Supply chain toughness and efficiency collaborative improvement method and system and electronic equipment

Abstract

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

Description

Technical Field

[0001] The present application relates to the field of supply chain management technology, and in particular to a method, system and electronic equipment for synergistically improving supply chain resilience and efficiency. Background Art

[0002] Supply chain refers to a functional chain that connects suppliers, manufacturers, distributors and end users around the core enterprise through the control of information flow, logistics flow and capital flow, starting from the purchase of raw materials, making intermediate products and final products, and then delivering products to consumers through sales networks. Supply chain management is the process of planning, organizing, coordinating and controlling the logistics, information flow and capital flow in the supply chain. The goal is to improve operational efficiency and enhance the competitiveness of enterprises in the market by optimizing resource allocation and processes.

[0003] With the increasing degree of globalization and the increasing uncertainty of the external environment, the world is currently undergoing a major change that has not been seen in a century, and supply chain risk shock events are frequent. For example, geopolitical issues have led to a supply shortage of key components (such as chips), extreme weather disasters have caused factories in some regions to stop production, and waterway blockages have caused some logistics to be blocked. These events can cause supply chain disruptions. In order to deal with such supply chain disruptions, traditional supply chain management solutions will adopt strategies such as adding a large amount of redundant inventory, dispersing the distribution of raw material suppliers, and increasing the layout of factories in different locations. However, these strategies may cause companies to pay high costs and reduce supply chain management efficiency. This way of resisting risk shocks at the expense of cost and efficiency cannot effectively improve the performance of the company, but may affect the sustainable development of the company.

[0004] Therefore, how to ensure the supply chain's ability to resist risk shocks and recover quickly while taking into account the efficiency of the supply chain has become a technical problem that companies urgently need to solve in order to improve the overall performance of the supply chain and maintain sustainable development. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a method, system and electronic device for synergistically improving supply chain resilience and efficiency, so as to ensure the supply chain's ability to resist risk shocks and its ability to recover quickly while taking into account the efficiency of the supply chain, further improve the comprehensive performance of the enterprise's supply chain, and maintain the sustainable development of the enterprise.

[0006] In a first aspect, an embodiment of the present application provides a method for collaboratively improving supply chain resilience and efficiency, wherein the method includes:

[0007] Obtain various supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient;

[0008] Based on the supply chain management variable data, various supply chain strategies to be evaluated that meet preset supply chain management constraints are screened out, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein:

[0009] The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order;

[0010] From the supply chain strategies to be evaluated, the optimal supply chain strategy with the largest supply chain comprehensive target value is output, wherein the supply chain comprehensive target value G is calculated based on the following formula:

[0011] G=w1·E+w2·R

[0012] Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

[0013] In some possible embodiments, the preset continuous supply constraint condition satisfies the following formula:

[0014]

[0015] in, The quantity of raw material j or component j currently provided by supplier i. There are N suppliers in total. j is the quantity of the same raw material j or component j currently in stock within the enterprise, y j To ensure the enterprise's demand for the same raw materials or components for normal and continuous production.

[0016] In some possible embodiments, the preset factory production capacity constraint condition satisfies the following formula:

[0017]

[0018] in, is the maximum output of the mth factory of the enterprise for the target product k, M is the number of factories owned by the enterprise, D k The total demand for the target product currently by the company's customers.

[0019] In some possible embodiments, the preset order fulfillment constraint condition satisfies the following formula:

[0020]

[0021] Among them, T q is the transportation time required by logistics provider q for a certain link in the supply chain, Q is the total number of logistics providers required by the enterprise to fulfill the target product, and T max The latest delivery time specified in the customer's order.

[0022] In some possible embodiments, the supply chain efficiency value E is calculated by the following formula:

[0023] E=E c ·C+E t ·T

[0024] Where C is the total cost of the supply chain strategy to be evaluated, E c is the weight coefficient of the total cost of the supply chain strategy to be evaluated, T is the total time of the supply chain strategy to be evaluated, and E t is the weight coefficient of the total time of the supply chain strategy to be evaluated;

[0025] The supply chain resilience value R is calculated by the following formula:

[0026] R=R p ·P+R s ·S

[0027] Where P is the risk impact resistance coefficient of the supply chain strategy to be evaluated, R p is the weight coefficient of the risk impact resistance coefficient of the supply chain strategy to be evaluated, S is the recovery coefficient of the supply chain strategy to be evaluated, R s is the weight coefficient of the recovery coefficient of the supply chain strategy to be evaluated.

