CN114648271A - Community group buying mode-based logistics data processing method, device and equipment - Google Patents
Community group buying mode-based logistics data processing method, device and equipment Download PDFInfo
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Abstract
The application provides a logistics data processing method, a logistics data processing device and logistics data processing equipment based on a community group purchase mode. According to the method, when the supply chain plan of the resource point is updated, the planning operation quantity of the resource point is determined according to the updated supply chain plan; according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, the recommended scheduling resource quantity of the resource point is determined, the recommended scheduling resource quantity is displayed on a capacity allocation page of the resource point, the recommended scheduling resource quantity can be updated in time when a supply chain plan is updated, accurate data guidance is provided for capacity allocation (scheduling) of capacity allocation personnel, the scheduling accuracy is improved, the capacity plan can be pulled through in time to be matched with the supply chain plan, the response timeliness of supply chain plan change is improved, and therefore the condition of bin explosion or excess capacity is reduced.
Description
Technical Field
The application relates to the technical field of computers, in particular to a logistics data processing method, a logistics data processing device and logistics data processing equipment based on a community group purchase mode.
Background
With the rapid development of internet technology, internet + community group buying has become a common logistics distribution mode of e-commerce platforms. The community group buying mode is as shown in fig. 1, one or more central bins are arranged under an e-commerce platform line, and group points (such as self-service points, off-line store stores and the like) are arranged in each cell or street and the like, a plurality of grid bins are arranged between the central bins and the group points, and the central bins and the grid bins are different types of resource points. The logistics distribution process based on the community group buying mode comprises the following steps: goods are sent from a delivery warehouse of a supplier to a central warehouse for warehousing; warehousing the obtained grid warehouse which is taken out from the central warehouse and is conveyed to the corresponding target team point of the goods; and then the goods are delivered out of the grid warehouse and conveyed to the target group of the goods. After the goods reach the target reunion, the consumers go to the reunion by themselves to pick up the goods, or the deliverers deliver the goods to the consumers.
In the current e-commerce platform based on the community group buying mode, the manager of the resource point fills up the weekly production capacity plan according to the total unit amount of the sales plan issued upstream in the current period (usually, one week or one month). However, in practical applications, the actual demand and the sales plan are often different, the capacity plan and the actual capacity are also different, and the problem of mismatching of warehouse entry and exit demand and capacity exists, which easily causes warehouse explosion or excess capacity.
Disclosure of Invention
The application provides a logistics data processing method, a logistics data processing device and logistics data processing equipment based on a community group purchase mode, which are used for solving the problem that the demand and the capacity are not matched in the existing logistics management based on the community group purchase mode, and the problem that warehouse explosion or excess capacity is easily caused.
On the one hand, the application provides a commodity circulation data processing method based on community group purchase mode, is applied to the electronic commerce platform of community group purchase mode, the electronic commerce platform corresponds and is provided with polytype resource point, include:
responding to an updating message of a supply chain plan of a resource point, and determining the planned job number of the resource point according to the updated supply chain plan;
determining the recommended production scheduling resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, wherein the historical operation efficiency information comprises at least one of the historical human effect and the historical vehicle effect of the logistics operation;
and displaying the recommended scheduling resource quantity on a capacity allocation page of the resource point.
On the other hand, this application provides a commodity circulation data processing apparatus based on community group purchase mode, includes:
the plan number determining module is used for responding to an updating message of a supply chain plan of a resource point and determining the plan job number of the resource point according to the updated supply chain plan;
the system comprises a recommended quantity determining module, a resource point scheduling module and a resource point scheduling module, wherein the recommended quantity determining module is used for determining the recommended production resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, and the historical operation efficiency information comprises at least one of the historical human effect and the historical vehicle effect of the logistics operation;
and the recommendation guidance module is used for displaying the recommended scheduling resource quantity on a capacity allocation page of the resource point.
In another aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer execution instructions stored in the memory to realize the logistics data processing method based on the community group buying mode.
In another aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are executed by a processor to implement the method for processing logistics data based on a community group buying mode.
According to the logistics data processing method, device and equipment based on the community group purchase mode, when a supply chain plan of a resource point is updated, the planned operation quantity of the resource point is determined according to the updated supply chain plan; according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, the recommended scheduling resource quantity of the resource point is determined, the recommended scheduling resource quantity is displayed on a capacity allocation page of the resource point, the recommended scheduling resource quantity can be updated in time when a supply chain plan is updated, accurate data guidance is provided for capacity allocation (scheduling) of capacity allocation personnel, the scheduling accuracy is improved, the capacity plan can be pulled through in time to be matched with the supply chain plan, the response timeliness of supply chain plan change is improved, and therefore the condition of bin explosion or excess capacity is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a resource point architecture in a community group purchase mode provided by the present application;
FIG. 2 is a flowchart of a method for processing logistics data based on a community group buying mode according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart of a logistics data processing method based on a community group buying mode according to another exemplary embodiment of the present application;
FIG. 4 is a diagram illustrating an exemplary capacity allocation page according to an exemplary embodiment of the present application;
fig. 5 is a schematic general flowchart of a logistics data processing method based on a community group purchase mode according to an exemplary embodiment of the application;
FIG. 6 is an exemplary diagram of an overall framework for an e-commerce platform provided by an exemplary embodiment of the present application;
fig. 7 is a schematic structural diagram of a logistics data processing apparatus based on a community group purchase mode according to an exemplary embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an example embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terms referred to in this application are explained first:
capacity: capacity refers to the total fixed assets that an enterprise participates in production, the number of products that it can produce, or the number of raw materials that it can process, under a given organizational technical condition, during a planning period. In the logistics scene, the capacity is the amount of work units that a specific logistics resource point can complete in a unit time.
