Textile garment supply chain collaborative management system and method

Abstract

The application provides a textile garment supply chain collaborative management system and method, comprising: collecting static master data and dynamic event flow of each node of the supply chain in real time; identifying a key disturbance event affecting an original collaborative plan based on the dynamic event flow; according to the type of the key disturbance event, combining the static master data, performing correlation analysis on capacity fluctuation information, work-in-process state change information and real-time sales and return information, and determining the influence range and transmission path of the key disturbance event in the supply chain; according to the influence range and transmission path, automatically triggering dynamic rescheduling of a local link in the original collaborative plan, and determining an adjustment scheme; and synchronizing the adjustment scheme to a manufacturing execution system or a warehouse management system of an affected node, so as to realize closed-loop correction of the collaborative plan. The application can reduce losses caused by information delay, reduce collaborative communication costs, and ensure efficient and stable operation of the overall supply chain.

Description

Technical Field

[0001] This application relates to the field of apparel supply chain management technology, and more specifically, to a collaborative management system and method for textile and apparel supply chains. Background Technology

[0002] The textile and apparel supply chain is typically long and involves many participants, encompassing multiple stages from raw material supply, textile printing and dyeing, garment manufacturing to logistics and distribution. Traditional supply chain management systems are mostly static planning systems, relying on historical data for periodic (e.g., weekly, monthly) demand forecasting and production scheduling. However, the textile and apparel industry is characterized by significant seasonality, fashion trends, and market uncertainty. Dynamic events frequently occur, such as equipment failures on the production floor, capacity fluctuations due to worker absences, and sudden sales spikes at the market terminal caused by changing fashion trends.

[0003] Existing technological solutions often exhibit delayed responses to such dynamic disturbances, typically requiring manual intervention to investigate the impact and coordinate adjustments to plans by all parties. This approach is inefficient and prone to information asymmetry. For example, a production line malfunction in a garment factory can lead to order delays for multiple downstream distributors, and this chain reaction is often only discovered and addressed several days after the problem occurs. Therefore, achieving real-time perception, dynamic response, and collaborative adjustment of the supply chain is a pressing technical challenge that the industry needs to address. Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of the prior art and provide a collaborative management system and method for the textile and apparel supply chain, so as to solve the technical problems of the prior art supply chain management system's lagging response to real-time dynamic events and inability to make rapid collaborative adjustments.

[0005] In a first aspect, the present invention provides a method for collaborative management of a textile and apparel supply chain, the method comprising: Real-time collection of static master data and dynamic event streams from each node of the supply chain; the dynamic event streams include production capacity fluctuation information, work-in-process status change information, and real-time sales and return information from the market terminal; Based on the dynamic event flow, key disturbance events that affect the original collaborative plan are identified. Based on the type of the key disturbance event, and in conjunction with the static master data, a correlation analysis is performed on the capacity fluctuation information, the work-in-process status change information, and the real-time sales and return information to determine the scope of impact and transmission path of the key disturbance event in the supply chain. Based on the scope of influence and the transmission path, the dynamic rescheduling of local links in the original collaborative plan is automatically triggered to determine the adjustment plan; The adjustment plan will be synchronized to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

[0006] Preferably, the identification of key disturbance events affecting the original collaborative plan based on the dynamic event stream includes: Based on the aforementioned capacity fluctuation information, sudden production line failure events can be identified; Based on the real-time sales and return information, events that cause unexpected fluctuations in terminal sales are identified. Alternatively, batch quality inspection anomalies can be identified based on the changes in the status of the work-in-process and the batch quality inspection information.

[0007] Preferably, when the critical disturbance event is a sudden production line failure, determining the scope and transmission path of the critical disturbance event in the supply chain includes: Based on the product bill of materials and process routes in the static master data, match the in-production orders and product models corresponding to the faulty production line; The current processing progress of the affected orders is determined based on the work-in-process status change information; Based on the customer order information in the static master data, predict the transmission impact of delayed delivery on downstream distribution nodes.

[0008] Preferably, the automatic triggering of dynamic rescheduling of local components in the original collaborative plan to determine the adjustment scheme includes: Based on the aforementioned transmission effect and the supplier basic information in the static master data, alternative suppliers with the same process capabilities are selected from the preset capacity candidate pool, and order transfer plans or delivery date adjustment plans are generated.

