Resource optimization configuration system of intelligent supply chain

Abstract

The invention discloses a resource optimization configuration system of an intelligent supply chain, and relates to the technical field of supply chain resource optimization, and the system comprises a manufacturing production module, a supply replenishment module, a distribution demand module and a logistics service module, formulates a manufacturer prediction distribution strategy, obtains raw material inventory data, formulates a supplier replenishment strategy, and carries out the resource optimization of the supply chain. According to historical sales data and consumer preference data, a demand prediction model is established, temporary order adding goods data is acquired, a manufacturer temporary distribution strategy is formulated, a transportation tool distribution strategy and a multi-order combination strategy are formulated, and through integration of technologies such as the Internet of Things and big data, accurate productivity evaluation, demand prediction and rapid response are realized. Through hierarchical manufacturer management and an intelligent distribution strategy, the resource utilization efficiency is improved, the operation cost is reduced, meanwhile, logistics transportation and order processing are optimized, rapid delivery is ensured, the transparency and controllability of a supply chain are enhanced, the customer satisfaction is improved, and sustainable development of the supply chain is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain resource optimization, and in particular to a resource optimization configuration system for a smart supply chain. Background Art

[0002] With the rapid development of the global economy and the rise of e-commerce, supply chain management is facing unprecedented challenges and opportunities. The traditional supply chain management model is unable to cope with complex and changing market demands, improve resource utilization efficiency, and reduce operating costs. Therefore, integrating advanced technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to achieve intelligent, automated, and efficient supply chains has become an important topic in today's supply chain technology field.

[0003] At present, a Chinese invention patent with application number CN202311188336.0 discloses a smart supply chain system, including a business entry layer, a business function layer, a shared service layer, an internal business layer and a technical base layer; the business entry layer, the business function layer, the shared service layer and the internal business layer jointly realize the information carrying and resource aggregation of the energy industry supply chain; the business entry layer includes an interactive application module and a demand perception module, the interactive application module is used to gather all users, and the interactive application module has a user unit, and the demand perception module is used to detect users with application needs in the user unit and establish interactive channels between related users.

[0004] This invention makes it difficult to predict the quantity of goods for replenishment and to classify product production according to the manufacturer's production capacity, which affects the product replenishment efficiency and production efficiency and causes waste of production capacity. Summary of the invention

[0005] The technical problem solved by the present invention is that it is difficult in the related art to predict the quantity of goods for replenishment and to classify product production according to the manufacturer's production capacity, which affects the replenishment efficiency and production efficiency of the products and causes waste of production capacity.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A resource optimization configuration system for a smart supply chain, including a manufacturing and production module, a supply and replenishment module, a distribution demand module, and a logistics service module: The manufacturing production module is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer and set weights respectively, calculate the weight value corresponding to each manufacturer, divide the manufacturer level according to the weight value, set the manufacturer's supply cycle according to the demand forecasting model of the distribution demand module, calculate the supply time and formulate the manufacturer's forecast allocation strategy; The supply replenishment module is used to obtain raw material inventory data, calculate the raw material demand quantity data based on the delivery time and supply quantity input by the manufacturing production module, and formulate supplier replenishment strategies based on the raw material inventory data and raw material inventory data; The distribution demand module is used to establish a demand forecasting model based on historical sales data and consumer preference data, obtain temporary order product data, the temporary order product data includes the temporary order product quantity and the temporary order product time limit, and formulate a manufacturer temporary allocation strategy based on the temporary order product data, manufacturer level and manufacturer forecast allocation strategy; The logistics service module is used to obtain the product batch number, product size, product quantity, product time limit and order delivery address from the distribution demand module, and formulate a transportation tool allocation strategy and a multi-order merging strategy.

[0007] Preferably, the manufacturing production module includes a capacity calculation unit, a capacity evaluation unit and a demand forecasting unit: The capacity calculation unit is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer. The on-time delivery rate is the ratio of the number of on-time deliveries to the total number of deliveries, the product quality qualification rate is the ratio of the number of qualified products to the total number of products, and the cost reduction rate is the ratio of the cost reduction amount to the initial cost amount; The capacity assessment unit is used to set weights for on-time delivery rate, product quality qualification rate and cost reduction rate, calculate the weight value corresponding to each manufacturer, and divide the manufacturers into levels according to the weight values. The manufacturer levels are divided into first-level manufacturers, second-level manufacturers, third-level manufacturers and fourth-level manufacturers.

[0008] Preferably, the demand forecasting unit is used to set the manufacturer's delivery cycle and obtain the production cycle according to the demand forecast commodity data generated by the demand forecasting model, and calculate the delivery time. The calculation expression of the delivery time is: Delivery time = the start time of the next manufacturer's delivery cycle + production cycle + manufacturer's delivery cycle; Develop manufacturer forecast allocation strategies based on supply time, supply volume, and manufacturer tier.

