Supply chain network diagnosis method and device and storage medium

Abstract

The invention discloses a supply chain network diagnosis method and device and a storage medium. The method comprises the following steps: determining a first performance indicator of each node in a supply chain network under a constraint condition according to a product design and performance association model; and on the basis of the first performance indicator and a second performance indicator of each node in historical data of an abnormal order, supply abnormal information of the supply chain network is determined, and the supply abnormal information of the supply chain network comprises a supply abnormal node or a product design abnormality. According to the supply chain network diagnosis method provided by the invention, the supply abnormal information can be quickly and accurately determined.

Description

Technical Field

[0001] The present application relates to the field of supply chain technology, and in particular to a supply chain network diagnosis method, device, and storage medium. Background Art

[0002] Supply chain network diagnosis is a method based on management science, operations research and systems engineering, which is used to digitally analyze and locate abnormalities in supply chain performance indicators.

[0003] The Supply Chain Operations Reference Model (SCOR) and other supply chain network diagnostic technologies in related technologies mainly define the planning, procurement, production, distribution, and return processes, taking into account operational management factors such as inventory, suppliers, and logistics. However, it is difficult to quickly and accurately identify supply anomalies. Summary of the Invention

[0004] The present application provides a supply chain network diagnosis method, device, and storage medium, which can quickly and accurately locate supply anomaly information.

[0005] In a first aspect, the present application provides a supply chain network diagnostic method. This method can be executed by a terminal or a server. In this method, based on a product design and performance correlation model, a first performance indicator for each node in the supply chain network under constraints is determined. Supply anomaly information about the supply chain network is determined based on the first performance indicator and a second performance indicator for each node in historical data on abnormal orders. This supply anomaly information includes abnormal supply nodes or product design anomalies.

[0006] In an embodiment of the present application, the performance indicators of each node that meets the constraints in theoretical conditions are determined by utilizing the product design and performance association model, and then compared with the performance indicators of each node in the historical data of actual abnormal orders to determine the supply anomaly information of the supply chain network. This method can quickly and accurately determine the supply anomaly information.

[0007] In the implementation of the present application, the product design and performance association model may include the relationship between product design strategies and supply strategies and performance indicators.

[0008] In the implementation of this application, supply strategy refers to the design of supply networks using different strategies to form different supply chain networks. For example, supply strategy includes the inventory level and supplier allocation ratio of each node in the supply chain network.

[0009] In the implementation of this application, product design strategies refer to the different strategies that can be used to design a product, resulting in different product family bill of materials (GBOM) networks and supply chain networks. For example, a mobile phone can be designed with two cameras or just one. Different designs will result in different GBOM networks and supply chain networks, leading to different performance indicators.

[0010] In the implementation of the present application, the performance indicator may include one or more performance indicators, which may include supply cycle, cost, asset operation efficiency, supply reliability, supply flexibility, etc.

[0011] For example, the performance indicator may include supply cycle and cost. When the performance indicator includes supply cycle and cost, the supply cycle may be used as a constraint condition, or the cost may be used as a constraint condition.

[0012] In the implementation of this application, constraints can be set based on performance indicators. For example, when the performance indicators include supply cycle and cost, the constraint can be to ensure that the supply cycle does not exceed a set value, and the corresponding objective function is to minimize cost. For example, the constraint can be to ensure that the cost does not exceed a set value, and the corresponding objective function is to minimize the supply cycle. The set values ​​here can be set as needed, and this application does not impose any restrictions on this.

[0013] Using constraints and corresponding objective functions, the product design strategy and supply strategy in the product design and performance association model are used as variables to find the supply cycle and cost that meet the constraints or are closest to the constraints, which is the first performance indicator mentioned above.

[0014] Optionally, the method further includes:

[0015] Based on the node information of each node in the GBOM network and the supply chain network, a product design and performance association model is established, and the GBOM network corresponds to the supply chain network.

[0016] Among them, the GBOM network is combined with the supply chain network to form a product family supply chain network, which constitutes the basis of modeling.

[0017] The following uses supply cycle and cost as performance indicators to illustrate the modeling scheme of the product design and performance correlation model:

[0018] For example, the node information may include inventory, number of suppliers, and information about each supplier, and the supplier information may include supply cycle information and cost information. In other examples, the node information or supplier information may also include other content, which is not limited here.

[0019] In this implementation, the product family supply chain network is digitally modeled as the basis for quantitative modeling and analysis, linking product design with supply design, breaking the original separation between independent operations of product design and supply operations, and ensuring that the model considers both design and supply factors during diagnosis, thereby ensuring diagnostic accuracy.

[0020] In the implementation of this application, a product design and performance association model is established based on the node information of each node in the GBOM network and the supply chain network, including:

[0021] Determine the adjacency matrix based on the GBOM network, and the element A in the adjacency matrix ij Represents the node P in the GBOM network i and node P j The composition relationship between them, i and j are node identifiers;

[0022] Determine the reuse degree of each node in the GBOM network based on the adjacency matrix;

[0023] Determine the planning accuracy of each node in the supply chain network based on the reuse degree of each node in the GBOM network;

[0024] Based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network, a product design and performance correlation model is established.

[0025] In this implementation, the product design and performance association model is a quantitative analysis model of design and supply. The model uses planning accuracy as an anchor point and quantitatively associates product design reuse with upstream and downstream planning accuracy, ensuring that the model considers both design and supply factors during diagnosis, thereby ensuring diagnostic accuracy.

[0026] Among them, the planning accuracy of each node in the supply chain network is determined based on the reuse of each node in the GBOM network, including: determining the planning accuracy of each node in the supply chain network based on the reuse of each node in the GBOM network and the planning accuracy of downstream nodes.

[0027] In the implementation of this application, a product design and performance correlation model is established based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network, including:

[0028] Based on historical order demand, the adjacency matrix, and the planning accuracy of each node in the supply chain network, determine the inventory quantity of each supplier under different inventory levels and supplier allocation ratios;

[0029] Based on the supply cycle and cost information of each supplier, determine the supply cycle and cost of each supplier under different inventory levels and supplier allocation ratios;

[0030] Based on the supply cycle and cost of each supplier under different inventory levels and supplier allocation ratios, a product design and performance correlation model is obtained.

