CN113095610A - Planning scheduling and allocating system, method and storage medium - Google Patents
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Abstract
The application provides a planning, scheduling and allocating system, a planning, scheduling and allocating method and a storage medium, wherein a data collection module acquires production data related to a target product; the data filtering module eliminates abnormal data which do not meet preset conditions in the production data; the data statistics module is used for carrying out statistics on the filtered production data from different dimensions to obtain sales data, warehousing data, production data and cost data of the target product; and the data analysis module generates a production plan and/or a goods allocation plan of the target product according to the sales data, the warehousing data, the production data and the cost data. The method and the device realize reasonable production and allocation of products and optimize utilization of resources.
Description
Technical Field
The present application relates to the field of resource management technologies, and in particular, to a system, a method, and a storage medium for scheduling and allocating planned production.
Background
With the development and application of big data technologies, more and more enterprises, especially big companies, apply big data technologies to visually and visually present a large amount of actual data, so that the data are visually and visually presented, the decision efficiency and scientificity are improved, the subjective decision awareness and the manual maintenance cost are reduced, the resource allocation is optimized, the efficiency is improved, and the economic benefit is promoted.
With the rapid increase of enterprise business volume, particularly the rapid increase of product sales volume of large companies, the traditional manual data statistics is time-consuming and labor-consuming, the decision making of a management layer by looking at a report form is not intuitive, and the data visualization can realize scientific decision making and improve the decision making efficiency to the maximum extent. Meanwhile, the rationalization and the high efficiency of the production plan and the logistics distribution of the product are realized through multi-dimensional statistical data. The traditional decision method adopts reports, the efficiency is low, the traditional method for producing air conditioners and electric products has the defects that the information is outdated and the market information is not fed back timely according to the historical report data of the last year or the last year, and the traditional method for sending the electric products to distributors all over the country randomly causes the pushing of warehouse goods or the shortage of goods, so that the resource waste is caused.
In the face of the expansion of logistics range and the increase of warehouse quantity, most enterprises establish logistics informatization systems to plan, coordinate, control and supervise logistics activities, so that the logistics activities are optimally coordinated and matched. However, the information types are more and more, the integration is more and more complex, and how to effectively utilize the information provides an efficient and accurate decision basis for enterprises, simplifies the operation flow, improves the enterprise benefit, and is the key point of the development of the resource management technology.
Disclosure of Invention
In view of the above problems, the present application provides a system, a method and a storage medium for scheduling and allocating production, so as to solve the technical problems of low information integration rate and poor production and logistics distribution efficiency in the prior art.
In a first aspect, the present application provides a scheduling and scheduling system, comprising:
the data collection module is used for acquiring production data related to a target product;
the data filtering module is used for eliminating abnormal data which do not meet preset conditions in the production data, and the abnormal data comprises incomplete data, error data and repeated data;
the data statistics module is used for counting the filtered production data from different dimensions so as to obtain sales data, warehousing data, production data and cost data of the target product;
and the data analysis module is used for generating a production plan and/or a goods allocation plan of the target product according to the sales data, the warehousing data, the production data and the cost data of the target product.
Still further, the system further comprises:
and the storage module is used for storing data and instructions generated in the operation process of the data collection module, the data filtering module, the data statistics module and the data analysis module.
Still further, the system further comprises:
and the timing module is used for setting timing information and sending the timing information to the data collection module, and the data collection module acquires the production data in a specified time period at a specified time according to the timing information.
Still further, the system further comprises:
and the data analysis module is also combined with the artificial planning data and generates the production plan and/or the goods allocation plan according to the sales data, the warehousing data, the production data and the cost data of the target product.
Still further, the system further comprises:
the production execution module is used for adjusting the production quantity and/or the production progress of the target product according to the production plan; and/or
And the allocation execution module is used for adjusting the allocation region and/or the allocation quantity of the target product according to the goods allocation plan.
