An AI-based intelligent supply chain management method and system
By building an AI-based intelligent supply chain management system, full-process data sharing and collaboration have been achieved, solving the problems of data isolation and decision-making bias in traditional supply chain management, and improving the intelligence level and operational efficiency of the supply chain.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIJING PRECISION TECHNOLOGY (ZHEJIANG) CO LTD
- Filing Date
- 2026-04-16
- Publication Date
- 2026-07-31
AI Technical Summary
Traditional supply chain management models rely on human experience, making it difficult to guarantee the comprehensiveness and real-time nature of data collection. Data from each link is isolated, and there is a lack of intelligent control over the entire process. This leads to a mismatch between production plans and material requirements, inventory backlogs or shortages, and unreasonable logistics and distribution, making it unable to adapt to the needs of large-scale and high-efficiency market operations.
By adopting an AI-based intelligent supply chain management approach, the system achieves end-to-end data sharing and collaboration through the collaborative work of multiple modules, including data acquisition, data analysis, decision optimization, and execution layers. Combined with machine learning modules, the system continuously optimizes decision-making algorithms, builds a full-link data transmission channel, supports upstream and downstream system integration, and realizes intelligent management from raw material procurement to distribution.
It enables precise control over all aspects of the supply chain, improves the scientific nature and flexibility of decision-making, breaks down data silos, ensures operational consistency and collaboration, continuously optimizes decision-making accuracy, reduces operating costs, and improves management efficiency.
Smart Images
Figure CN122492073A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of supply chain management technology, specifically to an AI-based intelligent supply chain management method and system. Background Technology
[0002] In the field of supply chain management, traditional management models rely heavily on human experience for the entire process. From raw material procurement planning and inventory control to logistics and distribution route planning, all require manual data collection and analysis for decision-making. This not only makes it difficult to guarantee the comprehensiveness and real-time nature of data collection, but also easily leads to decision-making biases due to the subjectivity of human judgment. At the same time, in the traditional model, data from different links in the supply chain are isolated, and there is a lack of effective data collaboration among procurement, production, inventory, and logistics modules. Real-time data sharing across the entire chain cannot be achieved, resulting in frequent problems such as mismatch between production plans and material requirements, inventory backlogs or stockouts, and unreasonable logistics and distribution routes. This significantly increases supply chain operating costs and makes it difficult to adapt to the needs of large-scale, high-efficiency market operations.
[0003] While existing supply chain management technologies incorporate some IT tools, they often only optimize single links, lacking a comprehensive intelligent control system for the entire process. They fail to establish a multi-level data analysis and decision-making optimization architecture, thus failing to achieve closed-loop management from data collection to execution feedback. Some technologies possess basic data collection and analysis capabilities, but lack the integration of AI algorithms for in-depth demand forecasting, inventory optimization, and logistics planning. This results in low decision-making accuracy and a lack of self-learning and optimization capabilities, preventing continuous improvement of decision-making models based on historical supply chain operational data. Furthermore, existing technologies suffer from insufficient system integration capabilities, hindering efficient data exchange with upstream and downstream systems. The connection between manual intervention and intelligent decision-making is weak, making it difficult to flexibly respond to unexpected situations in supply chain operations. Overall, the level of intelligence is low, failing to fundamentally address the core issues of high supply chain costs and low management efficiency. Summary of the Invention
[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an AI-based intelligent supply chain management method and system, enabling intelligent management of the entire supply chain process from raw material procurement to distribution.
[0005] To achieve the above objectives, the present invention adopts the following technical solution:
[0006] An AI-based intelligent supply chain management method includes the following steps:
[0007] S1. Collect data from the entire supply chain through multiple modules in the data acquisition layer, parse material identification information, collect material data, track production progress, and monitor inventory capacity and material flow status.
[0008] S2. The collected end-to-end data is transmitted to the data analysis layer to identify material requirements and calculate the material requirements, evaluate the comprehensive indicators of suppliers, analyze the delivery time and transportation costs of multiple orders, and plan delivery routes.
[0009] S3. The AI algorithm in the decision optimization layer processes the data analysis results, adjusts the production plan and scheduling strategy, intelligently adjusts the inventory configuration, generates delivery tasks and determines the optimal delivery route based on the order analysis results;
[0010] S4. The execution layer generates and sends purchase orders based on material requirements and selected suppliers, tracks the order delivery status in real time and updates inventory information synchronously, and adjusts the decision-making results of each link through the manual intervention module.
[0011] S5. Through the machine learning module, historical supply chain data is analyzed and learned to continuously optimize decision-making algorithms at each level. Data sharing and collaboration between levels are achieved in real time through the data interaction module, realizing full-process management of the supply chain from raw material procurement to distribution.