[0009] Preferably, the manufacturer's forecast allocation strategy is: Set the supply time threshold to divide the demand forecast product data into forecast urgent output product data, forecast medium-speed output product data and forecast slow output product data; set the demand quantity threshold to divide the demand forecast product data into forecast high demand output product data, forecast medium demand output product data and forecast low demand output product data; give priority to allocating forecast urgent output product data and forecast high demand output product data to first-tier manufacturers, give priority to allocating forecast medium-speed output product data and forecast medium demand output product data to second-tier manufacturers, and give priority to allocating forecast slow output product data and forecast low demand output product data to third-tier manufacturers; When the secondary manufacturer is unable to produce the allocated output goods, if the primary manufacturer has already produced the allocated output goods, the output goods data that the secondary manufacturer cannot produce will be allocated to the primary manufacturer; When the third-level manufacturer is unable to produce the allocated output goods, if the second-level manufacturer has already produced the allocated output goods, the output goods data that the third-level manufacturer cannot produce will be allocated to the second-level manufacturer; When the first-tier manufacturer, the second-tier manufacturer and the third-tier manufacturer are all unable to produce the allocated output goods, the output goods data that the first-tier manufacturer, the second-tier manufacturer and the third-tier manufacturer cannot produce are allocated to the fourth-tier manufacturer; A manufacturer forecast allocation table is generated according to the manufacturer forecast allocation strategy, and the manufacturer forecast allocation table is sent to the manufacturer.

[0010] Preferably, the supply replenishment module includes a demand calculation unit and a material replenishment unit: The demand calculation unit is used to obtain raw material inventory data from suppliers, and calculate the raw material demand quantity data according to the delivery time and supply quantity input by the manufacturing production module. The calculation expression of the raw material demand quantity data is: Raw material demand quantity data = supply quantity × quantity of raw materials required for the corresponding goods; The material replenishment unit is used to formulate a manufacturer forecast allocation strategy according to the supply time, supply quantity and manufacturer level. The manufacturer forecast allocation strategy is: Set the safety stock quantity, and calculate the replenishment quantity data according to the safety stock quantity, raw material inventory data and raw material demand quantity data. The calculation expression of the replenishment quantity data is: Replenishment quantity data = raw material demand quantity data - raw material inventory data + safety inventory; Establish a mapping relationship between replenishment quantity data, raw material names and delivery time; Integrate the mapping relationship and output it as a supplier replenishment table, and send the supplier replenishment table to the supplier.

[0011] Preferably, the distribution demand module includes a demand forecasting unit and a temporary allocation unit: The demand forecasting unit is used to establish a demand forecasting model based on historical sales data and consumer preference data, wherein the historical sales data includes sales volume, sales time, sales category, and consumer preference data includes consumer preference category, consumer preference purchase time, and consumer consumption amount. The historical sales data and consumer preference data are cleaned, and a demand forecasting model is established using time series analysis. Demand forecasting product data is generated based on the manufacturer's supply cycle. The demand forecasting product data includes the forecasted type of demanded products, the forecasted quantity of products, and the forecasted demand time of products. The temporary allocation unit is used to obtain temporary order product data, which includes the temporary order product quantity and the temporary order product time limit, and formulate the manufacturer's temporary allocation strategy based on the temporary order product data, manufacturer level and manufacturer forecast allocation strategy.

[0012] Preferably, the manufacturer's temporary allocation strategy is: Set the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer, and divide the temporary order addition data into temporary urgent output data, temporary medium-speed output data and temporary slow output data according to the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer. Allocate the temporary urgent output data to the highest-level manufacturer who has completed the production task of the manufacturer forecast allocation table, allocate the temporary medium-speed output data to the manufacturer at the next level of the highest-level manufacturer, and allocate the temporary slow output data to the manufacturers at the next two levels of the highest-level manufacturer; A manufacturer temporary allocation table is generated according to the manufacturer temporary allocation strategy, and the manufacturer temporary allocation table is sent to the manufacturer.

[0013] Preferably, the logistics service module includes an information management unit, a transportation allocation unit, an order merging unit and a relationship establishing unit; The information management unit is used to obtain and store the transported goods information, which includes the goods batch number, goods size, goods quantity, goods delivery time limit and order delivery address; The transport allocation unit is used to formulate a transport allocation strategy and generate a transport allocation table according to the size and quantity of goods; The order merging unit is used to formulate a multi-order merging strategy and generate an order merging allocation table according to the goods delivery time limit and the order delivery address; The relationship establishing unit is used to establish a corresponding relationship between the product batch number, the transportation tool allocation table and the order merging allocation table, and send the corresponding relationship to the logistics service provider.

[0014] Preferably, the transport allocation strategy is: Setting a threshold value for the size of goods and a threshold value for the number of goods, dividing the transported goods information into large-size goods, large-quantity goods, small-size goods and small-quantity goods according to the threshold value for the size of goods and the threshold value for the number of goods, setting the type of transport means, the type of transport means including large transport equipment and small transport equipment, allocating large-size goods and large-quantity goods to large transport equipment, and allocating small-size goods and small-quantity goods to small transport equipment; Generate a transport allocation table based on the transport allocation strategy.

