CN110428196B - Quantitative analysis method and system for site selection of logistics network points - Google Patents
Quantitative analysis method and system for site selection of logistics network points Download PDFInfo
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Abstract
The invention relates to the technical field of logistics data analysis, in particular to a quantitative analysis method and system for site selection of logistics network points. The method comprises the following steps: acquiring order data of customers in a designated website jurisdiction range; acquiring customer behavior data of a customer in a preset time period according to the order data; respectively calculating the customer value and the customer viscosity value of each customer according to the customer behavior data; determining a weight of the customer based on the customer value and the customer stickiness value; and calculating the geographical position of the optimal website according to the geographical position data and the weight of the client. The quantitative analysis method and the system for site selection of the logistics network points can judge the rationality of the network points by the quantitative analysis site selection method, provide an effective solution for scientifically adding, combining or splitting network points, and realize standardized site selection.
Description
Technical Field
The invention relates to the technical field of logistics data analysis, in particular to a quantitative analysis method and system for site selection of logistics network points.
Background
Based on the continuous increase of the commodity quantity of the logistics company, the existing network points are insufficient, and the network points need to be newly added/separated or whether the current network points are reasonable or not is judged. Because the selection of network points needs to consider a lot of factors, the existing logistics network point site selection theory generally considers the following factors: the method comprises the following steps of (1) distributing factors of customers, traffic conditions of site selection positions, property conditions of the site, operability and rent of the site and the like, wherein the distributing factors of the customers are the distributing conditions of the customers around the site selection positions, the site selection aims to take the distance from a driver to each website into consideration, and the site selection is carried out in a region where the customers are concentrated; the traffic condition of the site selection position comprises whether local traffic control has limitation on operating vehicles of a company and whether traffic rush hour has influence on the timeliness of a client assignment task; the property conditions of the site selection position comprise hydropower of a house, network supply and whether outdoor parking of a site is convenient or not; the operability of the site comprises factors such as the area size of the site, whether goods are easy to stack, whether goods are convenient to circulate in the site, whether rent is appropriate and the like. The existing site selection theory has large operation complexity, and the site selection position is qualitatively analyzed by depending on the experience of a network planning manager to a great extent.
In view of the above, it is an urgent technical problem in the art to provide a new quantitative analysis method and system for site selection of logistics network points to overcome the above drawbacks in the prior art.
Disclosure of Invention
The present invention is directed to a method and a system for quantitative analysis of site selection of logistics network points, which overcome the above-mentioned drawbacks of the prior art.
The object of the invention can be achieved by the following technical measures:
the invention provides a quantitative analysis method for site selection of a logistics network point, which comprises the following steps:
acquiring order data of customers in a designated website jurisdiction range;
acquiring customer behavior data of a customer in a preset time period according to the order data;
respectively calculating the customer value and the customer viscosity value of each customer according to the customer behavior data;
determining a weight for each customer based on the customer value and the customer stickiness value; and
and calculating the geographic position of the optimal network point according to the geographic position data of each client and the weight.
Preferably, the customer behavior data includes a billing weight and an operating time for placing an order for each order of the customer.
Preferably, the step of calculating the customer worth value and the customer stickiness value of each customer respectively according to the customer behavior data comprises:
counting according to the order placing and charging weight of each order in a preset time period by a client to obtain the total fee weight, wherein the total fee weight is a client value;
and counting to obtain the total service times according to the operation time of each order in a preset time period by the customer, wherein the total service times are the viscosity value of the customer.
Preferably, the step of determining a weight for each customer based on the customer value and the customer stickiness value comprises:
setting a weighting coefficient of the client value and a weighting coefficient of the client viscosity value; the sum of the weighting coefficient of the client value degree and the weighting coefficient of the client viscosity degree is 1;
and carrying out weighted summation on the customer value and the customer viscosity value to obtain the weight of the customer.