In a second aspect, the present application provides a scheduling method, comprising:
acquiring production data related to a target product;
rejecting abnormal data which do not meet preset conditions in the production data, wherein the abnormal data comprise incomplete data, error data and repeated data;
counting the production data after the abnormal data are removed from different dimensions to obtain sales data, warehousing data, production data and cost data of the target product;
and generating a production plan and/or a goods allocation plan according to the sales data, the warehousing data, the production data and the cost data.
Further, the acquiring production data related to the target product includes:
and acquiring the production data in a specified time period at a specified time according to the timing information.
Still further, the method further comprises:
acquiring artificially made plan data;
and combining the artificial planning data, and generating the production plan and/or the goods allocation plan according to the sales data, the warehousing data, the production data and the cost data of the target product.
Still further, the method further comprises:
when a production plan is generated according to the sales data, the warehousing data, the production data and the cost data, the production quantity and/or the production progress of the target product are/is adjusted according to the production plan;
and when a goods allocation plan is generated according to the sales data, the warehousing data, the production data and the cost data, adjusting the allocation region and/or the allocation quantity of the target product according to the goods allocation plan.
In a third aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by one or more processors, performs the steps of the method of the second aspect.
Compared with the prior art, the application has the following advantages or beneficial effects:
according to the plan scheduling and allocating system, the plan scheduling and allocating method and the storage medium, production record data, installation data and scanning data are collected and counted, integration analysis is carried out on the sales information, the production information, the storage information, the cost information and other dimensions, the production plan and the goods allocation plan obtained through analysis are accessed into the production execution module and the allocation execution module in real time, and reasonable production and efficient allocation of each product are achieved. The method breaks through an information isolated island, forms an information closed loop, optimizes reasonable production and allocation of resources, avoids resource waste and promotes improvement of economic benefits.
Drawings
The present application will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings:
FIG. 1 is a block diagram of a scheduling system according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a scheduling method according to an embodiment of the present disclosure;
fig. 3 is a flowchart of an air conditioner scheduling method according to an embodiment of the present application;
fig. 4 is a block diagram of a cargo deployment system according to a second embodiment of the present application;
fig. 5 is a flowchart of a cargo deployment method according to a second embodiment of the present application;
fig. 6 is a flowchart of an air conditioning cargo allocation method according to a second embodiment of the present application.
Detailed Description
The following detailed description will be provided with reference to the accompanying drawings and embodiments, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the scope of protection of the present application.
Example one
As shown in fig. 1, the present embodiment provides a scheduling system, which includes a data collection module 201, a data filtering module 202, a data statistics module 203, and a data analysis module 204.
The data collection module 201 is configured to obtain production data of a target product, where the production data includes production record data, installation data, and scan data.
The production record data can comprise one or more of parameters such as a start bar number, a stop bar number, an order number, a printing number, printing time, a base to which a factory belongs, installation data can comprise one or more of parameters such as a product type, a brand model, an internal machine bar code, an external machine bar code, a user telephone, a user name, province, a city, a village and town, a street and a vendor company name, and scanning data can comprise one or more of parameters such as a scanning module, a model, an electric appliance type, a brand, an ip address, a region, a user, time, a bar code and a browser.
The data can be acquired from a large data platform in a mode that a program interface simulates an HTTP request, or directly acquired from a target database in a mode of opening the database, or required data is analyzed from bottom layer data in a mode of directly acquiring the data.
The data filtering module 202 is configured to remove abnormal data that does not meet the condition in the production data, where the abnormal data is one or more of incomplete data, error data, and duplicate data.
For each piece of collected data, whether the data is empty, damaged or not, lost or not, meeting requirements or not, repeated or not and the like are judged at first, and if the data is empty, the data is removed to improve the effectiveness of the data and ensure the accuracy of statistical analysis results.
The data statistics module 203 is configured to perform classified statistics on the filtered production data to obtain sales data, warehousing data, production data, and cost data of the target product.
The statistics is performed from different dimensions, that is, statistics is performed from different angles, different parameters, and different directions, so as to obtain various types of data after statistics, for example, the sales data of the target product may include a total sales volume of the target product, a sales volume of the target product in a specific area, a sales distribution of a specific model of the target product, and the like, the warehousing data of the target product may include an inventory of the target product in a specific warehouse, a total inventory of the target product, and the like, the production data of the target product may include a total production volume of the target product, a production distribution of the target product, and the like, and the cost data of the target product may include a transportation cost and/or a transportation duration of the target product transported from a specific area to another specific area, a material procurement period of the target.