And (3) nodulation: the system refers to a self-service point or an off-line service store arranged on an e-commerce platform.
Human effect: the yield of packing/sorting/selecting operation can be specified per person at a resource point in unit time, and the yield unit is the number of pieces.
Vehicle effect: each vehicle can carry the output of the transportation operation under the designated resource point in unit time, and the output unit is the number of pieces or the group number.
Vehicle efficiency calculated by piece: each vehicle can carry the number of pieces of the transportation operation under the designated resource point in unit time.
Vehicle efficiency calculated by clique: each vehicle can load the group number of the transportation operation under the specified resource point in unit time. For the goods at the resource point, the goods are divided into a plurality of groups (or called groups) according to the delivery target address, each group corresponds to a group point, and the goods in the group list are delivered to the corresponding group point.
Supply chain planning: refers to a system for organizing and planning to execute and measure the overall logistics activities of an enterprise. It includes basic components such as forecast, stock plan and distribution demand plan.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the following examples, "plurality" means two or more unless specifically limited otherwise.
Aiming at the problem that warehouse explosion or excess capacity is easily caused due to the fact that the problem that demand is not matched with capacity exists in logistics management based on a community group buying mode, the logistics data processing method based on the community group buying mode can be applied to specific operation scenes of each logistics operation link of warehouse outgoing/warehousing, for example, the logistics operation links of warehouse outgoing can comprise goods picking, sorting, picking of medium pieces, shipping and the like; the logistics operation links of warehousing can comprise receiving goods, returning supply, reversing and the like. The method determines the planning operation quantity of the resource point according to the updated supply chain plan when the supply chain plan of the resource point is updated; according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, the recommended scheduling resource quantity of the resource point is determined, the recommended scheduling resource quantity is displayed on a capacity allocation page of the resource point, the recommended scheduling resource quantity can be updated in time when a supply chain plan is updated, accurate data guidance is provided for capacity allocation (scheduling) of capacity allocation personnel, the scheduling accuracy is improved, the capacity plan can be pulled through in time to be matched with the supply chain plan, the response timeliness rate of supply chain plan change is improved, and bin explosion or excess capacity is reduced.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. These several specific embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a logistics data processing method based on a community group purchase mode according to an exemplary embodiment of the application. The logistics data processing method based on the community group purchase mode provided by the embodiment can be specifically applied to electronic equipment such as a server of an electronic commerce platform based on the community group purchase mode. As shown in fig. 2, the method comprises the following specific steps:
step S201, responding to the update message of the supply chain plan of the resource point, and determining the planning job number of the resource point according to the updated supply chain plan.
The supply chain plan comprises a supply plan and a sales plan, and warehousing requirements of various resource points can be determined based on the supply plan, so that a warehousing plan is determined. The ex-warehouse demand of each resource point can be determined according to the sales plan, so that the ex-warehouse plan of each resource point is determined.
The electronic commerce platform is correspondingly provided with a plurality of types of resource points, including a center bin, a grid bin, a supply unit and the like. The supply unit is a storage point for realizing storage processing in the central bin, and one central bin may include a plurality of supply units.
The method of this embodiment can process for any resource point when the supply chain plan of the resource point is updated.
In practical applications, the supply plan and the sales plan can be adjusted in real time according to actual needs, so that the supply chain plan changes (i.e., is updated). When the supply chain plan of the resource point is updated, the server receives an update message of the supply chain plan of the resource point, acquires the updated supply chain plan, and determines the planned job number of the resource point based on the updated supply chain plan.
The planned operation number of the resource point is the number of goods planned to perform the logistics operation in the current specific operation scene.
Illustratively, when the supply plan of the central warehouse changes, an updated supply plan of the central warehouse is obtained, and the planned warehousing quantity of the central warehouse can be determined according to the updated supply plan.
Illustratively, when the sales plan of a grid bin changes, an updated sales plan of the grid bin is obtained, and the planned ex-warehouse quantity of the grid bin can be determined according to the updated sales plan.
Step S202, determining the recommended production scheduling resource quantity of the resource points according to the planned operation quantity of the resource points and the historical operation efficiency information of the resource points, wherein the historical operation efficiency information comprises at least one of the historical human effect and the historical vehicle effect of the logistics operation.
After the planned operation quantity of the resource point is determined, the server can calculate the recommended production resource quantity of the resource point according to the historical operation efficiency information and the planned operation quantity of the resource point, and can accurately predict the quantity of resources (operators and vehicles) needing to be produced by the resource point.
For example, the recommended number of production schedules (or the recommended number of vehicles for production schedule) of the resource point in the current production cycle (one day or one week) can be calculated according to the planned number of the resource points and the historical human efficiency (or the historical vehicle efficiency) when the resource points are delivered.
Illustratively, according to the historical human efficiency of the planned warehousing quantity of the resource point, the number of recommended production vehicles of the resource point in the current production capacity cycle (one day or one week) can be calculated.
Step S203, the recommended quantity of the scheduling resources is displayed on the capacity allocation page of the resource point.
After determining the recommended scheduled production resource amount of the resource point, the recommended scheduled production resource amount can be displayed on a capacity allocation page of the resource point to provide accurate data guidance for capacity allocation personnel to perform capacity allocation (scheduling), so that the capacity allocation personnel can perform accurate capacity allocation to obtain a capacity plan matched with a supply chain plan.