[0009] Preferably, when the key disturbance event is an unexpected fluctuation in terminal sales, determining the scope and transmission path of the key disturbance event in the supply chain includes: Based on the product bill of materials in the static master data, calculate the raw material requirements corresponding to the incremental demand; Assess the available additional production capacity for current orders based on the work-in-process status change information; Based on the customer order information in the static master data, the time window for demand transmission to upstream fabric suppliers and dyeing and printing plants is determined.

[0010] Preferably, the automatic triggering of dynamic rescheduling of local components in the original collaborative plan to determine the adjustment scheme includes: Within the demand transmission time window, based on the supplier basic information in the static master data, advance instructions for upstream fabric procurement plans and incremental instructions for dyeing and printing production scheduling plans are automatically generated.

[0011] Preferably, the automatic triggering of dynamic rescheduling of local components in the original collaborative plan to determine the adjustment scheme includes: Among several preset alternative adjustment strategies, the decision is based on the simulation results of the overall supply chain delivery time, cost and inventory levels. Based on the simulation results, the alternative adjustment strategy with the least impact is selected as the final adjustment scheme.

[0012] Secondly, this invention provides a textile and apparel supply chain collaborative management system, comprising: The data acquisition module is used to collect static master data and dynamic event streams from each node of the supply chain in real time; the dynamic event streams include production capacity fluctuation information, work-in-process status change information, and real-time sales and return information from the market terminal. The critical disturbance event determination module is used to identify critical disturbance events that affect the original collaborative plan based on the dynamic event stream; The impact scope and transmission path determination module is used to determine the impact scope and transmission path of the key disturbance event in the supply chain by performing correlation analysis on the capacity fluctuation information, the work-in-process status change information and the real-time sales and return information based on the type of the key disturbance event and in combination with the static master data. The adjustment scheme determination module is used to automatically trigger dynamic rescheduling of local links in the original collaborative plan based on the scope of influence and transmission path, and determine the adjustment scheme. The correction module is used to synchronize the adjustment plan to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

[0013] Thirdly, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0014] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0015] This invention provides a collaborative management system and method for the textile and apparel supply chain. By collecting dynamic event flows across the entire supply chain in real time (such as capacity fluctuations, work-in-process status, and market sales changes) and deeply integrating them with static master data, it achieves second-level perception and intelligent response to sudden disturbances. Its core lies in its ability to automatically perform correlation analysis based on the type of disturbance event, accurately quantifying the impact range and transmission path of the event in the complex supply chain network, and triggering dynamic rescheduling of local plans accordingly. After generating the optimal adjustment plan, it is then issued to the execution system in a closed loop. This mechanism effectively solves the problems of delayed response and difficult collaboration in traditional supply chain management systems, significantly improving the resilience, response speed, and automation level of the supply chain in the face of market changes and production anomalies. This reduces losses caused by information delays, lowers collaborative communication costs, and ensures the efficient and stable operation of the entire supply chain.

[0016] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0017] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a collaborative management method for the textile and apparel supply chain provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another textile and apparel supply chain collaborative management method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the composition of a textile and apparel supply chain collaborative management system according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] See Figure 1The illustration shows a specific embodiment of a collaborative management method for the textile and apparel supply chain provided by the present invention. In this embodiment, the collaborative management method for the textile and apparel supply chain includes:

[0021] Step 101: Collect static master data and dynamic event streams from each node of the supply chain in real time; the dynamic event streams include production capacity fluctuation information, work-in-process status change information, and real-time sales and return information from the market terminal. Specifically, the static master data in this embodiment refers to relatively stable basic descriptive information in the textile and apparel supply chain, including at least the Bill of Materials (BOM, which details the specifications and quantities of fabrics and accessories required for garment products), process routes (i.e., the process flow and equipment requirements of each production stage), basic supplier information (covering basic profiles, production capacity, historical delivery rate, and credit rating of raw material suppliers, dyeing and printing plants, and garment manufacturers), and customer order information (including order number, product model, quantity, delivery date, and priority). Dynamic event flow refers to event-type data with time-series characteristics generated in real time during the supply chain operation. Specifically, it includes production capacity fluctuation information at the production site (such as production line downtime, equipment failures, and fluctuations in actual output efficiency obtained in real time through manufacturing execution systems or equipment sensors), work-in-process status change information (such as the current process position, processing progress, and yield rate of each cut piece or semi-finished product tracked by RFID or barcode scanning), and real-time sales and return information at the market terminal (such as the instant sales volume, inventory changes, and return records of each SKU obtained through POS systems or e-commerce platform interfaces). By collecting the above two types of data across the entire domain, a complete data foundation is built for subsequent collaborative decision-making.

