Product delivery time prediction method and device, electronic equipment and storage medium

Abstract

The invention relates to a product delivery time prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-delivered order which comprises the number of to-be-delivered products and a product identifier; according to the order to be delivered, determining a material structure corresponding to the order to be delivered; and according to the material structure, and the material basic data, sales data, production data, purchase data, storage data and assembly transportation data corresponding to the to-be-delivered product, predicting delivery time of the to-be-delivered order. According to the method provided by the invention, when the delivery time of the to-be-delivered order is predicted, several influence factors which influence the delivery time, such as the material structure corresponding to the to-be-delivered order, the material basic data corresponding to the to-be-delivered product, the sales data, the production data, the purchase data, the storage data and the assembly transportation data, are considered; therefore, the delivery time predicted based on the influence factors is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a product delivery date prediction method, device, electronic device and storage medium. Background Art

[0002] In the production process of equipment manufacturing enterprises, the product delivery time will be affected by many factors, and different manufacturing supply strategies have different effects on the delivery time. In the production and manufacturing process of discrete manufacturing equipment enterprises, most of them adopt customized design and production of the order-oriented ETO model. The final product delivery will be affected by multiple manufacturing operations. Therefore, the product delivery date is longer and there are more influencing factors; at the same time, since the product is customized, different orders have different material structures due to different demands, and the demand for materials, procurement, and inventory also varies from order to order; in addition, due to the huge amount of product data being designed, produced, assembled and transported in the same time period, a small change will have a huge impact on the delivery date of different products. Therefore, the accurate prediction and calculation of product delivery dates of discrete equipment enterprises is a difficult problem to solve. The settlement results of the existing system can only achieve the prediction accuracy, and the order delay is large. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a product delivery date prediction method, device, electronic device and storage medium, aiming to solve at least one of the above technical problems.

[0004] In the first aspect, the technical solution of the present invention to solve the above technical problem is as follows: a product delivery date prediction method, the method comprising:

[0005] Obtaining an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the product to be delivered;

[0006] According to the order to be delivered, determine the material structure corresponding to the order to be delivered;

[0007] The delivery time of the order to be delivered is predicted based on the material structure, the basic material data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the product to be delivered.

[0008] The beneficial effect of the present invention is that when predicting the delivery time of an order to be delivered, the present scheme takes into account several factors affecting the delivery time, including the material structure corresponding to the order to be delivered, the basic material data corresponding to the product to be delivered, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data, so that the delivery time predicted based on these factors is more accurate.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Further, determining the material structure corresponding to the order to be delivered according to the order to be delivered includes:

[0011] According to the order to be delivered, determine the order material structure BOM tree corresponding to the order to be delivered, the order material structure BOM tree includes at least one level of nodes, each level represents the level to which the material of the product to be delivered belongs, each node represents the material corresponding to the product to be delivered, and the connection between the nodes corresponding to each two levels represents the parent-child relationship between the materials corresponding to the corresponding two nodes.

[0012] Further, predicting the delivery time of the order to be delivered according to the material structure, the basic material data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the product to be delivered, includes:

[0013] For any node in the order material structure BOM tree, the delivery time of the node is predicted according to the material corresponding to the node, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the material, until the delivery time corresponding to each node in the order material structure BOM tree is predicted;

[0014] For any node at the lowest level in the BOM tree of the order material structure, based on the delivery time of each node, trace back from the node to the previous level of the lowest level layer by layer, and determine the delivery time corresponding to the optimal path as the delivery time of the order to be delivered through the critical path algorithm, wherein the optimal path is the path with the earliest total delivery time among the paths formed by the nodes of each level, each path is composed of a node in each level, and the total delivery time of each path is determined based on the delivery time of each node corresponding to the path.

[0015] Further, the type of material corresponding to the product to be delivered includes at least one of a self-made type and a purchased type;

[0016] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a self-made type, the delivery time corresponding to each node is determined by the following method:

[0017] Determine the delivery time corresponding to the node based on the delivery time of the subnode corresponding to the node and the fixed production lead time of the material corresponding to the node;

[0018] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a procurement type, the delivery time corresponding to each node is determined in the following way:

[0019] The delivery time corresponding to the node is determined based on the available supply sources of the material corresponding to the node and the required quantity of the material corresponding to the node.