If the production data acquired by the data collection module 201 is data in one day, the various types of data counted by the data counting module 203 represent the conditions in one day; if the production data acquired by the data collection module 201 is data within one week, the various types of data counted by the data counting module 203 represent the conditions within one week.
The data analysis module 204 is configured to generate a production plan of the target product according to the sales data, the warehousing data, the production data, and the cost data of the target product.
The production plan is used for adjustment and modification of the production situation of the target product, such as increase or decrease in the number of production pieces of the target product in a specific area, increase or decrease in the production ratio of a specific model of the target product, and the like. And generating a next-day production plan for adjusting and modifying the next-day production according to the data representing the statistics of the conditions in one day, and generating a next-week production plan for adjusting and modifying the next-week production according to the data representing the statistics of the conditions in one week. The data analysis module 204 may upload the production plan to the big data platform for sharing through the program interface.
The scheduling system may also include a storage module 302 for storing various types of data, which may include, for example, instructions for any application or method in the scheduling system, as well as application-related data.
The scheduled production system may further include a timing module 207 configured to set timing information and send the timing information to the data collection module 201, and the data collection module 201 obtains production data in a specified time period at a specified time according to the timing information.
Specifically, the timing module 207 may send a trigger signal to the data collection module 201, and the data collection module 201 receives the trigger signal and then obtains the production data. The trigger signal may be a current signal or a voltage signal, or may be a level signal or a pulse signal. The timing module 207 can send out various types of trigger signals, for example, three different types and/or intensities of trigger signals to the data collection module 201 to respectively obtain the production record data, the installation data and the scan data. For example, 3:00 production records per day, 4:00 installations per day, 6:00, 12:00, 18:00, 24:00 scans per day. The production log data and the installation data may be data from 3:00 by yesterday to 3:00 by today, or data from 3:00 by last sunday to 3:00 by this sunday. The scan data may be data from 6:00 of the day to 12:00 of the day, or from 12:00 of the last week to 12:00 of the week. The timing module 207 may be implemented in software or hardware and may have a delay function.
In some embodiments, the scheduling system further includes a client 206 for inputting and transmitting the artificially planned production data to the data analysis module 204, wherein the artificially planned production data is a production plan customized by a user. The data analysis module 204 combines the artificially planned production data with the data counted by the data counting module 203 to generate a production plan. The client 206 is also used to receive and display the production plan, and can read from the storage module 205 or obtain from a big data platform.
The client 206 can realize data visualization, and a user can retrieve related information of a target product, such as sales ranking, sales or production quantity in each month, and the like, from different dimensions such as product information, production information, warehousing information, and the like through keywords. The client is preferably a mobile client, so that a user can conveniently adjust the plan in real time, retrieve and check the product condition and monitor the production and distribution scheme.
The client 206 may include an audio component for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 302 or transmitted through the communication component. The audio assembly also includes at least one speaker for outputting audio signals. The audio component is mainly used for realizing functions of reminding, alarming, confirming and the like.
The production execution module 208 is configured to adjust the production quantity and/or the production schedule of the target product according to the production plan.
The production execution module 208 may obtain the production plan from the big data platform through the data interface. The production plans vary from region to region, and accordingly, the production execution module 208 may include multiple subsystems to perform production adjustments for different regions. Similarly, the production plans of different products are different, and the production execution module 208 can be divided into a plurality of subsystems according to the product types, and the different subsystems execute the production adjustment of different products.
Accordingly, the present embodiment provides a scheduling method applied to the scheduling system, as shown in fig. 2, the method includes the following steps:
step S11, acquiring production data according to the timing information.
And step S12, eliminating abnormal data in the production data.
And step S13, counting the production data from different dimensions to obtain the sales data, the warehousing data, the production data and the cost data of the target product.