The method determines the planned job number of the resource point according to the updated supply chain plan when the supply chain plan of the resource point is updated; according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, the recommended scheduling resource quantity of the resource point is determined, the recommended scheduling resource quantity is displayed on a capacity allocation page of the resource point, the recommended scheduling resource quantity can be updated in time when a supply chain plan is updated, accurate data guidance is provided for capacity allocation (scheduling) of capacity allocation personnel, the scheduling accuracy is improved, the capacity plan can be pulled through in time to be matched with the supply chain plan, the response timeliness rate of supply chain plan change is improved, and bin explosion or excess capacity is reduced.
In an optional embodiment, after the capacity allocation page of the resource point displays the recommended quantity of the scheduled resources, the capacity plan information input on the capacity allocation page may be further obtained. Determining the actual capacity of the resource point according to the capacity plan information; according to the actual capacity and the planned operation quantity of the resource point, when the capacity of the resource point is determined to be abnormal, capacity abnormity early warning information is pushed, and early warning can be timely carried out when the actual capacity plan is not matched with the supply chain plan, so that a scheduling worker is guided to timely adjust the capacity plan, or related personnel can timely adjust the supply chain plan (such as additionally applying for capacity outside the supply chain plan) and timely pull through the capacity plan to be matched with the supply chain plan, so that the condition of bin explosion or excess capacity is further reduced.
Wherein the capacity plan information comprises: actual scheduling information of resource points and current operating efficiency information.
Referring to fig. 3, fig. 3 is a flowchart of a logistics data processing method based on a community group buying mode according to another exemplary embodiment of the present application. As shown in fig. 3, the method comprises the following specific steps:
step S301, responding to the update message of the supply chain plan of the resource point, and determining the planning job number of the resource point according to the updated supply chain plan.
The supply chain plan comprises a supply plan and a sales plan, and the warehousing requirements of the resource points can be determined based on the supply plan, so that the warehousing plan is determined. The ex-warehouse demand of each resource point can be determined according to the sales plan, so that the ex-warehouse plan of each resource point is determined.
Illustratively, the supply chain planner may update the daily sales target for the next capacity cycle (e.g., the next week) periodically (e.g., at 18 points per week) on a medium-to-long term business target category GMV (Gross trades Volume) basis.
The electronic commerce platform is correspondingly provided with a plurality of types of resource points, including a center bin, a grid bin, a supply unit and the like. The supply unit is a warehousing point for realizing warehousing processing in the central warehouse, and one central warehouse can comprise a plurality of supply units.
The method of this embodiment can process for any resource point when the supply chain plan of the resource point is updated.
In practical applications, the supply plan and the sales plan can be adjusted in real time according to actual needs, so that the supply chain plan changes (i.e., is updated). When the supply chain plan of the resource point is updated, the server receives an update message of the supply chain plan of the resource point, acquires the updated supply chain plan, and determines the planned operation quantity of the resource point based on the updated supply chain plan.
Illustratively, when the supply plan of the central warehouse changes, an updated supply plan of the central warehouse is obtained, and the planned warehousing quantity of the central warehouse can be determined according to the updated supply plan.
Illustratively, when the sales plan of a grid bin changes, an updated sales plan of the grid bin is obtained, and the planned ex-warehouse quantity of the grid bin can be determined according to the updated sales plan.
Step S302, according to the historical operation data of the resource point in the preset time period, determining the historical operation efficiency information of the resource point in the preset time period.
In the community group buying mode, the quantity of goods delivered from the warehouse can be measured by the number of the goods and the quantity of groups into which the goods are split. In calculating the vehicle efficiency, the vehicle efficiency by pieces of the vehicle may be calculated by pieces, or the vehicle efficiency by lumps of the vehicle may be calculated by lumps.
In this embodiment, the historical operating efficiency information of the resource point in the preset time period can be determined according to the historical operating data in a relatively close preset time period, and is closer to the actual operating efficiency information in the current capacity cycle.
Illustratively, the historical job data may include historical ex-warehouse records, historical in-warehouse records, historical orders, fulfillment data corresponding to orders, and so on.
For example, when processing is performed on different resource points, the used preset time periods may be different, so that the accuracy of the calculated historical job efficiency information is improved. The preset time period may be set for different resource points according to an actual operation scene, and is not specifically limited herein.
In addition, when different resource points are processed, the unit time for determining human efficiency or vehicle efficiency may also be different, and the specific calculation manner of human efficiency or vehicle efficiency may be configured for different resource point allocations according to the actual operation scene, which is not specifically limited herein.
For example, assuming that the capacity cycle is 1 day, in the delivery scene of the grid bin, the historical vehicle effect and the historical human effect of the grid bin in the last week can be determined, wherein the unit time is day when the grid bin is delivered.
For example, in the warehouse-out scenario of the central warehouse, the average human effect of the central warehouse in hours per unit time on the same day (day of week) in the last 3 weeks can be determined, and the amount of the production resources to be recommended can be determined based on the planned work amount in combination with the work duration of each day.
Wherein the historical job efficiency information includes at least one of:
the historical per-item human effect calculated according to the items when the vehicle enters the warehouse, the historical per-item human effect calculated according to the items when the vehicle leaves the warehouse, the historical vehicle effect calculated according to the items when the vehicle leaves the warehouse, and the historical vehicle effect calculated according to the group when the vehicle leaves the warehouse.
And step S303, determining the recommended scheduling resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point.
After the planned work quantity and the historical work efficiency information of the resource point are determined, the server can calculate and obtain the recommended production scheduling resource quantity of the resource point according to the historical work efficiency information and the planned work quantity of the resource point, and can accurately predict the quantity of resources (operators and vehicles) needing to be produced by the resource point.