[0012] Preferably, in step S1, the specific method for collecting supply chain data through multiple modules of the data acquisition layer is as follows: the identification and parsing module identifies and parses the identification information of materials to extract production records, supply records, and usage records of materials; the material information acquisition module comprehensively collects and structures the relevant data of material category, specifications, batch, and material; the production progress monitoring module captures the completion status of production processes, process time, and capacity load data in real time through production line sensing devices to track the work progress of the production line; and the inventory status monitoring module collects the warehouse storage capacity, material inbound quantity, outbound quantity, and inventory turnover rate data in real time to monitor the material flow status.
[0013] Preferably, in step S2, the specific method for the data analysis layer to identify material requirements and calculate material demand quantities, evaluate supplier comprehensive indicators, analyze multi-order delivery time and transportation costs, and plan delivery routes is as follows: The demand identification unit, based on identifier resolution data and historical demand data, uses AI algorithms to evaluate current inventory levels, predict future material demand trends, and accurately calculate material demand quantities for different time periods and different product categories; the supplier evaluation unit, based on material demand data, constructs an evaluation system and completes comprehensive indicator evaluation from the dimensions of supplier historical delivery on-time rate, product qualification rate, delivery capacity, pricing level, and service response speed; the order analysis unit decomposes and analyzes the delivery addresses, delivery time requirements, and cargo attributes of multiple orders, calculates the transportation costs of each delivery plan in conjunction with transportation methods and transportation unit prices, and initially plans delivery routes.
[0014] Preferably, in step S3, the decision optimization layer AI algorithm processes the data analysis results, adjusts the production plan and scheduling strategy, intelligently adjusts inventory configuration, and generates delivery tasks and determines the optimal delivery path based on the order analysis results. Specifically, the production scheduling unit dynamically adjusts the process layout and capacity allocation of the production plan based on real-time identifier resolution data and production progress data, and optimizes the production scheduling strategy; the inventory adjustment unit calculates the safety stock threshold by combining material demand forecast results and inventory status data with AI algorithms, realizing dynamic replenishment, allocation and clearing of inventory, and intelligently adjusting inventory configuration; and the logistics planning unit determines the optimal delivery path for each order by iteratively optimizing the preliminary path of the order analysis, combined with real-time traffic conditions and logistics resource distribution, through AI algorithms.
[0015] Preferably, in step S4, the execution layer generates and sends purchase orders based on material requirements and selected suppliers, tracks order delivery status in real time, and updates inventory information synchronously. The method for adjusting the decision results of each link through the manual intervention module is as follows: the order generation unit selects the optimal supplier based on the calculated material requirements and the comprehensive evaluation results of the supplier, creates a purchase order in a preset format, and automatically sends it to the corresponding supplier; the delivery tracking unit captures the logistics nodes and transportation locations of the purchase order in real time through the logistics tracking interface to achieve real-time tracking of the order delivery status. After the materials complete the warehousing or outbound operation, the inventory information of the inventory status monitoring module is updated synchronously; the manual intervention module supports operators to manually adjust the decision results of production plans, inventory configurations, delivery routes, and purchase orders, and the adjustment data is synchronized to each related link in real time.
[0016] Preferably, in step S5, the method for achieving real-time data sharing and collaboration through the data interaction module is as follows: the data interaction module establishes a full-chain data transmission channel for the supply chain, supports multiple standardized data interfaces, and realizes bidirectional data transmission and real-time sharing between the data acquisition layer, data analysis layer, decision optimization layer, and execution layer; at the same time, it realizes system integration with upstream supplier systems and downstream customer systems through the data interface, and achieves synchronous interaction and collaborative processing of cross-system data.
[0017] Preferably, in step S5, the method by which the machine learning module analyzes and learns from historical supply chain data to continuously optimize the decision-making algorithms at each layer is as follows: The machine learning module continuously collects historical data from the entire supply chain process, including raw data from the data collection layer, processing results from the data analysis layer, decision data from the decision optimization layer, and execution feedback data from the execution layer; it performs feature extraction, pattern analysis, and model training on the historical data; iteratively adjusts the algorithm models of the data analysis layer and the decision optimization layer based on the execution feedback data; continuously optimizes the algorithm parameters; and improves the accuracy and adaptability of the decision-making algorithm.
[0018] Preferably, the method by which the inventory adjustment unit intelligently adjusts inventory configuration through AI algorithms is as follows: the AI algorithm combines the time and quantity dimensions of material demand forecasts with data on material shelf life, warehousing costs, and procurement cycles to establish an inventory optimization model; the model calculates the optimal inventory level, replenishment trigger point, and replenishment quantity for each category of materials to achieve dynamic and intelligent inventory adjustment, while generating inventory handling suggestions for slow-moving materials and near-expiry materials.
