Supply chain planning method, apparatus and computer device

Abstract

Disclosed in the present application are a supply chain planning method, an apparatus, a computer device and a storage medium. Specifically, the disclosed supply chain planning method comprises: receiving information of an stock keeping unit and information of facilities; receiving information of activities, the information of the activities comprising the information of the stock keeping unit and the information of the facilities; and, according to the information of the stock keeping unit, the information of the facilities and the information of the activities, generating nodes and arcs to form a supply chain planning network, the nodes and the arcs respectively containing the number of the activities and decision variables of a traffic. In this way, supply chain planning can be abstracted into three different object layers and then, according to actual service requirements, constraint and object configuration are performed so as to form a network expression of the supply chain planning, such that a subsequent supply chain planning optimization result can be calculated as a reference for actual decision making.

Description

Supply chain planning method, device, and computer equipment Technical Field

[0001] The present application relates to the field of information processing, and in particular, to a method, apparatus, computer equipment, and storage medium for supply chain planning. Background Art

[0002] Supply chain planning optimization is an unavoidable topic during enterprise digital transformation. This topic encompasses a variety of issues, including Sales & Operations Planning (S&OP), Master Production Scheduling (MPS), Material Requirements Planning (MRP), Capacity Requirements Planning (CRP), Production Planning and Scheduling (CPS), Inventory Control, Vehicle Routing (VRP), Service Dispatching, Order Fulfillment, and Material Full-Kit. These issues have different names and distinct characteristics depending on the industry and process involved, presenting numerous challenges in management and decision-making.

[0003] Summary of the Invention

[0004] This summary is provided to introduce some selected concepts in a simplified form, which will be further described in the detailed description below. This summary is not intended to identify any key features or essential features of the claimed subject matter, nor is it intended to be used to help determine the scope of the claimed subject matter.

[0005] Based on this, the present application provides a supply chain planning method, which includes: receiving inventory unit information and facility information; receiving activity information, wherein the activity information includes inventory unit information and facility information; generating nodes and arcs based on the inventory unit information, facility information and activity information to form a supply chain planning network; wherein the nodes and arcs respectively contain decision variables for the quantity and flow of activities.

[0006] Through the above method, the supply chain can be abstracted into three different layers of objects, and then constraints and goals can be configured according to actual business needs to form a network representation of the supply chain plan, so that the subsequent supply chain plan optimization results can be calculated and used as a reference for actual decision-making.

[0007] Furthermore, it also includes: generating an expression of the objective function and an expression of the constraint conditions of the objective function according to the supply chain planning network and business requirements.

[0008] Through the above method, the expression of the supply chain objective function and the expression of the constraint conditions can be calculated, and the optimized supply chain planning results can be further obtained to serve as a reference for actual decision-making.

[0009] Furthermore, the generating a node includes generating a node including the name of the activity and the start time of the activity.

[0010] In the above manner, the nodes containing activities and start information can be used to form a supply chain network and calculate subsequent expression functions.

[0011] Furthermore, the generating node includes generating a node including an input inventory unit and quantity, an output inventory unit and quantity, a starting facility name, and an ending facility name.

[0012] Through the above method, the nodes can be better organized into a supply chain planning network to prepare for the subsequent calculation of expression functions and actual supply chain planning.

[0013] In addition, the present application also provides a supply chain planning device, which includes:

[0014] A physical layer module is used to receive information about inventory units and facilities;

[0015] An activity layer module, configured to receive activity information, wherein the activity information includes information about the inventory unit and information about the facility;

[0016] The network layer module is used to generate nodes and arcs based on the inventory unit information, facility information and activity information to form a supply chain planning network; wherein the nodes and arcs include decision variables of the quantity and flow of activities.

[0017] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the above method when executing the computer program.

[0018] The present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the above method when executed by a processor.

[0019] The present application also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions. When the computer-executable instructions are executed, the computer-executable instructions cause at least one processor to perform the method described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Implementations of the present disclosure are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate the same or similar parts.

[0021] FIG1 is a schematic diagram of a process of a supply chain planning method according to an embodiment of the present application.

[0022] FIG2 is a schematic diagram of a supply chain planning apparatus according to an embodiment of the present application.