[0028] In some possible embodiments, the method further includes:

[0029] Acquire the real-time status of the supply chain in real time, and dynamically adjust the supply chain according to the optimal supply chain strategy;

[0030] The real-time status and the dynamically adjusted supply chain status optimization results are displayed in real time in the user interaction interface.

[0031] In some possible embodiments, the method further includes:

[0032] The operation instruction input by the user in the user interaction interface is obtained and parsed, and in response to the operation instruction, the real-time status of the supply chain corresponding to the operation instruction is displayed on the user interaction interface.

[0033] In a second aspect, an embodiment of the present application provides a system for collaboratively improving supply chain resilience and efficiency, wherein the system includes:

[0034] A data collection module is used to obtain the supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient;

[0035] The first calculation module is used to screen out various supply chain strategies to be evaluated that meet preset supply chain management constraints based on the supply chain management variable data, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein:

[0036] The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order;

[0037] The second calculation module is used to output the optimal supply chain strategy with the largest supply chain comprehensive target value from each of the supply chain strategies to be evaluated, wherein the supply chain comprehensive target value G is calculated based on the following formula:

[0038] G=w1·E+w2·R

[0039] Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, wherein the electronic device includes:

[0041] A processor and a memory for storing programs,

[0042] Among them, the program includes instructions, and when the instructions are executed by the processor, the processor executes the method for synergistically improving supply chain resilience and efficiency as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to enable a computer to execute the method for synergistically improving supply chain resilience and efficiency as described in the first aspect.

[0044] Beneficial effects of this application:

[0045] The present application provides a method, system and electronic device for collaboratively improving supply chain resilience and efficiency, wherein the method obtains various supply chain management variable data of the supply chain, such as inventory levels, supplier attributes, customer orders, transportation time, cost, risk shock resistance coefficient, recovery coefficient, etc., and then based on the various supply chain management variable data, screens out various supply chain strategies to be evaluated that meet one or more of the preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints, and then outputs the optimal supply chain strategy with the largest supply chain comprehensive target value through the calculation formula of the supply chain comprehensive target value.

[0046] By selecting the embodiment of the present application and utilizing supply chain management variable data, different supply chain strategies are dynamically generated in multiple dimensions such as supplier selection, inventory decision-making, production planning, and transportation planning. Then, a calculation formula for the comprehensive target value constructed based on the supply chain efficiency value and the supply chain resilience value is adopted to determine an optimal supply chain strategy that can not only ensure supply chain efficiency but also resist risk shocks and recover quickly, thereby further helping to improve the comprehensive performance of the enterprise's supply chain and maintain the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A schematic diagram of a flow chart of a method for collaboratively improving supply chain resilience and efficiency provided in an embodiment of the present application is shown;

[0049] Figure 2 Another schematic diagram of a flow chart of a method for collaboratively improving supply chain resilience and efficiency provided in an embodiment of the present application is shown;

[0050] Figure 3 A schematic diagram of a system architecture of a supply chain resilience and efficiency collaborative improvement system provided by an embodiment of the present application is shown;

[0051] Figure 4 A schematic diagram of a processing flow of a system for collaboratively improving supply chain resilience and efficiency provided by an embodiment of the present application is shown;

[0052] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement an embodiment of the present application is shown. DETAILED DESCRIPTION

[0053] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0054] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0055] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0056] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0057] In order to achieve the goal of ensuring the supply chain's ability to resist risk shocks and its ability to recover quickly while taking into account the efficiency of the supply chain, thereby further improving the overall performance of the enterprise's supply chain and maintaining the sustainable development of the enterprise. The present application provides a method, system and electronic device for collaboratively improving supply chain resilience and efficiency. Among them, in the first aspect, the present application provides a method for collaboratively improving supply chain resilience and efficiency, which can be applied to any system or electronic device with supply chain management capabilities, wherein the system with supply chain management capabilities can be a software system or a hardware system. The types of electronic devices include, but are not limited to: personal mobile terminals, computers or servers, etc. Among them, in some embodiments, the method for collaboratively improving supply chain resilience and efficiency provided by the present application can be as follows: Figure 1 As shown, the following steps are included:

