CN113487259A - Ex-warehouse delivery method for e-commerce intelligent warehousing - Google Patents
Ex-warehouse delivery method for e-commerce intelligent warehousing Download PDFInfo
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Abstract
The invention discloses a delivery method for E-commerce intelligent storage, which comprises the steps of obtaining an order to be processed, obtaining a first warehouse which is delivered to a to-be-delivered receiving address of the order to be processed most quickly, judging whether the delivery pressure, the logistics pressure and the inventory quantity of the corresponding order to be processed of the first warehouse are all smaller than corresponding preset threshold values, if so, and sending the to-be-processed order to a first warehouse for ex-warehouse delivery, otherwise, acquiring other warehouses which can be delivered to the to-be-delivered receiving address within the preset time efficiency of the e-commerce, taking the other warehouses and the first warehouse as alternative warehouses, performing weighted calculation on all the alternative warehouses according to the ex-warehouse pressure, the logistics pressure and the inventory quantity of the corresponding to-be-processed order and the proportional relation of the corresponding preset threshold value to obtain the final scores of all the alternative warehouses, and sending the to-be-processed order to the alternative warehouse with the highest final score for ex-warehouse delivery. The invention can reduce the distribution prolonging phenomenon by the method.
Description
Technical Field
The invention relates to the technical field of intelligent warehousing, in particular to a delivery method for e-commerce intelligent warehousing.
Background
The intelligent warehousing system is a logistics activity which effectively plans, executes and controls the goods entering and leaving warehouse, storing, sorting, packaging, delivering and information thereof by using advanced technological means and equipment such as software technology, internet technology, automatic sorting technology, light guide technology, Radio Frequency Identification (RFID), voice control technology and the like. The method mainly comprises the following steps: identification system, handling system, storage system, letter sorting system and management system.
In the planning process of warehouse-out and delivery of the intelligent warehousing system, the existing mode is to select the nearest warehouse for delivery based on the principle of nearby warehouse delivery according to the receiving address of the goods and to deliver the goods according to the generated goods list. However, the stock quantity, the delivery pressure and the logistics pressure of different warehouses at different time periods are different, the delivery pressure of some warehouses at some time periods may be larger based on the principle that the warehouses are delivered nearby, the logistics pressure may be larger due to insufficient stock quantity and allocation, and the receiving addresses are too concentrated, and the like, and the problems may finally cause the delivery to be prolonged, so that the shopping experience of customers is greatly influenced. Therefore, a better delivery method for ex-warehouse is required to solve the above problems.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the ex-warehouse distribution method for the E-business intelligent warehouse is provided to reduce the phenomenon of prolonged distribution.
In order to solve the technical problems, the invention adopts the technical scheme that:
a delivery method for ex-warehouse of E-commerce intelligent warehousing comprises the following steps:
step S1, obtaining a to-be-processed order, obtaining a first warehouse of a to-be-sent receiving address which is most quickly sent to the to-be-processed order, judging whether the warehouse-out pressure, the logistics pressure and the inventory quantity corresponding to the to-be-processed order of the first warehouse are all smaller than corresponding preset threshold values, if yes, sending the to-be-processed order to the first warehouse for warehouse-out delivery, and otherwise, executing step S2;
and step S2, acquiring other warehouses which can be delivered to the to-be-delivered receiving address within the preset time period of the e-commerce, taking the other warehouses and the first warehouse as alternative warehouses, performing weighted calculation on all the alternative warehouses according to the ratio of ex-warehouse pressure, logistics pressure and the inventory quantity corresponding to the to-be-processed order to the corresponding preset threshold value to obtain the final scores of all the alternative warehouses, and sending the to-be-processed order to the alternative warehouse with the highest final score for ex-warehouse delivery.
The invention has the beneficial effects that: a delivery method for E-commerce intelligent warehousing distributes orders to be processed to a first warehouse for delivery to guarantee the timeliness of the orders if the first warehouse which is delivered fastest has no problems in delivery pressure, logistics pressure and corresponding inventory quantity. When one index of the first warehouse exceeds a preset threshold value, the warehouse is considered to be in an overload state at present, the delivery time can be possibly influenced, at the moment, all warehouses which can be delivered within the preset time limit of the e-commerce are taken as alternative warehouses for weighted calculation, the alternative warehouse with the highest final score is obtained for delivery and delivery, delivery within the preset time limit of the e-commerce is guaranteed, the problems that the delivery pressure of a certain warehouse is large, the number of warehouses in a certain warehouse is insufficient, the receiving addresses are too concentrated, the logistics pressure is large and the like can be avoided, and the phenomenon of delivery extension is reduced as far as possible.