[0023] FIG3 is a schematic diagram of a computer device for supply chain planning according to an embodiment of the present application.

[0024] FIG4 is a hierarchical diagram of a supply chain planning method according to an embodiment of the present application.

[0025] FIG5 is a schematic diagram of a supply chain planning network according to an embodiment of the present application.

[0026] FIG6 is a schematic diagram of a supply chain planning network according to an embodiment of the present application.

[0027] The reference numerals are as follows: S101-S103 Step 200: Apparatus 201: Module 202: Module 203: Module 204: Module 300: Computer device 302: Processor 304: Memory DETAILED DESCRIPTION

[0028] In the following description, for the purpose of explanation, a large number of specific details are set forth. However, it is understood that the present invention can be implemented without these specific details. In other examples, well-known circuits, structures, and technologies are not shown in detail so as not to affect the understanding of the description.

[0029] References throughout this specification to "an implementation," "an implementation," "an exemplary implementation," "some implementations," "various implementations," etc., indicate that the implementations of the invention being described may include particular features, structures, or characteristics. However, it does not imply that every implementation must include those particular features, structures, or characteristics. Furthermore, some implementations may have some, all, or none of the features described for other implementations.

[0030] Based on the description in the background technology, in essence, supply chain planning optimization problems are all about better utilizing resource supply to meet demand while complying with the constraints and goals specified by the business.

[0031] Therefore, operational research (OR) theory and methods are often employed to support these decisions. By abstractly representing and algorithmically solving custom planning problems, the resulting optimization results should ensure feasibility and improve optimality, surpassing existing manual decision-making processes in both effectiveness and efficiency. However, in practical applications, most challenges today often lie in the representation phase, not the solution phase.

[0032] In fact, although general OR algorithms, such as mathematical programming solvers, heuristic algorithms, and metaheuristic algorithms, have been well developed to a certain extent, each specific planning problem still requires a high degree of customization. For example, MPS and MPR are intuitively different, and the MPS of Company A and Company B are rarely exactly the same. This gap between divergent requirements and unified representation hinders the reusability of assets, limits the bandwidth of delivery, and blocks the possibility of scalability "as a service."

[0033] Conventionally, there are two main ways to formulate supply chain planning problems.

[0034] Purely customized representation: Customized on a case-by-case basis. The problem statement, as well as the subsequent model development and algorithm design, are all tightly centered around the specific scenario. This representation is straightforward, but at the expense of poor reusability and scalability.

[0035] Representation within the corresponding business suite. For example, using Advanced Planning and Scheduling (APS) software, such as Preactor, has predefined entities and relationships for production planning and scheduling scenarios. However, the disadvantage is that the scope of the problem is limited by the software. In many cases, the software cannot customize special constraints. In addition, no general software can cover the various planning scenarios mentioned above. In addition, the solution algorithm is highly coupled with the software itself, and most of them are rule-based heuristic algorithms. Their optimization or even feasibility cannot be guaranteed under complex customization conditions. Unless in special circumstances, customized optimization algorithms are integrated externally into the software, which falls into the above situation.

[0036] Therefore, the implementation scenario of this application can be to distinguish and abstract the elements and objects in the supply chain through three different layers of logical units, and generate nodes and arcs representing material supply, production relationship transfer, etc. through elements such as inventory units, facilities, and activities to form a corresponding network, or called a supply chain planning network, so as to further obtain the constraints of the supply chain plan and the expression of the objective function, and after solving, the optimized supply chain planning decision results can be obtained.

[0037] This application provides a supply chain planning method, which includes:

[0038] S101, receiving information of a stock keeping unit and information of a facility.

[0039] In some implementations, a stock keeping unit (SKU), also known as a minimum stock keeping unit (SKU), or an inventory unit (IU), represents the materials involved in a supply chain plan. Facilities represent the resources necessary for processing, producing, storing, and transporting the SKU. SKU information represents the basic attributes of various SKUs, while facility information represents the basic attributes of various facilities. Specifically, SKU information and facility information can be represented as follows:

[0040] Stock Keeping Unit (SKU)

[0041] All materials involved. Typical properties:

[0042] - Basic information: discrete / continuous form, weight, volume, price, etc.