[0031] In this implementation, by considering the supply cycle and cost of each supplier under different supply strategies, the relationship between supply strategy and supply cycle and cost is obtained. Combined with different product design strategies, the relationship between product design strategy and supply strategy and performance indicators can be obtained to achieve modeling.

[0032] The process of establishing the product design and performance correlation model given in the above implementation is described from the perspective of the calculation process. The process of establishing the product design and performance correlation model can also be described from the perspective of sub-models, for example:

[0033] Based on the planning accuracy and stocking level of each node, a supplier stocking sub-model is established; based on the supply cycle information and cost information of each supplier, a supply cycle and cost sub-model of each supplier under different stocking levels and supplier allocation ratios is established; based on the supplier stocking sub-model and the supply cycle and cost sub-model, a product design and performance correlation model is obtained.

[0034] Optionally, the method further includes:

[0035] Identify supply chain anomalies based on supply anomaly information in the order history data of the supply chain network;

[0036] Determine the possible causes of supply chain anomalies based on supply chain anomalies.

[0037] In this implementation, supply chain anomalies are determined through supply anomaly information, and then the possible causes of the supply chain anomalies are determined based on the supply chain anomalies, thereby achieving rapid diagnosis of the causes of the supply anomalies.

[0038] In an implementation of the present application, determining possible causes of supply chain anomalies based on supply chain anomalies may include: obtaining a supply anomaly pattern library; and determining possible causes of supply chain anomalies that match the supply chain anomalies in the supply anomaly pattern library.

[0039] Optionally, the method further includes:

[0040] Based on the product design and performance correlation model and the possible causes of supply chain anomalies, the sensitivity of the possible causes of supply chain anomalies is determined, and the possible causes of supply chain anomalies with high sensitivity are regarded as root causes.

[0041] In this implementation, based on sensitivity analysis of the impact of root causes on supply performance, improvement suggestions for the root causes are given to optimize the supply chain.

[0042] In the implementation of this application, based on the product design and performance correlation model and the possible causes of supply chain anomalies, the sensitivity of the possible causes of supply chain anomalies is determined, including:

[0043] Modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of supply chain anomalies;

[0044] Based on the product design and performance association model with modified variables, the sensitivity of determining the possible causes of supply chain anomalies is high when there is no supply anomaly in the supply chain network under constraints.

[0045] In this implementation, the model is combined with the control of design variables other than the root cause indicators. A sensitivity analysis of the root cause indicators is then performed. Specifically, each time a root cause indicator is modified as a variable, such as the order allocation ratio or inventory allocation ratio, the impact on supply performance and the resulting trends are examined. When a small change in a product design or supply design root cause indicator (such as improving the reuse of bottleneck node components by 10%) exceeds a certain threshold (e.g., improving supply cycle time by more than 20%), it can be confirmed that the root cause of poor supply performance is that root cause, and improvements to that root cause design indicator can be prioritized to improve supply performance.

[0046] In a second aspect, the present application provides a supply chain network diagnosis device, which includes:

[0047] a determination unit, configured to determine, based on a product design and performance correlation model, a first performance indicator of each node in the supply chain network under a constraint condition;

[0048] The diagnosis unit is used to determine the supply abnormality information of the supply chain network based on the first performance indicator and the second performance indicator of each node in the historical data of the abnormal order. The supply abnormality information of the supply chain network includes supply abnormal nodes or product design abnormalities.

[0049] Optionally, the product design and performance association model includes the relationship between product design strategies and supply strategies and performance indicators.

[0050] Optionally, the device further comprises:

[0051] The modeling unit is used to establish a product design and performance association model based on the node information of each node in the GBOM network and the supply chain network, and the GBOM network corresponds to the supply chain network.

[0052] Optionally, a modeling unit is used to determine an adjacency matrix based on the GBOM network, and the element A in the adjacency matrix ij Represents the node P in the GBOM network i and node P jThe composition relationship between them, i and j are node identifiers; the reuse of each node in the GBOM network is determined based on the adjacency matrix; the planning accuracy of each node in the supply chain network is determined based on the reuse of each node in the GBOM network; based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network, a product design and performance association model is established.

[0053] Optionally, the modeling unit is used to determine the stocking quantity of each supplier under different stocking levels and supplier allocation ratios based on historical order demand, the adjacency matrix and the planning accuracy of each node in the supply chain network; determine the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios based on the supply cycle information and cost information of each supplier; and obtain a product design and performance association model based on the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios.

[0054] Optionally, the device further comprises:

[0055] The analysis unit is used to determine supply chain anomalies based on supply anomaly information in order history data of the supply chain network; and to determine possible causes of the supply chain anomalies based on the supply chain anomalies.

[0056] Optionally, the analysis unit is further configured to determine the sensitivity of the possible causes of the supply chain anomaly based on the product design and performance correlation model and the possible causes of the supply chain anomaly, and to take the possible causes of the supply chain anomaly with high sensitivity as the root causes.

[0057] Optionally, the analysis unit is used to modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of the supply chain anomaly; based on the product design and performance association model after the modified variables, when there is no supply anomaly in the supply chain network under the constraint conditions, the sensitivity of determining the possible causes of the supply chain anomaly is high.

[0058] In a third aspect, an electronic device is provided. The electronic device includes a processor and a memory. The memory is used to store software programs and modules.

[0059] In one example, the electronic device may be a supply chain network diagnostic device, and accordingly, the processor implements the method of the above-mentioned first aspect or any possible implementation of the first aspect by running or executing the software program and / or module stored in the memory.

[0060] Optionally, there are one or more processors and one or more memories.

[0061] Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

[0062] In the specific implementation process, the memory can be a non-transitory memory, such as a read-only memory (ROM), which can be integrated on the same chip as the processor or be set on different chips. This application does not limit the type of memory and the setting method of the memory and the processor.

[0063] In a fourth aspect, a computer program product is provided, wherein the computer program product includes computer program code, and when the computer program code is executed by a computer, the computer executes the method in the first aspect or any possible implementation of the first aspect.

[0064] In a fifth aspect, the present application provides a computer-readable storage medium, which is used to store program codes executed by a processor, wherein the program codes include methods for implementing the above-mentioned first aspect or any possible implementation of the first aspect.