Drawings
Fig. 1 is a schematic flow chart of an ex-warehouse delivery method for e-commerce smart warehousing according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1, a delivery method for e-commerce smart warehousing includes:
step S1, obtaining a to-be-processed order, obtaining a first warehouse of a to-be-sent receiving address which is most quickly sent to the to-be-processed order, judging whether the warehouse-out pressure, the logistics pressure and the inventory quantity corresponding to the to-be-processed order of the first warehouse are all smaller than corresponding preset threshold values, if yes, sending the to-be-processed order to the first warehouse for warehouse-out delivery, and otherwise, executing step S2;
and step S2, acquiring other warehouses which can be delivered to the to-be-delivered receiving address within the preset time period of the e-commerce, taking the other warehouses and the first warehouse as alternative warehouses, performing weighted calculation on all the alternative warehouses according to the ratio of ex-warehouse pressure, logistics pressure and the inventory quantity corresponding to the to-be-processed order to the corresponding preset threshold value to obtain the final scores of all the alternative warehouses, and sending the to-be-processed order to the alternative warehouse with the highest final score for ex-warehouse delivery.
From the above description, the beneficial effects of the present invention are: and if the first warehouse which is delivered fastest has no problem in the delivery pressure, the logistics pressure and the corresponding inventory quantity, distributing the orders to be processed to the first warehouse for delivery and delivery so as to ensure the timeliness of the orders. When one index of the first warehouse exceeds a preset threshold value, the warehouse is considered to be in an overload state at present, the delivery time can be possibly influenced, at the moment, all warehouses which can be delivered within the preset time limit of the e-commerce are taken as alternative warehouses for weighted calculation, the alternative warehouse with the highest final score is obtained for delivery and delivery, delivery within the preset time limit of the e-commerce is guaranteed, the problems that the delivery pressure of a certain warehouse is large, the number of warehouses in a certain warehouse is insufficient, the receiving addresses are too concentrated, the logistics pressure is large and the like can be avoided, and the phenomenon of delivery extension is reduced as far as possible.
Further, the step S1 specifically includes the following steps:
acquiring an order to be processed, and setting all articles of the order to be processed as an article set to be delivered;
obtaining a first warehouse of a to-be-delivered receiving address which is most rapidly delivered to the to-be-processed order;
classifying the articles in the to-be-delivered article set, the inventory quantity of which exceeds a corresponding preset threshold value, in the first warehouse into a first sub-order, and classifying other articles in the to-be-delivered article set into a second sub-order;
judging whether the ex-warehouse pressure and the logistics pressure of the first warehouse are both smaller than corresponding preset thresholds, if so, sending the first sub-order to the first warehouse for ex-warehouse delivery and delivery, and executing the step S2 by taking the second sub-order as the to-be-processed order, otherwise, directly executing the step S2;
when the step S2 is executed with the second sub-order as the to-be-processed order, the final score of the first warehouse in the step S2 is a score obtained after a product between the score after the weighted calculation of the first warehouse and a preset coefficient greater than 1.
As can be seen from the above description, when an order of a user includes many articles, and all the articles may not be able to complete delivery in one warehouse, it is necessary to split the order into a plurality of sub-orders according to the stock quantity to perform delivery in different warehouses, so as to ensure that all the articles can reach the customer in time.
Further, the step S1 of obtaining the first warehouse of the to-be-delivered receiving address that reaches the to-be-processed order most quickly includes the following steps:
counting the delivery time from each warehouse to each logistics node in advance;
determining a final logistics node of the order to be processed based on the to-be-delivered receiving address of the order to be processed;
and taking the warehouse with the shortest delivery time length corresponding to the final logistics node as a first warehouse.
From the above description, the positions of the warehouse and the logistics nodes are fixed, and the delivery time length between each logistics node and the warehouse is known in advance, so that the warehouse with the shortest delivery time length can be directly and quickly obtained by determining the final logistics node of the order to be processed according to the delivery address, and the warehouse with the fastest delivery can be quickly and accurately obtained.
Further, if the first delivery time of the first warehouse in the step S1 exceeds the e-commerce preset time limit, the time limit of the other warehouses acquired in the step S2 is replaced by the e-commerce preset time limit by the first delivery time plus a preset number of days.
