CN114912968A - Method and system for personalized product selection of automobile accessory supply chain - Google Patents
Method and system for personalized product selection of automobile accessory supply chain Download PDFInfo
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Abstract
The invention discloses a method and a system for personalized selection of automobile accessories in a supply chain, belonging to the technical field of automobile accessories selection, and comprising the steps of constructing an automobile accessory sku candidate pool and determining the category of the automobile accessories; evaluating each sku in the candidate pool according to the store historical data to obtain an evaluation value of each sku; grouping and sequencing all skus in each category according to the evaluation values from large to small to generate a selection list; and determining the number of skus under each category, sequentially selecting skus from the selection list according to the arrangement sequence, and finally generating the personalized selection list. According to the method, the sales range is divided into different ranges, the characteristic values of the same sku in the different ranges are calculated, the evaluation value of each sku is obtained through weighting, sorting is carried out according to the evaluation value, and therefore the selection is more accurate and manual work is not needed.
Description
Technical Field
The invention relates to the technical field of automobile accessories, in particular to a method and a system for personalized selection of automobile accessories in an automobile supply chain.
Background
The post-market spare parts of the Chinese automobiles are various in variety, the holding capacity of the automobiles in 2020 all over the country is 2.81 hundred million, the average number of the spare parts of each automobile reaches about 1 ten thousand, and the scale of the whole spare parts is huge. And the current situation of domestic 'all-country vehicles', the types of the vehicles are various, the parts of different vehicles have great difference, and no domestic automobile aftermarket enterprise can supply all-kind automobile parts. And the enterprises behind the vapour provide the accessory to the local repair shop through the front-end storehouse that distributes all over the country, and the automobile type of different regions is kept the volume and is distributed the difference greatly, and market demand is diverse. Therefore, how to select a proper business class from each front-end warehouse of the post-automobile company can meet the market demand as much as possible and achieve the maximization of cost and income is a very challenging problem for any post-automobile company.
The scheme of selecting products in the front-end bin of the after-steam market is generated according to the industry experience, the market demand can be met to a certain extent, but the following problems which are difficult to solve exist due to manual limitation: the method has the advantages that a targeted selection scheme cannot be made according to the current situation of the area where the front-located warehouse is located, different selection strategies can be made for small-scale enterprises by increasing the number of employees, but for medium-and large-scale post-steam enterprises with the front-located warehouse distributed all over the country, manual work cannot make a targeted selection scheme for each front-located warehouse; the sale period of spare and accessory parts in the automobile rear market is long, the sale frequency of a single sku is low, and the manual work cannot quickly respond to the market change of each sku and timely update the selection scheme; and (4) manual selection is carried out, no better evaluation scheme is provided for the rationality of the selection scheme, and a service closed loop cannot be achieved.
Disclosure of Invention
Aiming at the defects in the problems, the invention provides a method and a system for personalized selection of products in an automobile accessory supply chain, wherein the method comprises the following steps:
constructing a sku candidate pool of the automobile parts, and determining the categories of the automobile parts;
evaluating each sku in the candidate pool according to historical data to obtain an evaluation value of each sku;
grouping and sequencing all skus in each category according to the evaluation values from large to small to generate an option list;
and determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence, and finally generating an individualized option list.
Preferably, constructing the auto-parts sku candidate pool includes:
constructing an automobile part basic candidate pool, wherein the automobile part basic candidate pool comprises all sold skus of other front bins, skus of an empty search and all sold skus of a skatecat car shop;
and filtering the automobile part basic candidate pool to obtain a final automobile part sku candidate pool.
Preferably, the filtering the automobile part basis candidate pool to obtain a final automobile part sku candidate pool includes:
filtering skus which do not meet the service requirement brands;
filtering sku of 20% of the vehicle types after the vehicle type retention quantity ranking;
filtering sku with abnormal purchasing state;
filtering sku pulled into the blacklist;
and sku filtering when the number of empty searching times of the sales platform is smaller than a preset threshold value.
Preferably, evaluating each sku in the candidate pool according to historical data, and obtaining the evaluation value of each sku includes:
dividing the sales area into a large area, a small area, a city and a front warehouse from large to small;
respectively calculating characteristic values of the same sku in different ranges;
and normalizing and weighting the characteristic values of the same sku in different ranges to obtain the evaluation value of each sku.
Preferably, the calculating the feature values of the same sku in different ranges respectively includes:
carrying out weighted summation according to the evaluation indexes to obtain the characteristic values of the same sku in different ranges;
the evaluation indexes comprise the number of times of empty searches in the range, the sales volume of the skatecat car raising store and the sales volume of the front warehouse.