[0020] Furthermore, after the delivery time of the to-be-delivered order is determined, the method further includes:

[0021] According to the product resource demand and supply resources corresponding to the to-be-delivered order, the resources in the supply resources occupied by the to-be-delivered order are determined, and a binding occupancy relationship between the to-be-delivered order and the resources occupied by the to-be-delivered order is established and saved.

[0022] Furthermore, the method further comprises:

[0023] According to the binding occupation relationship, searching for unoccupied resources and occupied resources in the supplied resources;

[0024] Based on the occupied resources, find the affected product materials and production orders.

[0025] Furthermore, if there are at least two orders to be delivered, and there is overlap between the materials required by the at least two orders to be delivered, the method further includes:

[0026] The overlapping materials are regarded as influencing materials, and each of the to-be-delivered orders is updated according to the influencing factors corresponding to each of the influencing materials, so as to minimize the change of each of the to-be-delivered orders.

[0027] In a second aspect, in order to solve the above technical problem, the present invention further provides a product delivery date prediction device, the device comprising:

[0028] An acquisition module, used for acquiring an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the product to be delivered;

[0029] A material structure determination module, used to determine the material structure corresponding to the order to be delivered according to the order to be delivered;

[0030] The delivery time prediction module is used to predict the delivery time of the order to be delivered based on the material structure, the basic material data corresponding to the product to be delivered, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data.

[0031] In a third aspect, in order to solve the above-mentioned technical problem, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the product delivery date prediction method of the present application is implemented.

[0032] In a fourth aspect, in order to solve the above-mentioned technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the product delivery date prediction method of the present application is implemented.

[0033] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments of the present invention are briefly introduced below.

[0035] Figure 1 A schematic diagram of a flow chart of a product delivery date prediction method provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of an order material structure BOM tree provided by an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of a node delivery cycle provided by an embodiment of the present invention;

[0038] Figure 4 A schematic diagram of a delivery date backtracking prediction calculation provided by an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of a resource and demand relationship binding storage structure provided by an embodiment of the present invention;

[0040] Figure 6 A schematic diagram of a dynamic settlement result of delivery period impact provided by an embodiment of the present invention;

[0041] Figure 7 A flowchart of five stages involved in a product delivery date prediction method provided by an embodiment of the present invention;

[0042] Figure 8 A schematic diagram of the structure of a product delivery date prediction device provided by one embodiment of the present invention;

[0043] Fig. 9 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0045] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0046] The solution provided by the embodiment of the present invention can be applied to any application scenario where the delivery time of pending orders needs to be predicted. The solution provided by the embodiment of the present invention can be executed by any electronic device, for example, it can be a user's terminal device, and the terminal device can be any device that can install applications, including at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0047] The embodiment of the present invention provides a possible implementation method, such as Figure 1 As shown, a flowchart of a method for predicting product delivery date is provided. The solution can be executed by any electronic device, for example, a terminal device, or a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below by taking the terminal device as the execution subject as an example. Figure 1 The method may include the following steps:

[0048] S10, obtaining an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the products to be delivered;

[0049] S20, determining a material structure corresponding to the order to be delivered according to the order to be delivered;

[0050] S30, predicting the delivery time of the order to be delivered based on the material structure, the basic material data corresponding to the product to be delivered, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data.

[0051] When predicting the delivery time of pending orders, this solution takes into account the factors that affect the delivery time, such as the material structure corresponding to the pending orders, the basic material data corresponding to the pending products, sales data, production data, procurement data, warehousing data, and assembly and transportation data. This makes the delivery time predicted based on these factors more accurate.

[0052] The scheme of the present invention is further explained below in conjunction with the following specific embodiments. The task of this scheme is to solve the optimization of the prediction and calculation process of order delivery time in the production process of discrete assembly manufacturing enterprises. Based on the concept of material requirement planning MRP, the order material structure BOM tree is forward expanded and reversely traced, and combined with the material basic data, sales data, production data, procurement data, warehousing data and other data bases provided in the information system of the production enterprise, dynamic delivery forecast calculation is realized to meet the functional requirements of demand management, production management, inventory management, and procurement management, and to achieve overall optimization of multiple orders, achieve the lowest order delay, achieve the highest order accuracy, maximize resource utilization, and maximize inventory turnover.

[0053] Based on this, a product delivery date prediction method is provided in this embodiment, which may include the following steps:

[0054] S10, obtaining an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the products to be delivered;

[0055] The pending orders may include different types of products or the same type of products. A pending order refers to an order to produce or purchase a specified number of products for delivery according to the requirements in the order. The product identifier can identify the type of product.