[0043] -Demand and inventory can be divided by time, type, etc. (demand, inventory, by time or by type)

[0044] Facility

[0045] All resources involved.

[0046] -Typical examples include: factory, shop, line, machine, manpower, warehouse, vehicle, route, supplier, customer, etc.

[0047] - Affiliation: sub facilities, for example, factory>>production line, shop>>manpower, affiliation is achieved through layering (affiliation: sub facilities eg, factory>>line, shop>>manpower, layered to present) and so on.

[0048] -Capacity: For example, production capacity, storage capacity, transportation capacity, etc., which can be distinguished by time, type, etc. (capacity, for example, by time, by type).

[0049] S102: Receive activity information, wherein the activity information includes information about the inventory unit and information about the facility.

[0050] Activity

[0051] Activities are planned objects. As shown in Figure 4(b), each activity can be viewed as a transformation from an input inventory unit of an input device to an output inventory unit of an output device, and has type and attribute information related to the transformation. Table 1 lists examples of typical activities.

[0052] Table 1 Some examples of activities

[0053] S103 , generating nodes and arcs based on the inventory unit information, facility information, and activity information to form a supply chain planning network; wherein the nodes and arcs respectively contain decision variables of the quantity and flow of activities.

[0054] In some embodiments, the supply chain planning network can be simply referred to as a network. It is composed of nodes and arcs. Nodes represent activities that can participate in the plan, such as processing and production, inventory storage, logistics transportation, raw material procurement, quality inspection, and fulfillment delivery. Arcs represent feasible connections between activities, such as the Sku output after production is used as the input for the start of quality inspection, and the Sku output from inventory storage is used as the input for logistics transportation. In some embodiments, the nodes and arcs can be used to generate a network. Their specific explanations can be as follows:

[0055] Network

[0056] At the top layer is the network representation of the decision problem, i.e., a feasible organization of activities through nodes and arcs on a directed graph.

[0057] Node

[0058] A node is essentially an activity with a start time. For an activity, there can be multiple nodes, representing this activity's multiple feasible start times. For example, as long as the factory has sufficient production capacity on days 1, 2, and 3, Sku1 can be produced on days 1, 2, and 3; if the production lead time is 2 days, Sku1 can be delivered on days 3, 4, and 5. Each node has a quantity decision variable that represents the planned scale of that node (i.e., the corresponding activity and time).

[0059] Arc

[0060] It is used to connect adjacent nodes and represents the feasible transition of some Sku among temporal activities. For example, the arc connecting the inventory node of Sku1 on day 1 and the production node of Sku2 on day 2 indicates that it is feasible to start the production of Sku2 on day 2 using the inventory of Sku1 on day 1 as raw materials. Each arc has a flow decision variable, which represents the planned scale of the arc (i.e., the corresponding Sku transition between nodes).

[0061] It should be noted that the aforementioned inventory units, facilities, activities, nodes, and arcs are conceptual. They do not necessarily correspond exactly to reality and can be virtual to support appropriate representation or deformation. Furthermore, the properties listed can be easily expanded or reduced to suit the needs of the scenario.

[0062] Based on the constructed abstract network, any planning result can be mapped to a set of values ​​regarding node quantity and arc flow. The business rules, or constraints, and optimization objectives that the planning result must follow are reflected in the following three aspects:

[0063] First, the network structure itself only contains feasible paths. For example, if an activity is not feasible to start at a certain time, the corresponding node will not appear in the network. Furthermore, for a node, its input / output arcs correspond to its activity's input / output skus. Therefore, if a skus is not feasible to connect two nodes (that is, cannot transit between two temporal activities), the corresponding arc will not appear in the network.

[0064] The second aspect is the self-consistency constraint. For each node, its quantity decision and the flow decision of its input / output arcs must conform to the proportional relationship in the corresponding activity attributes. This is actually a generalization of the flow conservation constraint in the network.

[0065] The third aspect is the specific business constraints and goals in business requirements. Generally speaking, these can be expressed in the form of expressions based on the number of nodes and the flow of arcs.

[0066] The specific implementation method can refer to the following examples and corresponding drawings. For example, a node can represent the type of activity as Activity type, the number of input inventory units as Sku in, the number of output inventory units as Sku out, the name of the starting facility as Facility In, the name of the ending facility as Facility Out, and the start time of the node or the start time of the activity as Node start time.