[0015] Preferably, the multi-order merging strategy is: Obtain an order delivery information table, the order delivery information table including a standard delivery area and a standard delivery time, extract the transport goods information that overlaps with the standard delivery area in the order delivery address and divide it into a number of same-area order information, extract the transport goods information corresponding to the delivery time limit included in the standard delivery time and divide it into a number of same-time-limit order information, and extract the transport goods information with repeated product batch numbers in the same-area order information and the same-time-limit order information; Generate an order merging allocation table based on the multi-order merging strategy.

[0016] Beneficial effects of the invention: The invention realizes accurate assessment of production capacity, demand forecasting and rapid response by integrating technologies such as the Internet of Things and big data. It improves resource utilization efficiency and reduces operating costs through hierarchical manufacturer management and intelligent allocation strategies. At the same time, it optimizes logistics and order processing to ensure rapid delivery, enhances supply chain transparency and controllability, improves customer satisfaction, and promotes sustainable development of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A basic flow chart of a resource optimization configuration system for a smart supply chain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0019] Example 1, reference Figure 1 , provides a resource optimization configuration system for a smart supply chain, including manufacturing and production modules, supply and replenishment modules, distribution demand modules and logistics service modules: The manufacturing production module is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer and set weights respectively, calculate the corresponding weight value for each manufacturer, divide the manufacturer levels according to the weight value, set the manufacturer's supply cycle according to the demand forecasting model of the distribution demand module, calculate the delivery time and formulate the manufacturer's forecast allocation strategy.

[0020] The supply replenishment module is used to obtain raw material inventory data, calculate the raw material demand quantity data based on the delivery time and supply quantity input by the manufacturing production module, and formulate supplier replenishment strategies based on the raw material inventory data.

[0021] The distribution demand module is used to establish a demand forecasting model based on historical sales data and consumer preference data, obtain temporary order product data, which includes the temporary order product quantity and temporary order product time limit, and formulate the manufacturer's temporary allocation strategy based on the temporary order product data, manufacturer level and manufacturer forecast allocation strategy.

[0022] The logistics service module is used to obtain the product batch number, product size, product quantity, product time limit and order delivery address from the distribution demand module, and formulate transportation tool allocation strategy and multi-order merging strategy.

[0023] The manufacturing production module includes a capacity calculation unit, a capacity assessment unit, and a demand forecasting unit: The capacity calculation unit is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer. The on-time delivery rate is the ratio of the number of on-time deliveries to the total number of deliveries, the product quality qualification rate is the ratio of the number of qualified products to the total number of products, and the cost reduction rate is the ratio of the cost reduction amount to the initial cost amount.

[0024] The capacity calculation unit provides detailed and accurate data support for subsequent capacity evaluation by accurately calculating each manufacturer's on-time delivery rate, product quality qualification rate and cost reduction rate. These data not only reflect the actual performance of manufacturers in terms of delivery time, product quality and cost control, but also provide a basis for decision-making for supply chain managers. By setting a unified calculation method and standard, it ensures the fairness and comparability of evaluations between different manufacturers, helps supply chain managers understand the actual situation of each manufacturer more objectively, and lays the foundation for subsequent capacity evaluation and allocation strategy formulation.

[0025] The capacity assessment unit is used to set the weights of on-time delivery rate, product quality qualification rate and cost reduction rate, calculate the weight value corresponding to each manufacturer, and divide the manufacturer levels according to the weight values. The weights are: Cost reduction rate: 0.2, on-time delivery rate: 0.35, product quality pass rate: 0.45.

[0026] The weight value corresponding to each manufacturer is calculated, and the manufacturer levels are divided according to the weight values. The manufacturer levels are divided into: First-tier manufacturer: weight value>=0.9.

[0027] Secondary manufacturers: 0.8<=weight value<0.9.

[0028] Third-tier manufacturers: 0.7<=weight value<0.8.

[0029] Level 4 manufacturer: weight value <0.7.

[0030] The capacity assessment unit achieves a comprehensive assessment of the manufacturer's comprehensive capabilities by setting weights for on-time delivery rate, product quality qualification rate and cost reduction rate, and calculating the corresponding weight value for each manufacturer. This assessment method not only takes into account the manufacturer's performance in delivery time, product quality and cost control, but also highlights the importance of different factors through weight setting, making the assessment results more scientific and reasonable. Dividing manufacturers into levels according to weight values ​​helps supply chain managers to carry out differentiated management and resource allocation for different manufacturers. First-level manufacturers, as high-quality resources, will have priority in obtaining orders and production resources, while low-level manufacturers need to actively strive for more orders and production opportunities while improving their own capabilities.

[0031] The demand forecasting unit is used to set the manufacturer's delivery cycle and obtain the production cycle according to the demand forecast commodity data generated by the demand forecasting model, and calculate the delivery time. The calculation expression of the delivery time is: Delivery time = start time of the next manufacturer's delivery cycle + production cycle + manufacturer's delivery cycle Develop manufacturer forecast allocation strategies based on supply time, supply volume, and manufacturer tier.