[0058] S11. Acquire various supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient;

[0059] S12. Based on the supply chain management variable data, various supply chain strategies to be evaluated that meet preset supply chain management constraints are screened out, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein:

[0060] The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order;

[0061] S13. Outputting the optimal supply chain strategy with the largest supply chain comprehensive target value from the supply chain strategies to be evaluated, wherein the supply chain comprehensive target value G is calculated based on the following formula:

[0062] G=w1·E+w2·R

[0063] Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

[0064] In the embodiments of the present application, collaborative improvement refers to improving the efficiency of the supply chain while improving the resilience of the supply chain, achieving the dual-objective optimization of supply chain resilience and efficiency, that is, maximizing the comprehensive target value of the supply chain. The embodiments of the present application are selected to utilize supply chain management variable data to dynamically generate different supply chain strategies in multiple dimensions such as supplier selection, inventory decision-making, production planning, and transportation planning. Then, a calculation formula for the comprehensive target value obtained based on the supply chain efficiency value and the supply chain resilience value is used to determine an optimal supply chain strategy that can not only ensure supply chain efficiency, but also resist risk shocks and recover quickly, further helping to improve the comprehensive performance of the enterprise's supply chain and maintain the sustainable development of the enterprise. The method for collaboratively improving supply chain resilience and efficiency provided in this application is helpful for enterprises to achieve a balance between redundancy and cost, a balance between risk prevention and control and operational efficiency, and a balance between short-term response and long-term growth, so as to achieve balanced growth in the resilience and efficiency of the entire supply chain.

[0065] Specifically, it can be combined with Figure 2 The process diagram shown in the figure can be understood. In the order production process, the method and system provided by this application are introduced to build a system that takes into account supply chain resilience and efficiency based on actual supply chain management variable data, output the optimal supply chain strategy, and then issue corresponding instructions to different suppliers and different factories based on the supply chain strategy, so as to achieve timely guarantee of customer order production under the condition of appropriate inventory costs, and finally deliver the corresponding order products to customers in a timely manner. In this way, the method provided by the embodiment of the present application can take into account the advantages of timely supply, efficient production, and on-time delivery, while being able to resist risks and stabilize operational advantages. Through the appropriate amount of inventory, factories, multiple suppliers and other resource reserves, it can enhance the adaptation and recovery to sudden risk shock events, thereby achieving both resilient and efficient supply chain management.

[0066] The following will describe the above steps S11 to S13 in detail with reference to specific examples:

[0067] In this article, resilience refers to the resilience of the supply chain, which refers to the ability of the supply chain to resist risks and recover quickly. Supply chain resilience is a supply chain's ability to respond and recover when faced with disruption risks. Supply chain resilience can help companies respond more quickly and forcefully after the supply chain is hit by disruption risks, thereby reducing supply chain losses and quickly recovering to normal production and operation. Among them, supply chain disruption risks include endogenous disruption risks and exogenous disruption risks. Endogenous disruption risks come from within the supply chain, such as factory shutdowns and production interruptions within a single enterprise. Exogenous disruption risks come from outside the supply chain, such as geopolitical conflicts, sudden outbreaks, natural disasters, etc. Efficiency refers to the efficiency of the supply chain, which refers to the ability of the supply chain to minimize costs and time while meeting customer needs.

[0068] In the embodiment of the present application, the supply chain management variable data is a general term for the quantity, cost, time and other information involved in each link of the entire supply chain. In the embodiment of the present application, the supply chain management variable data may include: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient. Among them:

[0069] Inventory level refers to the quantity of various raw materials, parts, semi-finished products, finished products and other materials related to the company's production and operation stored in temporary warehouses.

[0070] Supplier attributes refer to attributes such as the supplier's location, supplied product types, and supply capabilities.

[0071] A customer order refers to the demand made by a customer, including the quantity of products required by the customer, the delivery time required by the customer, and other information.

[0072] Transportation time refers to the turnaround time consumed by logistics providers in the process of product transportation.

[0073] Cost is a general term for all costs related to a product, including: raw material procurement cost, inventory cost, production cost, transportation cost, etc.