[0067] In short, this application proposes a unified formulation for supply chain planning optimization problems. The idea is to transform any specific planning problem into a general network flow representation. Two examples are given below:

[0068] Example 1: S&OP

[0069] This example is based on a solution provided to a Chinese dairy company. Figure 5(a) shows a schematic diagram of the supply chain. The planning horizon covers the next 52 weeks. Weekly demand for each product is forecasted for each distributor. S&OP determines weekly production, inventory, allocation, and delivery of each product to maximize demand while minimizing costs.

[0070] There are limits on the relationships between products and plants / machines (production), plants / warehouses and warehouses (distribution), and warehouses and distributors (delivery). In addition, there are upper and lower capacity limits for plants, machines, warehouses, and routes, as well as requirements related to prioritization and balancing.

[0071] Figure 5(b) is a partial diagram of the problem statement above. It shows several feasible paths to meet dealer 1’s demand for Sku1 in week 4:

[0072] - In factory 1, Sku1 is produced from stock raw material 1 using machine 1 or 2 and then transferred to warehouse 2.

[0073] -In factory 2, Sku1 is produced using machine 3 from the purchased raw material 1 and then transferred to warehouse 2.

[0074] -Use Inventory Sku1 from Warehouse 2.

[0075] By analogy, a complete network representation can be constructed, which already contains all feasible paths and self-consistent constraints. Based on this network, the expressions of constraints and objective functions can be formulated, such as

[0076] ●Factory1 capacity utilization at Week1(with upper limit)

[0077] ○=Sum of(quantity*Capacity utilize)of nodes with:

[0078] ■activity type of production

[0079] ■activity facility belonging to F1

[0080] ■node start-end covering Week1

[0081] ●Machine F1M1 production quantity at Week1(with lower limit)

[0082] ○=Sum of quantities of nodes with:

[0083] ■activity type of production

[0084] ■activity facility of F1M1

[0085] ■node start-end covering Week1

[0086] ●Factory1 production cost at Week1(to be minimized)

[0087] ○=Sum of[Cost function(quantity)]of nodes with:

[0088] ■activity type of production

[0089] ■activity facility belonging to F1

[0090] ■node start at Week1

[0091] ●SkuGroup1 fulfill quantity at Week1(with upper limit or to be maximized)

[0092] ○=Sum of quantities of nodes with:

[0093] ■activity type of fulfill

[0094] ■activity input sku belonging to SkuGroup1

[0095] ■Node start at Week 1

[0096] ●Route1 transport weight at Week1(with lower limit)

[0097] ○=Sum of quantities of nodes with:

[0098] ■activity type of fulfill

[0099] ■activity input / output facilities corresponding to Route1

[0100] Based on the above constraints and objective functions, intelligent algorithms can be used to calculate and solve the problems, thereby obtaining an optimized S&OP plan and providing decision support for actual operations.

[0101] Example 2: MPS

[0102] This example is based on a solution provided for a Siemens factory in China. MPS can be considered a more granular S&OP solution, also considering capacity constraints and material inventory. Furthermore, it often requires considering kitting, meaning that production plans are developed for each level of the BOM (Bill of Material). Figure 6(a) shows a partial BOM structure. As shown, Sku-D is both a level 2 and level 3 component of Sku-A, and Sku-D is also a component of Sku-A2.

[0103] Specifically, the planning period is for the next 8 weeks. Every week, the demand for each SKU is given. MPS will determine the weekly procurement, production, inventory, and product fulfillment to achieve the maximum fulfillment rate while minimizing costs. The relationship between products and production lines (production) is limited. Production lines have upper and lower capacity limits, and planning decisions also have requirements related to priority and balance. Figure 6(b) is a partial diagram of the problem statement. The figure shows several feasible paths to meet the demand for Sku-A in week 2:

[0104] - The demand for Sku-A can be met from inventory or from the production of Sku-B and C

[0105] - The Sku-B (and Sku-C) consumed in the production of Sku-A can come from the inventory or from the production of Sku-D

[0106] - The Sku-D consumed in the production of Sku-B and C can come from the inventory or from the production of Sku-E and F