[0056] S20, determining a material structure corresponding to the order to be delivered according to the order to be delivered;

[0057] Optionally, one possible implementation of the above S20 is:

[0058] According to the order to be delivered, determine the order material structure BOM tree corresponding to the order to be delivered, the order material structure BOM tree includes at least one level of nodes, each level represents the level to which the material of the product to be delivered belongs, each node represents the material corresponding to the product to be delivered, and the connection between the nodes corresponding to each two levels represents the parent-child relationship between the materials corresponding to the corresponding two nodes.

[0059] Among them, the parent-child relationship between two materials means that the two child materials are derived from the parent material.

[0060] As an example, the order material structure BOM tree can be referred to Figure 2 , Figure 2 , node A represents the material corresponding to the node at the highest level (first level), nodes B and C represent the materials corresponding to the nodes at the middle level (second level), nodes B and C are both child nodes corresponding to node A, nodes D and E are both materials corresponding to node D, and nodes D and E are both nodes at the lowest level (third level).

[0061] refer to Figure 3 , which is a schematic diagram of the delivery cycle of each node, where for a node, the delivery cycle of the node is the time from the release time to the delivery time of the material corresponding to the node. Based on the delivery cycle of each node, the delivery time of each node can be determined.

[0062] Optionally, the quota quantity of the material corresponding to each node can also be displayed in the order material structure BOM tree. The order material structure BOM tree can be used to clarify the material structure composition, which serves as the data basis for material reverse tracing.

[0063] S30, predicting the delivery time of the order to be delivered based on the material structure, the basic material data corresponding to the product to be delivered, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data.

[0064] Among them, basic material data refers to the data involved in the design process of the products to be delivered. Sales data, production data, procurement data and warehousing data refer to the data generated in the sales, production, procurement, warehousing, assembly and transportation processes of the products to be delivered. These data involve multiple manufacturing operation links of the products to be delivered.

[0065] One possible implementation of the above S30 is:

[0066] S301, for any node in the order material structure BOM tree, predict the delivery time of the node according to the material corresponding to the node, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the material, until the delivery time corresponding to each node in the order material structure BOM tree is predicted;

[0067] S302, for any node at the lowest level in the order material structure BOM tree, trace back from the node to the previous level of the lowest level layer by layer according to the delivery time of each node (as an example, see Figure 2 , starting from node E, backtracking to node C and node A in turn), through the critical path algorithm, determine the delivery time corresponding to the optimal path as the delivery time of the order to be delivered, wherein the optimal path is the path with the earliest total delivery time among the paths formed by the nodes of each level, each path is composed of a node in each level, and the total delivery time of each path is determined based on the delivery time of each node corresponding to the path.

[0068] Among them, each node corresponds to a delivery time, and the delivery time of a node refers to the time when the material corresponding to the node is obtained. For example, if the time when the material corresponding to the node is obtained is December 5, 2024, then the delivery time can be December 5, 2024.

[0069] For a route, the delivery time corresponding to the route refers to the time corresponding to all materials corresponding to the route. In a route, at least one material in each level may be included.

[0070] Optionally, the type of material corresponding to the product to be delivered includes at least one of a self-made type and a purchased type; wherein the self-made type means that the material is manufactured by the order supplier itself, and the purchased type means that the material is purchased by the order supplier from other suppliers.

[0071] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a self-made type, the delivery time corresponding to each node is determined by the following method:

[0072] Determine the delivery time corresponding to the node based on the delivery time of the subnode corresponding to the node and the fixed production lead time of the material corresponding to the node;

[0073] It is understandable that if the node has no corresponding child node, the delivery time corresponding to the node can be determined based on the fixed production lead time of the material corresponding to the node. The fixed production lead time refers to the time that must be consumed regardless of the size of the task batch.

[0074] Specifically, if a node (also called a parent node) corresponds to at least two child nodes, based on the delivery time of the child nodes corresponding to the node and the production fixed lead time of the material corresponding to the node, one implementation method for determining the delivery time corresponding to the node is:

[0075] Determine the aggregate time of the delivery times of all child nodes corresponding to the node.

[0076] Add the summary time to the fixed production lead time to get the delivery time corresponding to the node.

