Cloud baking raw material supply chain optimization management system

Abstract

The invention relates to the technical field of supply chain management, in particular to a cloud baking raw material supply chain optimization management system which comprises a demand dynamic adjustment module, a multi-source supplier collaborative management module, a logistics delivery tracking module, an emergency supply remediation module and a supply chain optimization module. Wherein the demand dynamic adjustment module is used for generating raw material demand prediction; the multi-source supplier collaborative management module is used for determining order allocation and delivery priorities through an optimization algorithm; the logistics delivery tracking module is used for monitoring the delivery state of the supplier in real time; the emergency supply remedy module is used for automatically selecting a proper alternative supplier under the condition of logistics abnormity; and the supply chain optimization module is used for optimizing supplier selection and order allocation. According to the invention, through dynamic optimization of supplier selection, order distribution and logistics abnormity response mechanisms, comprehensive optimization of supply chain management is realized in combination with user feedback information, and high efficiency, stability and flexibility of the supply chain are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a cloud-based baking raw material supply chain optimization management system. Background Art

[0002] With the increasing complexity of supply chain management, especially in the baking industry, companies usually rely on multiple suppliers to ensure the stability of raw material supply. Due to the variety of raw materials and the differences in price, delivery cycle, inventory status, etc. between suppliers of each raw material, companies need to use supply chain management systems to efficiently select suppliers and allocate orders. In addition, logistics management in each link of the supply chain, raw material transportation conditions, and user feedback on supplier service quality will directly affect the company's production efficiency and operating costs. Therefore, it is crucial to achieve collaborative management of multi-source suppliers, real-time tracking of logistics status, and optimized management of user feedback for the efficient operation of the supply chain.

[0003] However, existing supply chain management technologies have certain limitations in dealing with supplier selection, order allocation, and logistics exception response. First, the optimization algorithm for supplier selection and order allocation is relatively simple, and it is difficult to fully consider multi-dimensional data such as the supplier's historical cooperation records, inventory status, and logistics status; second, the logistics tracking and exception handling in the supply chain lack an automated mechanism, and it is impossible to respond in time when logistics are abnormal; in addition, the linkage between user feedback and supplier selection is insufficient, and it is difficult for the system to dynamically optimize supplier selection and order allocation based on the actual feedback of users. The existence of these problems makes the existing technology unable to meet the needs of enterprises for efficient operation and dynamic optimization of the supply chain. Therefore, it is necessary to propose a supply chain management system that can integrate multi-source supplier collaboration, logistics tracking, and user feedback optimization to solve the above technical problems. Summary of the invention

[0004] Based on the above objectives, the present invention provides a cloud-based baking ingredient supply chain optimization management system.

[0005] The cloud-based baking raw material supply chain optimization management system includes a demand dynamic adjustment module, a multi-source supplier collaborative management module, a logistics delivery tracking module, an emergency supply remediation module, and a supply chain optimization module; among which: Dynamic demand adjustment module: used to generate raw material demand forecasts based on historical order data, market fluctuations and seasonal changes, and adjust demand based on changes in real-time orders and production plans; Multi-source supplier collaborative management module: It is connected to the demand dynamic adjustment module, receives the raw material demand data transmitted by it, and determines the order allocation and delivery priority through the optimization algorithm based on the supplier's historical cooperation data, quotation, delivery cycle and inventory status, so as to coordinate the resource allocation of each supplier; Logistics delivery tracking module: used to monitor the supplier's delivery status in real time, obtain the transportation location of raw materials, estimated arrival time and environmental parameters during transportation, and transmit abnormal information to the emergency supply remediation module when logistics abnormalities occur; Emergency supply remediation module: connected with the logistics delivery tracking module, it is used to automatically call the backup supplier's data in the event of logistics anomalies or the supplier's failure to deliver on time, select a suitable alternative supplier through the supplier database, and generate a temporary emergency order to ensure the continuous operation of the supply chain; Supply Chain Optimization Module: It is connected with the Multi-Source Supplier Collaborative Management Module and the Logistics Delivery Tracking Module to collect user feedback on the supply chain and pass the feedback information to the Multi-Source Supplier Collaborative Management Module to optimize supplier selection and order allocation.

[0006] Optionally, the demand dynamic adjustment module includes a historical data processing unit, a market fluctuation analysis unit, a seasonal change analysis unit, a demand forecasting unit and a real-time adjustment unit; wherein: Historical data processing unit: used to collect and store the historical order data of baking enterprises, sort them in chronological order, and analyze the changes in order volume in different time periods through weighted analysis to generate a historical order trend model; Market fluctuation analysis unit: used to receive external market fluctuation data, analyze external factors including raw material price fluctuations, market demand growth rate and competitor activities, and input the analysis results into the demand forecasting unit; Seasonal Change Analysis Unit: used to analyze the impact of different seasons on baking raw material demand based on the periodic order fluctuations in historical data, especially the peak demand for raw materials during holidays or special events; Demand forecasting unit: connected with the historical data processing unit, the market fluctuation analysis unit and the seasonal change analysis unit, used to integrate the analysis results of each unit, generate raw material demand forecast data using the multivariate regression algorithm, and transmit the forecast results to the adjustment unit; Real-time adjustment unit: used to receive real-time order and production plan change data, and based on the difference between real-time data and demand forecast results, adjust the current demand through adaptive algorithms to ensure that demand forecasts can respond to changes in production plans at any time.