[0032] The manufacturer's forecast allocation strategy is: Set the supply time threshold to divide the demand forecast product data into predicted urgent output product data, predicted medium-speed output product data and predicted slow output product data; set the demand quantity threshold to divide the demand forecast product data into predicted high-demand output product data, predicted medium-demand output product data and predicted low-demand output product data; give priority to allocating the predicted urgent output product data and predicted high-demand output product data to first-tier manufacturers, give priority to allocating the predicted medium-speed output product data and predicted medium-demand output product data to second-tier manufacturers, and give priority to allocating the predicted slow-speed output product data and predicted low-demand output product data to third-tier manufacturers.

[0033] When the secondary manufacturer is unable to produce the allocated output goods, if the primary manufacturer has completed the production of the allocated output goods, the output goods data that the secondary manufacturer is unable to produce will be allocated to the primary manufacturer.

[0034] When the third-level manufacturer is unable to produce the allocated output goods, if the second-level manufacturer has completed the production of the allocated output goods, the output goods data that the third-level manufacturer is unable to produce will be allocated to the second-level manufacturer.

[0035] When the first-level manufacturer, the second-level manufacturer and the third-level manufacturer are all unable to produce the allocated output goods, the output goods data that the first-level manufacturer, the second-level manufacturer and the third-level manufacturer cannot produce are allocated to the fourth-level manufacturer.

[0036] A manufacturer forecast allocation table is generated according to the manufacturer forecast allocation strategy, and the manufacturer forecast allocation table is sent to the manufacturer.

[0037] The demand forecasting unit generates demand forecasting product data based on the demand forecasting model, which can more accurately reflect the changing trend of future market demand, and provide supply chain managers with an important basis for formulating production plans, adjusting inventory levels and optimizing resource allocation. It formulates manufacturer forecast allocation strategies based on delivery time, supply volume and manufacturer level, and realizes flexible allocation of production tasks. By giving priority to meeting urgent and high-demand orders and fully considering the manufacturer's production capacity and delivery cycle, the smooth completion of production tasks and the efficient operation of the supply chain are ensured. When the second- or third-tier manufacturers are unable to produce the allocated output goods, the demand forecasting unit can quickly adjust the allocation strategy and transfer the production tasks to higher-level manufacturers. This flexible risk response mechanism helps reduce the risk of production delays and supply chain disruptions, and ensures the stability and reliability of the supply chain.

[0038] The manufacturing and production module has significantly improved the response speed and flexibility of the supply chain through refined management and intelligent allocation. It can adjust production plans and allocation strategies in a timely manner according to changes in market demand and manufacturer capabilities to ensure the smooth completion of production tasks and efficient operation of the supply chain. By comprehensively evaluating the capabilities and performance of manufacturers and flexibly allocating them based on demand forecast results, the manufacturing and production module has achieved optimal allocation of supply chain resources, helping to reduce resource waste and production costs, and improving the overall efficiency and competitiveness of the supply chain. By ensuring optimization in product quality, delivery punctuality, and cost control, the manufacturing and production module can improve customer satisfaction and loyalty, help companies establish a good brand image and word-of-mouth effect, and lay a solid foundation for the long-term development of the company.

[0039] The supply replenishment module includes the demand calculation unit and the material replenishment unit: The demand calculation unit is used to obtain raw material inventory data from suppliers, and calculate the raw material demand quantity data according to the delivery time and supply quantity input by the manufacturing production module. The calculation expression of the raw material demand quantity data is: Raw material demand quantity data = supply quantity × quantity of raw materials required for the corresponding goods The demand calculation unit receives the delivery time and quantity information input by the manufacturing production module, and combines it with the quantity of raw materials required for the production of each product to accurately calculate the demand quantity data of raw materials, which helps to avoid surplus or shortage of raw materials and ensure the smooth progress of the production process. By obtaining raw material inventory data from suppliers in real time and connecting it with the needs of the manufacturing production module, the demand calculation unit promotes close coordination between the upstream and downstream of the supply chain, helps to reduce delays and errors in information transmission, and improves the response speed and flexibility of the entire supply chain. Based on accurate raw material demand forecasts, companies can arrange production plans more reasonably to ensure that sufficient raw materials are purchased at the right time to meet production needs, which helps to reduce production costs, improve production efficiency, and enhance the company's market competitiveness.

[0040] The material replenishment unit is used to formulate a manufacturer forecast allocation strategy based on the supply time, supply quantity and manufacturer level. The manufacturer forecast allocation strategy is: Set the safety stock quantity, and calculate the replenishment quantity data according to the safety stock quantity, raw material inventory data and raw material demand quantity data. The calculation expression of the replenishment quantity data is: Replenishment quantity data = raw material demand quantity data - raw material inventory data + safety stock quantity Establish a mapping relationship between replenishment quantity data, raw material name and delivery time.

[0041] Integrate the mapping relationship and output it as a supplier replenishment table, and send the supplier replenishment table to the supplier.