[0074] The risk shock resistance coefficient is used to characterize the ability of the supply chain to resist risk shocks. Specifically, it indicates the ability of the enterprise's supply chain to resist sudden risk shock events. It is measured by the length of time that the enterprise's supply chain remains unaffected after the shock event. Among them, the length of time that is not affected by the shock event is positively correlated with the risk shock resistance coefficient, that is, the longer the length of time that is not affected by the shock event, the greater the value of the risk shock resistance coefficient.

[0075] As an implementation method, the risk shock resistance coefficient can be obtained by constructing a simulation mathematical model, which is a neural network model, and can be obtained by training the impact events that occurred in the historical supply chain and the information associated with the impact events (such as the type, time, location, etc. of the event) as sample data, and using the neural network model to analyze the time the supply chain remains unaffected after the impact event occurs. In this way, when executing step S11, the impact event type, the duration of the impact event, and the location of the impact event can be input into the trained neural network model, and the neural network model can quickly output the risk shock resistance coefficient corresponding to different supply chain strategies based on the input parameters.

[0076] The recovery coefficient is used to characterize the resilience of the supply chain in the face of shocks, and specifically refers to the ability of the company's supply chain to recover from an interrupted or affected state to a normal state when responding to sudden risk shock events. It is specifically measured by the length of time from the beginning of the impact on the company's supply chain to the resumption of normal production and operations after the impact event. Among them, under the impact event, there is a negative correlation between the length of time from the beginning of the impact to the resumption of normal production and operations and the recovery coefficient, that is, the longer the length of time from the beginning of the impact to the resumption of normal production and operations, the smaller the value of the recovery coefficient. Similar to the above-mentioned method of obtaining the risk shock resistance coefficient, when executing step S11, a simulation mathematical model can be constructed based on historical data, model training can be performed, and then the recovery coefficient can be obtained based on the model output results obtained through training.

[0077] As an implementation method, step S11 is executed to obtain various supply chain management variable data of the supply chain. Various variable data in various links of the supply chain can be obtained through the set sensors. As an example, the real-time inventory quantity of a part A in the warehouse can be obtained through the smart sensors set in the warehouse, such as IOT (Internet of Things sensor) sensors.

[0078] As another embodiment, when executing step S11, the required supply chain management variable data can also be read from each data interface by docking with the data interface of each database of the existing supply chain management system. Exemplarily, the required supply chain management variable data can be obtained through the API (Application Programming Interface) data interface opened by each system such as the internal digital system of the enterprise, such as the ERP (Enterprise Resource Planning) system, the SRM (Supplier Relationship Management) system, the CRM (Customer Relationship Management System) system, the supply chain control tower, and the supply chain simulation system. As an example, the available quantity of a certain raw material at the supplier can be obtained through the API interface connected to the SRM system. The current demand situation of a certain product on the customer side can also be obtained through the API interface connected to the CRM system. The risk impact resistance coefficient and recovery coefficient output by the above-mentioned simulation network model can be obtained through the supply chain simulation system.

[0079] Based on the various supply chain management variable data obtained in step S11, step S12 is executed, and the various supply chain management data are used as input parameters and input into various preset constraints. The judgment conditions specified in the preset constraints are automatically calculated to automatically calculate whether they are met, and the supply chain strategies to be evaluated that meet one or more of the preset continuous supply constraints, the preset factory production capacity constraints, and the preset order fulfillment constraints are screened out.

[0080] Among them, the supply chain strategy here is a comprehensive solution to assist enterprises in making supply chain management decisions. Different supply chain strategies can be obtained according to different constraints that are met. Among them, only one of the three constraints can be met, or any two of the three constraints can be met. As a preferred implementation method, it can be stipulated that each supply chain strategy to be evaluated must meet the three constraints at the same time. In the embodiment of the present application, the supply chain strategy can be understood as a plan that has not yet been executed. For example, a deployment plan that meets the preset continuous supply constraint can be specifically determined as a supply chain strategy to be evaluated that meets the preset continuous supply constraint, wherein the deployment plan specifies which raw materials or components are to be deployed.

[0081] In some embodiments, the preset continuous supply constraint condition satisfies the following formula:

[0082]

[0083] in, The quantity of raw material j or component j currently provided by supplier i. There are N suppliers in total, which can be obtained through the SRM system. j is the quantity of the same raw material j or component j currently in stock within the enterprise, which can be obtained through sensors in the warehouse or inventory management system, y j In order to ensure the normal and continuous production of enterprises, the demand for the same raw materials or parts can be obtained through the ERP system. Among them, normal and continuous production means that there will be no interruption or stop of production.