[0065] In a sixth aspect, a chip is provided, comprising a processor, the processor being used to call and execute instructions stored in a memory from the memory, so that an electronic device equipped with the chip executes the method in the above-mentioned first aspect or any possible implementation of the first aspect.

[0066] In a seventh aspect, another chip is provided. The other chip includes an input interface, an output interface, a processor, and a memory. The input interface, the output interface, the processor, and the memory are connected via an internal connection path. The processor is configured to execute code in the memory. When the code is executed, the processor is configured to perform the method according to the first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of a supply chain network diagnosis method provided by an embodiment of the present application;

[0068] Figure 2 This is a flow chart of a supply chain network diagnosis method provided by an embodiment of the present application;

[0069] Figure 3 This is a flow chart of establishing a product design and performance correlation model provided by an embodiment of the present application;

[0070] Figure 4 This is a schematic diagram of the structure of a GBOM network provided in an embodiment of the present application;

[0071] Figure 5 This is a schematic diagram of the structure of an adjacency matrix provided in an embodiment of the present application;

[0072] Figure 6 This is a schematic diagram of the structure of a supply chain network provided by an embodiment of the present application;

[0073] Figure 7 This is a flow chart for determining supply anomaly information provided by an embodiment of the present application;

[0074] Figure 8 This is a block diagram of a supply chain network diagnostic device provided by an embodiment of the present application;

[0075] Figure 9 It is a structural diagram of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0076] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0077] This application will present various aspects, embodiments, or features in the context of systems that may include multiple devices, components, modules, etc. It should be understood and appreciated that each system may include additional devices, components, modules, etc., and / or may not include all of the devices, components, modules, etc. discussed in conjunction with the figures. Furthermore, combinations of these aspects may also be used.

[0078] In addition, in the embodiments of the present application, words such as "exemplarily" and "such as" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as an "example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present concepts in a concrete way. In the embodiments of the present application, "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent.

[0079] To facilitate understanding of the solutions provided in the embodiments of the present application, the technical terms used in the embodiments of the present application are explained below:

[0080] Product family: A group of related products with the same or similar functional structure or performance.

[0081] Group Bill of Materials (GBOM): The bill of materials (BOM) for each product in a product family is grouped together to form a group bill of materials, abbreviated as GBOM.

[0082] GBOM network: The integrated assembly relationship and reverse disassembly relationship of the product family constitute the GBOM network.

[0083] Supply chain network: This is also known as the supply chain network of a product family. The GBOM network links inventory, supplier capabilities, upstream and downstream supply information, and other information at each network node. The resulting network is called the product family supply chain network.

[0084] Supply chain network diagnosis: Based on the design-supply process and result data, automatically diagnose the nodes and causes in the supply process that affect performance such as delivery time and cost.

[0085] Planning Accuracy: The degree to which product inventory matches demand during the statistical period. Calculated as follows: Planning Accuracy = MIN (Inventory, Demand) / MAX (Inventory, Demand) * 100%.

[0086] Reuse: The total number of times a component is reused in the parent hierarchy.

[0087] Stocking level: Different supply chain nodes have different tendencies to stock up in advance after forecasting user demand. For example, they tend to stock up more than the forecasted amount, or less than the demanded amount. The stocking level can be expressed as a ratio relative to the expected demand.

[0088] Supplier allocation ratio: The same supply chain node can include multiple suppliers. The inventory ratio allocated to multiple suppliers when stocking is the supplier allocation ratio.

[0089] Supply critical path: Generally speaking, when analyzing the supply cycle, the path with the longest supply cycle from components to complete machines is called the supply critical path.

[0090] Failure mode and effects analysis (FMEA): Failure mode and effects analysis is a systematic risk assessment method used to identify and evaluate the possibility of equipment or system failure and its impact on the environment and personnel.

[0091] Sensitivity analysis: Sensitivity analysis is an uncertainty analysis technique that studies the degree of impact of certain changes in relevant factors on one or a group of key indicators from a quantitative analysis perspective.

[0092] Figure 1 This is a flow chart of a supply chain network diagnosis method provided by an embodiment of the present application. The method can be executed by a terminal or a server. Figure 1 As shown, the method includes the following steps.

[0093] S11: Based on the product design and performance correlation model, determine the first performance indicator of each node in the supply chain network under the constraint conditions.

[0094] In the implementation of the present application, the product design and performance association model may include the relationship between product design strategies and supply strategies and performance indicators.

[0095] In the implementation of this application, supply strategy refers to the design of supply networks using different strategies to form different supply chain networks. For example, supply strategy includes the inventory level and supplier allocation ratio of each node in the supply chain network.

[0096] In the implementation of this application, product design strategies refer to the different strategies a product can adopt to design it, thereby forming different GBOM networks and supply chain networks. For example, a mobile phone can be designed with two cameras or just one. Different designs will result in different GBOM networks and supply chain networks, leading to different performance indicators.

[0097] In the implementation of the present application, the performance indicator may include one or more performance indicators, which may include supply cycle, cost, asset operation efficiency, supply reliability, supply flexibility, etc.

[0098] For example, the performance indicator may include supply cycle and cost. When the performance indicator includes supply cycle and cost, the supply cycle may be used as a constraint condition, or the cost may be used as a constraint condition.

[0099] In the implementation of this application, constraints can be set based on performance indicators. For example, when the performance indicators include supply cycle and cost, the constraint can be to ensure that the supply cycle does not exceed a set value, and the corresponding objective function is to minimize cost. For example, the constraint can be to ensure that the cost does not exceed a set value, and the corresponding objective function is to minimize the supply cycle. The set values ​​here can be set as needed, and this application does not impose any restrictions on this.

[0100] Using constraints and corresponding objective functions, the product design strategy and supply strategy in the product design and performance association model are used as variables to find the supply cycle and cost that meet the constraints or are closest to the constraints, which is the first performance indicator mentioned above.

[0101] S12: Based on the first performance indicator and the second performance indicator of each node in the historical data of abnormal orders, determine the supply abnormality information of the supply chain network, where the supply abnormality information of the supply chain network includes supply abnormal nodes or product design abnormalities.