[0077] Optionally, for a parent node, if the parent node corresponds to at least two child nodes of a level, the summary time corresponding to the parent node is the latest delivery time among the delivery times of all child nodes;

[0078] As an example, for example, a parent node corresponds to two child nodes, the delivery time of child node A is December 5, 2024, and the delivery time of child node B is December 7, 2024, then the summary time is December 7, 2024.

[0079] If the parent node corresponds to at least two child nodes of at least two levels, the summary time corresponding to the parent node is the latest summary time among the summary times corresponding to each level.

[0080] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a procurement type, the delivery time corresponding to each node is determined in the following way:

[0081] The delivery time corresponding to the node is determined based on the available supply sources of the material corresponding to the node and the required quantity of the material corresponding to the node.

[0082] Among them, available supply sources include inventory on hand, purchase orders, and unaudited purchase plans.

[0083] Based on the available supply sources of the material corresponding to the node and the required quantity of the material corresponding to the node, we can know how much time is required to obtain the material corresponding to the node, and thus calculate the delivery time corresponding to the node.

[0084] Optionally, after determining the delivery time of the to-be-delivered order, the method further includes:

[0085] According to the product resource demand and supply resources (resources that can be provided) corresponding to the order to be delivered, the resources occupied by the order to be delivered in the supply resources are determined, and a binding occupancy relationship between the order to be delivered and the resources occupied by the order to be delivered is established and saved.

[0086] Among them, by binding the demand and resources through the above binding occupation relationship, it can be ensured that the resources simulated and occupied by the current product can be reserved as the supply source of the specified order demand, ensuring the availability of the calculation results. This calculation result can be generated and saved after the material requirement planning order is confirmed.

[0087] Optionally, the method further comprises:

[0088] According to the binding occupation relationship, searching for unoccupied resources and occupied resources in the supplied resources;

[0089] Based on the occupied resources, find the affected product materials and production orders (including orders to be delivered).

[0090] Optionally, if there are at least two orders to be delivered, and there is overlap between the materials required by the at least two orders to be delivered, the method further includes:

[0091] The overlapping materials are regarded as influencing materials, and each of the to-be-delivered orders is updated according to the influencing factors corresponding to each of the influencing materials, so as to minimize the change of each of the to-be-delivered orders.

[0092] The overlap between the materials required by at least two orders to be delivered means that the materials required by at least two orders have the same materials that need to be occupied.

[0093] Among them, the smallest change means that the quantity of products to be delivered is reduced the least, or the total order price of the orders to be delivered is reduced the least.

[0094] The impact factor of each material can be determined based on the importance of the material in the product. For example, if a material can be used in many types of products, the impact factor corresponding to the material is relatively large.

[0095] Optionally, the product delivery simulation prediction method supported by this method is divided into simulation calculation and formal calculation. The simulation calculation process can be repeated, which can support customers to continuously modify and optimize the fitting, so as to simulate the maximum accuracy of all product delivery dates and the minimum product delay under different data conditions and planning parameter configurations. When the calculation results that meet the customer's judgment are simulated, the results can be confirmed, and the final binding relationship between delivery time and demand resource occupancy will be updated to the data storage structure.

[0096] In order to better illustrate and understand the principle of the method provided by the present invention, the scheme of the present invention is described below in conjunction with an optional specific embodiment. It should be noted that the specific implementation of each step in the specific embodiment should not be understood as a limitation to the scheme of the present invention. On the basis of the principle of the scheme provided by the present invention, other implementations that can be thought of by those skilled in the art should also be considered within the scope of protection of the present invention.

[0097] See also Figure 7 This solution can be divided into five stages: material structure development, critical path backtracking, demand and resource relationship binding, dynamic calculation of delivery impact, and delivery simulation and confirmation. The following is a further description of this solution in combination with these five stages:

[0098] Material structure expansion: In this method, a storage structure and functional system for customized product material structure configuration will be provided to maintain and configure design-oriented product material results as the basic data source for delivery forecasting. The order material structure BOM tree of the sales order (order to be delivered) maintained by this system will be used for material structure expansion. Through material structure expansion, the material structure of the product in the order can be clearly displayed, including the material's hierarchy, parent-child relationship, quota quantity, etc. The material structure composition can be clarified as the data basis for reverse tracing of materials.

[0099] Critical path backtracking summary: After the product material structure is expanded, backtrack from the leaf node layer by layer, and calculate the optimal possible delivery time (i.e. the earliest delivery time) of each node according to the type of material corresponding to the node. Through the critical path algorithm, and then summarize upward according to the material structure level, the optimal possible delivery time of the product to be delivered (the delivery time of the order to be delivered) can be finally simulated and calculated.