[0019] An AI-based intelligent supply chain management system includes a data acquisition layer, a data analysis layer, a decision optimization layer, an execution layer, a machine learning module, and a data interaction module. The data acquisition layer includes an identifier resolution module, a material information acquisition module, a production progress monitoring module, and an inventory status monitoring module. The data analysis layer includes a demand identification unit, a supplier evaluation unit, and an order analysis unit. The decision optimization layer includes a production scheduling unit, an inventory adjustment unit, and a logistics planning unit. The execution layer includes an order generation unit, a delivery tracking unit, and a manual intervention module. The data interaction module connects to each layer and module to achieve real-time data sharing and collaboration. The machine learning module connects to the data analysis layer and the decision optimization layer to optimize decision-making algorithms. Each module executes the corresponding supply chain management process according to preset functions, achieving intelligent management of the entire process from raw material procurement to distribution.
[0020] Preferably, the data interaction module is equipped with multiple types of standardized data interfaces, supporting integration and docking with upstream supplier systems and downstream customer systems to achieve cross-system data interaction; the machine learning module includes a data mining unit, a model training unit, and an algorithm optimization unit. The data mining unit is used to extract features from historical data of the supply chain, the model training unit is used to train the decision algorithm model, and the algorithm optimization unit is used to iteratively adjust the algorithm parameters based on the training results to continuously optimize the decision algorithms at each level.
[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0022] The data acquisition layer is equipped with multiple modules, including identifier resolution, material information collection, production progress monitoring, and inventory status monitoring. It can comprehensively collect data from the entire supply chain, from raw materials to distribution, and achieve all-round, real-time collection of material information, production status, and inventory dynamics. This provides a complete and accurate data source for subsequent data analysis and decision optimization.
[0023] The data analysis layer, through layered processing of demand identification, supplier evaluation, and order analysis units, can accurately identify material requirements based on collected data, quantitatively assess the comprehensive capabilities of suppliers, and scientifically analyze order delivery costs and routes. This provides professional and systematic data analysis support for decision-making at all stages of the supply chain, enhancing the scientific nature of decision-making.
[0024] The decision optimization layer is equipped with AI algorithms, enabling the production scheduling, inventory adjustment, and logistics planning units to dynamically adjust production plans, intelligently and dynamically adjust inventory, and optimally plan delivery routes, respectively. This allows decisions at each stage of the supply chain to better align with actual operational needs, achieving precise control over production, inventory, and logistics.
[0025] The execution layer integrates order generation and delivery tracking units and sets up a manual intervention module. It can automatically generate and send purchase orders, track order delivery status in real time and update inventory information synchronously. At the same time, it supports flexible adjustment of decision results by humans, balancing the efficiency of intelligent execution with the flexibility to deal with emergencies.
[0026] The data interaction module establishes a real-time data transmission channel between various levels and modules, supports integration with multiple data interfaces and upstream and downstream systems, realizes full-link data sharing and collaboration within the supply chain and with upstream and downstream, breaks down data silos, and ensures the coordinated consistency of operations in all aspects.
[0027] The machine learning module continuously collects historical data on supply chain operations. Through data analysis and model training, iterative optimization of decision-making algorithms at each level enables the system to learn autonomously and continuously optimize, thereby improving the accuracy of demand forecasting, inventory optimization, and logistics planning, and achieving continuous upgrading of supply chain management capabilities.
[0028] The system establishes a closed-loop management system covering the entire process of data collection, analysis, decision-making, and execution. Each level and module performs its own duties while also working together. By combining AI algorithms and machine learning technologies, it achieves intelligent management of the entire supply chain process, from raw material procurement to product distribution, and promotes the efficient operation of each link in the supply chain. Attached Figure Description
[0029] Figure 1 This is a flowchart of the method of the present invention;
[0030] Figure 2 This is a structural block diagram of the system of the present invention. Detailed Implementation
[0031] The invention will now be further described with reference to the accompanying drawings.
[0032] like Figure 1 As shown, an AI-based intelligent supply chain management method includes the following steps:
[0033] S1. Collect supply chain data through multiple modules in the data acquisition layer:
[0034] The identification and resolution module identifies and parses the identification information of materials, extracting production records, supply records, and usage records; the material information collection module comprehensively collects and structures data related to the category, specifications, batch, and material of materials; the production progress monitoring module uses production line sensors to capture real-time data on the completion status of production processes, process time, and capacity load, tracking the progress of the production line; the inventory status monitoring module collects real-time data on warehouse storage capacity, material inbound quantity, outbound quantity, and inventory turnover rate, monitoring the material flow status.
[0035] Analyze material identification information, collect material data, track production progress, and monitor inventory capacity and material flow status;
[0036] S2. Transmit the collected end-to-end data to the data analysis layer to identify material requirements and calculate material demand quantities, evaluate comprehensive supplier indicators, analyze multi-order delivery times and transportation costs, and plan delivery routes:
[0037] The demand identification unit uses AI algorithms to assess current inventory levels and predict future material demand trends based on identifier resolution data and historical demand data, accurately calculating material demand for different time periods and product categories. The supplier evaluation unit constructs an evaluation system and completes a comprehensive indicator evaluation based on material demand data, considering factors such as the supplier's historical on-time delivery rate, product qualification rate, delivery capacity, pricing level, and service response speed. The order analysis unit breaks down and analyzes the delivery addresses, delivery time requirements, and cargo attributes of multiple orders, calculates the transportation costs of each delivery plan based on transportation methods and unit transportation prices, and initially plans delivery routes.