[0102] Among them, the product design and performance correlation model and abnormal orders are both for the same product, and the product designs of the two are the same or similar. That is, the product design corresponding to the product design and performance correlation model and the product design corresponding to the actual order are the same, or have slight differences, such as the different number of cameras mentioned above.

[0103] After determining the theoretical first performance indicator, by comparing it with the second performance indicator of each node in the actual order history data, the supply abnormality node, that is, the bottleneck node, in the supply chain network can be determined.

[0104] When determining a supply abnormality node, if there is a node where the difference between the first performance indicator and the second performance indicator exceeds a threshold, it is determined to be a supply abnormality node. The threshold here can be set based on needs and is not limited in this application.

[0105] In some examples, the difference between the first performance indicator and the second performance indicator of each node does not exceed the threshold. In this case, it is determined that there is no supply abnormality node, and the analysis can continue to determine whether it is a product design abnormality.

[0106] In an embodiment of the present application, the performance indicators of each node that meets the constraints in theoretical conditions are determined by utilizing the product design and performance association model, and then compared with the performance indicators of each node in the historical data of actual abnormal orders to determine the supply anomaly information of the supply chain network. This method can quickly and accurately determine the supply anomaly information.

[0107] Below we use supply cycle and cost as performance indicators, combined with Figure 2 The supply chain network diagnosis method provided in the embodiment of the present application is exemplified as follows:

[0108] Figure 2 This is a flow chart of a supply chain network diagnosis method provided by an embodiment of the present application. The method can be executed by a terminal or a server. Figure 2 As shown, the method includes the following steps.

[0109] S21: Based on the node information of each node in the GBOM network and the supply chain network, a product design and performance association model is established, and the GBOM network corresponds to the supply chain network.

[0110] For example, the node information may include inventory, number of suppliers, and information about each supplier, and the supplier information may include supply cycle information and cost information. In other examples, the node information or supplier information may also include other content, which is not limited here.

[0111] Exemplarily, the supply cycle information includes at least one of the order pre-processing cycle, production preparation cycle, logistics distribution cycle, etc.; the cost information includes at least one of the material cost, manufacturing cost, capital occupation cost, warehousing cost, scrap cost, logistics cost, etc.

[0112] The order pre-processing cycle is related to the manufacturer's level of information technology. The production stocking cycle is related to the manufacturer's single-product production cycle and production scale. The logistics distribution cycle and logistics costs are related to the manufacturer's location and distribution methods. Material costs are related to material composition, material price, and other factors. Manufacturing costs are related to the manufacturer's single-product costs. Warehousing costs are related to the manufacturer's single-product warehousing costs. Scrap costs are related to the manufacturer's yield rate. This supply cycle and cost information is stored in a database, and each node in the supply chain network is associated with the node information in the database. Node information in the database is updateable.

[0113] In the implementation of this application, the stocking quantity of each node is determined based on the product stocking quantity and the GBOM network. That is, based on the composition relationship between each node in the GBOM network, the stocking quantity required by each node is reversely deduced from the product stocking quantity. The demand at the product level is consistent with the total demand for abnormal orders. The stocking quantity of each node is determined based on the demand and stocking strategy.

[0114] Figure 3 This is a flow chart of establishing a product design and performance correlation model provided by the embodiment of this application. Figure 3 As shown, step S21 includes the following steps.

[0115] S31: Determine the adjacency matrix based on the GBOM network.

[0116] Among them, the element A in the adjacency matrix ij Represents the node P in the GBOM network i and node P j The composition relationship between them, i and j are node identifiers, that is, node serial numbers, and their values ​​are both positive integers.

[0117] In the implementation of this application, the GBOM network is input, which uses structured data representation, so it can more conveniently generate the adjacency matrix A, A ij Represents P in product family GBOM j By several P i The adjacency matrix A is the equivalent transformation of the product family GBOM, covering the product design strategy.

[0118] Figure 4 This is a schematic diagram of the structure of a GBOM network provided by an embodiment of the present application. Figure 4,The GBOM network describes the product integration relationship at multiple levels, such as components, modules, ,assemblies, bare metals, complete machines, and products. Figure 4 The six-layer structure shown describes a general scenario for analyzing a product from the top layer. For products of different forms, the structure may be decomposed into two or three layers, or more layers may be added, such as adding wafer manufacturing layers upstream of components. Different layers can be expanded according to the scope of analysis and diagnosis, and this application does not impose any restrictions on this.

[0119] In the GBOM network, a node represents each component in the BOM, and an edge represents the integration relationship between components. It is a directed edge, pointing from the upstream node to the downstream node. The number on the edge represents the integration ratio. Numbering the nodes in order from upstream to downstream, there are a total of N nodes, and we can get the component node set P = {P1, P2, ..., P N},by Figure 4 Taking the GBOM network shown in the figure as an example, P={P1,P2,…,P 19}.

[0120] In other examples, the GBOM network can also be represented by the reverse disassembly process, that is, the edges represent the disassembly relationship of the components, pointing from the downstream node to the upstream node.

[0121] Figure 5 This is a schematic diagram of the structure of an adjacency matrix provided in an embodiment of the present application. Figure 5 , the adjacency matrix can be used to describe the directed graph of the GBOM network. N nodes can form an N*N adjacency matrix A, A ij The value represents P j By several P i Composition. Figure 4 Taking the GBOM network shown in the figure as an example, these 19 nodes can form a 19*19 adjacency matrix, as shown in Figure 5 shown.

[0122] For example, A 10,14 is 2, indicating P 14 By 2P 10 composition.

[0123] S32: Determine the reuse degree of each node in the GBOM network based on the adjacency matrix.

[0124] For example, node i defines the reuse degree m i =∑ j:(i,j)∈A A ij .

[0125] by Figure 4 Node P in the product family GBOM shown 10 For example, the reuse degree m 10 =∑ j:(10,j)∈A Aij =A 10,14 +A 10,15 =2+3=5.

[0126] S33: Determine the planning accuracy of each node in the supply chain network based on the reuse degree of each node in the GBOM network.

[0127] Figure 6 This is a schematic diagram of the structure of a supply chain network provided by an embodiment of the present application. Figure 6 ,The supply chain network is constructed based on GBOM and ,combined with the node information of each node. ,The figure shows only one node as an example.

