CN117252400B - Coordination management method, system and application of automobile supply chain - Google Patents
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Abstract
The application discloses a method, a system and an application for coordination management of an automobile supply chain, which belong to the technical field of automobile production, and the method comprises the steps of collecting historical production data, stock data, sales data and industry average sales data of a host factory at the same level including the host factory to be managed; acquiring capacity data of a provider, electricity consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed; constructing a production demand prediction model and a supply chain accessory supply prediction model; the method and the system adopt the data of the collection host factory and the data of the supply chain supplier, construct the production demand prediction model and the supply chain accessory supply prediction model, realize the analysis of the automobile supply chain data, effectively solve the problem that the current application of the automobile supply chain data to the processing of the automobile is not ideal to influence the automobile production management, and further realize the coordination management of the automobile supply chain, and effectively identify risks and treatments.
Description
Technical Field
The application relates to the technical field of automobile production, in particular to an automobile supply chain coordination management method, an automobile supply chain coordination management system and an application.
Background
The main activities of the supply chain consist of three phases, namely raw material procurement, raw material processing into specific end products, and final product shipping and distribution to retailers; accordingly, a supply chain may be defined as the integration of various types of business entities (i.e., suppliers, manufacturers, distributors, and retailers) to complete an overall process including obtaining raw materials, converting the raw materials into specific end products, and then finally shipping and dispensing the end products to a retail terminal;
the more widely new energy automobiles are produced at present, the production needs of the new energy automobiles relate to an automobile supply chain, the management of the current supply chain is mostly managed in a manual tracking mode, and the application and analysis of the data of the automobile supply chain are lacked, so that the production management of the automobiles is influenced.
Disclosure of Invention
The method, the system and the application for coordination management of the automobile supply chain solve the problem that the application of the existing automobile supply chain data to processing of the automobile production management is not ideal in the prior art, realize the analysis of the automobile supply chain data, obtain the automobile production prediction and the supply chain accessory supply prediction, facilitate coordination management of the automobile supply chain, and effectively identify risks and treat the risks.
The application provides an automobile supply chain coordination management method, which comprises the following steps:
collecting historical production data, whole vehicle inventory data, sales data and industry average sales data of the same-level host factories including the host factories to be managed; acquiring capacity data of a provider, electricity consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed;
constructing a production demand prediction model and a supply chain accessory supply prediction model, and training by using the data acquired in the step S1; collecting production data, inventory data and sales data of a host factory to be managed and industry average sales data at a first moment; acquiring capacity data of a provider, power consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed at a first moment; calculating according to the production demand prediction model and the supply chain accessory supply prediction model to obtain predicted production data of the host factory and supply chain accessory supply prediction data at the second moment; and dividing risk grades and treatment plans according to the predicted production data and the supply chain accessory supply predicted data to perform coordination management of an automobile supply chain.
The application provides an automobile supply chain coordination management system, wherein the acquisition end is used for acquiring data of a host factory and a supplier; the server is used for analyzing and calculating an automobile supply chain; and the client terminal is used for analyzing, inputting and displaying the automobile supply chain.
The technical scheme provided by the application has at least the following technical effects or advantages:
the method and the system adopt the data of the collection host factory and the data of the supply chain supplier, construct the production demand prediction model and the supply chain accessory supply prediction model, realize the analysis of the automobile supply chain data, effectively solve the problem that the current application of the automobile supply chain data to the processing of the automobile is not ideal to influence the automobile production management, and further realize the coordination management of the automobile supply chain, and effectively identify risks and treatments.
Drawings
FIG. 1 is a flow chart of a method for coordination management of an automobile supply chain according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a coordination management system of an automobile supply chain according to an embodiment of the present application;
FIG. 3 is a block diagram of a collection end according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a client terminal and a user terminal according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a production demand prediction model and a supply chain fitting supply prediction model according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of supply chain coordination data set generation in accordance with an embodiment of the present application.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1-6, an embodiment of the present application provides an automobile supply chain coordination management system 100, where the system 100 includes an acquisition end 101 for acquiring data of a host factory and a vendor; a server 102 for automotive supply chain analysis calculations; and a client terminal 103 for the car supply chain analysis input and display.