[0038] S3. The AI algorithm at the decision optimization layer processes the data analysis results, adjusts production plans and scheduling strategies, intelligently regulates inventory allocation, generates delivery tasks based on order analysis results, and determines the optimal delivery route:
[0039] The production scheduling unit dynamically adjusts the process layout and capacity allocation of the production plan based on real-time identifier resolution data and production progress data, and optimizes the production scheduling strategy. The inventory adjustment unit uses AI algorithms combined with material demand forecasting results and inventory status data to calculate the safety stock threshold, realize the dynamic replenishment, allocation and clearing of inventory, and intelligently adjust the inventory configuration. The logistics planning unit determines the optimal delivery route for each order based on the preliminary route of order analysis, combined with real-time traffic conditions and logistics resource distribution, through iterative optimization using AI algorithms.
[0040] The inventory adjustment unit uses AI algorithms to intelligently adjust inventory configuration as follows: The AI algorithm combines the time and quantity dimensions of material demand forecasts with data on material shelf life, warehousing costs, and procurement cycles to establish an inventory optimization model; the model calculates the optimal inventory level, replenishment trigger point, and replenishment quantity for each category of materials to achieve dynamic and intelligent inventory adjustment, while generating inventory handling suggestions for slow-moving and near-expiry materials.
[0041] S4. The execution layer generates and sends purchase orders based on material requirements and selected suppliers, tracks order delivery status in real time and updates inventory information synchronously, and adjusts decision-making results at each stage through a manual intervention module.
[0042] The order generation unit selects the optimal supplier based on the calculated material requirements and the comprehensive evaluation results of the suppliers, creates a purchase order in a preset format, and automatically sends it to the corresponding supplier. The delivery tracking unit captures the logistics nodes and transportation locations of the purchase order in real time through the logistics tracking interface, realizing real-time tracking of the order delivery status. After the materials complete the warehousing or outbound operation, the inventory information of the inventory status monitoring module is updated synchronously. The manual intervention module allows operators to manually adjust the decision results of production plans, inventory configurations, delivery routes, and purchase orders, and the adjustment data is synchronized to all related links in real time.
[0043] S5. Analyze and learn from historical supply chain data through the machine learning module to continuously optimize decision-making algorithms at each level:
[0044] The machine learning module continuously collects historical data from the entire supply chain process, including raw data from the data acquisition layer, processing results from the data analysis layer, decision data from the decision optimization layer, and execution feedback data from the execution layer. It performs feature extraction, pattern analysis, and model training on the historical data, and iteratively adjusts the algorithm models of the data analysis layer and the decision optimization layer based on the execution feedback data, continuously optimizing algorithm parameters and improving the accuracy and adaptability of the decision-making algorithm.
[0045] Real-time data sharing and collaboration are achieved between different layers through data interaction modules, enabling end-to-end supply chain management from raw material procurement to distribution.
[0046] The data interaction module establishes a full-chain data transmission channel for the supply chain, supports multiple standardized data interfaces, and enables bidirectional data transmission and real-time sharing between the data acquisition layer, data analysis layer, decision optimization layer, and execution layer. At the same time, through this data interface, it enables system integration with upstream supplier systems and downstream customer systems, achieving synchronous interaction and collaborative processing of cross-system data.
[0047] like Figure 2As shown, an AI-based intelligent supply chain management system includes a data acquisition layer, a data analysis layer, a decision optimization layer, an execution layer, a machine learning module, and a data interaction module. The data acquisition layer includes an identifier resolution module, a material information acquisition module, a production progress monitoring module, and an inventory status monitoring module. The data analysis layer includes a demand identification unit, a supplier evaluation unit, and an order analysis unit. The decision optimization layer includes a production scheduling unit, an inventory adjustment unit, and a logistics planning unit. The execution layer includes an order generation unit, a delivery tracking unit, and a manual intervention module. The data interaction module connects to each layer and module to achieve real-time data sharing and collaboration. The machine learning module connects to the data analysis layer and the decision optimization layer to optimize decision-making algorithms. Each module executes the corresponding process of supply chain management according to preset functions, realizing intelligent management of the entire process from raw material procurement to distribution.
[0048] Furthermore, the data interaction module is equipped with multiple types of standardized data interfaces, supporting integration with upstream supplier systems and downstream customer systems to achieve cross-system data interaction; the machine learning module includes a data mining unit, a model training unit, and an algorithm optimization unit. The data mining unit is used to extract features from historical data of the supply chain, the model training unit is used to train the decision-making algorithm model, and the algorithm optimization unit is used to iteratively adjust the algorithm parameters based on the training results to continuously optimize the decision-making algorithms at each level.