[0007] Optionally, the demand forecasting unit includes: receiving the historical order trend data from the historical data processing unit, the market volatility correction data from the market volatility analysis unit, and the seasonal correction data from the seasonal change analysis unit as input variables of the regression model; A regression model is set, wherein the regression model includes variables of historical order trends, market fluctuation correction factors, and seasonal correction values, and the constant term of the regression model and the regression coefficients corresponding to the variables are used to represent their impact on raw material demand; Based on historical data, the least square method is used to solve the regression coefficient in the regression model to determine the impact of historical order trends, market fluctuations and seasonal changes on demand; The historical order trend data, market fluctuation correction data and seasonal correction data are brought into the model after the regression coefficient is solved to generate the raw material demand forecast data at the current moment.

[0008] Optionally, the real-time adjustment unit includes: First, receive and analyze real-time order data and updated data of production plan, compare these real-time data with previously generated demand forecast data, and calculate the difference between the two; Then, according to the calculated difference value, the adaptive adjustment algorithm is used to correct the current demand forecast data, and the correction range is determined according to the set adjustment coefficient; Next, continuously monitor the changes in real-time orders and production plans, and repeatedly revise the demand forecast data to ensure that the forecast data is always consistent with current demand; Finally, the revised demand forecast data is passed to the multi-source supplier collaborative management module to ensure that the supplier's order allocation strategy can be adjusted according to the updated demand.

[0009] Optionally, the multi-source supplier collaborative management module includes a demand receiving unit, a supplier data processing unit, an order allocation optimization unit, and a delivery priority determination unit; wherein: Demand receiving unit: used to receive the raw material forecast demand data transmitted by the real-time adjustment unit or the demand dynamic adjustment module, and determine the required raw material quantity and delivery time requirements based on the received data; Supplier data processing unit: used to receive and process data related to each supplier, including supplier's historical cooperation records, quotations, delivery cycles and inventory status. The supplier data processing unit generates a comprehensive score for the supplier by analyzing the supplier's past delivery punctuality, raw material quality and continuity of cooperation; Order allocation optimization unit: Based on the comprehensive supplier scores generated by the supplier data processing unit and combined with the raw material demand data, the order allocation ratio and delivery priority of each supplier are calculated using an optimization algorithm; Delivery priority determination unit: used to determine the supplier's delivery priority based on the supplier's comprehensive score and the calculation results of the optimization algorithm, and give priority to suppliers with abundant resources and short delivery cycles according to the delivery cycle and inventory status of each supplier to ensure timely execution of orders.

[0010] Optionally, the order allocation optimization unit includes: First, receive the supplier's comprehensive score and real-time raw material demand data, and use these data as input variables for the optimization algorithm; Then, the constraints of order allocation are set, including the maximum available supply, delivery cycle and quotation of the supplier, to ensure that the raw material demand can be met during the order allocation process, while minimizing the total cost and delivery time of the order when optimizing the allocation; Next, the order allocation ratio of each supplier is calculated through an optimization algorithm while satisfying all constraints; Finally, the supplier's delivery priority is determined based on each supplier's order allocation ratio and its delivery cycle.

[0011] Optionally, the logistics delivery tracking module includes a delivery monitoring unit, an environmental parameter monitoring unit, an anomaly detection unit, and an anomaly information transmission unit; wherein: Shipping monitoring unit: used to receive the supplier's shipping status information in real time, track the entire transportation process of raw materials from the supplier's warehouse to the destination, and obtain the transportation location, estimated arrival time and transportation route of the raw materials by connecting with the supplier's logistics system; Environmental parameter monitoring unit: used to monitor the environmental parameters of raw materials during transportation, including temperature, humidity and pressure; Abnormal detection unit: used to detect abnormal logistics conditions according to the set logistics standards and environmental parameter monitoring results. Abnormal conditions include transportation delays, environmental parameters exceeding the standard range, or transportation routes deviating from the predetermined path; Abnormal information transmission unit: connected to the emergency supply remediation module, used to transmit abnormal information to the emergency supply remediation module after detecting logistics abnormalities. The abnormal information includes the abnormal type, specific occurrence time, current location of raw material transportation and specific values ​​of environmental parameter abnormalities.

[0012] Optionally, the emergency supply remediation module includes an abnormal response unit, a backup supplier calling unit, a supplier selection unit and an emergency order generating unit; wherein: Abnormal response unit: connected with the logistics delivery tracking module, used to immediately start the emergency supply procedure after receiving the abnormal logistics information or the notification that the supplier cannot deliver on time, so as to identify the type, quantity and delivery time requirements of the raw materials that need urgent remediation; Backup supplier calling unit: used to call backup supplier data from the supplier database. Specifically, according to the emergency demand of each raw material, the backup supplier with corresponding inventory and fast delivery capability is retrieved from the supplier database. Supplier selection unit: used to select the corresponding alternative supplier according to multiple criteria after calling the data of the alternative supplier. The selection criteria include the inventory status, quotation, delivery cycle and historical delivery performance of the alternative supplier. The supplier selection unit comprehensively scores the relevant parameters of each supplier through a preset weight mechanism, and determines the best alternative supplier according to the scoring results; Emergency order generation unit: used to generate temporary emergency orders based on the alternative suppliers determined by the supplier selection unit. The order content includes the type, quantity, delivery time and transportation requirements of the raw materials. After the order is generated, it will be automatically sent to the alternative supplier.