[0042] The material replenishment unit ensures the stability of raw material supply by setting safety stock and calculating the replenishment quantity based on raw material inventory data and demand quantity data. When the raw material inventory is lower than the safety stock, the replenishment operation is triggered in time to avoid production interruptions caused by raw material shortages. Refined replenishment management: By establishing a mapping relationship between replenishment quantity data, raw material name and delivery time, the material replenishment unit realizes refined management of the replenishment process. This management method helps enterprises better grasp the replenishment progress and inventory status, and adjust the replenishment strategy in time to cope with market changes and uncertainties in production demand. After integrating the mapping relationship and outputting it as a supplier replenishment table, the material replenishment unit can automatically send replenishment information to suppliers. This automated information transmission method reduces manual intervention and errors, improves supplier management efficiency, and strengthens communication and collaboration between enterprises and suppliers.

[0043] The supply replenishment module achieves accurate prediction of raw material demand and timely replenishment through the synergy of the demand calculation unit and the material replenishment unit. This efficient supply chain management model helps to improve the response speed, flexibility and stability of the entire supply chain, reduce inventory costs, and improve production efficiency. By accurately calculating the demand quantity of raw materials and reasonably setting the safety inventory, the supply replenishment module can ensure the optimal allocation of raw material resources. This helps to reduce resource waste and production costs, improve resource utilization efficiency, and create greater economic benefits for enterprises. The implementation of the supply replenishment module helps enterprises better respond to market changes and uncertainties in customer demand. By improving the collaborative efficiency, stability and flexibility of the supply chain, enterprises can respond to market changes more quickly and launch products and services that meet market demand, thereby enhancing the market competitiveness and sustainable development capabilities of enterprises.

[0044] The distribution demand module includes a demand forecasting unit and a temporary allocation unit: The demand forecasting unit is used to establish a demand forecasting model based on historical sales data and consumer preference data, the historical sales data includes sales volume, sales time and product type, the consumer preference data includes consumer preferred product type, consumer preferred purchase time and consumer consumption amount, the historical sales data and consumer preference data are cleaned, and the demand forecasting model is established using time series analysis, and demand forecast product data is generated according to the manufacturer's supply cycle, and the demand forecast product data includes demand product forecast type, product forecast quantity and product forecast demand time.

[0045] The demand forecasting unit integrates historical sales data and consumer preference data, and uses advanced methods such as time series analysis to establish an efficient and accurate demand forecasting model to capture market dynamics and predict the quantity and time of consumer demand for different types of goods in the future, providing strong support for the company's production and sales decisions. Based on the demand forecast results, the company can arrange inventory management and production plans more accurately, while avoiding inventory backlogs, ensuring sufficient supply of key goods to meet market demand. This helps reduce inventory costs, improve capital turnover, and enhance the company's market competitiveness. By monitoring market changes in real time and quickly adjusting the forecasting model, the demand forecasting unit can help companies respond quickly to changes in market demand, which helps companies seize market opportunities, quickly launch new products that meet consumer needs, and further consolidate and expand market share.

[0046] The temporary allocation unit is used to obtain temporary order product data, wherein the temporary order product data includes the temporary order product quantity and the temporary order product time limit, and formulate the manufacturer's temporary allocation strategy according to the temporary order product data, manufacturer level and manufacturer forecast allocation strategy.

[0047] The manufacturer's temporary allocation strategy is: Set the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer; divide the temporary order addition data into temporary urgent output data, temporary medium-speed output data and temporary slow output data according to the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer; allocate the temporary urgent output data to the highest-level manufacturer who has completed the production task of the manufacturer forecast allocation table, allocate the temporary medium-speed output data to the manufacturer at the next level of the highest-level manufacturer, and allocate the temporary slow output data to the manufacturers at the next two levels of the highest-level manufacturer.

[0048] A manufacturer temporary allocation table is generated according to the manufacturer temporary allocation strategy, and the manufacturer temporary allocation table is sent to the manufacturer.

[0049] The temporary allocation unit specializes in processing data on temporary order items. Based on this data, manufacturer level and forecast allocation strategy, it quickly formulates manufacturer temporary allocation strategy. This flexible allocation mechanism ensures that the company can respond quickly to customers' temporary needs and avoid customer loss due to production delays. By setting the time limit threshold for temporary order items and the maximum amount of temporary manufacturing goods by the manufacturer, the temporary allocation unit can reasonably divide the priority and urgency of temporary order data. At the same time, according to the manufacturer level and current production load, temporary order items of different priorities are allocated to suitable manufacturers, which helps to make full use of manufacturer resources and improve production efficiency and resource utilization. By responding to customers' temporary needs in a timely manner and arranging production reasonably, the temporary allocation unit helps to improve customer satisfaction and loyalty, helps companies maintain their dominant position in the fierce market competition, and attracts more potential customers.

[0050] The distribution demand module achieves accurate prediction and flexible response to market demand and temporary demand through the synergy of the demand forecasting unit and the temporary allocation unit. This efficient supply chain management model helps to improve the response speed, flexibility and stability of the entire supply chain, reduce operating costs, and improve production efficiency. By accurately predicting market demand and reasonably arranging production plans for temporary demand, the distribution demand module can ensure the optimal allocation of enterprise resources, help reduce resource waste and production costs, improve resource utilization efficiency, and create greater economic benefits for the enterprise. The implementation of the distribution demand module helps enterprises better grasp market opportunities and respond to market challenges. By accurately predicting market demand and quickly responding to customer temporary needs, enterprises can launch products and services that better meet market demand and improve customer satisfaction and loyalty, which will further enhance the market competitiveness of enterprises and promote their sustainable development.