[0100] Among them, for the nodes corresponding to self-made materials, the earliest delivery time can be determined by adding the summary time of the sub-item nodes (sub-nodes) to the fixed production lead time of the material; for the nodes corresponding to purchased materials, the earliest delivery time can be determined by first calculating the available supply sources of the material, where the available supply sources include the existing inventory, purchase orders, and unaudited purchase plans, and then based on the available supply sources, calculate the earliest time when the required quantity of the material can be provided as the earliest delivery time, and then summarize upward. This method uses this bottom-up material structure backtracking to finally complete the prediction calculation of the product delivery time.

[0101] See also Figure 4 The schematic diagram of delivery date backcasting calculation is shown in Figure 4 For different types of materials, which materials' demand quantities can be provided by available supply resources, and which materials cannot be fully provided by available supply resources, based on different situations, the earliest delivery time of each type of material can be calculated.

[0102] Binding of demand and resource relationship: In the above process, the material on each node after the product material structure is expanded as the demand needs to be satisfied by resources before the simulation calculation process of the delivery date can be carried out. These resources can include approved or in-transit purchase orders, unapproved purchase plans, workshop production orders in process, and warehouse storage existing stock. This method provides a new data storage structure and storage method, which can bind the product resource demand and existing resources to save the occupation relationship. That is, the process of establishing and saving the binding occupation relationship between the to-be-delivered order and the resources occupied by the to-be-delivered order in the previous text. After the delivery date simulation calculation result is confirmed, this binding occupation relationship can be recorded. This method can ensure that the resources simulated and occupied by the current product can be reserved as the supply source of the specified order demand, ensuring the availability of the calculation result. This calculation result can be generated and saved after the material requirement planning order is confirmed.

[0103] See also Figure 5 The resource and demand relationship binding storage structure diagram shown in the figure can be Figure 5 The storage structure shown stores the above-mentioned binding occupancy relationship.

[0104] Dynamic calculation of delivery impact: Since there are often a large number of orders being designed and produced at the same time at the production site of discrete equipment manufacturing products, the relationship between material demand and supply resources is relatively complex. According to the product priority and production strategy, once there is an overlapping occupation of supply materials (that is, there is overlap between the materials required by at least two pending orders), it will affect the delivery date of the original confirmed delivery material, resulting in a change in the accuracy of the date. Similarly, the possibility of a large number of delivery changes will be extended. This method adopts a new method, which takes the impact of different materials on products as the influencing factor, and calculates the minimum possible combination of the influencing factors of the products affected by the delivery date (at least the pending products corresponding to each pending order in the two pending deliveries) through a linear programming-based algorithm to ensure the maximum accuracy of the delivery date.

[0105] See also Figure 6 The schematic diagram of the dynamic settlement results affected by the delivery period is shown in Figure 6 In the case where there is overlap between the materials required by at least two pending orders, the minimum change can be reflected in the reduction of the quantity of a certain product to be delivered in the pending orders.

[0106] Delivery simulation and result confirmation: The product delivery simulation prediction method supported by this method is divided into simulation calculation and formal calculation. The simulation calculation process can be repeated, which can support customers to continuously modify and optimize the fitting, so as to simulate the maximum accuracy of all product delivery and the minimum product delay under different data conditions and planning parameter configurations. When the calculation results that meet the customer's judgment are simulated, the results can be confirmed, and the final binding relationship between delivery time and demand resource occupancy will be updated to the data storage structure.

[0107] The solution of the present invention has the following beneficial effects:

[0108] 1. The method in this application can effectively improve the accuracy of product delivery dates of equipment manufacturing enterprises, reduce product delays, increase product completeness, reduce inventory levels, and reduce costs.

[0109] 2. Bidirectional expansion of material structure: The delivery time of material products is calculated through the two mechanisms of forward expansion and reverse tracing of product material results, realizing the simulation calculation of delivery time. The concept of low-level code is combined to improve the calculation performance, improve the accuracy of enterprise product delivery time calculation, reduce the uncertainty of orders of discrete equipment manufacturing enterprises, and improve the accuracy of results and calculation efficiency;

[0110] 3. Bidirectional binding of demand and resources: By bidirectionally binding product material demand and supply resource occupancy, on the one hand, it is possible to quickly find the resources that can be occupied, and on the other hand, after the locked resources are forcibly occupied, it is possible to find the affected product materials and production orders, thereby simulating and calculating the impact of delivery forecast occupancy on other low-priority materials, and generating the delivery plan with the best accuracy and the smallest delivery delay by calculating the minimum impact factor.

