WO2023279407A1

WO2023279407A1 - Outbound and distribution method for e-commerce intelligent warehousing

Info

Publication number: WO2023279407A1
Application number: PCT/CN2021/105644
Authority: WO
Inventors: 朱德金; 李侠
Original assignee: 深圳市通拓信息技术网络有限公司
Priority date: 2021-07-06
Filing date: 2021-07-12
Publication date: 2023-01-12
Also published as: CN113487259A; CN113487259B

Abstract

An outbound and distribution method for e-commerce intelligent warehousing, comprising: obtaining an order to be processed, obtaining a first warehouse fastest reaching a delivery address to be reached of said order, determining whether an outbound pressure, logistics pressure, and inventory quantity corresponding to said order of the first warehouse are all less than corresponding preset thresholds, if yes, sending said order to the first warehouse for outbound and distribution; otherwise obtaining other warehouses that can reach said delivery address within an e-commerce preset time limit, using the other warehouses and the first warehouse as alternative warehouses, performing weighting calculation on all the alternative warehouses according to the outbound pressure, logistics pressure, and inventory quantity corresponding to said order in accordance with the proportional relationship with the corresponding preset threshold values, obtaining final scores of all the alternative warehouses, and sending said order to the alternative warehouse having the highest final score for outbound and distribution. According to the method, the phenomenon of distribution delay can be reduced.

Description

A method of outbound distribution for e-commerce intelligent warehousing

technical field

The invention relates to the technical field of intelligent warehousing, in particular to a delivery method for e-commerce intelligent warehousing.

Background technique

The intelligent warehousing system uses software technology, Internet technology, automatic sorting technology, light guide technology, radio frequency identification (RFID), voice control technology and other advanced technological means and equipment to control the entry and exit, storage, sorting, packaging, distribution and storage of items. Its information for effective planning, execution and control of logistics activities. It mainly includes: identification system, handling system, storage system, sorting system and management system.

Among them, in the planning process of the outbound and delivery of the intelligent warehousing system, the existing mode is based on the receiving address of the goods, based on the principle of delivery to the nearest warehouse, the nearest warehouse is selected for delivery, and the goods are delivered according to the generated cargo list. Out of the warehouse. However, the inventory quantity, delivery pressure, and logistics pressure of different warehouses in different periods are also different. Based on the principle of distribution to the nearest warehouse, some warehouses may have higher delivery pressure in some periods, which may be due to the inventory quantity. Insufficient deployment and excessive logistics pressure caused by too concentrated receiving addresses will eventually lead to extended delivery, which will greatly affect the customer's shopping experience. Therefore need a kind of better outbound distribution method to solve the above problems.

technical problem

The technical problem to be solved by the present invention is to provide a delivery method for e-commerce smart warehousing, so as to reduce the phenomenon of delivery extension.

technical solution

In order to solve the problems of the technologies described above, the technical solution adopted in the present invention is:

A method for outbound delivery for e-commerce intelligent warehousing, comprising:

Step S1. Obtain the order to be processed, obtain the first warehouse that can be delivered to the delivery address of the order to be processed the fastest, and judge the delivery pressure and logistics pressure of the first warehouse and the corresponding order Whether the inventory quantity of each is less than the corresponding preset threshold, if so, send the pending order to the first warehouse for outbound delivery, otherwise execute step S2;

Step S2. Obtain other warehouses that can deliver to the delivery address within the e-commerce preset time limit, use the other warehouses and the first warehouse as candidate warehouses, and store all the candidate warehouses According to the outbound pressure, the logistics pressure and the inventory quantity corresponding to the order to be processed according to the proportional relationship with the corresponding preset threshold, the final scores of all the candidate warehouses are obtained, and the order to be processed is calculated. Send to the alternative warehouse with the highest final score for outbound delivery.