For example, the recommended number of production schedules (or the recommended number of vehicles for production schedule) of the resource point in the current production cycle (one day or one week) can be calculated according to the planned number of the resource points and the historical human efficiency (or the historical vehicle efficiency) when the resource points are delivered.
Illustratively, the number of recommended production vehicles of the resource point in the current production capacity cycle (one day or one week) can be calculated according to the planned warehousing number of the resource point and the historical human effect of the resource point during warehousing.
Step S304, the recommended quantity of the scheduling resources is displayed on the capacity allocation page of the resource point.
After determining the recommended scheduled production resource amount of the resource point, the recommended scheduled production resource amount can be displayed on a capacity allocation page of the resource point to provide accurate data guidance for capacity allocation personnel to perform capacity allocation (scheduling), so that the capacity allocation personnel can perform accurate capacity allocation to obtain a capacity plan matched with a supply chain plan.
Illustratively, the capacity plan includes actual scheduling information and current operating efficiency information, which can be obtained by manual filling.
Optionally, the e-commerce platform may include a labor scheduling system, and the labor scheduling system may implement functions of attendance checking, scheduling, and the like for personnel and vehicles of each resource node, and may collect the productivity information in a labor force collection manner.
In this step, the recommended scheduling resource quantity of the resource point may be displayed on the capacity allocation page of the labor scheduling system. The dispatcher of the resource point can refer to the displayed recommended scheduling resource quantity on the capacity allocation page of the labor scheduling system, perform capacity disassembly and allocation (can be assigned to specific people or vehicles), and determine a capacity plan.
Alternatively, the e-commerce platform can include a capacity filling system that provides a capacity allocation page for manually filling a capacity plan to implement capacity allocation.
In this step, the recommended scheduling resource quantity of the resource point can be displayed on the capacity allocation page of the capacity filling system. And (4) related personnel of the resource points refer to the displayed recommended production scheduling resource quantity on a production capacity allocation page of the production capacity allocation system to perform production capacity plan allocation, so that production capacity allocation (specific people or vehicles can be appointed) is realized.
Optionally, after the recommended production scheduling resource amount of the resource point is determined, the recommended production scheduling resource amount of the resource point may be pushed in a preset pushing manner, and related personnel may be notified in time.
The preset pushing mode may be sent by an email, sent by an instant messaging software, pushed to a client of an e-commerce platform, and the like, and may be set according to the needs of an actual job scenario, which is not specifically limited herein.
Exemplarily, fig. 4 is an exemplary diagram of a capacity allocation page of a capacity filling system, and fig. 4 shows a capacity allocation page in a warehousing scene of a certain resource point, where a predicted quantity is a planned warehousing quantity of a current resource point determined based on a supply chain plan, a recommended number of production lines and historical human effects are displayed in the capacity allocation page, an actual number of production lines and actual human effects can be filled through the page, the system can calculate and determine an actual capacity of warehousing of the resource point based on the actual number of production lines and the actual human effects, and corresponding warning information is displayed when determining that the capacity is excessive or insufficient based on the actual capacity of warehousing and the planned warehousing quantity of the resource point.
In addition, based on the capacity filling system, the capacity of a day can be specifically divided into a plurality of time slices within a day, the capacity information of each time slice is determined, and the capacity of different time slices can be different (such as the capacity information of the time slices shown in fig. 4).
Step S305, acquiring the capacity plan information input on the capacity allocation page, wherein the capacity plan information comprises: actual scheduling information of resource points and current operating efficiency information.
After the related personnel complete the capacity allocation and submit the capacity plan information, the server acquires the capacity plan information input on the capacity allocation page.
Step S306, determining the actual capacity of the resource point according to the capacity planning information.
In this step, the actual capacity of the resource point, that is, the number of ex-warehouse/in-warehouse of the resource point that can be actually completed in the current capacity cycle, can be calculated and determined according to the actual scheduling information of the resource point and the current operating efficiency information.
Step S307, according to the actual capacity and the planned operation quantity of the resource point, when the capacity of the resource point is determined to be abnormal, capacity abnormity early warning information is pushed.
In this embodiment, after the actual capacity of the resource point is determined, the actual capacity of the resource point is compared with the planned operation quantity, so as to determine the size relationship and the difference between the actual capacity and the planned operation quantity.
Optionally, according to the actual capacity and the planned operation number of the resource point, if the actual capacity is greater than the planned operation number, and the ratio of the difference between the actual capacity and the planned operation number to the planned operation number is greater than or equal to a first threshold, indicating that the part of the actual capacity exceeding the planned operation number is larger in the planned operation number, and the capacity is seriously excessive, then pushing capacity excess warning information.
The first threshold may be set and adjusted according to the needs of the actual operation scenario, and is not specifically limited herein.
Optionally, according to the actual capacity and the planned operation quantity of the resource point, if the actual capacity is smaller than the planned operation quantity, and the ratio of the difference between the actual capacity and the planned operation quantity to the planned operation quantity is greater than or equal to a second threshold, indicating that the part of the planned operation quantity exceeding the actual capacity is larger in the planned operation quantity ratio and seriously insufficient in capacity, pushing capacity shortage warning information.
The second threshold may be set and adjusted according to the needs of the actual operation scenario, and is not specifically limited herein.
For example, for the early warning of the grid warehouse, the early warning information can be timely pushed to a city manager responsible for the grid warehouse and a general responsible person of a Regional Distribution Center (RDC) in the province, so as to reduce the performance risk, avoid the problem of warehouse burst, and timely find the problem of excess capacity.