[0022] Step 102: Identify key disturbance events that affect the original collaborative plan based on the dynamic event flow; Furthermore, within the real-time collected dynamic event stream, a pre-set rule engine or anomaly detection algorithm identifies abnormal events that may impact established procurement, production, or distribution plans. Key disturbance events include at least three categories: First, based on capacity fluctuation information, when the downtime of equipment, the magnitude of output decline, or the degree of efficiency deviation at a production node exceeds a set threshold, it is identified as a sudden production line failure event. Second, based on real-time sales and return / exchange information, when the growth rate of terminal sales, cumulative sales, or return / exchange rate of a specific product significantly deviates from historical levels or the predicted range, it is identified as an unexpected fluctuation in terminal sales. Third, based on changes in work-in-process status, combined with batch quality inspection information obtained from the quality inspection process, when indicators such as the product yield rate, key process pass rate, or fabric color difference grade of a production batch exceed the quality control range, it is identified as a batch quality inspection anomaly event. These three types of events cover the most typical disturbances at the production, market, and quality ends of the textile and apparel supply chain, serving as the initial driving force for subsequent coordinated adjustments.

[0023] Step 103: Based on the type of key disturbance events and combined with static master data, conduct correlation analysis on capacity fluctuation information, work-in-process status change information, and real-time sales and return information to determine the scope of impact and transmission path of key disturbance events in the supply chain. Furthermore, the core of this step lies in placing isolated events within the entire chain network for causal correlation analysis, accurately locating the impact boundaries and propagation trajectory of disturbances. This correlation analysis is implemented in different ways for different types of key disturbance events:

[0024] When a critical disturbance event is a sudden production line failure, the process begins by matching the orders currently being produced on the affected production line with the corresponding product models based on the product bill of materials and process routes in the static master data, thus identifying the specific affected entities. Secondly, based on changes in the status of work-in-process, the current processing progress of these affected orders is traced, including completed processes, semi-finished product inventory, and their current workstations, to assess their "salvage value" and urgency. Finally, based on customer order information in the static master data, the affected orders are linked to their downstream customers to predict the impact of delayed order delivery on the inventory and sales plans of brand owners, distributors, or retailers, thereby constructing a complete impact chain of "production line failure - work-in-process blockage - order delay - downstream shortage."

[0025] When the key disturbance event is an unexpected fluctuation in terminal sales, the sales increase is first precisely broken down into the increase in raw material demand for corresponding specifications of fabrics, accessories, and packaging materials based on the product bill of materials in the static master data, thus realizing the transformation of market signals into procurement signals. Secondly, based on the information on changes in the status of work-in-process, the current order load, remaining capacity space, and semi-finished product reserves of each supplier, dyeing and finishing plant, and garment factory are traced upstream to assess the rapid response capability of the supply chain. Finally, based on the customer order information in the static master data, combined with the standard production cycle and logistics timeliness of each upstream supplier, the lead time required from issuing the adjustment instruction to meeting the new demand is calculated, i.e., the demand transmission time window, thus constructing a complete demand transmission path of "sales surge - raw material demand surge - upstream capacity assessment - replenishment window calculation".

[0026] When a critical disturbance event is a batch quality inspection anomaly, the process begins by first identifying the raw material batch, supplier, and corresponding garment order used in the quality inspection batch based on the product bill of materials in the static master data, thus determining the source and scope of the quality problem. Secondly, based on the work-in-process status change information, the current distribution of related work-in-process, semi-finished products, and finished products is traced, including those still on the production line, already in storage, and already shipped to distribution nodes. Finally, based on customer order information in the static master data, the customers corresponding to shipped products are identified, the necessity and cost of recall or replacement are assessed, and a complete quality risk transmission path of "quality inspection anomaly - source tracing - work-in-process location - shipped product tracking" is constructed.