Preferably, the evaluation value of each sku is calculated by the following formula:
Y＝0.4*(0.4A 1 +0.4A 2 +0.4A 3 +A 4 +A 5 )+0.3*(0.4B 1 +0.4B 2 +0.4B 3 +B 4 +B 5 )+0.2*
(0.4C 1 +0.4C 2 +0.4C 3 +C 4 +C 5 )+0.1*(0.4D 1 +0.4D 2 +0.4D 3 +D 4 +D 5 )；
in the formula: y is an evaluation value; a. the 1 The PV number of the empty search in the range of the front bin is obtained; a. the 2 The number of empty searching frequencies in the range of the front bin is obtained; a. the 3 Sales of the skatecat car-raising store in the front warehouse range; a. the 4 Sales frequency of the head bin which is in the head bin range; a. the 5 The sales volume of the front bin which is the range of the front bin; b is 1 Searching PV number for the city range; b is 2 The number of the empty search frequencies in the city range is obtained; b is 3 Sales for city-wide skatecat car shops; b is 4 Sales frequency for the pre-bins in the city range; b is 5 The sales volume of the pre-bin in the city range; c 1 The number of the PV of the empty search in the cell range; c 2 The number of the empty searching frequencies in the cell range is set; c 3 Sales for district-wide Tianmao car shops; c 4 The sales frequency of the front bin in the cell range; c 5 The sales volume of the front bin of the cell range; d 1 The number of empty search PV in a large area range; d 2 The number of empty search frequencies in a large area range; d 3 Sales for a large regional range of skatecat car shops; d 4 Sales frequency of the front bins in large area range; d 5 The sales of the pre-bins for a large area range.
Preferably, the grouping and sorting all skus in each category according to the evaluation value from large to small, and the generating of the option list includes:
and setting a threshold value and selecting the number in the threshold value for the same-brand and same-price bands with the wildcard relationship among skus in the category, and deleting the rest.
The invention also provides a system for the method for personalized selection of the automobile accessory supply chain, which comprises the following steps:
the building module is used for building an automobile part sku candidate pool and determining the category of the automobile part sku candidate pool;
the evaluation module is used for evaluating each sku in the candidate pool according to historical data to obtain an evaluation value of each sku;
the sorting module is used for carrying out grouping sorting on all skus in all the categories according to the evaluation values from large to small to generate an option list;
and the generating module is used for determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence, and finally generating an individualized option list.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the sales range is divided into different ranges, the characteristic values of the same sku in the different ranges are calculated, the evaluation value of each sku is obtained through weighting, sorting is carried out according to the evaluation value, and therefore the selection is more accurate and manual work is not needed.
Drawings
FIG. 1 is a flow chart of a method for personalized selection of items in a steam distribution supply chain according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The empty search in the present invention is defined as a sku that is searched on the sales platform and that has no goods.
The invention is described in further detail below with reference to the accompanying drawing 1:
as shown in fig. 1, the present invention provides a method and a system for personalized selection of products in a steam distribution supply chain, wherein the method comprises:
constructing a sku candidate pool of the automobile parts, and determining the categories of the automobile parts;
specifically, constructing the auto-parts sku candidate pool includes:
constructing an automobile part basic candidate pool, wherein the automobile part basic candidate pool comprises all sold skus of other front bins, skus of an empty search and all sold skus of a heaven car shop;
and filtering the automobile part basic candidate pool to obtain a final automobile part sku candidate pool.
Further, the step of filtering the automobile part basic candidate pool to obtain a final automobile part candidate pool comprises:
filtering skus which do not meet the service requirement brands;
filtering sku of 20% of the vehicle types after the vehicle type retention quantity ranking;
filtering sku with abnormal purchasing state;
filtering sku pulled into the blacklist;
and sku filtering when the number of empty searching times is smaller than a preset threshold value on the sales platform.
Evaluating each sku in the candidate pool according to the historical data to obtain an evaluation value of each sku;
specifically, the method comprises the steps of dividing a sales area into a large area, a small area, cities and front bins from large to small, wherein the large area consists of one to a plurality of provinces, and the small area consists of one to a plurality of cities;
respectively calculating characteristic values of the same sku in different ranges;
and normalizing and weighting the characteristic values of the same sku in different ranges to obtain the evaluation value of each sku.
Further, respectively calculating the feature values of the same sku in different ranges includes:
carrying out weighted summation according to the evaluation indexes to obtain the characteristic values of the same sku in different ranges;
the evaluation index includes the number of times of empty searches in the range, the sales volume of the skatecat car raising store, and the sales volume of the front warehouse.