In an optional embodiment, in step S301, in response to an update message of a supply chain plan of a resource point, determining a planned job number of the resource point according to the updated supply chain plan includes: in response to an update message of the supply chain plan for the resource point, the number of planned jobs for the resource point is determined from the updated supply chain plan.
Accordingly, in step S303, the recommended production resource amount of the resource point is determined according to the planned work amount of the resource point and the historical work efficiency information of the resource point, including at least one of the recommended production number and the recommended production vehicle number.
Specifically, the recommended scheduling number of the resource points is determined according to the planned job number of the resource points and the historical job effectiveness of the resource points.
And determining the recommended production vehicle number of the resource point according to the planned work number of the resource point and the historical work vehicle efficiency of the resource point.
Illustratively, the method provided by the embodiment is applicable to any one of the following scenarios: the system comprises a warehousing scene of a central bin, an ex-warehouse scene of the central bin, a warehousing scene of a grid bin and an ex-warehouse scene of the grid bin.
For example, taking the warehousing scene of the central warehouse as an example, the recommended number of production rate-scheduling persons when the central warehouse warehouses in the current production rate period may be: planning warehousing number of pieces/historical human effect, wherein the historical human effect can be balance average human effect in the same period (day of week) in nearly 3 weeks of the central warehouse.
For example, taking the warehouse-out scene of the central warehouse as an example, the recommended number of the production rate scheduling persons when the central warehouse is warehouse-out in the current production rate cycle may be: the number of warehouses/historical human effects is planned, wherein the historical human effects can be the average human effects/working hours (in hours) of the hours of the same period (day of week) in the last 3 weeks of the central warehouse.
For example, taking a grid warehouse entering scene as an example, the recommended number of production scheduling persons when the grid warehouse enters the grid warehouse in the current production period may be: planning the number of warehousing/historical human effect, wherein the historical human effect can be the average human effect of the balance of the grid warehouse in about 1 week.
For example, taking a grid warehouse-out scenario as an example, the recommended number of production schedules during warehouse-out of the grid warehouse in the current production capacity cycle may be: planning out the number of warehouses/historical human effects, wherein the historical human effects can be the balance average human effects of the grid bin in the last 1 week.
For example, taking a grid warehouse-out scenario as an example, the recommended number of vehicles in the grid warehouse-out during the current capacity cycle may be: planning out the number of warehouse pieces/historical vehicle effect, wherein the historical vehicle effect can be the balance average vehicle effect (calculated according to the piece) of the grid bin in about 1 week.
In addition, the calculation method of the recommended number of vehicles in the scheduled production is similar to that of the recommended number of people in the scheduled production, and no examples are given here.
In an optional embodiment, for the warehouse-out scenario of the grid warehouse, the resource point is the grid warehouse, and the supply chain plan is the warehouse-out plan of the grid warehouse.
In step S301, in response to the update message of the supply chain plan of the resource point, determining the planned job number of the resource point according to the updated supply chain plan, which may further include:
and in response to the update message of the delivery plan of the grid warehouse, determining the planned delivery number and the planned delivery group number of the grid warehouse according to the updated delivery plan, wherein the planned delivery group number refers to the number of groups to which the articles planned to be delivered are delivered.
Accordingly, in step S303, determining the recommended production resource amount of the resource point according to the planned job amount of the resource point and the historical job efficiency information of the resource point, may include:
determining the number of first production vehicles of the grid bin according to the planned number of groups of the grid bin and the historical vehicle effect calculated according to the groups when the grid bin is delivered; determining the number of second-row vehicles in the grid bin according to the planned number of delivery vehicles in the grid bin and the historical vehicle effect calculated according to the number of the delivery vehicles in the grid bin during delivery; and taking the maximum value of the first and second numbers of the vehicles in the bank as the recommended number of the vehicles in the bank corresponding to the warehouse-out plan.
For example, for a warehouse-out scenario of a grid warehouse, the recommended number of scheduled vehicles when the grid warehouse is warehouse-out in the current capacity cycle may be: MAX (number of planned ex-warehouse/historical car efficiency calculated per piece, number of planned ex-warehouse cliques/historical car efficiency calculated per clique). Where MAX () denotes a maximum value operation.
In this embodiment, a capacity model common to multiple resource points is configured. The method can be used for realizing the logistics data processing method of various different resource points in the community group purchase mode based on the same universal capacity model, so that the logistics data processing method based on the community group purchase mode can be applied to various resource points, and the description of the capacity information of different logistics resource points and the matching of production needs are supported.
Wherein, the capacity data based on the universal capacity model comprises: resource point type, resource point code, service mode, capacity plan source, capacity cycle, capacity type, capacity unit, capacity factor, expected capacity, actual capacity, and abnormal capacity status.
Illustratively, a capacity model common to multiple resource points may include the following key attributes:
1. resource point type (resourceType): can be a central bin, a grid bin, a supply unit, etc.
2. Resource point code (resourcecode): such as center bin coding, grid bin coding, supplier unit coding, and the like.
3. Service mode (serviceMode): such as normal temperature standard product, normal temperature fruit and vegetable, freezing, cold storage and cold chain. Different operation service modes provided by logistics can be provided with different production and energy resource points, and can be independently depicted for the resource points.
4. Energy source (capacitySource): such as manual reporting (capacity reporting system), unplanned application, capacity disassembly and allocation (labor scheduling system), etc.
5. Capacity cycle (capacityTime): determining a one-day capacity plan when the capacity plan period is one day; or the capacity cycle is one week, and a capacity plan in one week is determined.