[0051] The logistics service module includes information management unit, transportation allocation unit, order consolidation unit and relationship establishment unit.

[0052] The information management unit is used to obtain and store the transported goods information, which includes the goods batch number, goods size, goods quantity, goods delivery time limit and order delivery address.

[0053] The information management unit is the data foundation of the logistics service module. It is responsible for comprehensively and accurately collecting relevant information of the transported goods, including the batch number, size, quantity, delivery time limit and order delivery address of the goods. This information provides the necessary data support for subsequent logistics operations. By storing this information in a unified format and standard, the information management unit ensures the consistency and accessibility of the data, and facilitates the data processing and analysis of subsequent units. The information management unit also promotes the transparency of information in the logistics process, allowing each logistics link to obtain the required information in real time, thereby improving the overall logistics efficiency.

[0054] The transport allocation unit is used to formulate a transport allocation strategy and generate a transport allocation table based on the size and quantity of goods.

[0055] The transport allocation strategy is: A cargo size threshold and a cargo quantity threshold are set, and the transport cargo information is divided into large-size cargo, large-quantity cargo, small-size cargo and small-quantity cargo according to the cargo size threshold and the cargo quantity threshold. The types of transport vehicles are set, and the types of transport vehicles include large-scale transport equipment and small-scale transport equipment. Large-size cargo and large-quantity cargo are assigned to large-scale transport equipment, and small-size cargo and small-quantity cargo are assigned to small-scale transport equipment.

[0056] Generate a transport allocation table based on the transport allocation strategy.

[0057] According to the size and quantity of the goods, the transport allocation unit can formulate a reasonable transport tool allocation strategy to ensure that large and small transport equipment are fully utilized and avoid waste of resources. Through reasonable allocation of transport tools, the waiting time and empty load rate in the transportation process can be reduced, the transportation efficiency can be improved, and the logistics cost can be reduced. The strategy setting of the transport allocation unit is flexible, and the size threshold and quantity threshold of the goods can be adjusted according to the actual situation to meet the transportation needs of different types of goods.

[0058] The order merging unit is used to formulate a multi-order merging strategy and generate an order merging allocation table according to the goods delivery time limit and the order delivery address.

[0059] The multi-order merging strategy is: Get the order delivery information table, which includes the standard delivery area and standard delivery time. Extract the transport product information that overlaps with the standard delivery area in the order delivery address and divide it into several same-area order information. Extract the transport product information corresponding to the delivery time limit included in the standard delivery time and divide it into several same-time limit order information. Extract the transport product information with repeated product batch numbers in the same-area order information and the same-time limit order information.

[0060] Generate an order merging allocation table based on the multi-order merging strategy.

[0061] Through the multi-order merging strategy, the order merging unit can effectively integrate the transportation goods information of multiple orders, thereby reducing the number of delivery times and delivery costs. The merged orders can be delivered faster, shortening customer waiting time and improving customer satisfaction and loyalty. Based on the order delivery information table and the analysis of standard delivery areas and durations, the order merging unit can intelligently formulate merging strategies to improve the scientificity and accuracy of decision-making.

[0062] The relationship establishment unit is used to establish the corresponding relationship between the product batch number, the transportation tool allocation table and the order merger allocation table, and send the corresponding relationship to the logistics service provider.

[0063] The relationship building unit integrates the correspondence between the product batch numbers, transport tool allocation tables and order consolidation allocation tables, and sends them to the logistics service provider, ensuring that each link in the logistics process can be closely connected to reduce errors and delays. By clarifying the associations and dependencies between each link, logistics service providers can perform logistics tasks more efficiently and improve overall logistics execution efficiency. The relationship building unit also promotes the synchronous updating of logistics information between each link, ensuring the real-time and accuracy of the logistics process.

[0064] The logistics service module optimizes and automates the logistics process and improves logistics efficiency and benefits through the collaborative work of various units. Whether it is the reasonable allocation of transportation tools, the intelligent merging of orders or the real-time synchronization of logistics information, it helps to reduce logistics costs and improve customer satisfaction. The various strategy settings in the module are flexible and diverse, and can be adjusted and optimized according to actual conditions to adapt to the transportation needs of different types of goods and changes in the logistics environment. By comprehensively collecting, integrating and analyzing logistics information, the logistics service module has promoted the development of logistics informatization and intelligence, which not only improves the scientificity and accuracy of logistics management, but also provides strong support for the innovative development of the logistics industry.