[0111] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a product delivery date prediction device 20, such as Figure 8 As shown in , the product delivery date prediction device 20 may include an acquisition module 210, a material structure determination module 220 and a delivery time prediction module 230, wherein:

[0112] An acquisition module 210 is used to acquire an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the product to be delivered;

[0113] A material structure determination module 220, configured to determine a material structure corresponding to the order to be delivered according to the order to be delivered;

[0114] The delivery time prediction module 230 is used to predict the delivery time of the to-be-delivered order based on the material structure, the material basic data corresponding to the to-be-delivered product, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data.

[0115] Optionally, when determining the material structure corresponding to the order to be delivered according to the order to be delivered, the material structure determining module 220 is specifically configured to:

[0116] According to the order to be delivered, determine the order material structure BOM tree corresponding to the order to be delivered, the order material structure BOM tree includes at least one level of nodes, each level represents the level to which the material of the product to be delivered belongs, each node represents the material corresponding to the product to be delivered, and the connection between the nodes corresponding to each two levels represents the parent-child relationship between the materials corresponding to the corresponding two nodes.

[0117] Optionally, when predicting the delivery time of the order to be delivered according to the material structure, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the product to be delivered, the delivery time prediction module 230 is specifically used to:

[0118] For any node in the order material structure BOM tree, the delivery time of the node is predicted according to the material corresponding to the node, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the material, until the delivery time corresponding to each node in the order material structure BOM tree is predicted;

[0119] For any node at the lowest level in the BOM tree of the order material structure, based on the delivery time of each node, trace back from the node to the previous level of the lowest level layer by layer, and determine the delivery time corresponding to the optimal path as the delivery time of the order to be delivered through the critical path algorithm, wherein the optimal path is the path with the earliest total delivery time among the paths formed by the nodes of each level, each path is composed of a node in each level, and the total delivery time of each path is determined based on the delivery time of each node corresponding to the path.

[0120] Optionally, the type of material corresponding to the product to be delivered includes at least one of a self-made type and a purchased type;

[0121] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a self-made type, the delivery time corresponding to each node is determined by the following method:

[0122] Determine the delivery time corresponding to the node based on the delivery time of the subnode corresponding to the node and the fixed production lead time of the material corresponding to the node;

[0123] For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a procurement type, the delivery time corresponding to each node is determined in the following way:

[0124] The delivery time corresponding to the node is determined based on the available supply sources of the material corresponding to the node and the required quantity of the material corresponding to the node.

[0125] Optionally, after determining the delivery time of the order to be delivered, the device further includes:

[0126] The binding occupancy relationship determination module is used to determine the resources occupied by the order to be delivered in the supply resources according to the product resource requirements and supply resources corresponding to the order to be delivered, and to establish a binding occupancy relationship between the order to be delivered and the resources occupied by the order to be delivered for storage.

[0127] Optionally, the device further comprises:

[0128] The search module is used to search for unoccupied resources and occupied resources in the supply resources according to the binding occupancy relationship; and to search for affected product materials and production orders according to the occupied resources.

[0129] Optionally, if there are at least two orders to be delivered, and there is overlap between the materials required by the at least two orders to be delivered, the device further includes:

[0130] The order modification module is used to take overlapping materials as influencing materials, and update each of the pending orders according to the influencing factors corresponding to each of the influencing materials, so as to minimize the modification of each of the pending orders.

[0131] The product delivery prediction device of the embodiment of the present invention can execute the product delivery prediction method provided by the embodiment of the present invention, and the implementation principle is similar. The actions performed by each module and unit in the product delivery prediction device in each embodiment of the present invention correspond to the steps in the product delivery prediction method in each embodiment of the present invention. For the detailed functional description of each module of the product delivery prediction device, please refer to the description in the corresponding product delivery prediction method shown in the previous text, which will not be repeated here.