6. Capacity type (capacityType): and (5) taking out and warehousing.
7. Capacity unit (capacityUnit): such as number of pieces, number of boluses.
8. Capacity element (capacity element): and intervening logistics factor information calculated by actual production energy, such as historical human effect, historical vehicle effect calculated according to pieces, historical vehicle effect calculated according to groups, recommended number of people, recommended number of vehicles and the like under the scene of delivery capacity of the grid warehouse.
9. Capacity strategy (calcaultetstrategy): and selecting different capacity strategies through capacity strategy codes, and analyzing the capacity model into the capacity model under the specific operation scene of each resource point.
10. Expected capacity (expectQuantity): the capacity is split by upstream supply chain planning, or capacity splitting, to the capacity expected to be available at the resource point.
11. Actual capacity (actual quantity): the supportable capacity unit quantity is deduced through manual reporting by a planner, or labor force collection, or according to the quantity of the recommended scheduling resources.
12. Abnormal productivity state (abnormalStatus): unfilled, excess capacity, insufficient capacity, etc.
In addition, based on a universal capacity model, capacity strategy codes corresponding to different resource points can be configured, each capacity strategy code corresponds to a special capacity model aiming at a specific resource point, and the special capacity model of the resource point comprises data which is not included by the universal capacity model and is specific to the resource point.
Based on the universal capacity model, when the method is applied to a certain resource point, the special capacity model corresponding to the resource point can be obtained according to the capacity strategy code corresponding to the resource point, and all capacity data of the resource point can be reserved. In practical applications, only a small number of resource points have the dedicated capacity model, and the number of data items of the dedicated capacity model is small, so that most of the capacity data of the resource points can be stored in the capacity model.
For example, the capacity cycle, the resource point type, the capacity type and the capacity unit at a resource point can uniquely determine the capacity record of a capacity unit at the resource point, the universal capacity model can independently support multiple levels of capacity calculation appeal in the community group purchase mode through the design of the capacity element + the capacity strategy, pull up the expected capacity (planned out/warehouse in quantity) and the actual capacity plan of the scheduling, read different capacity strategies under different scenes, and perform the abnormal capacity state calculation in a differentiated manner.
Fig. 5 is a general flowchart of a logistics data processing method based on a community group buying mode according to an exemplary embodiment of the present application, and as shown in fig. 5, a supply chain plan is first obtained, which may include a supply plan and a sales plan, warehousing requirements of a resource point are determined based on the supply plan, and ex-warehouse requirements of the resource point are determined based on the sales plan. And determining the planned quantity group quantity (such as planned ex-warehouse quantity, planned ex-warehouse group quantity, planned in-warehouse quantity and the like) of the resource point based on the warehousing/ex-warehouse requirements of the resource point. Based on the data of entering and leaving the warehouse such as historical orders and performance data, the historical human effect and the historical vehicle effect of the resource points in the warehouse/leaving the warehouse can be calculated off line. And calculating the capacity according to the planned quantity group quantity of the resource points, the historical human effect and the historical vehicle effect, and determining the number of people recommending and scheduling the production and the number of vehicles recommending and scheduling the production so as to provide accurate reference for making a capacity plan. Capacity plans can be determined based on two ways: the method comprises the steps that firstly, a manual filling mode is adopted, and the productivity plan information is filled based on a productivity filling system; and secondly, a labor scheduling mode is adopted, the staff and vehicles are scheduled based on the shift attendance setting of the labor scheduling system, and a capacity plan is generated.
Fig. 6 is an exemplary diagram of a general framework of an e-commerce platform provided in an exemplary embodiment of the present application, and as shown in fig. 6, the e-commerce platform is intended to provide guidance for matching a supply chain plan with a supply and demand of a capacity platform through capacity filling, capacity dismantling, capacity pre-warning, and capacity coordination capabilities of a provided basis. The unified productivity filling system, the productivity assessment management platform and the like are realized in a digital mode. The unified capacity filling system is used for realizing the reserve management of physical resources such as people, vehicles and the like, carrying out capacity proportioning configuration, realizing capacity confirmation, making a capacity plan and the like. The unified productivity assessment management platform can realize productivity filling overview real-time reports, count productivity filling rate, achievement rate, satisfaction rate, generate corresponding reports and the like. The method provided by the application provides supply chain plan (how much to do) and supply and demand matching guidance of a capacity platform (how much to do energy saving of an actual logistics loop) so as to adjust a sales plan or perform warehousing/ex-warehouse capacity abnormity early warning when the production needs are not matched. In addition, based on the actual work attendance conditions of personnel, vehicles (drivers) and the like, the production scheduling is carried out to generate a capacity plan, the capacity plan is communicated with the actual work attendance conditions of the personnel and the vehicles, and the dynamic capacity adjustment is realized; and can adjust the personnel and vehicle scheduling of the grid bin when the production needs are not matched. On the basis of experience guarantee in management, the capacity coordination of each resource point is drawn, the capacity data coordination efficiency is improved, the target occlusion of a capacity plan and a supply chain plan is achieved, and the problem of production demand mismatch is solved. The reasonable number of vehicles for scheduling is provided in advance in combination with production demand matching in operation, the labor scheduling capacity is collected, the capacity filling accuracy is improved, and the resource waste is reduced; the early warning of the performance risk in the process improves the response timeliness rate of plan updating of the supply chain, helps to adjust the shift capacity in time, avoids the problem of warehouse explosion, and finds the problem of excess capacity in time.