Preferably, the step of calculating the geographical location of the best website according to the geographical location data of each customer and the weight comprises:
carrying out normalization processing on the weight of each client;
multiplying the longitude data of each client by the weight obtained by the normalization processing, and then summing the multiplied values to obtain longitude data of the optimal geographical position of the website;
and multiplying the latitude data of each client by the weight obtained by the normalization processing, and summing the result to obtain the latitude data of the optimal website geographical position.
The invention also provides a quantitative analysis system for site selection of the logistics network points, which comprises:
the order data collection module is used for acquiring order data of customers in the jurisdiction range of the appointed network;
the behavior data acquisition module is used for acquiring customer behavior data of the customer in a preset time period according to the order data;
the calculation module is used for calculating the customer value and the customer viscosity value of each customer according to the customer behavior data;
a weight determination module to determine a weight for each customer based on the customer value and the customer stickiness value; and
and the geographic position module is used for calculating the geographic position of the best network point according to the geographic position data of each client and the weight.
Preferably, the customer behavior data includes a billing weight and an operating time for placing an order for each order of the customer.
Preferably, the calculation module comprises:
the value calculation operator module is used for counting according to the order placing and charging weight of each order in a preset time period by a client to obtain the total charge weight, and the total charge weight is a client value;
and the viscosity calculation submodule is used for counting the total service times according to the operation time of each order in a preset time period of a customer, and the total service times are the viscosity value of the customer.
Preferably, the weight determination module includes:
the coefficient setting submodule is used for setting a weighting coefficient of the customer value and a weighting coefficient of the customer viscosity value; the sum of the weighting coefficient of the client value degree and the weighting coefficient of the client viscosity degree is 1;
and the weight calculation submodule is used for carrying out weighted summation on the customer value and the customer stickiness value to obtain the weight of the customer.
Preferably, the geographical location module comprises:
the normalization submodule is used for performing normalization processing on the weight of each client;
the longitude calculation submodule is used for multiplying the longitude data of each client by the weight obtained by the normalization processing and then summing the multiplied longitude data, and the obtained value is the longitude data of the best geographical position of the website;
and the latitude calculation submodule is used for multiplying the latitude data of each client by the weight obtained by the normalization processing and then summing the multiplied values, and the obtained value is the latitude data of the optimal website geographical position.
The invention provides a quantitative analysis method and a system for site selection of a logistics network, which are characterized in that customer behavior data are extracted according to order data in a network jurisdiction range, then a customer value and a customer viscosity value of each customer are calculated according to the customer behavior data, further the weight of each customer is calculated, and finally the geographical position of the network is calculated according to the geographical position data and the weight of each customer, so that the quantitative site selection is realized; the rationality of the site establishment can be objectively judged by a quantitative analysis site selection method, an effective solution is provided for scientifically adding, combining or splitting sites, and standardized site selection is realized.
Drawings
Fig. 1 is a flowchart of a quantitative analysis method for site selection of logistics nodes according to an embodiment of the present invention.
Fig. 2 is a block diagram of a quantitative analysis system for site selection of logistics nodes according to a first embodiment of the present invention.
Fig. 3 is a block diagram of a quantitative analysis system for site selection of logistics nodes according to a second embodiment of the present invention.
Fig. 4 is a block diagram of a quantitative analysis system for site selection of a website of a logistics system according to a third embodiment of the present invention.
Fig. 5 is a block diagram of a quantitative analysis system for site selection of a website according to a fourth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
In order to make the description of the present disclosure more complete and complete, the following description is given for illustrative purposes with respect to the embodiments and examples of the present invention; it is not intended to be the only form in which the embodiments of the invention may be practiced or utilized. The embodiments are intended to cover the features of the various embodiments as well as the method steps and sequences for constructing and operating the embodiments. However, other embodiments may be utilized to achieve the same or equivalent functions and step sequences.