[0065] The present invention significantly shortens the product cycle from production to delivery through accurate capacity calculation and forecast allocation, as well as rapid supply replenishment and distribution demand response. In particular, for urgent or high-demand products, the system can quickly adjust production plans and logistics arrangements to ensure on-time delivery, improve the overall response speed of the supply chain, and classify according to multi-dimensional indicators such as the manufacturer's capacity, product quality and cost efficiency, and formulate forecast allocation and temporary allocation strategies accordingly. This refined management method enables resources to tilt towards more efficient and reliable manufacturers, avoids waste and excessive concentration of resources, and improves the overall utilization efficiency of resources. Through accurate raw material demand calculation and timely replenishment arrangements, the system effectively reduces raw material inventory backlogs and out-of-stock risks, and reduces inventory costs and procurement costs. At the same time, through multi-order merging and intelligent transportation tool allocation, logistics costs are reduced, transportation efficiency is improved, thereby reducing the operating cost of the entire supply chain, real-time data collection and visual management, making information in each link of the supply chain more transparent and traceable. This not only helps managers to understand the operation status of the supply chain in real time and discover and solve problems in a timely manner, but also enhances the controllability and flexibility of the supply chain, enabling it to better respond to market changes. Through accurate demand forecasting and rapid response mechanisms, it ensures that products can be delivered to customers on time, in quantity and according to quality. This not only improves customer satisfaction and loyalty, but also enhances the company's market competitiveness. Environmental protection and sustainable development factors are taken into consideration during the design, and waste and pollution are reduced by optimizing resource allocation and reducing operating costs. At the same time, through intelligent transportation tool allocation and multi-order consolidation strategies, carbon emissions during transportation are reduced, promoting the green development and sustainable development of the supply chain.

[0066] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A resource optimization configuration system for a smart supply chain, characterized in that: Including manufacturing production module, supply replenishment module, distribution demand module and logistics service module: The manufacturing production module is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer and set weights respectively, calculate the weight value corresponding to each manufacturer, divide the manufacturer level according to the weight value, set the manufacturer's supply cycle according to the demand forecasting model of the distribution demand module, calculate the supply time and formulate the manufacturer's forecast allocation strategy; The supply replenishment module is used to obtain raw material inventory data, calculate the raw material demand quantity data based on the delivery time and supply quantity input by the manufacturing production module, and formulate supplier replenishment strategies based on the raw material inventory data and raw material inventory data; The distribution demand module is used to establish a demand forecasting model based on historical sales data and consumer preference data, obtain temporary order product data, the temporary order product data includes the temporary order product quantity and the temporary order product time limit, and formulate a manufacturer temporary allocation strategy based on the temporary order product data, manufacturer level and manufacturer forecast allocation strategy; The logistics service module is used to obtain the product batch number, product size, product quantity, product time limit and order delivery address from the distribution demand module, and formulate a transportation tool allocation strategy and a multi-order merging strategy.

2. A resource optimization configuration system for a smart supply chain as claimed in claim 1, characterized in that: The manufacturing production module includes a capacity calculation unit, a capacity evaluation unit and a demand forecasting unit: The capacity calculation unit is used to calculate the on-time delivery rate, product quality qualification rate and cost reduction rate of each manufacturer. The on-time delivery rate is the ratio of the number of on-time deliveries to the total number of deliveries, the product quality qualification rate is the ratio of the number of qualified products to the total number of products, and the cost reduction rate is the ratio of the cost reduction amount to the initial cost amount; The capacity assessment unit is used to set weights for on-time delivery rate, product quality qualification rate and cost reduction rate, calculate the weight value corresponding to each manufacturer, and divide the manufacturers into levels according to the weight values. The manufacturer levels are divided into first-level manufacturers, second-level manufacturers, third-level manufacturers and fourth-level manufacturers.

3. A resource optimization configuration system for a smart supply chain as claimed in claim 2, characterized in that: The demand forecasting unit is used to set the manufacturer's delivery cycle and obtain the production cycle according to the demand forecast commodity data generated by the demand forecasting model, and calculate the delivery time. The calculation expression of the delivery time is: Delivery time = start time of the next manufacturer's delivery cycle + production cycle + manufacturer's delivery cycle Develop manufacturer forecast allocation strategies based on supply time, supply volume, and manufacturer tier.

4. A resource optimization configuration system for a smart supply chain as claimed in claim 3, characterized in that: The manufacturer forecast allocation strategy is: Set the supply time threshold to divide the demand forecast product data into forecast urgent output product data, forecast medium-speed output product data and forecast slow output product data; set the demand quantity threshold to divide the demand forecast product data into forecast high demand output product data, forecast medium demand output product data and forecast low demand output product data; give priority to allocating forecast urgent output product data and forecast high demand output product data to first-tier manufacturers, give priority to allocating forecast medium-speed output product data and forecast medium demand output product data to second-tier manufacturers, and give priority to allocating forecast slow output product data and forecast low demand output product data to third-tier manufacturers; When the secondary manufacturer is unable to produce the allocated output goods, if the primary manufacturer has already produced the allocated output goods, the output goods data that the secondary manufacturer cannot produce will be allocated to the primary manufacturer; When the third-level manufacturer is unable to produce the allocated output goods, if the second-level manufacturer has already produced the allocated output goods, the output goods data that the third-level manufacturer cannot produce will be allocated to the second-level manufacturer; When the first-tier manufacturer, the second-tier manufacturer and the third-tier manufacturer are all unable to produce the allocated output goods, the output goods data that the first-tier manufacturer, the second-tier manufacturer and the third-tier manufacturer cannot produce are allocated to the fourth-tier manufacturer; A manufacturer forecast allocation table is generated according to the manufacturer forecast allocation strategy, and the manufacturer forecast allocation table is sent to the manufacturer.