Fig. 7 is a schematic structural diagram of a logistics data processing apparatus based on a community group purchase mode according to an exemplary embodiment of the present application. The logistics data processing device based on the community group purchase mode can execute the processing flow provided by the logistics data processing method based on the community group purchase mode. As shown in fig. 7, the logistics data processing apparatus 70 based on the community group buying mode includes: a plan number determination module 701, a recommendation number determination module 702, and a recommendation direction module 703.
A plan number determining module 701, configured to determine, in response to an update message of a supply chain plan of a resource point, a planned job number of the resource point according to the updated supply chain plan.
A recommended quantity determining module 702, configured to determine the recommended production resource quantity of the resource point according to the planned operation quantity of the resource point and historical operation efficiency information of the resource point, where the historical operation efficiency information includes at least one of historical human efficiency and historical vehicle efficiency of the logistics operation.
The recommendation guidance module 703 is configured to display the recommended quantity of the scheduled resources on a capacity allocation page of the resource point.
In an optional embodiment, in response to the update message of the supply chain plan of the resource point, when determining the planned job number of the resource point according to the updated supply chain plan, the planned number determination module is further configured to: and responding to the update message of the supply chain plan of the resource point, and determining the planned work piece number of the resource point according to the updated supply chain plan.
When the recommended production resource quantity of the resource point is determined according to the planned job quantity of the resource point and the historical job efficiency information of the resource point, the recommended quantity determining module is further used for performing at least one of the following processes:
determining the recommended scheduling number of the resource points according to the planned operation number of the resource points and the historical operation efficiency of the resource points;
and determining the recommended production vehicle number of the resource point according to the planned work number of the resource point and the historical work vehicle effect of the resource point.
In an alternative embodiment, the resource point is a grid bin and the supply chain plan is an ex-warehouse plan for the grid bin. When determining the planned job number of the resource point according to the updated supply chain plan in response to the update message of the supply chain plan of the resource point, the planned number determination module is further configured to: and in response to the update message of the delivery plan of the grid warehouse, determining the planned delivery number and the planned delivery group number of the grid warehouse according to the updated delivery plan, wherein the planned delivery group number refers to the number of groups to which the articles planned to be delivered are delivered.
Determining the recommended scheduling resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, wherein the recommended quantity determining module is further used for:
determining the number of first production vehicles of the grid bin according to the planned number of groups of the grid bin and the historical vehicle effect calculated according to the groups when the grid bin is delivered; determining the number of second-row vehicles in the grid bin according to the planned number of delivery vehicles in the grid bin and the historical vehicle effect calculated according to the number of the delivery vehicles in the grid bin during delivery; and taking the maximum value of the first and second numbers of the vehicles in the bank as the recommended number of the vehicles in the bank corresponding to the warehouse-out plan.
In an optional implementation manner, the recommendation amount determining module is further configured to:
before the recommended production resource quantity of the resource point is determined according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point, the historical operation efficiency information of the resource point in a preset time period is determined according to the historical operation data of the resource point in the preset time period.
Wherein the historical job efficiency information includes at least one of:
the historical per-item human effect calculated according to the items when the vehicle enters the warehouse, the historical per-item human effect calculated according to the items when the vehicle leaves the warehouse, the historical vehicle effect calculated according to the items when the vehicle leaves the warehouse, and the historical vehicle effect calculated according to the group when the vehicle leaves the warehouse.
In an optional embodiment, the logistics data processing apparatus based on the community group buying mode may further include:
an early warning module for: after the recommended quantity of the scheduling resources is displayed on the capacity allocation page of the resource point, capacity plan information input on the capacity allocation page is acquired, and the capacity plan information comprises: actual scheduling information and current operating efficiency information of the resource points; determining the actual capacity of the resource point according to the capacity plan information; and according to the actual capacity and the planned operation quantity of the resource point, when the capacity of the resource point is determined to be abnormal, pushing capacity abnormity early warning information.
In an optional embodiment, when the capacity abnormality warning information is pushed when determining that the capacity of the resource point is abnormal according to the actual capacity and the planned operation number of the resource point, the warning module is further configured to perform at least one of the following processes:
according to the actual capacity and the planned operation quantity of the resource points, if the actual capacity is larger than the planned operation quantity, and the ratio of the difference value of the actual capacity and the planned operation quantity to the planned operation quantity is larger than or equal to a first threshold value, pushing capacity excess early warning information;
and according to the actual capacity and the planned operation quantity of the resource points, if the actual capacity is smaller than the planned operation quantity and the ratio of the difference value of the actual capacity and the planned operation quantity to the planned operation quantity is larger than or equal to a second threshold value, pushing capacity shortage early warning information.
In an optional embodiment, the logistics data processing apparatus based on the community group buying mode may further include a capacity data storage module, configured to:
storing the capacity data of the resource point according to the configured universal capacity model, wherein the capacity data comprises: resource point type, resource point code, service mode, capacity plan source, capacity cycle, capacity type, capacity unit, capacity factor, expected capacity, actual capacity, and abnormal capacity status.
The apparatus provided in the embodiment of the present application may be specifically configured to execute the scheme provided in any one of the method embodiments, and specific functions and technical effects that can be achieved are not described herein again.
Fig. 8 is a schematic structural diagram of an electronic device according to an example embodiment of the present application. As shown in fig. 8, the electronic apparatus 80 includes: a processor 801, and a memory 802 communicatively coupled to the processor 801, the memory 802 storing computer-executable instructions.
The processor executes the computer execution instructions stored in the memory to implement the scheme provided by any of the above method embodiments, and the specific functions and the technical effects that can be achieved are not described herein again.
The embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement the solutions provided in any of the above method embodiments, and specific functions and technical effects that can be achieved are not described herein again.
An embodiment of the present application further provides a computer program product, where the program product includes: the computer program is stored in a readable storage medium, at least one processor of the electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to execute the scheme provided by any one of the above method embodiments, and specific functions and achievable technical effects are not described herein again.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. The utility model provides a commodity circulation data processing method based on community group purchase mode which characterized in that, is applied to the electronic commerce platform of community group purchase mode, electronic commerce platform corresponds and is provided with polytype resource point, includes:
responding to an updating message of a supply chain plan of a resource point, and determining the planned job number of the resource point according to the updated supply chain plan;
determining the quantity of recommended production scheduling resources of the resource points according to the planned operation quantity of the resource points and historical operation efficiency information of the resource points, wherein the historical operation efficiency information comprises at least one of historical human effect and historical vehicle effect of logistics operation;
and displaying the recommended scheduling resource quantity on a capacity allocation page of the resource point.
2. The method of claim 1, wherein determining the projected number of jobs for a resource point according to an updated supply chain plan in response to the update message for the supply chain plan for the resource point comprises:
responding to an update message of a supply chain plan of a resource point, and determining the number of planned jobs of the resource point according to the updated supply chain plan;
the determining the recommended scheduling resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point comprises at least one of the following items:
determining the recommended scheduling number of the resource points according to the planned operation number of the resource points and the historical operation effectiveness of the resource points;
and determining the recommended scheduling vehicle number of the resource point according to the planned operation number of the resource point and the historical operation vehicle effect of the resource point.
3. The method of claim 1 or 2, wherein the resource points are grid silos, the supply chain plan is an ex-warehouse plan for the grid silos,
the determining the planned job number of the resource point according to the updated supply chain plan in response to the update message of the supply chain plan of the resource point comprises:
the updating message responding to the delivery plan of the grid warehouse determines the planned delivery number and the planned delivery group number of the grid warehouse according to the updated delivery plan, wherein the planned delivery group number refers to the number of the group points to which the articles planned to be delivered are delivered;
the determining the recommended scheduling resource quantity of the resource point according to the planned operation quantity of the resource point and the historical operation efficiency information of the resource point further comprises:
determining the first production vehicle number of the grid bin according to the planned delivery cluster number of the grid bin and the historical vehicle effect calculated according to clusters when the grid bin is delivered;
determining a second production rate vehicle number of the grid bin according to the planned delivery number of the grid bin and the historical vehicle effect calculated according to the grid bin delivery time;
and taking the maximum value of the first and second numbers of vehicles in the bank as the recommended number of vehicles in the bank corresponding to the ex-warehouse plan.
4. The method of claim 1, wherein before determining the recommended quantity of resources to schedule for the resource point based on the projected quantity of jobs for the resource point and the historical job efficiency information for the resource point, further comprising:
determining historical operation efficiency information of the resource point in a preset time period according to historical operation data of the resource point in the preset time period;
the historical job efficiency information includes at least one of:
the historical per-item human effect calculated according to the items when the vehicle enters the warehouse, the historical per-item human effect calculated according to the items when the vehicle leaves the warehouse, the historical vehicle effect calculated according to the items when the vehicle leaves the warehouse, and the historical vehicle effect calculated according to the group when the vehicle leaves the warehouse.
5. The method as claimed in claim 1, wherein after the displaying the recommended scheduling resource amount on the capacity allocation page of the resource point, further comprising:
acquiring capacity plan information input on the capacity allocation page, wherein the capacity plan information comprises: actual scheduling information and current operating efficiency information of the resource points;
determining the actual capacity of the resource point according to the capacity plan information;
and according to the actual capacity of the resource point and the planned operation quantity, when the capacity of the resource point is determined to be abnormal, pushing capacity abnormity early warning information.
6. The method according to claim 5, wherein the pushing capacity abnormality warning information when determining that the capacity of the resource point is abnormal according to the actual capacity of the resource point and the planned operation quantity comprises at least one of:
according to the actual capacity of the resource point and the planned operation quantity, if the actual capacity is larger than the planned operation quantity, and the ratio of the difference value between the actual capacity and the planned operation quantity to the planned operation quantity is larger than or equal to a first threshold value, pushing capacity excess early warning information;
and according to the actual capacity of the resource points and the planned operation quantity, if the actual capacity is smaller than the planned operation quantity, and the ratio of the difference value between the actual capacity and the planned operation quantity to the planned operation quantity is larger than or equal to a second threshold value, pushing capacity shortage early warning information.
7. The method of claim 1, further comprising:
storing capacity data of the resource point according to the configured universal capacity model, wherein the capacity data comprises: resource point type, resource point code, service mode, capacity plan source, capacity cycle, capacity type, capacity unit, capacity factor, expected capacity, actual capacity, and abnormal capacity status.
8. A logistics data processing device based on community group purchase mode is characterized by comprising:
the plan number determining module is used for responding to an updating message of a supply chain plan of a resource point and determining the plan job number of the resource point according to the updated supply chain plan;
the system comprises a recommended quantity determining module, a resource point judging module and a resource management module, wherein the recommended quantity determining module is used for determining the quantity of recommended production scheduling resources of the resource point according to the quantity of planned operations of the resource point and historical operation efficiency information of the resource point, and the historical operation efficiency information comprises at least one of historical human efficiency and historical vehicle efficiency of logistics operation;
and the recommendation guidance module is used for displaying the recommended scheduling resource quantity on a capacity allocation page of the resource point.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-7.
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