From the above description, that is, when the overall logistics pressure is large or some goods are in short supply, and the like, and the delivery time cannot meet the preset time limit of the e-commerce, it is necessary to add the preset number of days to the shortest first delivery time to perform screening of other warehouses, so as to reduce the delivery delay problem caused by the above phenomena as much as possible.
Further, the order to be processed is a set of a preset number of individual orders with the same receiving address of the logistics node.
Further, the order to be processed is a set of all personal orders with the same logistics node as the receiving address in a preset time interval.
From the above description, the time or quantity is selected as the limiting condition according to different situations, so as to aggregate the individual orders with the same receiving address as the logistics node as the pending order, thereby reducing the calculation pressure.
Further, the warehouse-out pressure and the logistics pressure of the alternative warehouse are both lower than corresponding pressure upper limit values, and when the inventory quantity of a certain warehouse is 0 and article scheduling is needed, the time for article scheduling is accumulated to the delivery duration of the warehouse.
Wherein, the upper pressure limit value is the maximum processing capacity of the warehouse.
As can be seen from the above description, when the inventory quantity is 0, the delivery time spent on waiting for the article scheduling is needed, and the increased time is likely to exceed the time limit preset by the operator, so as to reduce the scheduling times between warehouses as much as possible to reduce the warehouse-out pressure and the logistics pressure at the same time.
Further, before the step S1, the method further includes:
in a preset peak period, acquiring historical total sales data of past years in the same peak period, historical item sales data of each item, a hot sales merchant corresponding to each historical hot sales product, historical preferential strength of each hot sales merchant and historical product trend of each historical hot sales product in the previous time of the corresponding peak period in advance;
predicting current total sales data of a current peak period according to the total sales data of the past years, predicting current category sales data of the current peak period according to historical category sales data of the past years, carrying out proportional calculation according to historical discount strength and current discount strength of the hot sales merchants to obtain discount coefficients, and converting according to the current total sales data, the current category sales data and the discount coefficients of each hot sales merchant to obtain predicted merchant sales data of each hot sales merchant in the current peak period;
taking iteration products corresponding to historical hot sales products of each hot sales merchant in the same peak period in the past year as predicted hot sales products, obtaining predicted product sales data of the predicted hot sales products according to the sales data of the predicted merchants and the current product evaluation and the proportion coefficient between the current product evaluation and the historical product evaluation according to the current product evaluation of the preset hot sales products in the previous time of the current peak period, judging whether the predicted product sales data exceed corresponding preset hot sales thresholds or not, if yes, adding the predicted hot sales products into a preset hot sales set, and otherwise, neglecting the predicted hot sales products;
performing region clustering analysis on the historical hot sales products of which the predicted hot sales products are in an iterative relationship in the past year to obtain a historical sales ratio of each predicted hot sales product in a region corresponding to each logistics node, and converting the predicted product sales data of the predicted hot sales products and the historical sales ratio of each logistics node to obtain a preset sales volume quantity of each logistics node corresponding to the predicted hot sales products in the current peak period;
based on the relationship between the warehouses and the logistics nodes, obtaining the optimal warehouse corresponding to each logistics node according to the fastest delivery principle, and obtaining the stock quantity of each warehouse for each predicted hot-sold product in the current peak period;
and performing pre-cargo scheduling based on the difference between the real-time inventory amount of each predicted hot-sold product in each warehouse and the required stock quantity, so that the real-time inventory amount of each predicted hot-sold product in each warehouse is within the allowable fluctuation range of the stock quantity.
From the above description, the sales data of past years are distinguished according to the classification of different products, so that the predicted sales data of corresponding products can be obtained more accurately. On the basis, new products are continuously pushed by merchants to iterate, and the series of iterated products often have strong relevance, on the basis, the preset sales data of the iterated current products are predicted according to hot sales products in past years, and meanwhile, the evaluation comparison among different generations of products in the iterative process is added to adapt to the situations of sudden increase and sudden drop of sales of different generations of products caused by the change of public praise in the iterative process, so that the finally obtained preset sales quantity is more accurate and real; therefore, based on more accurate preset sales quantity, according to regional sales conditions of past years, the warehouse-out quantity of each warehouse in the upcoming current peak period is predicted to carry out advanced stock preparation, so that warehouse-out idleness and logistics idleness before the peak period can be better utilized to reduce warehouse-out pressure and logistics pressure in the peak period, the condition of delivery delay caused by the fact that the warehouse needs to be allocated or needs to be farther for delivery due to the problem of inventory quantity is avoided, and the phenomenon that the delivery time of the cargos in the current peak period exceeds the preset time of an operator is reduced.
