warehouse A", the receiving location is "Production Line 1", the supplier is "Company B", and the delivery time is "before 14:00 on the same day". For requirements 9-10, the shipping location is "Warehouse B", the receiving location is "Production Line 2", the supplier is "Company A", and the delivery time is "before 16:00 on the same day". Based on adaptive order grouping factors (e.g., same shipping location + same receiving location), the delivery system automatically generates three documents: Document 1 (Company A - Warehouse A - Production Line 1 - before 14:00, including demands 1-4), Document 2 (Company B - Warehouse A - Production Line 1 - before 14:00, including demands 5-8), and Document 3 (Company A - Warehouse B - Production Line 2 - before 16:00, including demands 9-10), which are then pushed to the corresponding suppliers to achieve precise delivery. The adaptive order grouping factor allows for customized order grouping factors, supporting diverse logistics pull mechanisms.

[0031] The use of net demand ordering aims to quickly respond to material shortages at production lines and ensure uninterrupted production when production reaches dynamic points. Net demand deducts all available inventory (in transit, at checkpoints, in storage areas, and at production lines), directly reflecting the quantity of materials that "must be replenished immediately." After ordering, it can be immediately issued to suppliers to achieve emergency replenishment and avoid production stoppages caused by depletion of production line inventory.

[0032] In addition, the method further includes: when the production system advances to the dynamic location, calculating the total demand based on the production data and logistics data; and using an adaptive order grouping factor as a condition, assembling the total demand into a document and issuing it to the supplier to complete the delivery. At this time, using the total demand order grouping, the total demand is superimposed with the demand of vehicles that have not yet passed the point and the workstation reduction adjustment, reflecting the overall material gap "including the production plan for a future period of time", which can be used to generate batch delivery documents, allowing suppliers to plan their inventory and transportation in advance, adapting to long-cycle delivery or large-volume material replenishment.

[0033] The formula for calculating the total demand is as follows: Total demand = Demand for vehicles that have not yet passed through checkpoints + Σ[(Dynamic checkpoint workstation number - 1) × Reduction quantity] - In-transit inventory - Checkpoint inventory - Storage area inventory - Lineside inventory; Among them, the reduction quantity refers to the number of parts required for each workstation; the demand for vehicles that have not yet passed the point indicates the total number of parts required for vehicles that have not yet passed the workstation for production and assembly; Σ[(dynamic point workstation number - 1) × reduction quantity] represents the total cumulative reduction of parts for all related workstations from the first workstation to the previous workstation of the current dynamic point workstation.

[0034] This embodiment also provides a real-time data-driven precision replenishment and delivery system, using the aforementioned real-time data-driven precision replenishment and delivery method. The system includes a computer, which executes a computer program constructed according to the aforementioned real-time data-driven precision replenishment and delivery method.

[0035] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A precise replenishment and delivery method based on real-time data-driven methods, characterized in that: The method includes the following steps: Step S1: Establish communication connections between the distribution system and the production system and logistics system respectively; Step S2: Calculate and configure dynamic points; Step S3: In the delivery system, production data and logistics data are obtained from the production system and logistics system respectively by executing a predefined timed script; Step S4: When the production system advances to the dynamic point, calculate the net demand based on the production data and logistics data; Step S5: Using the adaptive group single factor as a condition, the net demand is grouped into a document and issued to the supplier to complete the delivery.

2. The precise replenishment and delivery method based on real-time data as described in claim 1, characterized in that: In step S1, communication connections between the delivery system and the production system and logistics system are established based on the HTTP protocol.

3. The method for precise replenishment and delivery based on real-time data as described in claim 2, characterized in that: The HTTP protocol adopts a publish / subscribe mechanism, with the delivery system as the core node, publishing logistics data acquisition requests to the logistics system and production data acquisition requests to the production system; The production and logistics systems act as response nodes, pushing logistics and production data to the delivery system in real time after receiving a request.

4. The precise replenishment and delivery method based on real-time data as described in claim 1, characterized in that: In step S2, the calculation of the dynamic point location is expressed by the following formula: Dynamic location = target workstation number - JPH * reported vacancy hours; JPH represents the production cycle time obtained from production data; shortage hour refers to the advance warning time reserved for material replenishment.

5. The precise replenishment and delivery method based on real-time data as described in claim 1, characterized in that: In step S3, the predefined timing script is used to automatically call the communication interfaces of the production system and logistics system via API at a preset frequency to obtain production data and logistics data.

6. The precise replenishment and delivery method based on real-time data as described in claim 5, characterized in that: The preset frequency is consistent with the production cycle of the production system.

7. The precise replenishment and delivery method based on real-time data as described in claim 1, characterized in that: In step S4, the formula for calculating net demand is as follows: Net demand = Total demand for parts - In-transit inventory - Gate inventory - Storage area inventory - Line-side inventory; The total demand for parts refers to the sum of the demand for the same part from all workstations between the dynamic point and the target workstation.

8. The precise replenishment and delivery method based on real-time data as described in claim 1, characterized in that: The method further includes: when the production system advances to the dynamic location, calculating the total demand based on the production data and logistics data; and using the adaptive grouping factor as a condition, assembling the total demand into a document and issuing it to the supplier to complete the delivery.

9. A precise replenishment and delivery method based on real-time data as described in claim 8, characterized in that: The formula for calculating the total demand is as follows: Total demand = Demand for vehicles that have not yet passed through checkpoints + Σ[(Dynamic checkpoint workstation number - 1) × Reduction quantity] - In-transit inventory - Checkpoint inventory - Storage area inventory - Lineside inventory; Among them, the reduction quantity refers to the number of parts required for each workstation; the demand for vehicles that have not yet passed the point indicates the total number of parts required for vehicles that have not yet passed the workstation for production and assembly; Σ[(dynamic point workstation number - 1) × reduction quantity] represents the total cumulative reduction of parts for all related workstations from the first workstation to the previous workstation of the current dynamic point workstation.

10. A real-time data-driven precision replenishment and delivery system, using the real-time data-driven precision replenishment and delivery method according to any one of claims 1-9, characterized in that: The system includes a computer for executing a computer program constructed according to any one of claims 1-9 for a real-time data-driven precision replenishment and delivery method.

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