[0128] Figure 6 The supply chain network shown is also known as a product family supply chain network. It integrates the product family network structure, the supply chain network structure, product design information, and supply base information. Each node in the network has product information and supply base information, which are parameters. Product information includes dimensions and weight, while supply base information includes supply cost and supply cycle information, such as the node's suppliers, each supplier's processing capabilities, and processing unit prices.

[0129] The supply cost basis information and the supply cycle basis information are used to determine the aforementioned node information and supplier information.

[0130] Each node can be divided into procurement nodes, processing and assembly nodes, and logistics and distribution nodes due to different supply attributes. The node information corresponding to different types of nodes may be different.

[0131] In this application's implementation, the planning accuracy of a node can be calculated based on product reuse and the planning accuracy of downstream nodes. The planning accuracy of the most downstream product is historical data and can be used as input. Planning accuracy at the component level is often not statistically available and needs to be calculated.

[0132] For example, based on the downstream node P j The planning accuracy f j , calculate the upstream node P connected to it i The planning accuracy is calculated as follows:

[0133]

[0134] By recursively inferring the planning accuracy of product-level nodes to the upstream layer, the planning accuracy of all nodes in the supply chain network can be obtained.

[0135] S34: Based on historical order demand, the adjacency matrix, and the planning accuracy of each node in the supply chain network, determine the inventory quantity of each supplier under different inventory levels and supplier allocation ratios.

[0136] For historical product supply anomaly orders, the product demand is known. Based on demand and planning accuracy, different supply strategies (such as stocking levels and stocking ratios for different suppliers) can be used to determine the planned stocking quantity for each node. Based on the planned stocking quantity for each node and the inventory levels at each level, the processing quantity for each node can be determined.

[0137] S35: Based on the supply cycle information and cost information of each supplier, determine the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios.

[0138] Based on the supply cycle information and cost information of each supplier, and based on the planned inventory and processing volume of each node, the supply cycle and cost of each supplier can be determined. This embodiment of the application does not elaborate on how to calculate the supply cycle and cost based on the supply cycle information and cost information.

[0139] The embodiments of the present application only use supply cycle and cost as examples. In other examples, other performance indicators can also be used for modeling.

[0140] S36: Based on the supply cycle and cost of each supplier under different inventory levels and supplier allocation ratios, a product design and performance correlation model is obtained.

[0141] In the implementation of this application, Figures 4 to 6 The figure shows a network under a product design. The same product may correspond to multiple GBOM networks and supply chain networks. Therefore, each network can determine the supply cycle and cost of each supplier under different inventory levels and supplier allocation ratios.

[0142] In the implementation of the present application, the product design and performance association model may include the relationship between the supply strategies, supply cycles, and costs corresponding to these various networks. Specifically, the product design and performance association model includes the relationship between product design strategies, supply strategies, and performance indicators. The supply strategies include the inventory levels and supplier allocation ratios for each node in the supply chain network.

[0143] Among them, through the process from step S34 to step S36, a product design and performance correlation model is established based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network.

[0144] This product design and performance correlation model is also a quantitative analysis model of design and supply. The model uses planning accuracy as an anchor point and quantitatively correlates product design reusability with upstream and downstream planning accuracy.

[0145] S22: Determine a first supply cycle and cost of each node in the supply chain network under a constraint condition based on the product design and performance association model, where the constraint condition includes at least one of the supply cycle and the cost.

[0146] Taking different product design strategies and supply strategies as design variables and the lowest cost under cycle constraints or the shortest cycle under cost constraints as the objective function, the product design strategies and supply strategies that meet the performance results are solved, and the corresponding supply cycle and cost are obtained.

[0147] Among them, the constraints can be adjusted based on performance. Here, only two common scenarios are taken as examples and are not intended to limit the embodiments of this application.

[0148] S23: Determine supply anomaly information of the supply chain network based on the first supply cycle and cost and the second supply cycle and cost of each node in the historical data of abnormal orders. The supply anomaly information of the supply chain network includes supply abnormal nodes or product design abnormalities.

[0149] Among them, the product design and performance correlation model and the abnormal orders are both for the same product, and the product designs of the two are the same or similar, that is, the product design corresponding to the product design and performance correlation model and the product design corresponding to the actual order are the same, or have slight differences, such as the different number of cameras mentioned above.

[0150] After determining the theoretical first supply cycle and cost, by comparing them with the second supply cycle and cost of each node in the actual order history data, the supply abnormality nodes, also known as bottleneck nodes, in the supply chain network can be determined.

[0151] When determining a supply abnormality node, if there is a node where the difference between the first supply cycle and cost and the second supply cycle and cost exceeds a threshold, it is determined to be a supply abnormality node. The threshold here can be set based on needs and is not limited in this application.

[0152] In some examples, if the difference between the first supply cycle and cost and the second supply cycle and cost for each node does not exceed a threshold, it is determined that there is no supply anomaly node, and further analysis can be performed to determine whether there is a product design anomaly. In other words, if it is determined that there is no supply anomaly, it may be a product design anomaly, requiring sensitivity analysis.

[0153] Figure 7 This is a flow chart of determining supply anomaly information provided by an embodiment of the present application. Figure 7The solution determined by the model is compared with historical data to identify bottleneck nodes. In supply cycle diagnosis, the path with the longest supply cycle from upstream to downstream is the supply anomaly critical path. For a single order, the bottleneck node can be different for each order, but the distribution across multiple large orders tends to be regular.

[0154] S24: Determine supply chain anomalies based on supply anomaly information in order history data of the supply chain network.

[0155] In order history data, supply cycle and cost include not only the supply cycle and cost of the node as a whole, but also the cycle and cost of each component, such as production cycle, transportation cycle, etc.

[0156] When determining the first supply cycle and cost in S22 , the cycles and costs of the various components of the node, such as the production cycle, the transportation cycle, etc., can also be determined.

[0157] After obtaining the above two aspects of information, the cycle and cost of each component of each node in the order history data can be compared with the cycle and cost of each component in the first supply cycle and cost to determine supply chain anomalies.

[0158] For example, when there is a bottleneck node, the part of the bottleneck node with the largest or largest difference between the history and the model is determined as a supply chain anomaly.