[0132] Among them, the above-mentioned product delivery date prediction device can be a computer program (including program code) running in a computer device, for example, the product delivery date prediction device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0133] In some embodiments, the product delivery prediction device provided by the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the product delivery prediction device provided by the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the product delivery prediction method provided by the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0134] In other embodiments, the product delivery date prediction device provided by the embodiment of the present invention can be implemented in software. Figure 8A product delivery date prediction device stored in a memory is shown, which may be software in the form of a program or plug-in, and includes a series of modules, including an access request acquisition module 210, a target web page package determination module 220, and a product delivery date prediction module 230, for implementing the product delivery date prediction method provided in an embodiment of the present invention.

[0135] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0136] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0137] In an alternative embodiment, an electronic device is provided, such as Fig. 9 As shown, Fig. 9 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0138] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may 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, etc.

[0139] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0140] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0141] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0142] The electronic device may also be a terminal device. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0143] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0144] According to another aspect of the present invention, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0145] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0146] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] The computer-readable storage medium provided by the embodiment of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0148] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0149] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

Claims

1. A product delivery date prediction method, characterized in that: The following steps are involved: Obtaining an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the product to be delivered; According to the order to be delivered, determine the material structure corresponding to the order to be delivered; The delivery time of the order to be delivered is predicted based on the material structure, the basic material data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the product to be delivered.

2. The method according to claim 1, characterized in that The determining, according to the order to be delivered, a material structure corresponding to the order to be delivered, includes: According to the order to be delivered, determine the order material structure BOM tree corresponding to the order to be delivered, the order material structure BOM tree includes at least one level of nodes, each level represents the level to which the material of the product to be delivered belongs, each node represents the material corresponding to the product to be delivered, and the connection between the nodes corresponding to each two levels represents the parent-child relationship between the materials corresponding to the corresponding two nodes.

3. The method according to claim 2, characterized in that The method of predicting the delivery time of the order to be delivered according to the material structure, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the product to be delivered, comprises: For any node in the order material structure BOM tree, the delivery time of the node is predicted according to the material corresponding to the node, the material basic data, sales data, production data, procurement data, warehousing data and assembly and transportation data corresponding to the material, until the delivery time corresponding to each node in the order material structure BOM tree is predicted; For any node at the lowest level in the BOM tree of the order material structure, based on the delivery time of each node, trace back from the node to the previous level of the lowest level layer by layer, and determine the delivery time corresponding to the optimal path as the delivery time of the order to be delivered through the critical path algorithm, wherein the optimal path is the path with the earliest total delivery time among the paths formed by the nodes of each level, each path is composed of a node in each level, and the total delivery time of each path is determined based on the delivery time of each node corresponding to the path.

4. The method according to claim 3, characterized in that The type of material corresponding to the product to be delivered includes at least one of a self-made type and a purchased type; For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a self-made type, the delivery time corresponding to each node is determined by the following method: Determine the delivery time corresponding to the node based on the delivery time of the subnode corresponding to the node and the fixed production lead time of the material corresponding to the node; For any node in the BOM tree of the order material structure, if the type of the material corresponding to the node is a procurement type, the delivery time corresponding to each node is determined in the following way: The delivery time corresponding to the node is determined based on the available supply sources of the material corresponding to the node and the required quantity of the material corresponding to the node.

5. The method according to any one of claims 1 to 4, characterized in that After determining the delivery time of the order to be delivered, the method further includes: According to the product resource demand and supply resources corresponding to the to-be-delivered order, the resources in the supply resources occupied by the to-be-delivered order are determined, and a binding occupancy relationship between the to-be-delivered order and the resources occupied by the to-be-delivered order is established and saved.

6. The method according to claim 5, characterized in that The method further comprises: According to the binding occupation relationship, searching for unoccupied resources and occupied resources in the supplied resources; Based on the occupied resources, find the affected product materials and production orders.

7. The method according to any one of claims 1 to 4, characterized in that If there are at least two orders to be delivered, and there is overlap between the materials required by the at least two orders to be delivered, the method further includes: The overlapping materials are regarded as influencing materials, and each of the to-be-delivered orders is updated according to the influencing factors corresponding to each of the influencing materials, so as to minimize the change of each of the to-be-delivered orders.

8. A product delivery date prediction device, characterized in that: include: An acquisition module, used for acquiring an order to be delivered, wherein the order to be delivered includes the quantity and product identification of the product to be delivered; A material structure determination module, used to determine the material structure corresponding to the order to be delivered according to the order to be delivered; The delivery time prediction module is used to predict the delivery time of the order to be delivered based on the material structure, the basic material data corresponding to the product to be delivered, the sales data, the production data, the procurement data, the warehousing data and the assembly and transportation data.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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