In an exemplary embodiment, the collection end 101 includes a host factory data collection unit 1011 and a data collection unit 1012 of a supply chain provider;
the host factory data acquisition unit 1011 acquires historical production data, stock data, sales data and industry average sales data of the host factories of the same level including the host factories to be managed;
the data acquisition unit 1012 of the supply chain supplier acquires capacity data of the supplier, power consumption data of the supplier, key raw material purchasing data and supply chain accessory data of a host factory to be managed;
in an exemplary embodiment, the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms, and the like. The server is an electronic device for providing background services, for example, in the present implementation environment, the server provides cloud computing and cloud storage services for the client terminal;
in an exemplary embodiment, the client terminal 103 may be used for a client that provides a coordination and management function of an automobile supply chain, and may be an electronic device such as a desktop computer, a notebook computer, a tablet computer, a smart phone, etc., which is not limited herein;
the client 1031 provides a coordination management function of the automobile supply chain, for example, the management platform may be in the form of an application program or a web page, and accordingly, a user interface of the client for performing coordination management of the automobile supply chain may be in the form of a program window or a web page, which is not limited herein.
In an exemplary embodiment, the mobile terminal 104 further includes an electronic device such as a smart phone, a tablet computer, a notebook computer, a desktop computer, an intelligent control panel, and other devices with display and control functions, which are not limited herein; the user terminal can run the client 1031; and the management of a supply chain is facilitated.
The embodiment of the application provides an automobile supply chain coordination management method, which comprises the following steps:
s1, collecting historical production data, whole vehicle inventory data, sales data and industry average sales data of a same-level host factory including a host factory to be managed; acquiring capacity data of a provider, electricity consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed;
s2, constructing a production demand prediction model and a supply chain accessory supply prediction model, and training by using the collected data in the S1 to obtain a production demand prediction model and a supply chain accessory supply prediction model after training;
s3, collecting production data, inventory data and sales data of a host factory to be managed at a first moment and industry average sales data; acquiring capacity data of a provider, power consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed at a first moment;
s4, calculating according to the production demand prediction model and the supply chain accessory supply prediction model by using the data acquired in the S3 to obtain predicted production data of a host factory and supply chain accessory supply prediction data at a second moment;
s5, dividing risk grades and treatment plans according to the forecast production data and the supply chain accessory supply forecast data in the S4, and carrying out coordination management on an automobile supply chain.
In an exemplary embodiment, the historical production data, the inventory data, the sales data and the industry average sales data of the same-level host factories including the host factories collected in the step S1 are assigned and collected as follows:
，
wherein, the method comprises the steps of, wherein,in order to be a time step, the time step,is the sequence length;is the firstAt the moment of time of day,the data sets of the same-level host factories comprise historical production data, stock data of the whole automobile, sales data and industry average sales data; in particular, the method comprises the steps of,historical production data of a first host factory with the same level, stock data of the whole vehicle, sales data and industry average sales data;historical production data of a second host factory with the same level, stock data of the whole vehicle, sales data and industry average sales data;is the firstHistorical production data of the host factories of the same level, stock data of the whole vehicle, sales data and industry average sales data;is the slaveTo the point ofTime of dayHistorical production data of the host factories of the same level, stock data of the whole vehicle, sales data and industry average sales data.
In an exemplary embodiment, the assignment and aggregation process for the capacity data of the supplier, the electricity consumption data of the supplier, the key raw material purchasing data, and the supply chain accessory data of the host factory to be managed acquired in S1 is as follows:
，
，in order to be a time step, the time step,is the sequence length;is the firstAt the moment of time of day,a data set of the suppliers, wherein the data set of the suppliers comprises capacity data of the suppliers, power consumption data of the suppliers, key raw material purchasing data and supply chain accessory data of a host factory to be managed; in particular, the method comprises the steps of,the method comprises the steps of providing capacity data, electricity consumption data, key raw material purchasing data and supply chain accessory data of a host factory to be managed for a first supplier;the method comprises the steps of providing capacity data, electricity consumption data, key raw material purchasing data and supply chain accessory data of a host factory to be managed for a second provider;is the firstCapacity data, electricity consumption data, key raw material procurement data, and supply chain accessory data of a host factory to be managed are provided for each supplier.