[0013] Optionally, the supplier selection unit includes: Receive the relevant parameters of the backup supplier, including the supplier's inventory status, quotation, delivery cycle and historical delivery performance, and set the weight coefficient of each parameter, namely inventory weight , Quote Weight , Delivery cycle weight and historical delivery performance weights ; Standardize the parameters of each supplier, convert the parameters of different dimensions into a unified scoring standard, and set the standardized parameters as the inventory score , Quote Rating , Delivery cycle score and historical delivery performance scores ; The standardized scores of each supplier are weighted using a preset weighting mechanism, the formula is: ,in, The overall rating of the supplier. , , , are the weights of each parameter, , , , They are the supplier's inventory score, quotation score, delivery cycle score and historical delivery performance score; According to the calculated comprehensive score, all alternative suppliers are ranked and the supplier with the highest comprehensive score is selected as the best alternative supplier.

[0014] Optionally, the supply chain optimization module includes a feedback collection unit, a feedback analysis unit, a feedback transmission unit and an optimization adjustment unit; wherein: Feedback collection unit: used to collect user feedback on various links of the supply chain, including supplier service quality, timeliness of raw material delivery, raw material quality and overall user satisfaction; Feedback analysis unit: used to analyze the collected user feedback information, quantify and score different types of feedback information, including supplier service quality score, delivery timeliness score and raw material quality score, and generate feedback scores by combining different types of analysis results; Feedback transmission unit: used to transmit the feedback score generated by the feedback analysis unit to the multi-source supplier collaborative management module. The transmitted information includes the comprehensive feedback score of each supplier; Optimization and adjustment unit: After receiving feedback information in the multi-source supplier collaborative management module, the optimization algorithm is used to re-evaluate the supplier selection criteria and adjust the supplier's comprehensive score and order allocation ratio based on the supplier's historical data, quotation, inventory status and delivery cycle.

[0015] Beneficial effects of the present invention: The present invention, by integrating multi-source supplier collaborative management, logistics delivery tracking and supply chain optimization modules, solves the problems of inaccurate supplier selection and order allocation, untimely handling of logistics exceptions and inability to dynamically optimize supplier selection based on user feedback in existing supply chain management. It also optimizes order allocation based on comprehensive factors such as suppliers' historical data, quotations, inventory status, etc. through diversified data analysis and optimization algorithms, and automatically calls backup suppliers when logistics exceptions occur to generate emergency orders, thereby ensuring the continuity and stability of the supply chain.

[0016] The present invention realizes dynamic optimization of supply chain management by collecting and analyzing user feedback information on the supply chain and optimizing supplier selection criteria and order allocation strategies. The system can automatically adjust the supplier's comprehensive score based on the feedback information, thereby improving the supplier's service quality and response speed, and ultimately improving the overall efficiency of the supply chain and the production benefits of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a supply chain optimization management system according to an embodiment of the present invention; Figure 2 Schematic diagram of a multi-source supplier collaborative management module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0020] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1-Figure 2 As shown, the cloud-based baking raw material supply chain optimization management system includes a demand dynamic adjustment module, a multi-source supplier collaborative management module, a logistics delivery tracking module, an emergency supply remediation module, and a supply chain optimization module; among which: Dynamic demand adjustment module: used to generate raw material demand forecasts based on historical order data, market fluctuations and seasonal changes, adjust demand based on changes in real-time orders and production plans, and pass the updated demand data to the multi-source supplier collaborative management module; Multi-source supplier collaborative management module: It is connected to the demand dynamic adjustment module, receives the raw material demand data transmitted by it, and determines the order allocation and delivery priority through the optimization algorithm based on the supplier's historical cooperation data, quotation, delivery cycle and inventory status, so as to coordinate the resource allocation of each supplier; Logistics delivery tracking module: used to monitor the supplier's delivery status in real time, obtain the transportation location of the raw materials, the estimated arrival time and the environmental parameters during transportation (such as temperature and humidity), and transmit the abnormal information to the emergency supply remediation module when a logistics abnormality occurs; Emergency supply remediation module: connected with the logistics delivery tracking module, it is used to automatically call the backup supplier's data in the event of logistics anomalies or the supplier's failure to deliver on time, select a suitable alternative supplier through the supplier database, and generate a temporary emergency order to ensure the continuous operation of the supply chain; Supply Chain Optimization Module: It is connected with the Multi-Source Supplier Collaborative Management Module and the Logistics Delivery Tracking Module to collect user feedback on the supply chain and pass the feedback information to the Multi-Source Supplier Collaborative Management Module to optimize supplier selection and order allocation.