5. The resource optimization configuration system for a smart supply chain as claimed in claim 4, characterized in that: The supply replenishment module includes a demand calculation unit and a material replenishment unit: The demand calculation unit is used to obtain raw material inventory data from suppliers, and calculate the raw material demand quantity data according to the delivery time and supply quantity input by the manufacturing production module. The calculation expression of the raw material demand quantity data is: Raw material demand quantity data = supply quantity × quantity of raw materials required for the corresponding goods The material replenishment unit is used to formulate a manufacturer forecast allocation strategy according to the supply time, supply quantity and manufacturer level. The manufacturer forecast allocation strategy is: Set the safety stock quantity, and calculate the replenishment quantity data according to the safety stock quantity, raw material inventory data and raw material demand quantity data. The calculation expression of the replenishment quantity data is: Replenishment quantity data = raw material demand quantity data - raw material inventory data + safety stock quantity Establish a mapping relationship between replenishment quantity data, raw material names and delivery time; Integrate the mapping relationship and output it as a supplier replenishment table, and send the supplier replenishment table to the supplier.

6. A resource optimization configuration system for a smart supply chain as claimed in claim 5, characterized in that: The distribution demand module includes a demand forecasting unit and a temporary allocation unit: The demand forecasting unit is used to establish a demand forecasting model based on historical sales data and consumer preference data, wherein the historical sales data includes sales volume, sales time, sales category, and consumer preference data includes consumer preference category, consumer preference purchase time, and consumer consumption amount. The historical sales data and consumer preference data are cleaned, and a demand forecasting model is established using time series analysis. Demand forecasting product data is generated based on the manufacturer's supply cycle. The demand forecasting product data includes the forecasted type of demanded products, the forecasted quantity of products, and the forecasted demand time of products. The temporary allocation unit is used to obtain temporary order product data, which includes the temporary order product quantity and the temporary order product time limit, and formulate the manufacturer's temporary allocation strategy based on the temporary order product data, manufacturer level and manufacturer forecast allocation strategy.

7. A resource optimization configuration system for a smart supply chain as claimed in claim 6, characterized in that: The manufacturer temporary allocation strategy is: Set the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer, and divide the temporary order addition data into temporary urgent output data, temporary medium-speed output data and temporary slow output data according to the time limit threshold for temporary order addition and the maximum amount of temporary manufactured goods by the manufacturer. Allocate the temporary urgent output data to the highest-level manufacturer who has completed the production task of the manufacturer forecast allocation table, allocate the temporary medium-speed output data to the manufacturer at the next level of the highest-level manufacturer, and allocate the temporary slow output data to the manufacturers at the next two levels of the highest-level manufacturer; A manufacturer temporary allocation table is generated according to the manufacturer temporary allocation strategy, and the manufacturer temporary allocation table is sent to the manufacturer.

8. A resource optimization configuration system for a smart supply chain as claimed in claim 7, characterized in that: The logistics service module includes an information management unit, a transportation allocation unit, an order merging unit and a relationship establishment unit; The information management unit is used to obtain and store the transported goods information, which includes the goods batch number, goods size, goods quantity, goods delivery time limit and order delivery address; The transport allocation unit is used to formulate a transport allocation strategy and generate a transport allocation table according to the size and quantity of goods; The order merging unit is used to formulate a multi-order merging strategy and generate an order merging allocation table according to the goods delivery time limit and the order delivery address; The relationship establishing unit is used to establish a corresponding relationship between the product batch number, the transportation tool allocation table and the order merging allocation table, and send the corresponding relationship to the logistics service provider.

9. A resource optimization configuration system for a smart supply chain as claimed in claim 8, characterized in that: The transport allocation strategy is: Setting a threshold value for the size of goods and a threshold value for the number of goods, dividing the transported goods information into large-size goods, large-quantity goods, small-size goods and small-quantity goods according to the threshold value for the size of goods and the threshold value for the number of goods, setting the type of transport means, the type of transport means including large transport equipment and small transport equipment, allocating large-size goods and large-quantity goods to large transport equipment, and allocating small-size goods and small-quantity goods to small transport equipment; Generate a transport allocation table based on the transport allocation strategy.

10. A resource optimization configuration system for a smart supply chain as claimed in claim 9, characterized in that: The multi-order merging strategy is: Obtain an order delivery information table, the order delivery information table including a standard delivery area and a standard delivery time, extract the transport goods information that overlaps with the standard delivery area in the order delivery address and divide it into a number of same-area order information, extract the transport goods information corresponding to the delivery time limit included in the standard delivery time and divide it into a number of same-time-limit order information, and extract the transport goods information with repeated product batch numbers in the same-area order information and the same-time-limit order information; Generate an order merging allocation table based on the multi-order merging strategy.

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