The calculation formula of the evaluation value of each sku is:
Y＝0.4*(0.4A 1 +0.4A 2 +0.4A 3 +A 4 +A 5 )+0.3*(0.4B 1 +0.4B 2 +0.4B 3 +B 4 +B 5 )+0.2*
(0.4C 1 +0.4C 2 +0.4C 3 +C 4 +C 5 )+0.1*(0.4D 1 +0.4D 2 +0.4D 3 +D 4 +D 5 )；
in the formula: y is an evaluation value; a. the 1 The number of the empty PV searches in the range of the front bin; a. the 2 The number of empty searching frequencies in the range of the front bin is obtained; a. the 3 Sales of the skatecat car-raising store in the front warehouse range; a. the 4 Sales frequency of the front bin which is the range of the front bin; a. the 5 The sales volume of the front bin which is the range of the front bin; b is 1 Searching PV number for the city range; b 2 The number of the empty search frequencies in the city range is obtained; b is 3 Sales for city-wide skatecat car shops; b is 4 Sales frequency for the pre-bins in the city range; b is 5 The sales volume of the pre-bin in the city range; c 1 Null search P for cell rangeA V number; c 2 The number of idle searches in the cell range is set; c 3 Sales for district-wide Tianmao car shops; c 4 The sales frequency of the front bin in the cell range; c 5 The sales volume of the front bin of the cell range; d 1 The number of empty search PV in a large area range; d 2 The number of empty search frequencies in a large area range; d 3 Sales for a large territorial range of skatecat car shops; d 4 Sales frequency of the front bins in large area range; d 5 The sales of the pre-bins for a large area range.
Grouping and sequencing all skus in each category according to the evaluation values from large to small to generate a selection list;
specifically, for the same-brand and same-price bands with wildcard relations among skus in the category, setting a threshold value, selecting the number in the threshold value, and deleting the rest skus.
And determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence, and finally generating an individualized option list.
Example 1 selection of old shop
In this embodiment, the brand in the cell to which the store belongs is selected, and 95 categories that are selectable for the selection are defined.
Constructing the candidate pool of old shop auto-parts sku includes:
adding skus which are not sold in other preposed warehouses and Tianmao car shops at present and skus searched on a sales platform on the basis of the original old shop, wherein the number of times that the skus have no goods is larger than a preset threshold value;
according to the set regional brands of the large area, the small area and the city and the classified target brands with three, four and five levels, the brands which do not accord with the rule are filtered and deleted;
filtering and deleting sku of 20% of vehicle types after the vehicle type retention quantity ranking in the community;
deleting the filtering that the purchasing state is abnormal, namely the purchasing can not be normally performed;
and deleting sku filtering in a blacklist established by all store-shops in the cell.
Evaluating each sku in the candidate pool according to the historical data to obtain an evaluation value of each sku;
sequencing all skus in the category according to the evaluation value from large to small to generate a selection list;
specifically, for the same-brand and same-price bands with wildcard relations among skus in the category, setting a threshold value, selecting the number in the threshold value, and deleting the rest skus.
And determining the number of skus under each category, sequentially selecting skus from the selection list according to the arrangement sequence, and finally generating the personalized selection list.
In this embodiment, the number of skus in each category is determined to be the number of skus in each category that are proportionally expanded on the original list, and the number of skus in each category needs to be limited to a certain extent, in this embodiment, the maximum expansion is 1.5 times, and the total number of skus is not higher than 10000.
Example 2 selection of New shop
In this embodiment, the brand within the cell to which the store belongs is selected and 26 initial stock categories of the selection are limited. The initial stock list of the new store needs to meet the minimum sku richness of the front-end bin, and meanwhile, the customer needs to be mined continuously and iteratively.
Constructing a new store auto-parts sku candidate pool includes:
judging whether a pre-positioned bin exists in a city where the new store is positioned, and if so, counting sku sales in the city; if not, counting sku sales in the cell;
according to the set regional brands of the large area, the small area and the city and the classified target brands with three, four and five levels, the brands which do not accord with the rule are filtered and deleted;
filtering and deleting sku of 20% of vehicle types after the vehicle type retention quantity ranking in the community;
deleting the filtering that the purchasing state is abnormal, namely the purchasing can not be normally performed;
sku filtering in the blacklist is deleted for all store owners located in the cell.
And limiting the types of sku under the conditions of integration and categories to obtain a final personalized option list given by a new store. The number of the whole skus is less than 5000, and the number of the single category is less than 1000. Under the condition that the market is unknown, cost can be well controlled by limiting sku number, and a selection list is continuously perfected in a step-by-step iteration mode.
The invention also provides a system for the method for personalized product selection of the automobile accessory supply chain, which comprises the following steps:
the building module is used for building an automobile part sku candidate pool and determining the category of the automobile part sku candidate pool;
the evaluation module is used for evaluating each sku in the candidate pool according to historical data to obtain an evaluation value of each sku;
the sorting module is used for carrying out grouping sorting on all skus in all the categories according to the evaluation values from large to small to generate an option list;
and the generating module is used for determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence, and finally generating an individualized option list.