[0027] Step 104: Based on the scope of impact and transmission path, automatically trigger dynamic rescheduling of local links in the original collaborative plan to determine the adjustment plan; Furthermore, based on the quantitative results obtained from the above correlation analysis, the system automatically generates targeted collaborative adjustment plans, making precise corrections only to the affected links and avoiding a complete overhaul of the original plan. The logic for generating adjustment plans for different types of disturbance events is as follows:

[0028] In response to sudden production line failures, based on the determined scope of delayed delivery, the system automatically selects alternative suppliers with the same process capabilities, sufficient remaining capacity, and high historical delivery date achievement rates from the preset capacity pool, using the basic supplier information in the static master data. After comprehensive evaluation, it generates an order transfer plan (allocating part or all of the affected orders to alternative suppliers) or a delivery date adjustment plan (updating the order delivery date after consultation with downstream customers and simultaneously modifying the system records).

[0029] In response to unexpected fluctuations in terminal sales, within the calculated demand transmission time window, the system automatically generates and sends adjustment instructions to upstream collaborative nodes based on the supplier basic information in the static master data: issuing advance instructions for procurement plans to fabric suppliers, requiring them to adjust production schedules or prioritize shipments; issuing incremental instructions for production scheduling to dyeing and printing plants, requiring them to insert emergency production batches into the existing production sequence; and simultaneously issuing adjustment instructions for transportation plans to logistics service providers to match the new material flow rhythm.

[0030] In response to batch quality inspection anomalies, the system automatically triggers multiple handling instructions based on the identified scope of the quality problem: issuing instructions to suspend processing or isolate for inspection to the production workshop where the work-in-process is located; issuing instructions to lock inventory and suspend shipments to the finished goods warehouse; and automatically generating recall notices or replacement procedures for products already shipped or delivered, based on customer importance and the severity of the problem. At the same time, based on the traced raw material supplier information, the system updates the supplier evaluation score and triggers the warehousing re-inspection process for the same batch of raw materials.

[0031] Step 105: Synchronize the adjustment plan to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

[0032] Furthermore, after finalizing the adjustment plan, the system transforms the plan into executable instructions via standardized application programming interfaces (APIs) or enterprise service buses (ESBs) and distributes them to the corresponding business execution systems in real time. Specifically, for garment manufacturers, the adjustment plan is synchronized to their Manufacturing Execution System (MES) in the form of production work order changes, process adjustments, or pause instructions; for warehousing and logistics nodes, the adjustment plan is synchronized to their Warehouse Management System (WMS) or Transportation Management System (TMS) in the form of shipping plan updates, inventory locks, or transportation route adjustments; for upstream suppliers, the adjustment plan is synchronized to their supplier collaboration portal or ERP system in the form of purchase order changes or emergency replenishment notifications. Through this automated distribution mechanism, it is ensured that operators and execution equipment at all affected nodes can obtain the latest collaborative instructions at the same time, thereby achieving a complete closed-loop correction from event perception, impact analysis, plan decision-making to instruction execution, ensuring the continuity and collaboration of the overall supply chain operation.

[0033] Figure 1 The embodiments shown are merely basic examples of the method of the present invention. Other preferred embodiments of the method can be obtained by making certain optimizations and extensions based on them.

[0034] like Figure 2The image shows another specific embodiment of a collaborative management method for the textile and apparel supply chain according to the present invention. This embodiment further describes the method based on the foregoing embodiments, and includes the following steps:

[0035] Step 201: Among multiple preset alternative adjustment strategies, determine the one based on the simulation results of the overall supply chain delivery time, cost and inventory level; Specifically, after the system generates multiple feasible alternative adjustment strategies for a key disturbance event, it does not directly select one but first enters the simulation and deduction stage. In this embodiment, the simulation and deduction refers to using a pre-built supply chain digital twin model or discrete event simulation engine, taking each alternative adjustment strategy as input variables, to simulate its dynamic execution process in the actual supply chain network. The simulation and deduction results specifically include three dimensions of quantitative evaluation indicators: first, the impact on delivery time, i.e., the expected delay time of orders at each node, changes in on-time delivery rate, and the degree of impact on end-customer service levels after the strategy is implemented; second, the cost impact, covering incremental costs such as additional capacity switching costs, emergency procurement premiums, expedited logistics costs, and potential order default penalties incurred due to strategy adjustments; and third, the impact on inventory levels, including fluctuations in the inventory levels of raw materials, semi-finished products, and finished products at each node, potential obsolescence risks, and the consumption of safety stock. By running the alternative strategies in a simulation environment, the system can anticipate the chain reactions of different decisions over time, providing objective data support for subsequent optimal decision-making, rather than relying solely on static rules or experience.

[0036] Step 202: Select the alternative adjustment strategy with the least impact as the final adjustment plan based on the simulation results.

[0037] Furthermore, after obtaining the simulation results of various alternative strategies, the system initiates a multi-objective optimization decision-making mechanism to comprehensively evaluate the predicted indicators across three dimensions: delivery time, cost, and inventory. Selecting the alternative adjustment strategy with the least impact does not simply seek the optimal value for a single indicator, but rather seeks a balance among multiple conflicting objectives through a pre-defined weighting algorithm or Pareto optimal solution set screening. For example, one strategy might minimize the impact on delivery time but bring significant cost increases and inventory backlog risks; another strategy might be cost-optimal but lead to widespread order delays. By comparing the simulation results of various strategies, the system identifies the solution with the least disturbance to the overall supply chain performance indicators, i.e., the mildest overall negative impact. The final adjustment plan, determined through simulation verification, is the optimal solution and will be output to the execution phase, ensuring that the supply chain can respond quickly to disturbances while avoiding secondary risks caused by improper decision-making, achieving a balance between supply chain resilience and economy.

[0038] This invention also provides a collaborative management system for the textile and apparel supply chain. See also... Figure 3 The image shows a specific embodiment of a collaborative management system for the textile and apparel supply chain provided by the present invention. This embodiment of the system is used to execute... Figures 1-2 The physical apparatus of the method. Its technical solution is essentially the same as the embodiments described above, and the corresponding descriptions in the embodiments above also apply to this embodiment. The system includes:

[0039] The data acquisition module 301 is configured to collect static master data and dynamic event streams from each node of the supply chain in real time; the dynamic event streams include production capacity fluctuation information, work-in-process status change information, and real-time sales and return information from the market terminal. The critical disturbance event determination module 302 is configured to identify critical disturbance events that affect the original collaborative plan based on a dynamic event stream; The impact scope and transmission path determination module 303 is configured to perform correlation analysis on capacity fluctuation information, work-in-process status change information and real-time sales and return information based on the type of key disturbance events and combined with static master data, in order to determine the impact scope and transmission path of key disturbance events in the supply chain. The adjustment plan determination module 304 is configured to automatically trigger dynamic rescheduling of local links in the original collaborative plan based on the scope of impact and transmission path, and determine the adjustment plan. The correction module 305 is configured to synchronize the adjustment plan to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

[0040] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0041] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, and other types. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0042] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0043] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into memory and then executes them. Alternatively, it can obtain the corresponding execution instructions from other devices to form a textile and apparel supply chain collaborative management device at the logical level. The processor executes the execution instructions stored in memory to implement the textile and apparel supply chain collaborative management method provided in any embodiment of the present invention.

[0044] The above is as described in the present invention. Figure 3 The method for implementing a collaborative management system for a textile and apparel supply chain provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed through integrated logic circuits in the processor's hardware or through software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0045] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0046] This invention also proposes a readable medium storing execution instructions. When these instructions are executed by a processor of an electronic device, the device can perform a textile and apparel supply chain collaborative management method provided in any embodiment of this invention, specifically for executing, for example... Figure 1 , Figure 2 The method shown.

[0047] The electronic devices in the foregoing embodiments may be computers.