And step S14A, generating a production plan of the target product according to the sales data, the warehousing data, the production data and the cost data.
And step S15, adjusting the production quantity and/or the production progress of the target product according to the production plan.
In some embodiments, the step S14A may be replaced by the following steps:
step S134, acquiring production data of a man-made plan;
and step S14B, generating the production plan according to the artificially planned production data, the sales data, the warehousing data, the production data and the cost data.
The artificially planned production data may be obtained by the client 206 as initial data or default data. It can also be understood that artificial regulation is added in objective statistical data to reduce the interference of uncontrollable factors and enhance the redundancy and flexibility of the resource regulation and control method. The artificially planned production data need not be filtered and statistically processed, and can be utilized directly by the data analysis module 204.
Specifically, taking the generation of the production plan of the air conditioner as an example, the implementation process of the plan scheduling method is described.
The data collection module 201 acquires production record data, installation data, and scan data of all air conditioners. The data filtering module 202 then eliminates the missing data, the error data and the repeated data in the data. Next, the data statistics module 203 performs statistics on the filtered data. In practical applications, the counted data may be pushed to the data analysis module 204 every 8 hours according to the timing information set by the timing module 207.
As shown in fig. 3, the data analysis module 204 obtains the above-mentioned counted data through the data interface module 210, including the total sales data of the air conditioners, the sales data of the air conditioners in each area, the model ranking of the sales of the air conditioners in each area, the inventory of the air conditioners, the procurement period of the materials required for producing the air conditioners, the transportation cost and the transportation duration of the air conditioners, and then generates the air conditioner production plan by combining with the artificially planned production data. The air conditioner production plan may be presented in the form of a table. In practical applications, the data can be read every 12 hours according to the timing information set by the timing module 207 and a production plan can be generated.
Meanwhile, the data analysis module 204 can also obtain the sales volume of other electrical appliances (electric cooker, refrigerator, washing machine), the model proportion of each region, the stock of other electrical appliances, the purchasing period of materials required by other electrical appliances, and the like, and generate the production plan of other electrical appliances by combining with artificially made plan production data. The production plans of other electric appliances and the air conditioner production plan can be generated simultaneously and displayed in the same table.
In the process of generating the production plan according to the artificially planned production data, the sales data, the warehousing data, the production data and the cost data, unnecessary production plans can be eliminated. For example, only the production plans of the air conditioner and the refrigerator need to be generated, and the production plan of the washing machine can be eliminated; or if the sales volume of a certain type of the air conditioner is less than a set value, rejecting the production plan of the type of the air conditioner; or if the purchase period of the materials required by a certain electric appliance is greater than a set value, the production plan of the electric appliance is rejected; or if the stock of a certain type of refrigerator is larger than a set value, the production plan of the type of refrigerator is rejected; or the production of a certain electric appliance does not accord with the standard value specified by the country or the company, the production plan of the electric appliance is removed.
Then, the data analysis module 204 sends out the required production plan of the air conditioner and other electric appliances through the data interface module 211, and the production execution module 208 obtains the production plan to adjust the production quantity and/or the production schedule of the air conditioner and other electric appliances.
Example two
As shown in fig. 4, the present embodiment provides a cargo deployment system, which includes a data collection module 201, a data filtering module 202, a data statistics module 203, a data analysis module 204, a storage module 205, a client 206, a timing module 207, and a deployment execution module 209.
The data collection module 201, the data filtering module 202, the data statistics module 203, the storage module 205, and the timing module 207 are the same as those in the first embodiment, and are not described herein again.
The data analysis module 204 generates a cargo allocation plan of the target product according to the sales data, the warehousing data, the production data and the cost data of the target product.
The goods allocation plan is used for adjusting and modifying the allocation condition of the target product, such as the change of the target product to a specific region, the increase or decrease of the goods distribution amount of the target product and the like. And generating a tomorrow goods allocation plan for adjusting and modifying the allocation situation of the next day according to the data representing the statistics of the conditions in one day, and generating a next week goods allocation plan for adjusting and modifying the allocation situation of the next week according to the data representing the statistics of the conditions in one week. The data analysis module 204 may upload the deployment plan to the big data platform for sharing through the program interface.