[0049] Example 1
[0050] This embodiment is applied to intelligent supply chain management in the fast-moving consumer goods (FMCG) industry. The specific process is as follows:
[0051] The data acquisition layer initiates full-link data acquisition. The identifier resolution module identifies and resolves the identifier information of FMCG raw materials, extracting production, supply, and usage records. The material information acquisition module collects relevant data such as the category, specifications, and batch of raw materials and completes structured parsing. The production progress monitoring module tracks the completion status of production line processes and capacity load in real time to understand the production progress. The inventory status monitoring module collects real-time data on warehouse storage capacity, raw material and finished product inbound and outbound quantities, and inventory turnover rate to monitor the material flow status. All collected data is transmitted to each level in real time through the data interaction module.
[0052] The data analysis layer receives and processes the collected data. The demand identification unit, based on the identifier resolution data and historical demand data, uses AI algorithms to assess the current inventory levels of raw materials and finished products, predict future market material demand trends, and accurately calculate the demand for each category of raw materials at different times. The supplier evaluation unit, based on material demand data, constructs an evaluation system from the dimensions of historical delivery on-time rate, product qualification rate, delivery capacity, price level, and service response speed to complete a comprehensive evaluation of each raw material supplier. The order analysis unit breaks down and analyzes the delivery addresses and delivery time requirements of multiple finished product delivery orders, calculates the transportation costs of each delivery plan based on the transportation unit price of various transportation methods, and initially plans the delivery routes.
[0053] The decision optimization layer processes the data analysis results using AI algorithms. The production scheduling unit dynamically adjusts the process layout and capacity allocation of the production plan based on real-time identifier resolution data and production progress data, optimizing the production scheduling strategy. The inventory adjustment unit uses AI algorithms to combine material demand forecasting results and inventory status data to calculate the safety stock threshold, generate replenishment suggestions for raw materials with insufficient inventory, and plan allocation schemes for slow-moving finished products, achieving dynamic and intelligent inventory adjustment. The logistics planning unit determines the optimal delivery route for each finished product delivery order based on the preliminary route analysis of the order, combined with real-time traffic conditions and logistics resource distribution, through iterative optimization using AI algorithms.
[0054] The execution layer carries out execution operations based on the decision results. The order generation unit selects the optimal supplier for each category of raw materials based on the calculated raw material demand and the comprehensive evaluation results of the suppliers, creates purchase orders in a preset format, and automatically sends them to the corresponding suppliers. The delivery tracking unit captures the logistics nodes and transportation locations of the purchase orders in real time through the logistics tracking interface to achieve real-time tracking of the raw material delivery status. After the raw materials are put into storage and the finished products are put out of storage, the inventory information of the inventory status monitoring module is updated synchronously. Throughout the process, the decision results such as production plan, inventory configuration, and delivery route can be manually adjusted through the manual intervention module, and the adjustment data is synchronized to all related links in real time.
[0055] The machine learning module continuously collects historical data from the entire supply chain management process, including raw data, data analysis results, decision data, and execution feedback data. It performs feature extraction, pattern analysis, and model training on the historical data. Based on the execution feedback data, it iteratively adjusts the algorithm models of the data analysis layer and the decision optimization layer, optimizes the algorithm parameters, and provides more accurate algorithm support for subsequent supply chain management. The data interaction module ensures real-time data sharing and collaboration between the layers, realizing intelligent supply chain management for FMCG products from raw material procurement to finished product distribution.
[0056] Example 2
[0057] This embodiment applies to intelligent supply chain management in the electronic components industry. The specific process is as follows:
[0058] The data acquisition layer conducts end-to-end data acquisition. The identification and resolution module identifies and resolves the identification information of raw materials and semi-finished products for electronic components, obtaining a complete record of the production, supply, and use of each material. The material information acquisition module comprehensively collects and resolves relevant data such as the category, specifications, material, and batch of materials. The production progress monitoring module uses production line sensors to capture data such as the completion status and time consumption of each process on the electronic component production line in real time, accurately tracking the production progress. The inventory status monitoring module collects data such as the capacity of each storage location in the warehouse, the amount of materials entering and leaving the warehouse, and the inventory turnover rate in real time, dynamically monitoring the material flow status. All collected data is synchronized to the data analysis layer in real time through the data interaction module.
[0059] The data analysis layer professionally processes the collected data. The demand identification unit, based on the identifier resolution data and the market demand patterns of the electronic component industry, uses AI algorithms to assess the current inventory levels of various material categories, predict material demand trends for different periods in the future, and accurately calculate the material quantities required at each stage. The supplier evaluation unit, based on the material demand data and considering the supply characteristics of electronic component raw materials, completes a comprehensive evaluation of suppliers from the dimensions of historical on-time delivery rate, product qualification rate, emergency delivery capability, pricing level, and technical service capability, and selects high-quality suppliers. The order analysis unit breaks down multiple electronic component delivery orders, analyzes the delivery address, cargo attributes, and delivery time requirements of each order, and calculates the comprehensive cost of each delivery plan by combining the transportation costs and efficiency of different transportation methods, and initially plans the delivery route.