[0107] - The Sku-E (and Sku-F) consumed in the production of Sku-D can come from procurement or inventory. Similarly, a complete network representation can be constructed, which already contains all feasible paths and self-consistent constraints. Based on this network, the expressions of constraints and objective functions can be formulated, for example:

[0108] ●Assemble capacity utilization at Week1(with upper limit)

[0109] ○=Sum of(quantity*Capacity utilize)of nodes with:

[0110] ■activity type of production

[0111] ■activity facility corresponding to Assemble

[0112] ■node start-end covering Week1

[0113] ●Sku-A inventory balance at Week2 (with lower and upper limits)

[0114] ○=Sum of quantities of nodes with:

[0115] ■activity type of inventory

[0116] ■activity input sku of Sku-A

[0117] ■Node start at Week 2

[0118] ●Material(eg,Sku-E)stock consumption for finished goods(eg,Sku-A)(to be balanced)

[0119] ○=Sum of flows of arcs with:

[0120] ■Source node activity type of inventory

[0121] ■Sink node activity type of production(for A)

[0122] Similarly, through the above constraints and objective functions, intelligent algorithms can be used to calculate and solve, thereby obtaining an optimized MPS plan and providing decision support for actual operations.

[0123] Furthermore, the solution of this application can be considered an intermediate state between current solutions. It provides a unified underlying logic when expressing customized problems, while retaining the freedom of users to abstract modeling based on their own skills and experience.

[0124] Compared to pure customization, this approach converges diverse customizations into configurations and operations for defined entities, activities, and networks. Standardization of network elements reduces the difficulty of bottom-up construction, while the enrichment of network elements supports a wide range of scenario coverage.

[0125] Compared to formulations within corresponding business suites, this solution is software-independent, supporting a wide range of supply chain planning decisions regardless of the software itself. By decoupling the optimization problem formulation from the application software, the most advanced algorithmic solution techniques can be freely selected to ensure feasibility and enhance optimization.

[0126] There are two main advantages of this application:

[0127] Cost savings. The three-layer abstraction itself reduces the effort required to understand and translate various supply chain planning optimization problems. Based on the solution proposed in this application, reusable code can be developed to support the translation of supply chain planning problems, as well as further mathematical modeling and algorithmic solutions.

[0128] -Flexibility and scalability. The solution in this application can also be provided as a service. Users can use the defined network elements as building blocks and independently build and adjust their customized solutions through the application programming interface (API) or user interface (UI), without relying on existing systems or the cloud.

[0129] The performance characteristics of the solution of this application when applied may be as follows:

[0130] - Explicitly, the description / explanation of the method used clearly indicates that an abstract formulation of the solution is used for the supply chain planning optimization problem.

[0131] - It is inferred that in the interface specifications / user interaction functions / system workflow, the implicit method is based on the solution of this application.

[0132] -Implicitly, in publicity / promotion information, there are keywords such as unified expression, generalized abstraction, network flow, etc.

[0133] It should be understood that, although the steps in the flowcharts of the various figures are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the various figures may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0134] FIG2 provides a supply chain planning device 200, which includes:

[0135] The physical layer module 201 is used to receive the information of the inventory unit and the information of the facility;

[0136] An activity layer module 202 is configured to receive activity information, wherein the activity information includes information about the inventory unit and information about the facility;

[0137] The network layer module 203 is used to generate nodes and arcs based on the inventory unit information, facility information, and activity information to form a supply chain planning network; wherein the nodes and arcs respectively contain decision variables of quantity and flow.

[0138] Furthermore, the device 200 further includes:

[0139] The expression generation module 204 is used to generate an expression of the objective function and an expression of the constraint condition of the objective function according to the supply chain planning network and business requirements.

[0140] Furthermore, the network layer module 203 is used to generate a node including the name of the activity and the start time of the activity.

[0141] Furthermore, the network layer module 203 is further configured to generate an input inventory unit and quantity, an output inventory unit and quantity, a start facility name, and an end facility name.