Compared with the first embodiment, the client 206 inputs the manually-made plan deployment data, and the manually-made plan deployment data is the deployment plan defined by the user. At this time, the data analysis module 204 also generates a cargo allocation plan according to the artificial planning allocation data and by combining the sales data, the warehousing data, the production data and the cost data. The client 206 is also used for receiving and displaying the cargo deployment plan. Other functions and structures of the client 206 are the same as those of the first embodiment.
The allocation executing module 209 is configured to obtain a cargo allocation plan and adjust an allocation area and an allocation amount of the target product according to the cargo allocation plan. The deployment execution module 209 may obtain the cargo deployment plan from the big data platform through the data interface. The cargo allocation plans may be different for each region, and accordingly, the allocation execution module 209 may include a plurality of subsystems for performing the adjustment of the cargo allocation for different regions. Similarly, the goods allocation plans of different products are different, and the allocation executing module 209 may be divided into a plurality of subsystems according to the product types, and the different subsystems may execute the adjustment of the goods allocation of different products.
Correspondingly, the present embodiment further provides a cargo deployment method applied to the cargo deployment system, as shown in fig. 5, the method includes the following steps:
step S21, acquiring production data according to the timing information.
And step S22, eliminating abnormal data in the production data.
And step S23, counting the production data from different dimensions to obtain the sales data, the warehousing data, the production data and the cost data of the target product.
And step S24A, generating a goods allocation plan of the target product according to the sales data, the warehousing data, the production data and the cost data.
In some embodiments, the step S24A may be replaced by the following steps:
step S234, acquiring artificial planning and allocating data;
and step S24B, combining the artificial planning and allocation data with the sales data, the warehousing data, the production data and the cost data to generate a goods allocation plan of the target product.
The human-planned deployment data may be obtained by the client 206 as initial data or default data. The method can also be understood as adding artificial regulation in objective result data to reduce the interference of uncontrollable factors and enhance the redundancy and flexibility of the resource regulation and control method.
Specifically, the implementation process of the cargo allocation method is described by taking the allocation plan of the generated air conditioner as an example.
The data collection module 201 acquires production record data, installation data, and scan data of all air conditioners. The data filtering module 202 then eliminates the missing data, the error data and the repeated data in the data. The data statistics module 203 then performs statistics on the filtered data. In practical applications, the counted data may be pushed to the data analysis module 204 every 8 hours according to the timing information set by the timing module 207.
As shown in fig. 6, the data analysis module 204 obtains the above-mentioned counted data through the data interface module 210, including the total sales data of the air conditioners, the sales data of the air conditioners in each area, the model ranking of the sales of the air conditioners in each area, the inventory of the air conditioner warehouse, the procurement period of the materials required for producing the air conditioners, the transportation cost and the transportation duration of the air conditioners, and then generates the air conditioner deployment plan by combining with the artificially made plan deployment data. The air conditioning schedule may be presented in the form of a table. In practical applications, the data can be read every morning and a blending plan can be generated according to the timing information set by the timing module 207.
Meanwhile, the data analysis module 204 can also obtain the sales volume of other electrical appliances (electric cooker, refrigerator, washing machine), the model proportion of each area, the stock of other electrical appliances, the purchasing period of materials required by other electrical appliances, and the like, and generate the allocation plan of other electrical appliances by combining with the manually made plan allocation data. The generation of the allocation plan of other electrical appliances and the generation of the allocation plan of the air conditioner can be carried out simultaneously and are displayed in the same table.
In the process of generating the goods allocation plan of the target product by combining the artificially planned allocation data and the sales data, the warehousing data, the production data and the cost data, the unnecessary allocation plan can be eliminated. For example, only the allocation plan of the air conditioner and the refrigerator needs to be generated, and the allocation plan of the washing machine can be eliminated; or the client adds a list, and a certain model of the air conditioner does not need to be allocated, and the allocation plan of the model of the air conditioner is removed.