[0084] Through the preset continuous supply constraint formula provided in the embodiment of the present application, the sum of the quantity of raw materials or components that can be provided by the supplier and the quantity of the same raw materials or components currently in stock within the enterprise can be quickly determined based on the supply chain management variable data, so as to ensure the normal and continuous production of the enterprise. In this way, even if a sudden risk impact event occurs, the raw material supplier and the corresponding quantity of raw materials that can ensure the normal and continuous production of the enterprise can be quickly determined based on this constraint formula, thereby ensuring that the production of the enterprise is not interrupted while reducing the inventory cost required to consume, so as to achieve low-cost response to sudden risk impact events.

[0085] In some possible embodiments, the preset factory production capacity constraint condition satisfies the following formula:

[0086]

[0087] in, is the maximum output of the enterprise's mth factory for the target product k, M is the number of factories owned by the enterprise, which can be obtained through the enterprise ERP system, D k The total demand of the customer for the target product at present can be obtained through the CRM system.

[0088] Through the preset factory production capacity constraints provided in the embodiments of the present application, the production plan can be quickly constrained based on the current production capacity of the mth factory for the target product k and the current total customer demand for the target product, and it is stipulated that the target products produced by the production plan need to meet the overall customer demand, so as to ensure the delivery of customer orders while reducing the consumption of inventory costs by the number of target products.

[0089] In some embodiments, the preset order fulfillment constraint condition satisfies the following formula:

[0090]

[0091] Among them, T qis the transportation time required by logistics provider q for the supply chain, which can be determined based on the transportation time statistics of the transportation positioning and tracking sensor set by logistics provider q for a certain product. Q is the total number of logistics providers required by the enterprise to fulfill the target product, which can be determined based on the enterprise ERP system. T max The latest delivery time specified for a customer order can be determined through the CRM system.

[0092] By selecting the preset order fulfillment constraints provided in the embodiment of the present application, it is possible to screen out, from among numerous supply chain strategies, supply chain strategies to be evaluated whose transportation time meets the latest delivery time specified in the customer order, thereby ensuring that all produced products can be delivered before the latest delivery time required by the customer order, thereby achieving the effect of ensuring supply chain efficiency.

[0093] Through the above-mentioned preset continuous supply constraint formula, the preset factory production capacity constraint condition satisfies the following formula, and the preset order fulfillment constraint condition satisfies the following formula, it is possible to gradually screen out supply chain strategies that can support the normal continuous production of enterprises under impact events and ensure the normal delivery of products. However, it is very likely that the number of supply chain strategies screened out is still large. How to determine the optimal supply chain strategy from the screened supply chain strategies has become a problem that needs to be solved to further balance the coordinated improvement of supply chain resilience and supply chain efficiency. In the embodiment of the present application, by constructing the calculation formula for the comprehensive target value G:

[0094] G=w1·E+w2·R

[0095] Each of the screened supply chain strategies to be evaluated is screened again, that is, step S13 is executed to determine the comprehensive target value of each supply chain strategy to be evaluated, and then the supply chain strategy with the largest supply chain comprehensive target value is determined as the optimal supply chain strategy. As a possible implementation method, a multi-objective optimization model can be constructed through the above-mentioned comprehensive target value G calculation formula, and the supply chain strategy that minimizes cost and time and maximizes the supply chain's ability to resist risk shocks and rapid recovery capabilities can be determined through the multi-objective optimization model, that is, the supply chain strategy corresponding to maxG is determined through the above formula.

[0096] Wherein, E is the supply chain efficiency value, which is obtained by weighted summing the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated. For example, assuming that supply chain strategy A is evaluated, the supply chain efficiency value of supply chain A is calculated based on the total cost and total time of supply chain A. It can be calculated by the following formula:

[0097] E=E c ·C+E t ·T

[0098] Where C is the total cost of the supply chain strategy to be evaluated, E c is the weight coefficient of the total cost of the supply chain strategy to be evaluated, T is the total time of the supply chain strategy to be evaluated, and E t is the weight coefficient of the total time of the supply chain strategy to be evaluated. Among them, C, as the total cost of the supply chain strategy to be evaluated, includes: the sum of procurement cost, inventory cost, production cost, and transportation cost. That is, C = the sum of procurement cost + the sum of inventory cost + the sum of production cost + the sum of transportation cost. Similarly, T, as the total time of the supply chain strategy to be evaluated, includes production time and transportation time, that is, T = production time + transportation time.