[0142] It should be noted that the apparatus may include more or fewer modules to implement the described functionality. For example, at least one module in FIG. 2 may be further divided into a plurality of different submodules, each of which is configured to perform at least a portion of the operations described herein in conjunction with the corresponding module. Furthermore, in some examples, the apparatus 200 may further include additional modules for performing other operations already described in the specification. Furthermore, those skilled in the art will appreciate that the exemplary apparatus 200 may be implemented using software, hardware, firmware, or any combination thereof.

[0143] Figure 3 provides a computer device. According to one embodiment, the computer device 300 may include a processor 302, which executes a computer program stored in a memory 304. When the computer program is executed by the processor, the above method is implemented.

[0144] Those skilled in the art will understand that the structure shown in Figure 3 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] Those skilled in the art will appreciate that all or part of the processes in the methods for implementing the above-mentioned embodiments can be accomplished by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include processes for the implementation of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0146] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the above steps when the computer program is executed by a processor.

[0147] The present application also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions. When the computer-executable instructions are executed, the computer-executable instructions cause at least one processor to perform the above method.

[0148] Furthermore, the computer program can be stored and run in the cloud to perform the method. Furthermore, the components of the program can be deployed on multiple devices and the cloud. For example, the corresponding steps can be deployed and run on a local or local computer, or run on different cloud devices, and transmit signals through a communication connection, or can also be deployed and run on a local or local computer. This application does not limit the manner or method, and the corresponding technology can be flexibly deployed to make full use of equipment and technologies such as the cloud, big data, and supercomputing capabilities to execute and complete the method.

[0149] Some implementations of the present disclosure may include articles of manufacture. Articles of manufacture may include storage media for storing logic. Examples of storage media may include one or more types of computer-readable storage media capable of storing electronic data, including volatile or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writable or rewritable memory, and the like. Examples of logic may include various software units, such as software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, processes, software interfaces, application program interfaces (APIs), instruction sets, computing codes, computer codes, code segments, computer code segments, words, values, symbols, or any combination thereof. In some implementations, for example, articles of manufacture may store executable computer program instructions that, when executed by a processor, cause the processor to perform the methods and / or operations described herein. Executable computer program instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like. Executable computer program instructions can be implemented according to a predefined computer language, method or syntax for commanding a computer to perform a specific function. The instructions can be implemented using any appropriate high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language.

[0150] What has been described above includes examples of the disclosed architecture. It is, of course, not possible to describe every conceivable combination of components and / or methodologies, but those skilled in the art will appreciate that many other combinations and permutations are possible. Therefore, the novel architecture is intended to embrace all such alternatives, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A supply chain planning method, wherein: include: receiving SKU information and facility information; receiving information about an activity, wherein the information about the activity includes information about the inventory unit and information about the facility; Based on the information of the inventory units, the information of the facilities and the information of the activities, nodes and arcs are generated to form a supply chain planning network; wherein the nodes and arcs respectively contain decision variables of the quantity and flow of the activities.

2. The method according to claim 1, wherein Also includes: According to the supply chain planning network and business requirements, an expression of the objective function and an expression of the constraint conditions of the objective function are generated.

3. The method according to claim 1, wherein The generation node includes: A node is generated that includes the name of the activity and the start time of the activity.

4. The method according to claim 3, wherein: The generation node further includes: Generates a node that includes the input stock keeping units and their quantities, the output stock keeping units and their quantities, the start facility name, and the end facility name.

5. A supply chain planning device (200), wherein: include: A physical layer module (201) is used to receive information about inventory units and facility information; An activity layer module (202) is configured to receive information about an activity, wherein the information about the activity includes information about the inventory unit and information about the facility; The network layer module (203) is used to generate nodes and arcs based on the inventory unit information, facility information and activity information to form a supply chain planning network; wherein the nodes and arcs respectively contain decision variables of the quantity and flow of activities.

6. The device (200) according to claim 5, wherein Also includes: An expression generation module (204) is used to generate an expression of an objective function and an expression of a constraint condition of the objective function according to the supply chain planning network and business requirements.

7. The device (200) according to claim 5, wherein The network layer module (203) is used to generate a node containing the name of the activity and the start time of the activity.

8. The device (200) according to claim 7, wherein The network layer module (203) is further configured to generate a node including an input inventory unit and its quantity, an output inventory unit and its quantity, a start facility name, and an end facility name.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

11. A computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the method according to any one of claims 1 to 4.

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