[0023] The demand dynamic adjustment module includes a historical data processing unit, a market fluctuation analysis unit, a seasonal change analysis unit, a demand forecasting unit, and a real-time adjustment unit; among which: Historical data processing unit: used to collect and store the historical order data of baking enterprises, sort them in chronological order, and analyze the changes in order volume in different time periods through weighted analysis to generate a historical order trend model; the weighted analysis formula for historical orders is: ,in, Indicates time The historical demand forecast value, Indicates time Next Historical order data points, Represents historical order data points The relevant weighting coefficient, Represents the total number of historical order data points used for analysis; then the expression of the historical order trend model is: ,in, Indicates time Historical order trend value; , , They represent the historical demand forecast values ​​at several previous time points, , , Respectively represent and time , , The corresponding weighting coefficient is Adjust constants for the model; Market fluctuation analysis unit: used to receive external market fluctuation data, analyze external factors including raw material price fluctuations, market demand growth rate and competitor activities, and input the analysis results into the demand forecasting unit; the calculation formula for the impact of market fluctuations is: ,in, Indicates time Correction factor of market fluctuation on raw material demand, Indicates the price volatility of raw materials, represents the growth rate of market demand, Indicates the influence factor of competitors’ activities; Seasonal change analysis unit: used to analyze the impact of different seasons on baking raw material demand based on the periodic order fluctuations in historical data, especially the peak demand for raw materials during holidays or special events; seasonal change analysis is expressed using the Fourier transform formula: ,in, Indicates time The seasonal fluctuations in demand are corrected. Indicates The amplitude of the seasonal component, is the sequence number of the Fourier term, Represents the cycle of seasonal changes, is the total number of Fourier terms; Demand forecasting unit: connected with the historical data processing unit, the market fluctuation analysis unit and the seasonal change analysis unit, used to integrate the analysis results of each unit, generate raw material demand forecast data using the multivariate regression algorithm, and transmit the forecast results to the adjustment unit; Real-time adjustment unit: used to receive real-time order and production plan change data, and based on the difference between real-time data and demand forecast results, adjust the current demand through adaptive algorithms to ensure that demand forecasts can respond to changes in production plans at any time; the above units conduct comprehensive analysis of multi-dimensional data such as historical data, market fluctuations and seasonal changes. The demand dynamic adjustment module can generate more accurate raw material demand forecasts. At the same time, through the dynamic adjustment of the real-time adjustment unit, it ensures that when orders and production plans change, demand forecasts can respond quickly, thereby effectively improving the adaptability and flexibility of the supply chain.

[0024] The demand forecasting unit includes: receiving the historical order trend data from the historical data processing unit, the market volatility correction data from the market volatility analysis unit, and the seasonal correction data from the seasonal change analysis unit as input variables of the regression model; Set up a regression model, which includes variables such as historical order trends, market volatility correction factors, and seasonal correction values, and use the constant term of the regression model and the regression coefficients corresponding to each variable to represent their impact on raw material demand; Based on historical data, the least square method is used to solve the regression coefficient in the regression model to determine the impact of historical order trends, market fluctuations and seasonal changes on demand; The historical order trend data, market fluctuation correction data and seasonal correction data are brought into the model after the regression coefficient is solved to generate the raw material demand forecast data at the current moment.

[0025] The calculation steps for generating raw material demand forecast data using the multivariate regression algorithm are as follows: Step 1: Receive the historical order trend value generated by the historical data processing unit , the market volatility correction factor generated by the market volatility analysis unit , and the seasonal correction values ​​generated by the seasonal variation analysis unit ; Step 2: Take the received factors as independent variables of the regression model and set the form of the regression model as follows: ,in, Indicates time The predicted raw material demand value, is the constant term of the regression model, Represent the regression coefficients of historical order trends, market volatility correction factors, and seasonal correction values, respectively; Step 3: Determine the regression coefficient using the least squares method based on the historical data collected , the specific regression coefficient calculation formula is: ,in, Indicates the first The independent variable in time The value of For the The mean value of the independent variables, is the actual order data, is the average value of actual order data, is the number of data samples; Step 4: Use the obtained regression coefficient , substitute the historical order trend, market volatility correction factor and seasonal correction value into the regression equation to calculate the time Forecasted raw material demand ; Through the above steps, the demand forecasting unit can effectively integrate the data of each unit and use the multivariate regression algorithm to generate accurate raw material demand forecast results; through comprehensive analysis of historical order trends, market fluctuations and seasonal changes, it can better reflect the impact of these factors on future demand and optimize the overall efficiency of the supply chain.

[0026] The real-time adjustment unit includes: First, receive and analyze real-time order data and updated data of production plan, compare these real-time data with previously generated demand forecast data, and calculate the difference between the two; Then, based on the calculated difference value, the adaptive adjustment algorithm is used to correct the current demand forecast data. The correction range is determined by the set adjustment coefficient, which determines the system's response speed to real-time data changes, ensuring that the corrected data is closer to the actual situation. Next, we continuously monitor the changes in real-time orders and production plans, and repeatedly revise the demand forecast data, so that the system can dynamically track and respond to actual order changes, ensuring that the forecast data is always consistent with current demand; Finally, the revised demand forecast data is passed to the multi-source supplier collaborative management module to ensure that the supplier's order allocation strategy can be adjusted according to the updated demand, thereby ensuring the efficient operation of the supply chain.

[0027] The calculation steps for adjusting the current demand through the adaptive algorithm are as follows: Step 1: Receive real-time order data and production plan update data. The real-time data is recorded as , the predicted demand data is recorded as , and calculate the difference between real-time data and predicted demand , whose expression is: ,in, Indicates time The demand forecast error at the moment Indicates time Real-time order data, Indicates time Forecast demand data; Step 2: Based on the calculated demand forecast error , an adaptive adjustment algorithm is used to correct the current demand, and the correction formula is: ,in, represents the corrected demand. is the adaptive adjustment coefficient, which indicates the sensitivity of the adjustment. This coefficient can be set according to the response speed requirements of the system to ensure that the system responds to errors in a timely manner; Step 3: Continuously monitor the difference between real-time order data and forecast demand data, and repeat the adaptive adjustment process to ensure that when order data or production plans change, the system can dynamically adjust the demand forecast value according to the real-time situation at any time; Step 4: Update the demand forecast data Delivered to the multi-source supplier collaborative management module to ensure that the supplier allocation strategy can be adjusted according to the updated demand, thereby ensuring the continuity and accuracy of the supply chain.