[0099] R is the supply chain resilience value, which is obtained by weighted summing the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated. For example, assuming that supply chain strategy A is evaluated, the supply chain resilience value of supply chain strategy A is calculated, which can be obtained by weighted summing the risk shock resistance coefficient of supply chain strategy A and the recovery coefficient of supply chain strategy A. R can be calculated specifically by the following formula:

[0100] R=R p ·P+R s ·S

[0101] Where P is the risk impact resistance coefficient of the supply chain strategy to be evaluated, R p is the weight coefficient of the risk impact resistance coefficient of the supply chain strategy to be evaluated, S is the recovery coefficient of the supply chain strategy to be evaluated, R s is the weight coefficient of the recovery coefficient of the supply chain strategy to be evaluated.

[0102] In the embodiment of the present application, the weight coefficients are: the weight coefficient w1 of the supply chain efficiency value, the weight coefficient w2 of the supply chain resilience value, the weight coefficient E of the total cost of the supply chain strategy to be evaluated c , the weight coefficient E of the total time of the supply chain strategy to be evaluated t , the weight coefficient R of the risk impact resistance coefficient of the supply chain strategy to be evaluated p , the weight coefficient R of the recovery coefficient of the supply chain strategy to be evaluated s . Among them, w1 and w2 need to satisfy w1+w2=1. In this way, the effect of synergistically improving supply chain resilience and supply chain efficiency can be achieved. The above weight coefficients can be flexibly set by the enterprise according to its own development needs, or set by industry experts. This application does not strictly limit the specific values.

[0103] In addition, in the embodiment of the present application, according to the definition of supply chain efficiency, it can be seen that supply chain efficiency is usually negatively correlated with supply chain cost and supply chain time, and the definition of supply chain resilience can be seen that in the embodiment of the present application, supply chain resilience is positively correlated with the risk resistance and rapid recovery capabilities of the supply chain, so in general, the weight coefficient E c and weight coefficient E t The values ​​of are all negative, and the weight coefficient R p and weight coefficient R s All are positive values.

[0104] As an implementation method, when executing step S13, the optimal supply chain strategy can be solved by utilizing optimization algorithms such as particle swarm optimization algorithm and genetic algorithm, based on different calculation formulas of the above-mentioned comprehensive target value G, until the optimal supply chain strategy with the largest comprehensive target value G is output, thereby providing suggestions for enterprise supply chain management.

[0105] As an implementation method, suppliers, warehouses, factories, logistics providers, etc. all belong to various links in the supply chain. The method provided in the embodiment of the present application may also include:

[0106] The real-time status of the supply chain is obtained in real time, and the supply chain is dynamically adjusted according to the optimal supply chain strategy. The real-time status obtained and the optimization results of the dynamically adjusted supply chain status are then displayed in real time in the user interaction interface.

[0107] Among them, real-time acquisition of the real-time status of the supply chain can be to obtain the real-time operation status of suppliers, warehouses, factories, and logistics providers, and then determine which suppliers the company should purchase raw materials or parts from, what level the inventory should be maintained at, which factories to select for product production, and which logistics providers to select for specific transportation based on the goals. Based on the difference in quantity before and after the adjustment, the percentage of change can be given as the optimization result and displayed in real time on the user interaction interface. As an example, the applicability (i.e. robustness) of the given optimal supply chain strategy under different conditions can also be verified by scenario simulation.

[0108] As an implementation method, the method provided in the embodiment of the present application may further include:

[0109] The operation instruction input by the user in the user interaction interface is obtained and parsed, and in response to the operation instruction, the real-time status of the supply chain corresponding to the operation instruction is displayed on the user interaction interface.

[0110] Specifically, the operation instructions input by the user can be parsed through a preset natural language processing model to determine the variable data or supply chain strategy of the supply chain that the user needs to adjust, and then the real-time status of the supply chain corresponding to the user's operation instructions can be displayed on the user interaction interface, so that the user can understand the latest supply chain status and optimization results in real time, thereby facilitating the implementation of the corresponding supply chain strategy.