[0060] The decision optimization layer utilizes AI algorithms to optimize decisions. The production scheduling unit, based on real-time identifier resolution data and production progress data, and considering the process relationships in electronic component production, dynamically adjusts the process layout of the production plan and the capacity allocation of each production line to optimize production scheduling strategies. The inventory adjustment unit uses AI algorithms to combine material demand forecasting results and inventory status data, while also considering factors such as the shelf life and warehousing requirements of electronic components, to establish an inventory optimization model, calculate the safety stock threshold for each category of materials, and realize dynamic replenishment, allocation, and clearing of inventory, intelligently adjusting inventory configuration. The logistics planning unit, based on the preliminary path analysis of orders, combined with real-time traffic conditions, logistics network distribution, and transportation vehicle resources, uses AI algorithms for iterative optimization to determine the optimal delivery path for each order, while simultaneously planning the allocation scheme of logistics resources.
[0061] The execution layer implements various decision-making instructions. The order generation unit selects the best suppliers for each category of raw materials based on the calculated material requirements and the comprehensive evaluation results of suppliers, automatically creates purchase orders, and sends them to the corresponding supplier systems. The delivery tracking unit tracks the logistics transportation status of purchase orders in real time through the logistics tracking interface, and synchronously collects material delivery node information. After materials are put into storage and finished products are put out of storage, the inventory information in the inventory status monitoring module is updated immediately to ensure that the inventory data is real-time and accurate. In response to emergencies in the electronic component supply chain, the manual intervention module can manually adjust the decision results such as production plans, inventory configuration, and delivery routes. The adjustment data is synchronized to all related links in real time to ensure the smooth operation of the supply chain.
[0062] The data interaction module integrates the system with upstream raw material supplier systems and downstream finished product customer systems through multiple standardized data interfaces, enabling synchronous interaction and collaborative processing of cross-system data. The machine learning module continuously collects historical data from the entire supply chain management process, extracts features and analyzes patterns in the data, and iteratively adjusts the algorithm models of the data analysis layer and decision optimization layer through model training. It continuously optimizes algorithm parameters, improves the accuracy and adaptability of decision algorithms, and realizes intelligent supply chain management of electronic components from raw material procurement to finished product distribution.
[0063] Example 3
[0064] This embodiment applies to intelligent supply chain management in the food processing industry, and the specific process is as follows:
[0065] The data acquisition layer initiates full-link data acquisition. The identifier resolution module identifies and resolves the identifier information of food processing raw materials, extracting production records, supply records, quality inspection records, and usage records. The material information acquisition module collects relevant data such as the category, specifications, batch, and preservation requirements of raw materials and completes structured parsing. The production progress monitoring module tracks the completion status, capacity load, and production efficiency of each process in the food production line in real time to accurately grasp the production progress. The inventory status monitoring module collects real-time data on the storage capacity, material inbound and outbound quantities, and inventory turnover rate of raw material warehouses and finished product warehouses, while monitoring the storage environment data of fresh raw materials to dynamically grasp the material flow status. All collected data is transmitted to each level in real time through the data interaction module and shared.
[0066] The data analysis layer performs in-depth processing of the collected end-to-end data. The demand identification unit, based on the identifier resolution data and the consumption cycle characteristics of the food industry, uses AI algorithms to assess the current inventory levels of raw materials and finished products, and predicts material demand for different periods in the future based on market consumption trends, accurately calculating the real-time demand for each category of raw materials. The supplier evaluation unit, based on material demand data, constructs an evaluation system considering the preservation and delivery characteristics of food raw materials, from the dimensions of supplier historical delivery timeliness, product qualification rate, cold chain delivery capability, price level, and after-sales guarantee capability, to complete a comprehensive evaluation of each supplier's indicators. The order analysis unit breaks down and analyzes multiple food finished product delivery orders, combining the order's delivery address, delivery time, and food preservation requirements, analyzing the transportation costs and cold chain guarantee capabilities of different transportation methods, calculating the comprehensive cost of each delivery plan, and initially planning the delivery route.