Further, the lower limit value of the allowable fluctuation range of the stock quantity is [ 90%, 110% ], and the upper limit value of the allowable fluctuation range of the stock quantity is greater than 120%.
Referring to fig. 1, a first embodiment of the present invention is:
a delivery method for ex-warehouse of E-commerce intelligent warehousing comprises the following steps:
step S1, obtaining the order to be processed, obtaining a first warehouse which is delivered to the receiving address to be delivered to the order to be processed most quickly, judging whether the delivery pressure, the logistics pressure and the stock quantity of the corresponding order to be processed of the first warehouse are all smaller than corresponding preset threshold values, if yes, sending the order to be processed to the first warehouse for delivery, and if not, executing step S2;
in this embodiment, the to-be-processed order is a set of a plurality of individual orders whose shipping addresses are the same logistics node, and in this case, the to-be-processed order may be a set of a preset number of individual orders whose shipping addresses are the same logistics node, or may be a set of all individual orders whose shipping addresses are the same logistics node within a preset time interval. Or a combination of the two: namely, when the number of the individual orders with the same receiving address as the logistics node reaches the preset number or the current duration time reaches the preset time interval, the orders are collected into one order to be processed. For example, if the preset quantity is 10, and the preset time interval is 1 minute, the time and the quantity are accumulated in real time, and if the quantity of the individual orders with the same receiving address at the same logistics node reaches 10 or 1 minute has elapsed, the 10 individual orders or all the individual orders within 1 minute are taken as an order to be processed.
It should be noted that, the preset threshold is a positive integer, and the inventory quantity is kept less than that directly taking the inventory quantity as 0 as a judgment basis. At this time, when the weighting calculation is carried out, the previous first warehouse is also taken into consideration, the whole warehouse-out delivery to other warehouses or other warehouses to be delivered to the receiving address in the preset time limit of the e-commerce does not exist in some extreme cases, compared with the first warehouse, at this time, the warehouse-out delivery needs to be carried out by returning to the first warehouse, and if the direct warehouse-out delivery is carried out until the number of the warehouses is 0, the warehouses cannot be delivered in the preset time limit of the e-commerce under the extreme cases, so that the problem of goods delivery delay in the extreme cases can be reduced. For example, if the inventory quantity of an article in the first warehouse is 3 and is just a preset threshold value, the article is not delivered first, if other warehouses can be delivered within the preset time limit of the e-commerce, other warehouses are delivered, and if no other warehouse can meet the time limit, the article can be delivered from the first warehouse, so that the number of the preset threshold values corresponds to the number of times of extreme conditions which can be eliminated.
In this embodiment, step S1 specifically includes the following steps:
step S11, acquiring the order to be processed, and setting all the articles of the order to be processed as an article set to be delivered;
step S12, obtaining a first warehouse of the to-be-delivered receiving address which is delivered to the to-be-processed order most quickly;
step S13, classifying the items of which the stock quantity in the first warehouse exceeds the corresponding preset threshold value in the item set to be delivered as first sub-orders, and classifying other items in the item set to be delivered as second sub-orders;
and S14, judging whether the warehouse-out pressure and the logistics pressure of the first warehouse are both smaller than corresponding preset thresholds, if so, sending the first sub-order to the first warehouse for warehouse-out delivery, and executing the step S2 by taking the second sub-order as a to-be-processed order, otherwise, directly executing the step S2.
When step S2 is executed with the second sub-order as the to-be-processed order, the final score of the first warehouse in step S2 is the score obtained after the product of the score after the weighted calculation of the first warehouse and a preset coefficient greater than 1.
For example, if the preset coefficient is 1.1, the value of the optimal warehouse in the second sub-order is 100, and the value of the first warehouse is 95, in this case, the second sub-order is delivered from the optimal warehouse or the first warehouse almost, and the optimal warehouse is delivered for warehouse-out, logistics and distribution according to the values, so that resources for warehouse-out, logistics and distribution at one time are wasted, and meanwhile, a consumer needs to wait for notification of express delivery twice and take express delivery twice, so that the material experience of the consumer is not good.
In this embodiment, step S12 specifically includes the following steps:
counting the delivery time from each warehouse to each logistics node in advance;
determining a final logistics node of the order to be processed based on the to-be-delivered receiving address of the order to be processed;
and taking the warehouse with the shortest delivery time length corresponding to the final logistics node as a first warehouse.