[0111] In a second aspect, the embodiments of the present application provide a system for collaboratively improving supply chain resilience and efficiency. Figure 3 As shown, the system 30 includes:

[0112] The data collection module 301 is used to obtain various supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient;

[0113] The first calculation module 302 is used to screen out various supply chain strategies to be evaluated that meet preset supply chain management constraints based on the supply chain management variable data, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein:

[0114] The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order;

[0115] The second calculation module 303 is used to output the optimal supply chain strategy with the largest supply chain comprehensive target value from the supply chain strategies to be evaluated, wherein the supply chain comprehensive target value G is calculated based on the following formula:

[0116] G=w1·E+w2·R

[0117] Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

[0118] As an implementation method, the implementation process of the supply chain resilience and efficiency collaborative improvement system provided in the embodiment of the present application can be as follows: Figure 4 As shown, it includes the following steps:

[0119] Data collection, model initialization, optimization solution, dynamic adjustment, result output and verification.

[0120] Specifically, in the data collection link, execute the above step S11, and collect various supply chain management variable data through the API data interface connection of sensors, internal enterprise digital systems (such as ERP, SRM, CRM, supply chain control tower, supply chain simulation system, etc.) and external data sources, such as the real-time inventory level of a certain material of each supplier and its own warehouse, supplier attributes, logistics provider's transportation time, cost price, supplier's risk resistance coefficient, supplier's recovery coefficient, etc.

[0121] Based on the supply chain management variable data collected by the data collection module, the model is initialized. First, the above three types of constraints are set, and then the calculation model is set based on the calculation formula of the supply chain resilience value R, the supply chain efficiency value E, and the supply chain comprehensive target value G, and the values ​​of the weight coefficients involved in the calculation model are set. The values ​​of the weight coefficients can be determined by industry experts, or they can be automatically and flexibly adjusted by the enterprise according to its own supply chain management needs and strategic development direction.

[0122] Furthermore, with the help of the calculation model after initialization, the optimal supply chain strategy with the maximum comprehensive target value G is continuously optimized. Specifically, the particle swarm optimization PSO algorithm, genetic algorithm GA algorithm and other optimization algorithms can be used to solve the calculation formula of the comprehensive target value G, and the optimal supply chain strategy corresponding to max G is obtained as the optimal strategy output.

[0123] Furthermore, by real-time monitoring of the supply chain status, such as changes in the supplier's supply capacity, fluctuations in the company's own inventory, logistics delays of logistics providers, etc., supply chain management decisions can be adjusted dynamically to achieve dynamic adjustment optimization results. Alternatively, through the artificial intelligence big model, a dialogue can be conducted with the system provided by the embodiment of the present application in natural language, so that the company can understand the latest supply chain status and optimization results in real time, thereby promoting the execution of the corresponding supply chain strategy.

[0124] Finally, the specific optimization results can be displayed through a visual user interaction interface, and the scenario simulation function can be used to verify the robustness of the optimization scheme under different conditions.

[0125] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0126] In a third aspect, the exemplary embodiments of the present application further provide an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform a method according to an embodiment of the present application when executed by the at least one processor.

[0127] The exemplary embodiment of the present application further provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present application.

[0128] The exemplary embodiments of the present application further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to enable the computer to execute the method according to the embodiment of the present application.

[0129] refer to Figure 5 , the structural block diagram of the electronic device 500 that can be used as the server or client of the present application will now be described, which is an example of hardware devices that can be applied to various aspects of the present application. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0130] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0131] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, and the input unit 506 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 507 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 may include, but is not limited to, a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0132] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the aforementioned supply chain resilience and efficiency synergistic improvement method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. In some embodiments, the computing unit 501 may be configured to execute the aforementioned supply chain resilience and efficiency synergistic improvement method by any other appropriate means (e.g., by means of firmware).

[0133] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0134] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0135] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0136] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0137] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0138] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

Claims

1. A method for collaboratively improving supply chain resilience and efficiency, characterized in that: The method comprises: Obtain various supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient; Based on the supply chain management variable data, various supply chain strategies to be evaluated that meet preset supply chain management constraints are screened out, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein: The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order; From the supply chain strategies to be evaluated, the optimal supply chain strategy with the largest supply chain comprehensive target value is output, wherein the supply chain comprehensive target value G is calculated based on the following formula: G=w1·E+w2·R Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

2. The method according to claim 1, characterized in that The preset continuous supply constraint condition satisfies the following formula: in, The quantity of raw material j or component j currently provided by supplier i. There are N suppliers in total. j is the quantity of the same raw material j or component j currently in stock within the enterprise, y j To ensure the enterprise's demand for the same raw materials or components for normal and continuous production.