In an exemplary embodiment, for the acquisition in S1 above，Normalizing the data to obtain；The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofIn order to normalize the data set,respectively normalized historical production data of the same-level host factories, stock data of the whole vehicle, sales data, industry average sales data, capacity data of suppliers, electricity consumption data of the suppliers, key raw material purchasing data and supply chain accessory data of the host factories to be managed,is a time variable.
In an exemplary embodiment, a production demand prediction model and a supply chain accessory supply prediction model are constructed:
；
wherein,a production demand prediction model;the predicted production data of the host factory to be managed at the time t+1;a production demand prediction model;supply forecast data for supply chain accessories at time t +1,the method comprises the steps of producing data of a host factory at the time t, stock data of the whole vehicle, sales data and industry average sales data;and training the capacity data of the supplier, the electricity consumption data of the supplier, the key raw material purchasing data and the supply chain accessory data of the host factory to be managed at the time t by utilizing the data acquired in the step S1 to obtain a production demand prediction model and a supply chain accessory supply prediction model after training.
In an exemplary embodiment, theModel for predicting production demandThe method comprises the steps that a production demand prediction model is respectively constructed based on a BP neural network, wherein the BP neural network is provided with an input layer, an hidden layer and an output layer, the input layer is provided with 8 inputs, and the output layer is provided with 2 outputs; the activation function of the hidden layer isA function.
In an exemplary embodiment, based onProduction demand prediction modelOutput by a production demand prediction modelAndbuilding a risk assessment model:
；
wherein:，is a risk assessment parameter; the large data risk prediction, evaluation and management of the supply chain are facilitated; in an exemplary embodiment, according toOutput by a production demand prediction modelConstructing a supply chain coordination data set by adopting a diffusion modeWherein, the method comprises the steps of, wherein,supplying forecast data for supply chain accessories of the 1 st supplier at time t+1;supplying forecast data for supply chain accessories of the 2 nd supplier at time t+1;for the (t+1) th timeThe supply chain accessories of the suppliers supply forecast data,supplying forecast data for supply chain accessories of an mth supplier at time t+1; m is the number of suppliers;，，..... A.The sum isThe method comprises the steps of carrying out a first treatment on the surface of the And realizing the coordination and distribution management of the supply chain.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art, within the scope of the present application, should apply to the present application, and all equivalents and modifications as fall within the scope of the present application.
Claims (8)
1. A method for coordinated management of an automotive supply chain, comprising:
s1, collecting historical production data, whole vehicle inventory data, sales data and industry average sales data of a same-level host factory including a host factory to be managed;
acquiring capacity data of a provider, electricity consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed;
s2, constructing a production demand prediction model and a supply chain accessory supply prediction model:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofA production demand prediction model; />The predicted production data of the host factory to be managed at the time t+1; />Supplying a predictive model for a supply chain accessory; />Supply forecast data for supply chain accessories at time t +1,the method comprises the steps of producing data of a host factory at the time t, stock data of the whole vehicle, sales data and industry average sales data; />The method comprises the steps of providing capacity data of a provider, electricity consumption data of the provider, key raw material purchase data and supply chain accessory data of a host factory to be managed for a time t;
training by using the data acquired in the step S1;
s3, collecting production data, inventory data and sales data of a host factory to be managed at a first moment and industry average sales data; acquiring capacity data of a provider, power consumption data of the provider, key raw material purchasing data and supply chain accessory data of a host factory to be managed at a first moment;
s4, calculating according to the production demand prediction model and the supply chain accessory supply prediction model to obtain predicted production data of a host factory and supply chain accessory supply prediction data at a second moment;
s5, according to the predicted production data and the supply chain accessory supply predicted data, dividing risk grades and treatment plans, and carrying out coordination management on an automobile supply chain;
based on the output of the production demand prediction model and the supply chain accessory supply prediction modelAndbuilding a risk assessment model:
；
wherein:，/>is a risk assessment parameter.