[0028] The multi-source supplier collaborative management module includes a demand receiving unit, a supplier data processing unit, an order allocation optimization unit, and a delivery priority determination unit; among which: Demand receiving unit: used to receive the raw material forecast demand data transmitted by the real-time adjustment unit or the demand dynamic adjustment module, and determine the required raw material quantity and delivery time requirements based on the received data; Supplier data processing unit: used to receive and process data related to each supplier, including supplier's historical cooperation records, quotations, delivery cycles and inventory status. The supplier data processing unit generates a comprehensive score for the supplier by analyzing the supplier's past delivery punctuality, raw material quality and continuity of cooperation for subsequent allocation optimization; the formula for the supplier's comprehensive score is: ,in, The overall rating of the supplier. The supplier's on-time delivery rate, Provide suppliers with quality scores for raw materials, The duration of cooperation between the supplier and the enterprise, , , are weight coefficients related to punctuality, quality score and duration of cooperation, respectively. The performance of suppliers can be comprehensively evaluated through the score, and the results can be used for subsequent order allocation optimization; Order allocation optimization unit: Based on the comprehensive supplier scores generated by the supplier data processing unit and combined with the raw material demand data, the optimization algorithm is used to calculate the order allocation ratio and delivery priority of each supplier, so as to optimize the order allocation plan while meeting the raw material demand and reasonably coordinate the supplier's resources; Delivery priority determination unit: used to determine the delivery priority of suppliers based on the comprehensive scores of suppliers and the calculation results of the optimization algorithm, and give priority to suppliers with abundant resources and short delivery cycles according to the delivery cycle and inventory status of each supplier to ensure the timely execution of orders; through the synergy of multiple units, the multi-source supplier collaborative management module can receive raw material demand forecast data, comprehensively analyze the supplier's historical cooperation data, quotations, delivery cycles and inventory status, and use the optimization algorithm to reasonably determine order allocation and delivery priority. This module can effectively coordinate the resource allocation of each supplier to ensure the stability of raw material supply and the efficient operation of the supply chain.

[0029] The order allocation optimization unit includes: First, receive the supplier's comprehensive score and real-time raw material demand data, and use these data as input variables for the optimization algorithm; Then, the constraints of order allocation are set, including the maximum available supply, delivery cycle and quotation of the supplier, to ensure that the raw material demand can be met during the order allocation process, while minimizing the total cost and delivery time of the order when optimizing the allocation; Next, under the condition that all constraints are met, the order allocation ratio of each supplier is calculated through an optimization algorithm, with the supplier's comprehensive score and maximum supply capacity as the main basis, ensuring that suppliers with high scores and large supply capacity receive more orders; Finally, the supplier's delivery priority is determined based on the order allocation ratio of each supplier and its delivery cycle. The supplier's comprehensive score and delivery cycle jointly determine the delivery priority. Suppliers with high scores and short delivery cycles will be given priority in executing orders.

[0030] The specific steps of using the optimization algorithm for calculation are as follows: Step 1: Receive the supplier comprehensive score generated by the supplier data processing unit and real-time raw material demand data , and use the supplier comprehensive score as the input variable of the optimization algorithm; Step 2: Set constraints, including the maximum quantity each supplier can supply , Delivery cycle and quote To ensure that the cost and delivery time of order allocation are minimized while meeting the raw material demand; the objective function of the order allocation optimization model is defined as: ,in, The total cost assigned to the order, For the Suppliers' quotations, To allocate to The order volume of each supplier, For the The lead time of each supplier, is the weight coefficient, reflecting the impact of delivery cycle on cost, is the total number of suppliers; Step 3: According to the objective function of the optimization algorithm, the order allocation ratio of each supplier is calculated by solving the objective function. The specific calculation formula of the order allocation ratio is: ,in, To allocate to The order volume of each supplier, For the The overall rating of the suppliers, For the The maximum available supply from a supplier; Step 4: Determine the supplier's delivery priority by calculating the order allocation ratio of each supplier and combining the supplier's delivery cycle and comprehensive score. The calculation formula for delivery priority is: ,in, For the The delivery priority of each supplier, For its delivery cycle, the higher the delivery priority, the supplier will be given priority in order execution; through the above steps, the order allocation optimization unit can reasonably allocate orders and determine delivery priorities based on the supplier's comprehensive score and raw material requirements. The use of the optimization algorithm ensures the rational allocation of resources, reduces the overall cost of the supply chain, and makes the best arrangement on delivery time.