Therefore, the positions of the warehouse and the logistics nodes are fixed, and the delivery time length between each logistics node and the warehouse is known in advance, so that the warehouse with the shortest delivery time length can be directly and quickly obtained by determining the final logistics node of the order to be processed according to the delivery address, and the warehouse with the fastest delivery can be quickly and accurately obtained. For example, the shortest logistics time is obtained from warehouse A to logistics nodes in market B according to the existing path specification, so that the time data of the warehouses and the logistics nodes are known before calculation, and then the final logistics nodes know which warehouse is the nearest.
And step S2, acquiring other warehouses which can be delivered to the to-be-delivered receiving address within the preset time efficiency of the e-commerce, taking the other warehouses and the first warehouse as alternative warehouses, performing weighted calculation on all the alternative warehouses according to the ratio of the warehouse-out pressure, the logistics pressure and the inventory quantity of the corresponding to-be-processed orders to the corresponding preset threshold value to obtain the final scores of all the alternative warehouses, and sending the to-be-processed orders to the alternative warehouse with the highest final score for warehouse-out delivery and delivery.
The preset time limit of the e-commerce is the promised day of the day, day of the next day, and the like. When the material flow pressure is small, the existing material flow system can be delivered according to the preset time period of the electricity merchant basically.
If the first delivery time of the first warehouse in the step S1 exceeds the e-commerce preset time limit, the time limit of the other warehouses acquired in the step S2 is replaced by the first delivery time plus the preset number of days.
At this time, the pressure of the whole warehouse-out logistics is large, so that the warehouse-out logistics cannot be delivered within the preset time limit of the e-commerce, and the screening of other warehouses naturally needs to increase the preset number of days, otherwise, other warehouses do not exist. Wherein the preset number of days may be 1 day or 0.5 days.
The delivery pressure and the logistics pressure of the alternative warehouse including the first warehouse are lower than the corresponding upper pressure limit values, so that the preset threshold value indicated in step S1 is smaller than the upper pressure limit value. And when the inventory quantity of a certain warehouse is 0 and article scheduling is needed, the time for article scheduling is accumulated to the delivery time of the warehouse. After all, for the customer, the time span from ordering to goods taking is taken as the whole logistics time, and the time delay caused at the time point can aggravate the logistics time and needs to be overcome and considered by the merchant during distribution.
Referring to fig. 1, the second embodiment of the present invention is:
on the basis of the first or second embodiment, before step S1, the method for delivering electronic commerce smart warehouse further includes:
step S01, in a preset peak period, acquiring historical total sales data of past years in the same peak period, historical item sales data of each item, a hot sales merchant corresponding to each historical hot sales product, historical preferential power of each hot sales merchant and historical product wind rating of each historical hot sales product in the previous time of the corresponding peak period in advance;
the peak time of the e-commerce, such as the existing "double 11" and "618", is substantially due to the greater privilege, and thus the privilege of each merchant has a direct influence on the shopping desire of the user. On the basis, the homogenization of the existing product is serious, the main functions of different products are always the same, and the evaluation of the iterative product influences the selection of customers, so the preferential strength and the product evaluation are taken as one of predicted data sources.
In this embodiment, the past year may be three to five years, and the hot sales product may be determined as the top number of the ranking list or as the hot sales product when the sales number reaches.
Step S02, predicting current total sales data of the current peak according to the total sales data of the past years, predicting current category sales data of the current peak according to historical category sales data of the past years, carrying out proportional calculation according to historical discount strength and current discount strength of hot sales merchants to obtain discount coefficients, and converting according to the current total sales data, the current category sales data and the discount coefficients of each hot sales merchant to obtain predicted merchant sales data of each hot sales merchant in the current peak;
therefore, the sales data of the past years are distinguished according to the classification of different products, so that the predicted sales data of the corresponding product classes can be obtained more accurately. And balancing the numerical value according to the discount coefficient to ensure the accuracy of predicting the sales data of the merchant.
Step S03, taking iterative products corresponding to historical hot sales products of each hot sales merchant in the same peak period in the past year as predicted hot sales products, obtaining predicted product sales data of the predicted hot sales products according to the sales data of the predicted merchants and the current product evaluation and the proportion coefficient between the current product evaluation and the historical product evaluation according to the current product evaluation of the preset hot sales products in the previous time of the current peak period, judging whether the predicted product sales data exceed corresponding preset hot sales thresholds or not, if yes, adding the predicted hot sales products into a preset hot sales set, and otherwise, neglecting the predicted hot sales products;
on the basis, the preset sales data of the current iterative product are predicted according to the hot sales products in past years, and meanwhile, the evaluation comparison among different generations of products in the iterative process is added to adapt to the situations of sales volume explosion and explosion caused by the change of public praise in the iterative process of different generations of products, so that the finally obtained preset sales volume is more accurate and real.