Then, the data analysis module 204 sends out the required air conditioner and other electrical appliance allocation plan through the data interface module 211, and the allocation execution module 209 obtains the allocation plan to adjust the allocation area and/or the allocation amount of the air conditioner and other electrical appliances.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by one or more processors, implements the steps of the scheduling method according to the first embodiment and/or the cargo allocation method according to the second embodiment.
The planned scheduling method and the cargo allocation method are described in detail in the first embodiment and the second embodiment, and are not described herein again.
The storage medium may be a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc.
The processor may be an integrated circuit chip having information processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like.
In summary, the planned scheduling and allocation system, the planned scheduling and allocation method and the storage medium provided by the application are used for acquiring production record data, installation data and scanning data, performing integrated analysis on sales information, warehousing information, production information, cost information and other dimensions to obtain a production plan and a cargo allocation plan, and realizing reasonable production of each product and efficient allocation of cargos. The system breaks through an information isolated island, forms an information closed loop, optimizes product production and logistics distribution, avoids resource waste and promotes economic benefit improvement.
In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed system and method may be implemented in other ways. The system and method embodiments described above are merely illustrative.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments disclosed in the present application are described above, the above descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (10)
1. A system for scheduling and allocating, the system comprising:
the data collection module is used for acquiring production data related to a target product;
the data filtering module is used for eliminating abnormal data which do not meet preset conditions in the production data, and the abnormal data comprises incomplete data, error data and repeated data;
the data statistics module is used for counting the filtered production data from different dimensions so as to obtain sales data, warehousing data, production data and cost data of the target product;
and the data analysis module is used for generating a production plan and/or a goods allocation plan of the target product according to the sales data, the warehousing data, the production data and the cost data of the target product.
2. The planned scheduling and allocation system of claim 1, further comprising:
and the storage module is used for storing data and instructions generated in the operation process of the data collection module, the data filtering module, the data statistics module and the data analysis module.
3. The planned scheduling and allocation system of claim 2, further comprising:
and the timing module is used for setting timing information and sending the timing information to the data collection module, and the data collection module acquires the production data in a specified time period at a specified time according to the timing information.
4. The planned scheduling and allocation system of claim 3, further comprising:
and the data analysis module is also combined with the artificial planning data and generates the production plan and/or the goods allocation plan according to the sales data, the warehousing data, the production data and the cost data of the target product.
5. The planned scheduling and allocation system according to claim 1 or 4, further comprising:
the production execution module is used for adjusting the production quantity and/or the production progress of the target product according to the production plan; and/or
And the allocation execution module is used for adjusting the allocation region and/or the allocation quantity of the target product according to the goods allocation plan.
6. A planned scheduling and allocation method is characterized by comprising the following steps:
acquiring production data related to a target product;
rejecting abnormal data which do not meet preset conditions in the production data, wherein the abnormal data comprise incomplete data, error data and repeated data;
counting the production data after the abnormal data are removed from different dimensions to obtain sales data, warehousing data, production data and cost data of the target product;
and generating a production plan and/or a goods allocation plan according to the sales data, the warehousing data, the production data and the cost data.
7. The method of claim 6, wherein the obtaining production data associated with the target product comprises:
and acquiring the production data in a specified time period at a specified time according to the timing information.
8. The method of scheduling a project according to claim 7, wherein the method further comprises:
acquiring artificially made plan data;
and combining the artificial planning data, and generating the production plan and/or the goods allocation plan according to the sales data, the warehousing data, the production data and the cost data of the target product.
9. The method of scheduling production and allocation according to claim 6 or 8, further comprising:
when a production plan is generated according to the sales data, the warehousing data, the production data and the cost data, the production quantity and/or the production progress of the target product are/is adjusted according to the production plan;
and when a goods allocation plan is generated according to the sales data, the warehousing data, the production data and the cost data, adjusting the allocation region and/or the allocation quantity of the target product according to the goods allocation plan.
10. A storage medium having stored thereon a computer program, characterized in that the computer program, when executed by one or more processors, implements the steps of the method of any one of claims 6 to 9.
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