[0159] Among them, supply chain anomalies include but are not limited to procurement node cycles that are longer than the promised time, long processing times for manufacturers within the node, large differences in processing times for manufacturers within the node, long transportation waiting times, inaccurate reserve inventory locations, and inaccurate demand plans.

[0160] S25: Determine the possible causes of supply chain anomalies based on supply chain anomalies.

[0161] In an implementation of the present application, this step may include: obtaining a supply anomaly pattern library; and determining possible causes of supply chain anomalies that match supply chain anomalies in the supply anomaly pattern library.

[0162] Among them, the supply abnormal pattern library is a structured summary of the experience knowledge of product design and supply design experts with reference to FMEA in the field of reliability.

[0163] Table 1 is an example of a supply anomaly pattern library provided in an embodiment of the present application. Referring to Table 1, the supply anomaly pattern library includes phenomena, root causes, and supply design or product design indicators. The root causes of the problem types corresponding to supply delays can be divided into two categories: 1. Supply design reasons: the reasons affecting supply delays are caused by specific bottleneck nodes; 2. Product design reasons: the reasons affecting supply delays are caused by overall network supply delays (that is, each node is not much different from the theoretical value determined by the model, but the overall delivery requirements cannot be met). It is believed that the corresponding root cause may be caused by product design.

[0164] Table 1 Supply exception pattern library

[0165]

[0166] S26: Based on the product design and performance correlation model and the possible causes of supply chain anomalies, determine the sensitivity of the possible causes of supply chain anomalies and take the possible causes of supply chain anomalies with high sensitivity as the root causes.

[0167] Step S25 only determines the possible cause of the supply chain anomaly, and sensitivity analysis is needed to determine whether it is accurate.

[0168] The higher the sensitivity, the more accurate the cause is, and the lower the sensitivity, the less accurate the cause is.

[0169] For example, based on the product design and performance correlation model and the possible causes of supply chain anomalies, the sensitivity of the possible causes of supply chain anomalies is determined, including:

[0170] Modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of supply chain anomalies;

[0171] Based on the product design and performance association model with modified variables, the sensitivity of determining the possible causes of supply chain anomalies is high when there is no supply anomaly in the supply chain network under constraints.

[0172] In steps S24 and S25, if the root cause product design indicators or supply design indicators that may affect supply performance are found, the model is combined with the design variables other than the root cause indicators to remain unchanged. A sensitivity analysis of the root cause indicators is then performed. This involves changing one root cause indicator at a time, such as the order allocation ratio or inventory allocation ratio, to examine its impact on supply performance results and any trends. When a small change in a product design or supply design root cause indicator (such as improving the reuse of bottleneck node components by 10%) has an impact on supply performance results that exceeds a certain threshold (such as an improvement in supply cycle time of more than 20%), it can be determined that the sensitivity of the possible cause of the supply chain anomaly is high, and it is determined that the possible cause of the supply chain anomaly is the root cause of poor supply performance. Focus can be placed on improving this root cause design indicator to improve supply performance.

[0173] In the embodiment of the present application, supply chain network diagnosis can be divided into three parts: (1) Structural mapping: using multi-layer physical models such as components, modules, assemblies, bare metal, complete machines, and products to describe the product composition, using directed graphs and adjacency matrices to describe the integration and proportional relationship of the GBOM network, and combining supply information to form a product family supply network model, digitally modeling the product family supply chain network as a structural basis for quantitative modeling analysis and abnormal diagnosis. (2) Association model: Constructing a model that includes quantitative association relationships within and between supply nodes, using planning accuracy as an anchor point, and establishing a quantitative association model between product design reuse and upstream and downstream planning accuracy to provide a quantitative basis for diagnosis. Linking product design with supply design breaks the original separation of product design and supply operation, which are independent operations. (3) Diagnostic Analysis: Based on historical data and models, optimization theory is applied to identify the product configuration and supply design solution that achieves theoretically optimal performance. This is then compared with historical data from various stages to identify bottleneck nodes that affect supply performance indicators (not limited to supply cycle, supply cost, etc.). The supply anomaly pattern library is used to quickly diagnose the root cause of supply anomalies. Based on sensitivity analysis, the root cause's impact on supply performance is analyzed to provide improvement suggestions. This diagnostic analysis method, which combines the supply anomaly pattern library with models, ensures rapid diagnosis and provides design improvement suggestions.

[0174] Figure 8 This is a block diagram of a supply chain network diagnostic device provided by an embodiment of the present application. The supply chain network diagnostic device can be implemented as all or part of a terminal or server through software, hardware, or a combination of both. The device is used to perform Figures 1 to 7 The supply chain network diagnosis method shown in any one of the items may include: a determination unit 401 and a diagnosis unit 402 .

[0175] The determining unit 401 is configured to determine the first performance indicator of each node in the supply chain network under the constraint condition according to the product design and performance association model;

[0176] The diagnosis unit 402 is used to determine the supply abnormality information of the supply chain network based on the first performance indicator and the second performance indicator of each node in the historical data of abnormal orders. The supply abnormality information of the supply chain network includes supply abnormal nodes or product design abnormalities.

[0177] Optionally, the product design and performance association model includes the relationship between product design strategies and supply strategies and performance indicators, and the supply strategies include the inventory levels and supplier allocation ratios of each node in the supply chain network.

[0178] Optionally, the device further comprises:

[0179] The modeling unit 403 is used to establish a product design and performance association model based on the node information of each node in the GBOM network and the supply chain network, and the GBOM network corresponds to the supply chain network.

[0180] Optionally, the modeling unit 403 is configured to determine an adjacency matrix based on the GBOM network, wherein the element A in the adjacency matrix is ij Represents the node P in the GBOM network i and node P j The composition relationship between them, i and j are node identifiers; the reuse of each node in the GBOM network is determined based on the adjacency matrix; the planning accuracy of each node in the supply chain network is determined based on the reuse of each node in the GBOM network; based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network, a product design and performance association model is established.

[0181] Optionally, the modeling unit 403 is used to determine the stocking quantity of each supplier under different stocking levels and supplier allocation ratios based on historical order demand, the adjacency matrix and the planning accuracy of each node in the supply chain network; determine the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios based on the supply cycle information and cost information of each supplier; and obtain a product design and performance association model based on the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios.