[0031] The logistics delivery tracking module includes a shipment monitoring unit, an environmental parameter monitoring unit, an anomaly detection unit, and an anomaly information transmission unit; among which: Shipping monitoring unit: used to receive the supplier's shipping status information in real time, track the entire transportation process of raw materials from the supplier's warehouse to the destination, and obtain the transportation location, estimated arrival time and transportation route of the raw materials by connecting with the supplier's logistics system; Environmental parameter monitoring unit: used to monitor the environmental parameters of raw materials during transportation, including temperature, humidity and pressure. The unit collects data in real time through sensors installed in the transportation vehicle and transmits it to the system to ensure that the quality of the raw materials is not affected during transportation; Abnormal detection unit: used to detect abnormal logistics conditions according to the set logistics standards and environmental parameter monitoring results. Abnormal conditions include transportation delays, environmental parameters exceeding the standard range, or transportation routes deviating from the predetermined path; Abnormal information transmission unit: connected to the emergency supply remediation module, used to transmit abnormal information to the emergency supply remediation module after detecting logistics abnormalities. The abnormal information includes the abnormal type, specific occurrence time, current location of raw material transportation and specific values ​​of environmental parameter abnormalities, so that the emergency supply remediation module can take emergency measures in time according to the abnormal situation; through the logistics delivery tracking module, the system can monitor the supplier's delivery status in real time, obtain various key parameters in the transportation process, and promptly transmit information to the emergency supply remediation module when abnormalities are detected, ensuring that the supply chain can respond quickly when logistics abnormalities occur.

[0032] The emergency supply remediation module includes an abnormal response unit, a backup supplier calling unit, a supplier selection unit and an emergency order generation unit; wherein: Abnormal response unit: connected with the logistics delivery tracking module, used to immediately start the emergency supply procedure after receiving the abnormal logistics information or the notification that the supplier cannot deliver on time, so as to identify the type, quantity and delivery time requirements of the raw materials that need urgent remediation; Backup supplier calling unit: used to call backup supplier data from the supplier database. Specifically, according to the emergency demand of each raw material, the backup supplier with corresponding inventory and fast delivery capability is retrieved from the supplier database. Supplier selection unit: After calling the backup supplier's data, it selects the corresponding alternative supplier based on multiple criteria. The selection criteria include the backup supplier's inventory, quotation, delivery cycle and historical delivery performance. The supplier selection unit uses a preset weight mechanism to comprehensively score the relevant parameters of each supplier and determine the best alternative supplier based on the scoring results. Emergency order generation unit: used to generate temporary emergency orders based on the alternative suppliers determined by the supplier selection unit. The order content includes the type, quantity, delivery time and transportation requirements of raw materials. After the order is generated, it will be automatically sent to the alternative supplier and the relevant departments will be notified to ensure the rapid execution of the order; through the emergency supply remediation module, the system can quickly call the data of backup suppliers, select the most suitable alternative supplier, and generate emergency orders when logistics are abnormal or the supplier cannot deliver on time. This module ensures that the supply chain can maintain a stable and continuous supply of raw materials in the event of an emergency through an automated emergency response mechanism.

[0033] The supplier selection module includes: Receive the relevant parameters of the backup supplier, including the supplier's inventory status, quotation, delivery cycle and historical delivery performance, and set the weight coefficient of each parameter, namely inventory weight , Quote Weight , Delivery cycle weight and historical delivery performance weights ; Standardize the parameters of each supplier, convert the parameters of different dimensions into a unified scoring standard, and set the standardized parameters as the inventory score , Quote Rating , Delivery cycle score and historical delivery performance scores ; The standardized scores of each supplier are weighted using a preset weighting mechanism, the formula is: ,in, The overall rating of the supplier. , , , are the weights of each parameter, , , , They are the supplier's inventory score, quotation score, delivery cycle score and historical delivery performance score; According to the calculated comprehensive score, all backup suppliers are sorted and the supplier with the highest comprehensive score is selected as the optimal alternative supplier. Through the preset weight mechanism, the supplier selection unit can comprehensively score the key parameters of the backup suppliers such as inventory, quotation, delivery cycle and historical delivery performance, and determine the optimal alternative supplier based on the scoring results. This mechanism ensures that in emergency situations, the best supplier can be quickly selected to ensure the continuity and efficiency of the supply chain.

[0034] The supply chain optimization module includes a feedback collection unit, a feedback analysis unit, a feedback transmission unit, and an optimization adjustment unit; wherein: Feedback collection unit: used to collect user feedback information on various links of the supply chain. The feedback information includes supplier service quality, timeliness of raw material delivery, raw material quality and overall user satisfaction. This unit collects data through the user interface or automated feedback system and organizes and classifies the data; Feedback analysis unit: used to analyze the collected user feedback information and quantify the scores of different types of feedback information, including supplier service quality score, delivery timeliness score and raw material quality score, and generate feedback scores by combining different types of analysis results for subsequent optimization process; Feedback transmission unit: used to transmit the feedback scores generated by the feedback analysis unit to the multi-source supplier collaborative management module. The transmitted information includes the comprehensive feedback score of each supplier, which will serve as a reference for supplier selection and order allocation optimization; Optimization and adjustment unit: After receiving feedback information in the multi-source supplier collaborative management module, the optimization algorithm is used to re-evaluate the supplier selection criteria based on the supplier's historical data, quotation, inventory status and delivery cycle, and adjust the supplier's comprehensive score and order allocation ratio. The optimized order allocation strategy ensures that supplier selection is more accurate and order execution is more efficient. Through the supply chain optimization module, the system can effectively collect and process user feedback information on the supply chain, and pass the feedback information to the multi-source supplier collaborative management module, thereby optimizing supplier selection and order allocation. Through the feedback closed-loop mechanism, this module can continuously improve the overall operating efficiency and service quality of the supply chain.