The site selection of the network points comprises two factors of quantification and qualification, the two factors are more dependent on personal experience of network point planning managers at present, direct theoretical support is lacked, and the network point site selection is cluttered. In order to standardize site selection and deposit business experience, a quantitative part is urgently required to be dropped into a computer system in combination with an algorithm theory in business, iteration is carried out step by step to deposit artificial experience knowledge, and a powerful site selection solution is provided to support quantitative analysis of site selection decision of the site so as to realize standardized site selection.
The technical scheme analyzes the customer behavior data and quantitatively selects a reasonable and scientific website address.
Fig. 1 shows an embodiment of the quantitative analysis method for site selection of logistics nodes in the invention. As shown in fig. 1, in this embodiment, the quantitative analysis method for site selection of a logistics site includes the following steps:
s101, obtaining order data of customers in the jurisdiction range of the appointed network.
In this embodiment, the "jurisdiction of a specific site" represents a service area of a specific site, for example, when it is determined whether the location of the current site is reasonable, the jurisdiction of the specific site is the jurisdiction of the current site; and when judging whether the two network points are combined or not, the jurisdiction of the specified network point is the combination of the jurisdictions of the two network points. As defined herein: a given site R jurisdiction R = {1,2,3.. I.. N }, where i represents the ith customer in the jurisdiction R region. The order data is derived from information in the customer's logistics order, and may include, but is not limited to, one or more of the following: name, geographic location, billing weight, type of goods, type of service (pickup or dispatch), and order time, the order data extracted from the order information including: client code, billing weight, client service type, client address longitude, client address latitude, and operating time, see table 1.
Table 1 order data example table
| Name of field | Examples of such applications are |
| Customer code | Kye-20033668 |
| Billing weight | 300 |
| Type of customer service | Get one |
| Longitude of client address | 113.45434 |
| Client address latitude | 24.02383 |
| Time of operation (dispatch/fetch) | 2019-03-09 12:51:51 |
And S102, acquiring the customer behavior data of the customer in a preset time period according to the order data.
In this embodiment, the preset time period may be half a year, and preferably, the half a year before the current date is selected, which may take into account the influence of the newly added customer on site selection. In this embodiment, the customer behavior data includes the order placement billing weight and the operating time for each order of the customer.
And S103, respectively calculating the customer value and the customer stickiness value of each customer according to the customer behavior data.
In the embodiment of the present invention, the "client value" represents the value of the contribution of the client to the company in the preset time period, and specifically, the total charge weight is obtained by statistics according to the order placement charging weight of each order of the client in the preset time period, and the total charge weight reflects how much the client has value to the company in the period, so the total charging weight is the client value. If the total cost weight of the orders placed in a preset time period of a certain client is larger, the client is more important, and the site selection result of the network point should be close to the client.
The calculation formula of the customer value is as follows: v i ＝log(W i + 1) of, wherein, W i The total charge weight in a preset time period for the client i, i =1,2, \8230;, n.
"customer stickiness value" represents the degree of dependence of the customer on corporate services over a preset time period. Specifically, the total service times are obtained through statistics according to the operation time of each order of the customer in a preset time period, each operation time is equivalent to one time, and the total service times reflect how much the customer has viscosity (dependence degree) on the company in the period, so the total service times are the viscosity value of the customer. If the total times of picking up and sending the parts of a certain client by a company are more, the client is more dependent on company service, the frequency of corresponding company service is higher, and if the client is far away, the corresponding service cost is higher, and the site selection result of a network point is close to the client. Wherein the customer stickiness value is expressed as N i ，N i The total number of services within a time period, i =1,2, \ 8230;, n, is preset for customer i.
S104, determining the weight of each customer based on the customer value and the customer stickiness value.
Specifically, a weighting coefficient of a customer value and a weighting coefficient of a customer stickiness value are set; the sum of the weighting coefficient of the customer value degree and the weighting coefficient of the customer viscosity degree is 1; and then, carrying out weighted summation on the client value and the client viscosity value to obtain the weight of the client.