[0067] The decision optimization layer uses AI algorithms to optimize supply chain decisions. The production scheduling unit, based on real-time identifier resolution data and production progress data, and considering the timeliness requirements of food processing, dynamically adjusts the process layout of the production plan and the capacity allocation of each production line to optimize production scheduling strategies and ensure production efficiency. The inventory adjustment unit uses AI algorithms to combine material demand forecasting results and inventory status data, while considering the shelf life and freshness requirements of food raw materials and finished products, to calculate the safety stock threshold and replenishment trigger point for each category of materials, and provides processing suggestions for near-expiry materials, achieving dynamic and intelligent inventory adjustment and reducing material loss. The logistics planning unit, based on the preliminary path analysis of orders, combined with real-time traffic conditions, cold chain logistics resource distribution, and distribution network layout, uses AI algorithms for iterative optimization to determine the optimal cold chain delivery path for each food finished product delivery order, ensuring the freshness requirements during food delivery.
[0068] The execution layer carries out specific execution operations based on the decision results. The order generation unit selects the optimal supplier for each category of raw materials based on the calculated raw material demand and the comprehensive evaluation results of the suppliers, automatically creates purchase orders and sends them to the corresponding suppliers, and specifies the delivery and preservation requirements of the raw materials. The delivery tracking unit captures information such as cold chain logistics nodes, transportation locations, and warehousing environments of purchase orders in real time through the logistics tracking interface to achieve real-time tracking of the delivery status of raw materials. After the raw materials are put into storage and the finished products are put out of storage, the inventory information of the inventory status monitoring module is updated synchronously to ensure the real-time and accuracy of inventory data. In response to emergencies in the food supply chain, the decision results such as production plans, inventory configurations, and delivery routes can be manually adjusted through the manual intervention module, and the adjusted data is synchronized to all related links in real time.
[0069] The data interaction module enables bidirectional data transmission and real-time sharing between different layers within the system, as well as between the system and upstream supplier systems and downstream customer systems. The machine learning module continuously collects historical data from the entire food processing supply chain management process, extracts features, analyzes patterns, and trains models based on the data. Iteratively adjusts the algorithm models of the data analysis layer and decision optimization layer based on execution feedback data, continuously optimizes algorithm parameters, and improves the adaptability of the algorithm to food industry supply chain management. This enables intelligent supply chain management of the entire food processing process, from raw material procurement to finished product distribution, ensuring the efficient and stable operation of the food supply chain.
Claims
1. An AI-based intelligent supply chain management method, characterized by, Includes the following steps: S1. Collect data from the entire supply chain through multiple modules in the data acquisition layer, parse material identification information, collect material data, track production progress, and monitor inventory capacity and material flow status. S2. The collected end-to-end data is transmitted to the data analysis layer to identify material requirements and calculate the material requirements, evaluate the comprehensive indicators of suppliers, analyze the delivery time and transportation costs of multiple orders, and plan delivery routes. S3. The AI algorithm in the decision optimization layer processes the data analysis results, adjusts the production plan and scheduling strategy, intelligently adjusts the inventory configuration, generates delivery tasks and determines the optimal delivery route based on the order analysis results; S4. The execution layer generates and sends purchase orders based on material requirements and selected suppliers, tracks the order delivery status in real time and updates inventory information synchronously, and adjusts the decision-making results of each link through the manual intervention module. S5. Through the machine learning module, historical supply chain data is analyzed and learned to continuously optimize decision-making algorithms at each level. Data sharing and collaboration between levels are achieved in real time through the data interaction module, realizing full-process management of the supply chain from raw material procurement to distribution.
2. The AI-based intelligent supply chain management method of claim 1, wherein, In step S1, the specific method for collecting supply chain data through multiple modules of the data acquisition layer is as follows: the identification and parsing module identifies and parses the identification information of materials to extract production records, supply records, and usage records of materials; the material information acquisition module comprehensively collects and structures data related to the category, specifications, batch, and material of materials; and the production progress monitoring module uses production line sensing devices to capture data on the completion status of production processes, process time, and capacity load in real time to track the progress of the production line. The inventory status monitoring module collects real-time data on warehouse storage capacity, material inbound volume, outbound volume, and inventory turnover rate to monitor the material flow status.
3. The AI-based intelligent supply chain management method of claim 1, wherein, In step S2, the data analysis layer identifies material requirements and calculates the required quantity of materials, evaluates the comprehensive indicators of suppliers, analyzes the delivery time and transportation costs of multiple orders, and plans delivery routes. Specifically, the demand identification unit, based on identifier resolution data and historical demand data, uses AI algorithms to assess current inventory levels, predict future material demand trends, and accurately calculate the material requirements for different time periods and product categories. The supplier evaluation unit, based on material demand data, constructs an evaluation system and completes a comprehensive indicator evaluation from the dimensions of supplier historical delivery on-time rate, product qualification rate, delivery capacity, pricing level, and service response speed. The order analysis unit breaks down and analyzes the delivery addresses, delivery time requirements, and cargo attributes of multiple orders, calculates the transportation costs of each delivery plan based on transportation methods and unit transportation prices, and initially plans delivery routes.