2. The method for coordination management of an automobile supply chain according to claim 1, wherein the step of assigning and collecting the historical production data, the stock data, the sales data and the industry average sales data of the same-level host factories including the host factories collected in the step S1 is as follows:
，wherein->For the time step->Is the sequence length; />Is->Time of day (I)>The data sets of the same-level host factories comprise historical production data, stock data of the whole automobile, sales data and industry average sales data; />Historical production data of a first host factory with the same level, stock data of the whole vehicle, sales data and industry average sales data;historical production data of a second host factory with the same level, stock data of the whole vehicle, sales data and industry average sales data; />Is->Historical production data of the host factories of the same level, stock data of the whole vehicle, sales data and industry average sales data; />To be from->To->Time->Historical production data of the host factories of the same level, stock data of the whole vehicle, sales data and industry average sales data.
3. A method for coordinated management of an automotive supply chain according to claim 2,
and (3) assigning and collecting the capacity data of the suppliers, the electricity consumption data of the suppliers, the key raw material purchasing data and the supply chain accessory data of the host factory to be managed, wherein the capacity data, the electricity consumption data and the key raw material purchasing data are acquired in the step (S1), and the supply chain accessory data of the host factory to be managed are processed as follows:
，，/>for the time step->Is the sequence length; />Is->Time of day (I)>A data set of the suppliers, wherein the data set of the suppliers comprises capacity data of the suppliers, power consumption data of the suppliers, key raw material purchasing data and supply chain accessory data of a host factory to be managed; />The method comprises the steps of providing capacity data, electricity consumption data, key raw material purchasing data and supply chain accessory data of a host factory to be managed for a first supplier; />The method comprises the steps of providing capacity data, electricity consumption data, key raw material purchasing data and supply chain accessory data of a host factory to be managed for a second provider;is->Capacity data, electricity consumption data, key raw material procurement data, and supply chain accessory data of a host factory to be managed are provided for each supplier.
4. A method for coordinated management of an automotive supply chain according to claim 3,
normalizing the data acquired in the step S1 to obtain；/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein->For normalizing the dataset +.>Is a time variable +.>Normalized +.>At the moment, historical production data of the same-level host factories, stock data of the whole vehicle, sales data, industry average sales data, capacity data of suppliers, electricity consumption data of the suppliers, key raw material purchasing data and supply chain accessory data of the host factories to be managed are supplied.
5. A method for coordinated management of an automotive supply chain according to claim 1,
the saidPredictive model for production demand +.>The supply forecast model for the supply chain fittings is respectively constructed based on BP neural network, the BP neural network has an input layer, an hidden layer and an output layer, the input layer has 8The input and output layers have 2 outputs; the activation function of the hidden layer is +.>A function.
6. A method for coordinated management of an automotive supply chain according to claim 1,
output according to the supply chain fitting supply prediction modelConstructing a supply chain coordination data set by adopting a diffusion mode>Wherein->Supplying forecast data for supply chain accessories of the 1 st supplier at time t+1; />Supplying forecast data for supply chain accessories of the 2 nd supplier at time t+1; />Supply forecast data for supply chain accessories of the u-th supplier at time t+1, +.>Supplying forecast data for supply chain accessories of an mth supplier at time t+1; m is the number of suppliers; />，/>，.....>The sum is->。
7. A vehicle supply chain coordination management system applying the vehicle supply chain coordination management method of any one of claims 1 to 6, characterized in that,
the acquisition end is used for acquiring data of a host factory and a supplier;
the server is used for analyzing and calculating an automobile supply chain;
and the client terminal is used for analyzing, inputting and displaying the automobile supply chain.
8. An application of the coordination management system of the automobile supply chain in the production field of fuel automobiles or new energy automobiles.
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