The weighting coefficient of the customer value and the weighting coefficient of the customer stickiness value of each customer are set individually. In an actual service scene, the client value degree weighting coefficient and the client viscosity value weighting coefficient can be initially set and subsequently dynamically adjusted according to the focus of attention and the change of client behavior data in a service range, for example, if the value of contribution of a client to a company is inclined, the client value degree weighting coefficient can be set to be larger than the client viscosity value weighting coefficient; conversely, if the dependence of the client on the company is inclined, the weighting coefficient of the client value can be set to be smaller than that of the client viscosity value; if the customer contribution value becomes lower, the weighting coefficient of the customer value is reduced; if the dependence degree of the client on the company becomes higher, the weighting coefficient of the client viscosity value is increased.
Further, before setting the weighting coefficient of the client worth value and the weighting coefficient of the client viscosity value, the client worth value and the client viscosity value are standardized to obtain a client worth value standardized value and a client viscosity standardized value of each client. The calculation formula of the normalized value of the customer value degree is as follows:
the customer viscosity normalized value is calculated as follows:
Setting a weighting coefficient of the client worth value and a weighting coefficient of the client viscosity value, and correspondingly converting the weighting coefficients into a weighting coefficient of a client worth value standardization value and a weighting coefficient of a client viscosity standardization value; the sum of the weighting coefficients of the two is still 1; and then, carrying out weighted summation on the client value standardization value and the client viscosity standardization value to obtain the weight of the client.
Specifically, the formula for calculating the weight of the customer is as follows:
Q i ＝λV i N +ρN i N wherein, V i N Normalizing the value, N, for the customer value of customer i i N The value is normalized for the client's i client's viscosity, λ is the weighting factor for the client's value, ρ is the weighting factor for the client's viscosity value, where λ + ρ =1,0 ≦ λ ≦ 1,0 ≦ ρ ≦ 1, i =1,2, \ 8230, n.
When the value of lambda is 0, the weight of the client only considers the viscosity value of the client; when rho is 0, the weight of the client only considers the value of the client value; and when the lambda and the rho are not equal to 0, representing the weight of the client and simultaneously considering the value and the viscosity value of the client.
And S105, calculating the geographical position of the best network point according to the geographical position data of each client and the weight.
In the embodiment of the invention, firstly, the weight of each client is normalized; specifically, the calculation formula is: normalized weight of client i
Wherein Q is i I =1,2, \ 8230;, n, the weight of customer i.
Further, multiplying the longitude data of each client by the weight obtained by the normalization processing, and then summing the multiplied values to obtain longitude data of the best geographical position of the website, wherein the specific calculation formula is as follows:
wherein, X i For the longitude data of client i->
For the normalized weight of customer i, i =1,2, \ 8230;, n; and multiplying the latitude data of each client by the weight obtained by the normalization processing, and then summing the result to obtain the latitude data of the optimal website geographical position, wherein the specific calculation formula is as follows: />
Wherein, Y i For latitude data of client i->
I =1,2, \ 8230;, n, for the normalized weight of customer i.
The optimal network point address position obtained by calculation can be used for judging whether the current network point is set reasonably, if not, the network point can be split or combined with other network points according to the optimal network point address position and the actual service condition, or the network point is added in the jurisdiction range based on the network point. It should be noted that the calculated optimal site address position is not the final site of the site setup, for example, if the calculated optimal site address position is in the middle of a river, the site setup position of the site is planned and adjusted according to the actual situation.
In the embodiment, the customer behavior data is extracted according to the order data in the jurisdiction range of the network node, the customer value and the customer viscosity value of each customer are calculated according to the customer behavior data, the weight of each customer is further calculated, and finally the geographical position of the network node is calculated according to the geographical position data and the weight of each customer, so that quantitative site selection is realized; the rationality of the site establishment can be objectively judged by the quantitative site selection method, an effective solution is provided for scientifically adding, combining or splitting sites, and standardized site selection is realized.