[0182] Optionally, the device further comprises:

[0183] The analyzing unit 404 is configured to determine supply chain anomalies based on supply anomaly information in order history data of the supply chain network; and determine possible causes of the supply chain anomalies based on the supply chain anomalies.

[0184] Optionally, the analysis unit 404 is further configured to determine the sensitivity of the possible causes of the supply chain anomaly based on the product design and performance association model and the possible causes of the supply chain anomaly, and to take the possible causes of the supply chain anomaly with high sensitivity as the root causes.

[0185] Optionally, the analysis unit 404 is used to modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of the supply chain anomaly; based on the product design and performance association model after the variables are modified, when there is no supply anomaly in the supply chain network under the constraint conditions, the sensitivity of determining the possible causes of the supply chain anomaly is high.

[0186] It should be noted that the supply chain network diagnostic device provided in the above embodiment only uses the division of the above-mentioned functional units as an example when performing supply chain network diagnosis. In actual applications, the above-mentioned functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. In addition, the supply chain network diagnostic device provided in the above embodiment and the supply chain network diagnostic method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0187] Figure 9 FIG1 shows a schematic diagram of the structure of the device 150 provided in an embodiment of the present application. The device 150 may be a supply chain network diagnosis device or a retrieval device. Figure 9 The device 150 shown is used to perform the above Figures 1 to 7 The operations involved in any of the supply chain network diagnosis methods shown in FIG. 1 : The device 150 can be implemented by a general bus architecture.

[0188] like Figure 9 As shown, the device 150 includes at least one processor 151 , a memory 153 , and at least one communication interface 154 .

[0189] The processor 151 is, for example, a general-purpose central processing unit (CPU), a digital signal processor (DSP), a network processor (NP), a data processing unit (DPU), a microprocessor, or one or more integrated circuits for implementing the solution of the present application. For example, the processor 151 includes an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or other programmable logic devices, a transistor logic device, a hardware component, or any combination thereof. The PLD is, for example, a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. It can implement or execute the various logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0190] Optionally, the device 150 further includes a bus. The bus is used to transmit information between the components of the device 150. The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0191] The memory 153 is, for example, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 153 is, for example, independent and connected to the processor 151 via a bus. The memory 153 can also be integrated with the processor 151.

[0192] The communication interface 154 uses any transceiver-like device to communicate with other devices or communication networks. The communication network can be Ethernet, a radio access network (RAN), or a wireless local area network (WLAN). The communication interface 154 can include a wired communication interface and a wireless communication interface. Specifically, the communication interface 154 can be an Ethernet interface, a Fast Ethernet (FE) interface, a Gigabit Ethernet (GE) interface, an Asynchronous Transfer Mode (ATM) interface, a wireless local area network (WLAN) interface, a cellular network communication interface, or a combination thereof. The Ethernet interface can be an optical interface, an electrical interface, or a combination thereof. In the embodiment of the present application, the communication interface 154 can be used for the device 150 to communicate with other devices.

[0193] In a specific implementation, as an embodiment, the processor 151 may include one or more CPUs, such as Figure 9 0 and CPU1 are shown in FIG. Each of these processors can be a single-CPU processor or a multi-CPU processor. A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0194] In a specific implementation, as an embodiment, the device 150 may include multiple processors, such as Figure 9 1 and 155. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor here can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0195] In a specific implementation, as an embodiment, the device 150 may further include an output device and an input device. The output device communicates with the processor 151 and can display information in a variety of ways. For example, the output device can be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector. The input device communicates with the processor 151 and can receive user input in a variety of ways. For example, the input device can be a mouse, a keyboard, a touch screen device, or a sensor device.

[0196] In some embodiments, the memory 153 is used to store program code 1510 for executing the solution of the present application, and the processor 151 can execute the program code 1510 stored in the memory 153. That is, the device 150 can implement the method provided by the method embodiment by executing the program code 1510 in the memory 153 through the processor 151. The program code 1510 may include one or more software modules. Optionally, the processor 151 itself may also store program code or instructions for executing the solution of the present application.

[0197] In a specific embodiment, the device 150 of the embodiment of the present application may correspond to the controller in each of the above method embodiments, and the processor 151 in the device 150 reads the instructions in the memory 153 so that Figure 9 The illustrated device 150 is capable of performing all or a portion of the operations performed by a controller.

[0198] Specifically, the processor 151 is used to determine the first performance indicator of each node in the supply chain network under constraint conditions based on the product design and performance association model; based on the first performance indicator and the second performance indicator of each node in the historical data of abnormal orders, determine the supply abnormality information of the supply chain network, and the supply abnormality information of the supply chain network includes supply abnormal nodes or product design abnormalities.

[0199] For the sake of brevity, other optional implementations will not be described here in detail.

[0200] in, Figures 1 to 7 Each step of the supply chain network diagnostic method shown in any one of the figures is completed by the hardware integrated logic circuit or software instructions in the processor of the device 150. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0201] An embodiment of the present application further provides a chip comprising an input interface, an output interface, a processor, and a memory. The input interface, the output interface, the processor, and the memory are connected via an internal connection path. The processor is configured to execute code in the memory. When the code is executed, the processor is configured to perform any of the aforementioned supply chain network diagnostic methods.

[0202] It should be understood that the processor may be a CPU, or other general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the ARM architecture.

[0203] Furthermore, in an optional embodiment, there are one or more processors and one or more memories. Alternatively, the memories may be integrated with the processors, or provided separately from the processors. The memories may include read-only memory and random access memory, and provide instructions and data to the processors. The memories may also include non-volatile random access memory. For example, the memories may also store reference blocks and target blocks.

[0204] The memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be ROM, PROM, EPROM, EEPROM, or flash memory. The volatile memory may be RAM, which serves as an external cache. By way of example and not limitation, many forms of RAM are available, including, for example, SRAM, DRAM, SDRAM, DDR SDRAM, ESDRAM, SLDRAM, and DR RAM.

[0205] In an embodiment of the present application, a computer-readable storage medium is also provided, which stores computer instructions. When the computer instructions stored in the computer-readable storage medium are executed by an electronic device, the electronic device executes the supply chain network diagnosis method provided above.