3. The method according to claim 1, characterized in that The preset factory production capacity constraint condition satisfies the following formula: in, is the maximum output of the mth factory of the enterprise for the target product k, M is the number of factories owned by the enterprise, D k The total demand for the target product currently by the company's customers.

4. The method according to claim 1, characterized in that: The preset order fulfillment constraint condition satisfies the following formula: Among them, T q is the transportation time required by logistics provider q for a certain link in the supply chain, Q is the total number of logistics providers required by the enterprise to fulfill the target product, and T max The latest delivery time specified in the customer's order.

5. The method according to claim 1, characterized in that The supply chain efficiency value E is calculated by the following formula: E=E c ·C+E t ·T Where C is the total cost of the supply chain strategy to be evaluated, E c is the weight coefficient of the total cost of the supply chain strategy to be evaluated, T is the total time of the supply chain strategy to be evaluated, and E t is the weight coefficient of the total time of the supply chain strategy to be evaluated; The supply chain resilience value R is calculated by the following formula: R=R p ·P+R s ·S Where P is the risk impact resistance coefficient of the supply chain strategy to be evaluated, R p is the weight coefficient of the risk impact resistance coefficient of the supply chain strategy to be evaluated, S is the recovery coefficient of the supply chain strategy to be evaluated, R s is the weight coefficient of the recovery coefficient of the supply chain strategy to be evaluated.

6. The method according to claim 1, characterized in that The method further comprises: Acquire the real-time status of the supply chain in real time, and dynamically adjust the supply chain according to the optimal supply chain strategy; The real-time status and the dynamically adjusted supply chain status optimization results are displayed in real time in the user interaction interface.

7. The method according to claim 6, characterized in that The method further comprises: The operation instruction input by the user in the user interaction interface is obtained and parsed, and in response to the operation instruction, the real-time status of the supply chain corresponding to the operation instruction is displayed on the user interaction interface.

8. A system for collaboratively improving supply chain resilience and efficiency, characterized in that: The system comprises: A data collection module is used to obtain various supply chain management variable data of the supply chain, wherein the supply chain management variable data includes: inventory level, supplier attributes, customer orders, transportation time, cost, risk impact resistance coefficient, and recovery coefficient; The first calculation module is used to screen out various supply chain strategies to be evaluated that meet preset supply chain management constraints based on the supply chain management variable data, wherein the preset supply chain management constraints are one or more of preset continuous supply constraints, preset factory production capacity constraints, and preset order fulfillment constraints; wherein: The preset continuous supply constraint condition stipulates that the sum of the quantity of raw materials or parts currently provided by the supplier and the quantity of the same raw materials or parts currently in stock within the enterprise is greater than or equal to the enterprise's demand for the same raw materials or parts to ensure normal and continuous production; the preset factory production capacity constraint condition stipulates that the sum of the current maximum output of the target product by each factory of the enterprise is greater than or equal to the total demand of the customer for the target product; the preset order fulfillment constraint condition stipulates that the sum of the transportation time required for each link of the supply chain is less than or equal to the latest delivery time required by the customer's order; The second calculation module is used to output the optimal supply chain strategy with the largest supply chain comprehensive target value from each of the supply chain strategies to be evaluated, wherein the supply chain comprehensive target value G is calculated based on the following formula: G=w1·E+w2·R Among them, E is the supply chain efficiency value, which is obtained by weighted summing up the total cost of the supply chain strategy to be evaluated and the total time of the supply chain strategy to be evaluated, w1 is the weight coefficient of the supply chain efficiency value, R is the supply chain resilience value, which is obtained by weighted summing up the risk shock resistance coefficient of the supply chain strategy to be evaluated and the recovery coefficient of the supply chain strategy to be evaluated, w2 is the weight coefficient of the supply chain resilience value, and w1+w2=1.

9. An electronic device, characterized in that: The electronic device comprises: A processor and a memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-7.

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