Wherein, the iterative products such as iPhone series, iPad series and iMac series of the iPhone, the computer iterates the whole machine according to the updating of the processor or the display card every year, and the clothing iterates according to the different popular elements every year, etc.
Step S04, performing region clustering analysis on historical hot sales products of which the predicted hot sales products are in an iterative relationship in the past year to obtain the historical sales ratio of each predicted hot sales product in the region corresponding to each logistics node, and converting the predicted product sales data of the predicted hot sales products and the historical sales ratio of each logistics node to obtain the preset sales volume quantity of each logistics node corresponding to the predicted hot sales products in the current peak period;
step S05, based on the relationship between the warehouse and the logistics nodes, obtaining the optimal warehouse corresponding to each logistics node according to the fastest delivery principle, and obtaining the stock quantity of each warehouse for each predicted hot-sold product in the current peak period;
therefore, based on more accurate preset sales quantity, according to regional sales conditions of past years, the warehouse-out quantity of each warehouse in the upcoming current peak period is predicted to carry out advanced stock preparation, so that warehouse-out idleness and logistics idleness before the peak period can be better utilized, warehouse-out pressure and logistics pressure in the peak period are reduced, the condition that delivery delay is caused by the fact that the warehouse needs to be allocated or needs to be farther for delivery due to the problem of the inventory quantity is avoided, and the phenomenon that the delivery time of the cargos in the current peak period exceeds the preset time of a commercial is reduced.
Step S06, pre-scheduling the goods based on the difference between the real-time inventory amount for each predicted hot sell product and the required stock quantity in each warehouse, so that the real-time inventory amount for each predicted hot sell product in each warehouse is within the allowable fluctuation range of the stock quantity.
In the present embodiment, the lower limit value of the allowable fluctuation range of the stock quantity is [ 90%, 110% ], and the upper limit value of the allowable fluctuation range of the stock quantity is greater than 120%.
Referring to fig. 1, a third embodiment of the present invention is:
based on the second embodiment, the lower limit value of the allowable fluctuation range of the stock quantity is [ 100%, 110% ], and at this time, step S06 specifically includes the following steps:
step S061, counting the preset total amount of the preset hot-sold products which can be stored in the warehouse before the current peak period to obtain a ratio A between the preset total amount and the predicted product sales number in the predicted product sales data, judging whether the ratio A is larger than or equal to the upper limit value of the allowable fluctuation range, if so, executing step S062, otherwise, judging whether the ratio A is larger than or equal to the lower limit value of the allowable fluctuation range, if so, executing step S063, otherwise, executing step S064;
step S062, based on the difference between the real-time inventory amount and the required stock quantity of each predicted hot-sold product in each warehouse and the ratio A, performing pre-cargo scheduling, and scheduling the predicted hot-sold products to the rest of warehouses by the warehouse in which the ratio between the real-time inventory amount and the required stock quantity is greater than the ratio A [1,1.2] so that the ratio between the real-time inventory amount and the stock quantity of each predicted hot-sold product in each warehouse approaches to the ratio A;
wherein, step S062 means, for example, if the ratio a is 1.3, the lower limit of the allowable fluctuation range is 120%, and the above [1,1.1] is 1.1, then for some warehouses where the ratio between the real-time inventory amount and the required stock quantity exceeds 1.3 × 1.1, that is, for those warehouses where the ratio between the real-time inventory amount and the required stock quantity exceeds 1.43, it is necessary to schedule to a warehouse where the ratio between the real-time inventory amount and the required stock quantity is less than 1.3, and finally, the ratio between the real-time inventory amount and the required stock quantity in all warehouses approaches 1.3.