[0035] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0036] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Cloud-based baking raw material supply chain optimization management system, characterized by: It includes demand dynamic adjustment module, multi-source supplier collaborative management module, logistics delivery tracking module, emergency supply remediation module and supply chain optimization module; among which: Dynamic demand adjustment module: used to generate raw material demand forecasts based on historical order data, market fluctuations and seasonal changes, and adjust demand based on changes in real-time orders and production plans; Multi-source supplier collaborative management module: It is connected to the demand dynamic adjustment module, receives the raw material demand data transmitted by it, and determines the order allocation and delivery priority through the optimization algorithm based on the supplier's historical cooperation data, quotation, delivery cycle and inventory status, so as to coordinate the resource allocation of each supplier; Logistics delivery tracking module: used to monitor the supplier's delivery status in real time, obtain the transportation location of raw materials, estimated arrival time and environmental parameters during transportation, and transmit abnormal information to the emergency supply remediation module when logistics abnormalities occur; Emergency supply remediation module: connected with the logistics delivery tracking module, it is used to automatically call the backup supplier's data in the event of logistics anomalies or the supplier's failure to deliver on time, select a suitable alternative supplier through the supplier database, and generate a temporary emergency order to ensure the continuous operation of the supply chain; Supply Chain Optimization Module: It is connected with the Multi-Source Supplier Collaborative Management Module and the Logistics Delivery Tracking Module to collect user feedback on the supply chain and pass the feedback information to the Multi-Source Supplier Collaborative Management Module to optimize supplier selection and order allocation.

2. The cloud-based baking raw material supply chain optimization management system according to claim 1, characterized in that: The demand dynamic adjustment module includes a historical data processing unit, a market fluctuation analysis unit, a seasonal change analysis unit, a demand forecasting unit and a real-time adjustment unit; wherein: Historical data processing unit: used to collect and store the historical order data of baking enterprises, sort them in chronological order, and analyze the changes in order volume in different time periods through weighted analysis to generate a historical order trend model; Market fluctuation analysis unit: used to receive external market fluctuation data, analyze external factors including raw material price fluctuations, market demand growth rate and competitor activities, and input the analysis results into the demand forecasting unit; Seasonal Change Analysis Unit: used to analyze the impact of different seasons on baking raw material demand based on the periodic order fluctuations in historical data, especially the peak demand for raw materials during holidays or special events; Demand forecasting unit: connected with the historical data processing unit, the market fluctuation analysis unit and the seasonal change analysis unit, used to integrate the analysis results of each unit, generate raw material demand forecast data using the multivariate regression algorithm, and transmit the forecast results to the adjustment unit; Real-time adjustment unit: used to receive real-time order and production plan change data, and based on the difference between real-time data and demand forecast results, adjust the current demand through adaptive algorithms to ensure that demand forecasts can respond to changes in production plans at any time.

3. The cloud-based baking raw material supply chain optimization management system according to claim 2, characterized in that: The demand forecasting unit comprises: receiving the historical order trend data from the historical data processing unit, the market volatility correction data from the market volatility analysis unit, and the seasonal correction data from the seasonal change analysis unit as input variables of the regression model; A regression model is set, wherein the regression model includes variables of historical order trends, market fluctuation correction factors, and seasonal correction values, and the constant term of the regression model and the regression coefficients corresponding to the variables are used to represent their impact on the demand for raw materials; Based on historical data, the least square method is used to solve the regression coefficient in the regression model to determine the impact of historical order trends, market fluctuations and seasonal changes on demand; The historical order trend data, market fluctuation correction data and seasonal correction data are brought into the model after the regression coefficient is solved to generate the raw material demand forecast data at the current moment.

4. The cloud-based baking raw material supply chain optimization management system according to claim 3 is characterized in that: The real-time adjustment unit comprises: First, receive and analyze real-time order data and updated data of production plan, compare these real-time data with previously generated demand forecast data, and calculate the difference between the two; Then, according to the calculated difference value, the adaptive adjustment algorithm is used to correct the current demand forecast data, and the correction range is determined according to the set adjustment coefficient; Next, continuously monitor the changes in real-time orders and production plans, and repeatedly revise the demand forecast data to ensure that the forecast data is always consistent with current demand; Finally, the revised demand forecast data is passed to the multi-source supplier collaborative management module to ensure that the supplier's order allocation strategy can be adjusted according to the updated demand.

5. The cloud-based baking raw material supply chain optimization management system according to claim 1, characterized in that: The multi-source supplier collaborative management module includes a demand receiving unit, a supplier data processing unit, an order allocation optimization unit and a delivery priority determination unit; wherein: Demand receiving unit: used to receive the raw material forecast demand data transmitted by the real-time adjustment unit or the demand dynamic adjustment module, and determine the required raw material quantity and delivery time requirements based on the received data; Supplier data processing unit: used to receive and process data related to each supplier, including supplier's historical cooperation records, quotations, delivery cycles and inventory status. The supplier data processing unit generates a comprehensive score for the supplier by analyzing the supplier's past delivery punctuality, raw material quality and continuity of cooperation; Order allocation optimization unit: Based on the comprehensive supplier scores generated by the supplier data processing unit and combined with the raw material demand data, the order allocation ratio and delivery priority of each supplier are calculated using an optimization algorithm; Delivery priority determination unit: used to determine the supplier's delivery priority based on the supplier's comprehensive score and the calculation results of the optimization algorithm, and give priority to suppliers with abundant resources and short delivery cycles according to the delivery cycle and inventory status of each supplier to ensure timely execution of orders.