Based on the same inventive concept, the embodiment of the invention also provides a quantitative analysis system for site selection of the logistics network points, as in the following embodiment. Because the principle of solving the problems of the quantitative analysis system for the site selection of the logistics network points is similar to that of the quantitative analysis method for the site selection of the logistics network points, the implementation of the quantitative analysis system for the site selection of the logistics network points can refer to the implementation of the quantitative analysis method for the site selection of the logistics network points, and repeated details are not repeated. As used hereinafter, the terms "unit" or "sub-module" or "module" may implement a combination of software and/or hardware of predetermined functions. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 2 illustrates an embodiment of the quantitative analysis system for site selection of logistics sites according to the present invention. As shown in fig. 2, in this embodiment, the quantitative analysis system for site selection of logistics nodes includes: order data collection module 10, behavioral data acquisition module 20, calculation module 30, weight determination module 40, and geographic location module 50.
The order data collection module 10 is used for acquiring order data of customers in a jurisdiction range of a specified network; a behavior data obtaining module 20, configured to obtain, according to the order data, customer behavior data of the customer in a preset time period; a calculating module 30, configured to calculate a customer worth value and a customer stickiness value of each customer according to the customer behavior data; a weight determination module 40 for determining a weight for each customer based on the customer value and the customer stickiness value; and a geographic location module 50, configured to calculate a geographic location of the best website according to the geographic location data of each customer and the weight.
In this embodiment, the preset time period may be half a year, and preferably, the half a year before the current date is selected, which may take into account the influence of the newly added customer on site selection. In this embodiment, the customer behavior data includes the customer's order placement billing weight and operating time for each order.
On the basis of the embodiment shown in fig. 2, in other embodiments, as shown in fig. 3, the calculation module 30 further includes: the system comprises a value calculation operator module 301 and a viscosity calculation operator module 302, wherein the value calculation operator module 301 is used for obtaining total charge weight according to the order placing and charging weight of each order in a preset time period by a client through statistics, and the total charge weight is a client value; the viscosity calculator module 302 is configured to obtain a total service time according to the operation time of each order within a preset time period, where the total service time is a viscosity value of the customer.
Based on the embodiment shown in fig. 2, in other embodiments, as shown in fig. 4, the weight determining module 40 includes: a coefficient setting submodule 401 and a weight calculation submodule 402, wherein the coefficient setting submodule 401 is configured to set a weighting coefficient of a customer worth value and a weighting coefficient of a customer viscosity value, and a sum of the weighting coefficient of the customer worth value and the weighting coefficient of the customer viscosity value is 1; and the weight calculation submodule 402 is configured to perform weighted summation on the customer value and the customer stickiness value to obtain a weight of the customer.
In the present embodiment, the weighting factor of the customer-worth value and the weighting factor of the customer-stickiness value of each customer are set individually. In an actual business scene, the client value degree weighting coefficient and the client viscosity value weighting coefficient can be initially set and then dynamically adjusted according to the focus of attention and the change of client behavior data in a service range, for example, if the value of a client contributing to a company is inclined, the client value degree weighting coefficient can be set to be larger than the client viscosity value weighting coefficient; conversely, if the dependence of the client on the company is inclined, the weighting coefficient of the client value can be set to be smaller than that of the client viscosity value; if the customer contribution value becomes lower, the weighting coefficient of the customer value is reduced; if the dependence degree of the client on the company becomes higher, the weighting coefficient of the client viscosity value is increased.
Further, the weight determining module 40 further includes: the standardization submodule 403 is configured to standardize the client value and the client viscosity value to obtain a client value standardization value and a client viscosity standardization value of each client; the weight calculation sub-module 402 is configured to perform a weighted summation on the client value normalization value and the client viscosity normalization value to obtain a weight of the client.
Setting a weighting coefficient of the client value and a weighting coefficient of the client viscosity value, and correspondingly converting the weighting coefficients into the weighting coefficient of the client value standardized value and the weighting coefficient of the client viscosity standardized value; the sum of the two weighting factors is still 1.