Step S063, based on the difference between the real-time inventory amount of each predicted hot-sell product and the required stock quantity in each warehouse, making the real-time inventory amount of each predicted hot-sell product in each warehouse within the allowable fluctuation range of the stock quantity;
step S064, generating early warning information, limiting the shopping amount of the predicted hot-sold products in the area corresponding to the logistics network corresponding to each warehouse, and scheduling the predicted hot-sold products to other warehouses based on the difference between the real-time inventory amount of each predicted hot-sold product and the required stock quantity in each warehouse and the ratio A, so that the ratio between the real-time inventory amount and the required stock quantity is greater than the ratio A [1,1.1], and the ratio between the real-time inventory amount of each predicted hot-sold product and the stock quantity in each warehouse is close to the ratio A;
in step S064, for example, if the ratio a is 0.8, the lower limit of the allowable fluctuation range is 100%, and the above-mentioned ratio coefficient [1,1.1] is 1.05, then for some warehouses where the ratio between the real-time inventory amount and the required stock amount exceeds 0.8 × 1.05, that is, for those warehouses where the ratio between the real-time inventory amount and the required stock amount exceeds 0.84, it is necessary to schedule to a warehouse where the ratio between the real-time inventory amount and the required stock amount is less than 0.8, and finally, the ratio between the real-time inventory amount and the required stock amount in all warehouses is close to 0.8.
Therefore, through the goods scheduling, under the condition of sufficient goods, each warehouse is guaranteed to have the redundancy capability and the condition that the goods scheduling is carried out in the current peak period is reduced, and under the condition that the goods are insufficient, the times of the goods scheduling are reduced through the value coefficient, and each warehouse can be guaranteed to have proper goods to be sold to balance each area, so that the goods scheduling system is better suitable for various conditions in the current peak period.
In summary, the delivery distribution method for the e-commerce intelligent warehouse provided by the invention predicts the delivery quantity of each warehouse in the upcoming current peak period before the peak period comes, and performs advanced stock according to the actual situation of the goods to different degrees, so as to better utilize the delivery idleness and the logistics idleness before the peak period to reduce the delivery pressure and the logistics pressure in the peak period, avoid the condition of delivery delay caused by the need of distribution or the need of distribution by a farther warehouse due to the problem of inventory quantity, and reduce the occurrence of the phenomenon that the delivery time of the goods in the current peak period exceeds the preset time of the e-commerce. And in the peak period, the order to be processed is distributed to the first warehouse for ex-warehouse distribution so as to guarantee the timeliness of the first warehouse, when one index of the first warehouse exceeds a preset threshold value, the candidate warehouse which can be reached in the preset timeliness of the e-commerce is subjected to weighted calculation, and the candidate warehouse with the highest final score is obtained for ex-warehouse distribution, so that the candidate warehouse can be reached in the preset timeliness of the e-commerce, the problems that the ex-warehouse pressure of a certain warehouse is high, the stock quantity of a certain warehouse is insufficient, the receiving address is too concentrated, the logistics pressure is high and the like can be avoided, and the distribution prolonging phenomenon can be reduced as far as possible. Meanwhile, one order is divided into a plurality of sub-orders according to the stock quantity so as to carry out warehouse-out distribution of different warehouses, and all articles can be guaranteed to be delivered to a customer in time.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (9)
1. A delivery method for ex-warehouse of E-commerce intelligent warehouse is characterized by comprising the following steps:
step S1, obtaining a to-be-processed order, obtaining a first warehouse of a to-be-sent receiving address which is most quickly sent to the to-be-processed order, judging whether the warehouse-out pressure, the logistics pressure and the inventory quantity corresponding to the to-be-processed order of the first warehouse are all smaller than corresponding preset threshold values, if yes, sending the to-be-processed order to the first warehouse for warehouse-out delivery, and otherwise, executing step S2;
and step S2, acquiring other warehouses which can be delivered to the to-be-delivered receiving address within the preset time period of the e-commerce, taking the other warehouses and the first warehouse as alternative warehouses, performing weighted calculation on all the alternative warehouses according to the ratio of ex-warehouse pressure, logistics pressure and the inventory quantity corresponding to the to-be-processed order to the corresponding preset threshold value to obtain the final scores of all the alternative warehouses, and sending the to-be-processed order to the alternative warehouse with the highest final score for ex-warehouse delivery.
2. The ex-warehouse distribution method for the e-commerce intelligent warehouse as claimed in claim 1, wherein the step S1 specifically comprises the following steps:
acquiring an order to be processed, and setting all articles of the order to be processed as an article set to be delivered;
obtaining a first warehouse of a to-be-delivered receiving address which is most rapidly delivered to the to-be-processed order;
classifying the articles in the to-be-delivered article set, the inventory quantity of which exceeds a corresponding preset threshold value, in the first warehouse into a first sub-order, and classifying other articles in the to-be-delivered article set into a second sub-order;
judging whether the ex-warehouse pressure and the logistics pressure of the first warehouse are both smaller than corresponding preset thresholds, if so, sending the first sub-order to the first warehouse for ex-warehouse delivery and delivery, and executing the step S2 by taking the second sub-order as the to-be-processed order, otherwise, directly executing the step S2;
when the step S2 is executed with the second sub-order as the to-be-processed order, the final score of the first warehouse in the step S2 is a score obtained after a product between the score after the weighted calculation of the first warehouse and a preset coefficient greater than 1.