6. The cloud-based baking raw material supply chain optimization management system according to claim 5, characterized in that: The order allocation optimization unit comprises: First, receive the supplier's comprehensive score and real-time raw material demand data, and use these data as input variables for the optimization algorithm; Then, the constraints of order allocation are set, including the maximum available supply, delivery cycle and quotation of the supplier, to ensure that the raw material demand can be met during the order allocation process, while minimizing the total cost and delivery time of the order when optimizing the allocation; Next, the order allocation ratio of each supplier is calculated through an optimization algorithm while satisfying all constraints; Finally, the supplier's delivery priority is determined based on each supplier's order allocation ratio and its delivery cycle.

7. The cloud-based baking raw material supply chain optimization management system according to claim 1, characterized in that: The logistics delivery tracking module includes a delivery monitoring unit, an environmental parameter monitoring unit, an anomaly detection unit and an anomaly information transmission unit; wherein: Shipping monitoring unit: used to receive the supplier's shipping status information in real time, track the entire transportation process of raw materials from the supplier's warehouse to the destination, and obtain the transportation location, estimated arrival time and transportation route of the raw materials by connecting with the supplier's logistics system; Environmental parameter monitoring unit: used to monitor the environmental parameters of raw materials during transportation, including temperature, humidity and pressure; Abnormal detection unit: used to detect abnormal logistics conditions according to the set logistics standards and environmental parameter monitoring results. Abnormal conditions include transportation delays, environmental parameters exceeding the standard range, or transportation routes deviating from the predetermined path; Abnormal information transmission unit: connected to the emergency supply remediation module, used to transmit abnormal information to the emergency supply remediation module after detecting logistics abnormalities. The abnormal information includes the abnormal type, specific occurrence time, current location of raw material transportation and specific values ​​of environmental parameter abnormalities.

8. The cloud-based baking raw material supply chain optimization management system according to claim 1, characterized in that: The emergency supply remediation module includes an abnormal response unit, a backup supplier calling unit, a supplier selection unit and an emergency order generation unit; wherein: Abnormal response unit: connected with the logistics delivery tracking module, used to immediately start the emergency supply procedure after receiving the abnormal logistics information or the notification that the supplier cannot deliver on time, so as to identify the type, quantity and delivery time requirements of the raw materials that need urgent remediation; Backup supplier calling unit: used to call backup supplier data from the supplier database. Specifically, according to the emergency demand of each raw material, the backup supplier with corresponding inventory and fast delivery capability is retrieved from the supplier database. Supplier selection unit: used to select the corresponding alternative supplier according to multiple criteria after calling the data of the alternative supplier. The selection criteria include the inventory status, quotation, delivery cycle and historical delivery performance of the alternative supplier. The supplier selection unit comprehensively scores the relevant parameters of each supplier through a preset weight mechanism, and determines the best alternative supplier according to the scoring results; Emergency order generation unit: used to generate temporary emergency orders based on the alternative suppliers determined by the supplier selection unit. The order content includes the type, quantity, delivery time and transportation requirements of the raw materials. After the order is generated, it will be automatically sent to the alternative supplier.

9. The cloud-based baking raw material supply chain optimization management system according to claim 8, characterized in that: The supplier selection unit comprises: Receive the relevant parameters of the backup supplier, including the supplier's inventory status, quotation, delivery cycle and historical delivery performance, and set the weight coefficient of each parameter, namely inventory weight , Quote Weight , Delivery cycle weight and historical delivery performance weights ; Standardize the parameters of each supplier, convert the parameters of different dimensions into a unified scoring standard, and set the standardized parameters as the inventory score , Quote Rating , Delivery cycle score and historical delivery performance scores ; The standardized scores of each supplier are weighted using a preset weighting mechanism, the formula is: ,in, The overall rating of the supplier. , , , are the weights of each parameter, , , , They are the supplier's inventory score, quotation score, delivery cycle score and historical delivery performance score; According to the calculated comprehensive score, all alternative suppliers are ranked and the supplier with the highest comprehensive score is selected as the best alternative supplier.

10. The cloud-based baking raw material supply chain optimization management system according to claim 1, characterized in that: The supply chain optimization module includes a feedback collection unit, a feedback analysis unit, a feedback transmission unit and an optimization adjustment unit; wherein: Feedback collection unit: used to collect user feedback on various links of the supply chain, including supplier service quality, timeliness of raw material delivery, raw material quality and overall user satisfaction; Feedback analysis unit: used to analyze the collected user feedback information, quantify and score different types of feedback information, including supplier service quality score, delivery timeliness score and raw material quality score, and generate feedback scores by combining different types of analysis results; Feedback transmission unit: used to transmit the feedback score generated by the feedback analysis unit to the multi-source supplier collaborative management module. The transmitted information includes the comprehensive feedback score of each supplier; Optimization and adjustment unit: After receiving feedback information in the multi-source supplier collaborative management module, the optimization algorithm is used to re-evaluate the supplier selection criteria and adjust the supplier's comprehensive score and order allocation ratio based on the supplier's historical data, quotation, inventory status and delivery cycle.

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