[0206] In an embodiment of the present application, a computer program product containing instructions is also provided. When the computer program product is run on an electronic device, the electronic device executes the supply chain network diagnosis method provided above.

[0207] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described herein are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0208] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0209] The above are merely optional embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0210] Unless otherwise defined, the technical or scientific terms used herein shall have the usual meaning understood by persons of ordinary skill in the field to which this application belongs. The words “first”, “second”, “third” and similar terms used in the patent application specification and claims of this application do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as “a” or “an” do not indicate a quantitative limitation, but rather indicate the presence of at least one. Words such as “include” or “comprising” and similar words mean that the elements or objects appearing before “include” or “comprising” cover the elements or objects listed after “include” or “comprising” and their equivalents, and do not exclude other elements or objects.

[0211] The above is only an embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A supply chain network diagnosis method, characterized in that: The method comprises: According to the product design and performance correlation model, determine the first performance indicator of each node in the supply chain network under the constraint conditions; Based on the first performance indicator and the second performance indicator of each node in the historical data of abnormal orders, the supply abnormality information of the supply chain network is determined, and the supply abnormality information of the supply chain network includes supply abnormal nodes or product design abnormalities.

2. The method according to claim 1, characterized in that The product design and performance association model includes the relationship between product design strategy and supply strategy and performance indicators.

3. The method according to claim 1 or 2, characterized in that The method further comprises: The product design and performance association model is established based on the product family bill of materials (GBOM) network and the node information of each node in the supply chain network, and the GBOM network corresponds to the supply chain network.

4. The method according to claim 3, characterized in that The product design and performance association model is established based on the GBOM network and the node information of each node in the supply chain network, including: An adjacency matrix is ​​determined based on the GBOM network, and the element A in the adjacency matrix is ij Represents the node P in the GBOM network i and node P j The composition relationship between them, i and j are node identifiers; Determining the reuse degree of each node in the GBOM network based on the adjacency matrix; Determining the planning accuracy of each node in the supply chain network based on the reuse degree of each node in the GBOM network; The product design and performance association model is established based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network.

5. The method according to claim 4, characterized in that The establishing of the product design and performance association model based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network includes: Determining the inventory quantity of each supplier under different inventory levels and supplier allocation ratios based on historical order demand, the adjacency matrix, and the planning accuracy of each node in the supply chain network; Based on the supply cycle information and cost information of each supplier, determining the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios; Based on the supply cycles and costs of the various suppliers under the different stocking levels and supplier allocation ratios, the product design and performance association model is obtained.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Determine supply chain anomalies based on supply anomaly information in historical order data of the supply chain network; Determine possible causes of the supply chain anomaly based on the supply chain anomaly.

7. The method according to claim 6, characterized in that The method further comprises: Based on the product design and performance association model and the possible causes of the supply chain anomaly, the sensitivity of the possible causes of the supply chain anomaly is determined.

8. The method according to claim 7, characterized in that Determining the sensitivity of the possible causes of the supply chain anomaly based on the product design and performance association model and the possible causes of the supply chain anomaly includes: Modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of the supply chain anomaly; Based on the product design and performance association model after modifying the variables, when there is no supply anomaly in the supply chain network under the constraint conditions, the sensitivity of determining the possible cause of the supply chain anomaly is high.

9. A supply chain network diagnostic device, characterized in that: The device comprises: a determination unit, configured to determine, based on a product design and performance correlation model, a first performance indicator of each node in the supply chain network under a constraint condition; A diagnostic unit is used to determine supply anomaly information of the supply chain network based on the first performance indicator and the second performance indicator of each node in the historical data of abnormal orders, where the supply anomaly information of the supply chain network includes supply abnormal nodes or product design anomalies.

10. The device according to claim 9, characterized in that The product design and performance association model includes the relationship between product design strategy and supply strategy and performance indicators.

11. The device according to claim 9 or 10, characterized in that The device further comprises: A modeling unit is used to establish the product design and performance association model based on the GBOM network and the node information of each node in the supply chain network, and the GBOM network corresponds to the supply chain network.

12. The device according to claim 11, characterized in that The modeling unit is used to determine an adjacency matrix based on the GBOM network, wherein the element A in the adjacency matrix ij Represents the node P in the GBOM network i and node P j The composition relationship between them, i and j are node identifiers; Determining the reuse degree of each node in the GBOM network based on the adjacency matrix; Determining the planning accuracy of each node in the supply chain network based on the reuse degree of each node in the GBOM network; The product design and performance association model is established based on the planning accuracy of each node in the supply chain network and the node information of each node in the supply chain network.

13. The device according to claim 12, characterized in that The modeling unit is used to determine the stocking quantity of each supplier under different stocking levels and supplier allocation ratios based on historical order demand, the adjacency matrix and the planning accuracy of each node in the supply chain network; determine the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios based on the supply cycle information and cost information of each supplier; and obtain the product design and performance association model based on the supply cycle and cost of each supplier under different stocking levels and supplier allocation ratios.

14. The device according to any one of claims 9 to 13, characterized in that The device further comprises: The analysis unit is used to determine supply chain anomalies based on the supply anomaly information in the order history data of the supply chain network; and determine possible causes of the supply chain anomalies based on the supply chain anomalies.

15. The device according to claim 14, characterized in that The analysis unit is further configured to determine the sensitivity of the possible causes of the supply chain anomaly based on the product design and performance association model and the possible causes of the supply chain anomaly.

16. The device according to claim 15, characterized in that The analysis unit is used to modify the variables corresponding to the parameters in the product design and performance association model based on the parameters corresponding to the possible causes of the supply chain anomaly; based on the product design and performance association model after the variables are modified, when there is no supply anomaly in the supply chain network under the constraint conditions, the sensitivity of determining the possible causes of the supply chain anomaly is high.

17. A supply chain network diagnostic device, characterized in that: It comprises a memory and one or more processors, the memory is used to store computer programs; the one or more processors are used to execute the computer programs in the memory, so that the supply chain network diagnosis device performs the method as described in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that The storage medium stores a computer program or instruction. When the computer program or instruction is executed by a computer, the method according to any one of claims 1 to 8 is implemented.

19. A computer program product, characterized in that When a computer reads and executes the computer program product, the computer is caused to execute the method according to any one of claims 1 to 8.

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