In addition to the embodiment shown in fig. 2, in other embodiments, as shown in fig. 5, the geographic location module 50 includes: a normalization submodule 501, a longitude calculation submodule 502 and a latitude calculation submodule 503, wherein the normalization submodule 501 is used for performing normalization processing on the weight of each client; a longitude calculation submodule 502, configured to multiply and sum longitude data of each client by a weight obtained through normalization processing, where the obtained value is longitude data of an optimal geographical position of a website; and the latitude calculating submodule 503 is configured to multiply the latitude data of each client with the weight obtained through the normalization processing, and then sum the multiplied values, so that the obtained value is the latitude data of the best website geographical position.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. A method for quantitative analysis of site selection for a point of sale of a commodity stream, the method comprising:
acquiring order data of customers in a designated website jurisdiction range;
obtaining customer behavior data of a customer in a preset time period according to the order data, wherein the customer behavior data comprises the order placing and charging weight and the operation time of each order of the customer;
counting according to the order placing charging weight of each order in a preset time period by a client to obtain the total fee weight, and calculating the value of the client according to the total fee weight, wherein the calculation formula of the value of the client is V i ＝log(W i +1)，W i The total fee weight in a preset time period is set for a client i, i =1,2, \8230, n;
counting according to the operation time of each order in a preset time period by a customer to obtain the total service times, wherein the total service times are the viscosity value of the customer;
determining a weight for each customer based on the customer value and the customer stickiness value; and
carrying out normalization processing on the weight of each client;
multiplying the longitude data of each client by the weight obtained by the normalization processing, and then summing the multiplied values to obtain longitude data of the optimal geographical position of the website;
and multiplying the latitude data of each client by the weight obtained by the normalization processing, and then summing the result to obtain the latitude data of the optimal website geographical position.
2. The quantitative analysis method for website selection of logistics sites of claim 1, wherein the step of determining the weight of each customer based on the customer value and the customer stickiness value comprises:
setting a weighting coefficient of the client value and a weighting coefficient of the client viscosity value; the sum of the weighting coefficient of the client value degree and the weighting coefficient of the client viscosity degree is 1;
and carrying out weighted summation on the customer value and the customer viscosity value to obtain the weight of the customer.
3. A quantitative analysis system for site selection of a logistics site, the system comprising:
the order data collection module is used for acquiring order data of customers in the jurisdiction range of the appointed network;
the behavior data acquisition module is used for acquiring customer behavior data of a customer in a preset time period according to the order data, wherein the customer behavior data comprises the order placing and charging weight and the operation time of each order of the customer;
a calculation module for obtaining total charge weight according to the order placing charging weight statistics of each order in a preset time period by a client, calculating a client value according to the total charge weight, wherein the calculation formula of the client value is V i ＝log(W i +1)，W i The total charge weight in a preset time period for a client i, i =1,2, \ 8230;, n; the system is also used for obtaining the total service times according to the operation time statistics of each order in a preset time period by the customer, wherein the total service times are the viscosity value of the customer;
a weight determination module to determine a weight for each customer based on the customer value and the customer stickiness value; and
the normalization submodule is used for performing normalization processing on the weight of each client;
the longitude calculation submodule is used for multiplying the longitude data of each client by the weight obtained by the normalization processing and then summing the multiplied values, and the obtained value is the longitude data of the optimal geographical position of the website;
and the latitude calculation submodule is used for multiplying the latitude data of each client by the weight obtained by the normalization processing and then summing the multiplied values, and the obtained value is the latitude data of the optimal website geographical position.
4. The quantitative analysis system for site selection of logistics sites of claim 3, wherein the weight determination module comprises:
the coefficient setting submodule is used for setting a weighting coefficient of the client value and a weighting coefficient of the client viscosity value; the sum of the weighting coefficient of the client value degree and the weighting coefficient of the client viscosity degree is 1;
and the weight calculation submodule is used for carrying out weighted summation on the customer value and the customer stickiness value to obtain the weight of the customer.
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