4. The AI-based intelligent supply chain management method of claim 1, wherein, In step S3, the decision optimization layer AI algorithm processes the data analysis results, adjusts the production plan and scheduling strategy, and intelligently adjusts the inventory configuration. The specific method for generating delivery tasks and determining the optimal delivery route based on the order analysis results is as follows: the production scheduling unit dynamically adjusts the process layout and capacity allocation of the production plan based on real-time identifier parsing data and production progress data, and optimizes the production scheduling strategy; the inventory adjustment unit uses AI algorithms combined with material demand forecasting results and inventory status data to calculate the safety stock threshold, realize the dynamic replenishment, allocation and clearing of inventory, and intelligently adjust the inventory configuration. Based on the preliminary route analysis of orders, combined with real-time traffic conditions and logistics resource distribution, the logistics planning unit uses AI algorithms to iteratively optimize and determine the optimal delivery route for each order.
5. The AI-based intelligent supply chain management method of claim 1, wherein, In step S4, the execution layer generates and sends purchase orders based on material requirements and selected suppliers, tracks the order delivery status in real time and updates inventory information synchronously. The method of adjusting the decision results of each link through the manual intervention module is as follows: the order generation unit selects the best supplier based on the calculated material requirements and the comprehensive evaluation results of the supplier, creates a purchase order in a preset format and automatically sends it to the corresponding supplier. The delivery tracking unit captures the logistics nodes and transportation location of purchase orders in real time through the logistics tracking interface, enabling real-time tracking of order delivery status. After materials complete the warehousing or outbound operation, the inventory information of the inventory status monitoring module is updated synchronously. The manual intervention module allows operators to manually adjust the decision results of production plans, inventory configurations, delivery routes, and purchase orders, and the adjusted data is synchronized to all related links in real time.
6. The AI-based intelligent supply chain management method as described in claim 1, characterized in that, In step S5, the method for achieving real-time data sharing and collaboration through the data interaction module is as follows: The data interaction module builds a full-chain data transmission channel for the supply chain, supports multiple standardized data interfaces, and realizes bidirectional data transmission and real-time sharing between the data acquisition layer, data analysis layer, decision optimization layer, and execution layer; at the same time, it realizes system integration with upstream supplier systems and downstream customer systems through this data interface, and achieves synchronous interaction and collaborative processing of cross-system data.
7. The AI-based intelligent supply chain management method as described in claim 1, characterized in that, In step S5, the machine learning module analyzes and learns from historical supply chain data to continuously optimize the decision-making algorithms at each layer. Specifically, the machine learning module continuously collects historical data from the entire supply chain process, including raw data from the data collection layer, processing results from the data analysis layer, decision data from the decision optimization layer, and execution feedback data from the execution layer. It performs feature extraction, pattern analysis, and model training on the historical data. Based on the execution feedback data, iteratively adjusts the algorithm models of the data analysis layer and the decision optimization layer, continuously optimizes the algorithm parameters, and improves the accuracy and adaptability of the decision-making algorithms.
8. The AI-based intelligent supply chain management method as described in claim 4, characterized in that, The inventory adjustment unit uses AI algorithms to intelligently adjust inventory configuration as follows: The AI algorithm combines the time and quantity dimensions of material demand forecasts with data on material shelf life, warehousing costs, and procurement cycles to establish an inventory optimization model; the model calculates the optimal inventory level, replenishment trigger point, and replenishment quantity for each category of materials to achieve dynamic and intelligent inventory adjustment, while generating inventory handling suggestions for slow-moving and near-expiry materials.
9. An AI-based intelligent supply chain management system, used to execute the method according to any one of claims 1 to 8, characterized in that, It comprises a data acquisition layer, a data analysis layer, a decision optimization layer, an execution layer, a machine learning module, and a data interaction module. The data acquisition layer includes an identifier resolution module, a material information acquisition module, a production progress monitoring module, and an inventory status monitoring module. The data analysis layer includes a demand identification unit, a supplier evaluation unit, and an order analysis unit. The decision optimization layer includes a production scheduling unit, an inventory adjustment unit, and a logistics planning unit. The execution layer includes an order generation unit, a delivery tracking unit, and a manual intervention module. The data interaction module connects to each layer and module to achieve real-time data sharing and collaboration. The machine learning module connects to the data analysis layer and the decision optimization layer to optimize decision-making algorithms. Each module executes the corresponding process of supply chain management according to preset functions, realizing intelligent management of the entire process from raw material procurement to distribution.
10. The AI-based intelligent supply chain management system as described in claim 9, characterized in that, The data interaction module is equipped with multiple types of standardized data interfaces, supporting integration with upstream supplier systems and downstream customer systems to achieve cross-system data interaction; the machine learning module includes a data mining unit, a model training unit, and an algorithm optimization unit. The data mining unit is used to extract features from historical data of the supply chain, the model training unit is used to train the decision-making algorithm model, and the algorithm optimization unit is used to iteratively adjust the algorithm parameters based on the training results to continuously optimize the decision-making algorithm at each level.