[0048] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can be implemented in a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0049] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0050] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0051] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A collaborative management method for the textile and apparel supply chain, characterized in that, The method includes: Real-time collection of static master data and dynamic event streams from each node of the supply chain; the dynamic event streams include production capacity fluctuation information, work-in-process status change information, and real-time sales and return information from the market terminal; Based on the dynamic event flow, key disturbance events that affect the original collaborative plan are identified. Based on the type of the key disturbance event, and in conjunction with the static master data, a correlation analysis is performed on the capacity fluctuation information, the work-in-process status change information, and the real-time sales and return information to determine the scope of impact and transmission path of the key disturbance event in the supply chain. Based on the scope of influence and the transmission path, the dynamic rescheduling of local links in the original collaborative plan is automatically triggered to determine the adjustment plan; The adjustment plan will be synchronized to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

2. The method according to claim 1, characterized in that, The key disturbance events that affect the original collaborative plan, identified based on the dynamic event flow, include: Based on the aforementioned capacity fluctuation information, sudden production line failure events can be identified; Based on the real-time sales and return information, events that cause unexpected fluctuations in terminal sales are identified. Alternatively, batch quality inspection anomalies can be identified based on the changes in the status of the work-in-process and the batch quality inspection information.

3. The method according to claim 1, characterized in that, When the critical disturbance event is a sudden production line failure, determining the scope and transmission path of the critical disturbance event in the supply chain includes: Based on the product bill of materials and process routes in the static master data, match the in-production orders and product models corresponding to the faulty production line; The current processing progress of the affected orders is determined based on the work-in-process status change information; Based on the customer order information in the static master data, predict the transmission impact of delayed delivery on downstream distribution nodes.

4. The method according to claim 3, characterized in that, The automatic triggering of dynamic rescheduling of certain parts of the original collaborative plan, and the determination of the adjustment scheme, includes: Based on the aforementioned transmission effect and the supplier basic information in the static master data, alternative suppliers with the same process capabilities are selected from the preset capacity candidate pool, and order transfer plans or delivery date adjustment plans are generated.

5. The textile and apparel supply chain collaborative management method according to claim 1, characterized in that, When the key disturbance event is an unexpected fluctuation in terminal sales, determining the scope and transmission path of the key disturbance event in the supply chain includes: Based on the product bill of materials in the static master data, calculate the raw material requirements corresponding to the incremental demand; Assess the available additional production capacity for current orders based on the work-in-process status change information; Based on the customer order information in the static master data, the time window for demand transmission to upstream fabric suppliers and dyeing and printing plants is determined.

6. The textile and apparel supply chain collaborative management method according to claim 5, characterized in that, The automatic triggering of dynamic rescheduling of certain parts of the original collaborative plan, and the determination of the adjustment scheme, includes: Within the demand transmission time window, based on the supplier basic information in the static master data, advance instructions for upstream fabric procurement plans and incremental instructions for dyeing and printing production scheduling plans are automatically generated.

7. The textile and apparel supply chain collaborative management method according to claim 1, characterized in that, The automatic triggering of dynamic rescheduling of certain parts of the original collaborative plan, and the determination of the adjustment scheme, includes: Among several preset alternative adjustment strategies, the decision is based on the simulation results of the overall supply chain delivery time, cost and inventory levels. Based on the simulation results, the alternative adjustment strategy with the least impact is selected as the final adjustment scheme.

8. A collaborative management system for the textile and apparel supply chain, characterized in that, include: The data acquisition module is used to collect static master data and dynamic event streams from each node of the supply chain in real time. The dynamic event stream includes production capacity fluctuation information from the production site, work-in-process status change information, and real-time sales and return information from the market terminal. The critical disturbance event determination module is used to identify critical disturbance events that affect the original collaborative plan based on the dynamic event stream; The impact scope and transmission path determination module is used to determine the impact scope and transmission path of the key disturbance event in the supply chain by performing correlation analysis on the capacity fluctuation information, the work-in-process status change information and the real-time sales and return information based on the type of the key disturbance event and in combination with the static master data. The adjustment scheme determination module is used to automatically trigger dynamic rescheduling of local links in the original collaborative plan based on the scope of influence and transmission path, and determine the adjustment scheme. The correction module is used to synchronize the adjustment plan to the manufacturing execution system or warehouse management system of the affected nodes to achieve closed-loop correction of the collaborative plan.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 7.

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