3. The warehouse-out distribution method for e-commerce intelligent warehousing as claimed in claim 2, wherein the step S1 of obtaining the first warehouse of the to-be-delivered receiving address which is the fastest to the to-be-processed order specifically comprises the following steps:
counting the delivery time from each warehouse to each logistics node in advance;
determining a final logistics node of the order to be processed based on the to-be-delivered receiving address of the order to be processed;
and taking the warehouse with the shortest delivery time length corresponding to the final logistics node as a first warehouse.
4. The ex-warehouse distribution method for the e-commerce intelligent warehouse according to claim 1, wherein if the first arrival time of the first warehouse in the step S1 exceeds an e-commerce preset time limit, the time limit of the other warehouses acquired in the step S2 is replaced by the e-commerce preset time limit with the first arrival time plus a preset number of days.
5. The ex-warehouse delivery method for the e-commerce intelligent warehouse according to claim 1, wherein the order to be processed is a collection of a preset number of individual orders with the same receiving address as the logistics node.
6. The ex-warehouse distribution method for the e-commerce intelligent warehouse according to claim 1, wherein the to-be-processed orders are a set of all personal orders with the same logistics node as the receiving address in a preset time interval.
7. The ex-warehouse distribution method for E-commerce intelligent warehousing as claimed in claim 1, wherein the ex-warehouse pressure and the logistics pressure of the alternative warehouse are lower than corresponding upper pressure limit values, and when the inventory quantity of a certain warehouse is 0 and article scheduling is required, the time for performing article scheduling is accumulated to the delivery time of the warehouse.
8. The ex-warehouse distribution method for the e-commerce smart warehouse of claim 7, further comprising, before the step S1:
in a preset peak period, acquiring historical total sales data of past years in the same peak period, historical item sales data of each item, a hot sales merchant corresponding to each historical hot sales product, historical preferential strength of each hot sales merchant and historical product trend of each historical hot sales product in the previous time of the corresponding peak period in advance;
predicting current total sales data of a current peak period according to the total sales data of the past years, predicting current category sales data of the current peak period according to historical category sales data of the past years, carrying out proportional calculation according to historical discount strength and current discount strength of the hot sales merchants to obtain discount coefficients, and converting according to the current total sales data, the current category sales data and the discount coefficients of each hot sales merchant to obtain predicted merchant sales data of each hot sales merchant in the current peak period;
taking iteration products corresponding to historical hot sales products of each hot sales merchant in the same peak period in the past year as predicted hot sales products, obtaining predicted product sales data of the predicted hot sales products according to the sales data of the predicted merchants and the current product evaluation and the proportion coefficient between the current product evaluation and the historical product evaluation according to the current product evaluation of the preset hot sales products in the previous time of the current peak period, judging whether the predicted product sales data exceed corresponding preset hot sales thresholds or not, if yes, adding the predicted hot sales products into a preset hot sales set, and otherwise, neglecting the predicted hot sales products;
performing region clustering analysis on the historical hot sales products of which the predicted hot sales products are in an iterative relationship in the past year to obtain a historical sales ratio of each predicted hot sales product in a region corresponding to each logistics node, and converting the predicted product sales data of the predicted hot sales products and the historical sales ratio of each logistics node to obtain a preset sales volume quantity of each logistics node corresponding to the predicted hot sales products in the current peak period;
based on the relationship between the warehouses and the logistics nodes, obtaining the optimal warehouse corresponding to each logistics node according to the fastest delivery principle, and obtaining the stock quantity of each warehouse for each predicted hot-sold product in the current peak period;
and performing pre-cargo scheduling based on the difference between the real-time inventory amount of each predicted hot-sold product in each warehouse and the required stock quantity, so that the real-time inventory amount of each predicted hot-sold product in each warehouse is within the allowable fluctuation range of the stock quantity.
9. The ex-warehouse delivery method for the E-commerce intelligent warehouse according to claim 8, wherein the lower limit value of the allowable fluctuation range of the stock quantity is [ 90%, 110% ], and the upper limit value of the allowable fluctuation range of the stock quantity is more than 120%.
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