The present invention has been described in terms of the preferred embodiment, and it is not intended to be limited to the embodiment. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for personalized selection of items in a steam distribution supply chain, comprising:
constructing a sku candidate pool of the automobile parts, and determining the categories of the automobile parts;
evaluating each sku in the candidate pool according to historical data to obtain an evaluation value of each sku;
grouping and sequencing all skus in each category according to the evaluation values from large to small to generate an option list;
and determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence, and finally generating an individualized option list.
2. The method of claim 1, wherein constructing the candidate pool of auto parts sku comprises:
constructing an automobile part basic candidate pool, wherein the automobile part basic candidate pool comprises all sold skus of other front bins, skus of an empty search and all sold skus of a skatecat car shop;
and filtering the automobile part basic candidate pool to obtain a final automobile part sku candidate pool.
3. The method as claimed in claim 2, wherein the step of filtering the auto parts base candidate pool to obtain the final auto parts sku candidate pool comprises:
filtering sku which does not accord with the service requirement brand;
filtering sku of 20% of the vehicle types after the vehicle type retention quantity ranking;
filtering sku with abnormal purchasing state;
filtering sku pulled into the blacklist;
and sku filtering when the number of empty searching times of the sales platform is smaller than a preset threshold value.
4. The method of claim 3, wherein each sku in the candidate pool is evaluated according to historical data, and obtaining the evaluation value of each sku comprises:
dividing the sales area into a large area, a small area, a city and a front warehouse from large to small;
respectively calculating characteristic values of the same sku in different ranges;
and normalizing and weighting the characteristic values of the same sku in different ranges to obtain the evaluation value of each sku.
5. The method of claim 4, wherein calculating the feature values of the sku in different ranges comprises:
carrying out weighted summation according to the evaluation indexes to obtain the characteristic values of the same sku in different ranges;
the evaluation index includes the number of empty searches in the range, the sales volume of the skatecat car raising store, and the sales volume of the front warehouse.
6. The method of claim 5, wherein the evaluation value of each sku is calculated by the formula:
Y＝0.4*(0.4A 1 +0.4A 2 +0.4A 3 +A 4 +A 5 )+0.3*(0.4B 1 +0.4B 2 +0.4B 3 +B 4 +B 5 )+0.2*(0.4C 1 +0.4C 2 +0.4C 3 +C 4 +C 5 )+0.1*(0.4D 1 +0.4D 2 +0.4D 3 +D 4 +D 5 )；
in the formula: y is an evaluation value; a. the 1 The PV number of the empty search in the range of the front bin is obtained; a. the 2 The number of empty searching frequencies in the range of the front bin is obtained; a. the 3 Sales of the skatecat car-raising store in the front warehouse range; a. the 4 Sales frequency of the front bin which is the range of the front bin; a. the 5 The sales volume of the front bin which is the range of the front bin; b 1 Searching PV number for the city range; b is 2 The number of the empty search frequencies in the city range is obtained; b is 3 Sales for city-wide skatecat car shops; b is 4 Sales frequency of the pre-bin in the city range; b is 5 The sales volume of the pre-bin in the city range; c 1 The number of the PV of the empty search in the cell range; c 2 The number of the empty searching frequencies in the cell range is set; c 3 Sales for district-wide Tianmao car shops; c 4 The sales frequency of the front bin in the cell range; c 5 The sales volume of the front bin of the cell range; d 1 The number of empty search PV in a large area range; d 2 The number of empty search frequencies in a large area range; d 3 Sales for a large regional range of skatecat car shops; d 4 Sales frequency for large area coverage front bins; d 5 The sales of the pre-bins for a large area range.
7. The method of claim 6, wherein the step of grouping and sorting all skus in each category according to the evaluated value from large to small, and generating the option list comprises:
and setting a threshold value and selecting the number in the threshold value for the same-brand and same-price bands with the wildcard relationship among skus in the category, and deleting the rest.
8. A system for a method of personalizing an offering from a supply chain of a vehicle according to any one of claims 1 to 7, comprising:
the building module is used for building an automobile part sku candidate pool and determining the category of the automobile part;
the evaluation module is used for evaluating each sku in the candidate pool according to historical data to obtain an evaluation value of each sku;
the sorting module is used for carrying out grouping sorting on all skus in all the categories according to the evaluation values from large to small to generate an option list;
and the generation module is used for determining the number of skus under each category, sequentially selecting skus from the option list according to the arrangement sequence and finally generating an individualized option list.
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