Beneficial effect

The beneficial effect of the present invention lies in: a delivery method for e-commerce intelligent storage, if the first warehouse with the fastest delivery has no problems with delivery pressure, logistics pressure and corresponding inventory quantity, Assign pending orders to the first warehouse for outbound distribution to ensure their timeliness. And when the first warehouse exceeds the preset threshold in one of the indicators, it is considered that the warehouse is currently in an "overloaded" state, which may affect the delivery time. The warehouse is used as an alternative warehouse for weighted calculation, and the candidate warehouse with the highest final score is obtained for outbound delivery, so as to ensure delivery within the e-commerce preset time limit, and to avoid the occurrence of a certain warehouse's high outbound pressure and certain warehouses. Insufficient inventory in the first warehouse and high logistics pressure caused by too concentrated receiving addresses, so as to minimize the occurrence of delivery extensions.

Description of drawings

FIG. 1 is a schematic flowchart of a delivery method for e-commerce smart warehousing according to an embodiment of the present invention.

Embodiments of the present invention

In order to describe the technical content, achieved goals and effects of the present invention in detail, the following descriptions will be made in conjunction with the embodiments and accompanying drawings.

Please refer to Figure 1, an outbound delivery method for e-commerce smart warehousing, including:

It can be seen from the above description that the beneficial effect of the present invention is that if the first warehouse that is delivered the fastest has no problems with the outbound pressure, logistics pressure and corresponding inventory quantity, the order to be processed is allocated to the first warehouse Carry out outbound distribution to ensure its timeliness. And when the first warehouse exceeds the preset threshold in one of the indicators, it is considered that the warehouse is currently in an "overloaded" state, which may affect the delivery time. The warehouse is used as an alternative warehouse for weighted calculation, and the candidate warehouse with the highest final score is obtained for outbound delivery, so as to ensure delivery within the e-commerce preset time limit, and to avoid the occurrence of a certain warehouse's high outbound pressure and certain warehouses. Insufficient inventory in the first warehouse and high logistics pressure caused by too concentrated receiving addresses, so as to minimize the occurrence of delivery extensions.

Further, the step S1 specifically includes the following steps:

Obtain pending orders, and set all items of the pending orders as a collection of items to be shipped;

Obtain the first warehouse to be delivered to the delivery address of the order to be processed the fastest;

Classify the items in the set of items to be shipped whose inventory quantity in the first warehouse exceeds the corresponding preset threshold as the first sub-order, and classify other items in the set of items to be shipped as the second sub-order child order;

Judging whether the outbound pressure and logistics pressure of the first warehouse are both lower than the corresponding preset thresholds, if so, sending the first sub-order to the first warehouse for outbound delivery, and sending the second sub-order to the first warehouse for outbound delivery. The sub-order is used as the order to be processed to perform the step S2, otherwise the step S2 is directly performed;

When the second sub-order is used as the order to be processed to execute the step S2, the final score of the first warehouse in the step S2 is the score after the weighted calculation of the first warehouse and a score greater than 1 The score obtained after the product between the preset coefficients.

As can be seen from the above description, when a user's order includes many items, and all items may not be delivered in one warehouse, it is necessary to disassemble an order into multiple sub-orders according to the inventory quantity for different warehouses. Delivery out of the warehouse to ensure that all items can be delivered to customers in a timely manner.

Further, in the step S1, obtaining the first warehouse to be delivered to the delivery address of the order to be processed the fastest includes the following steps:

Calculate the delivery time from each warehouse to each logistics node in advance;

determining the final logistics node of the order to be processed based on the delivery address of the order to be processed;

The warehouse with the shortest delivery time among all the delivery times corresponding to the final logistics node is used as the first warehouse.

It can be seen from the above description that the positions of warehouses and logistics nodes are fixed, and the delivery time between each logistics node and warehouse is known in advance, so the final logistics node of the order to be processed can be determined directly and quickly according to the delivery address. The warehouse with the shortest arrival time, so that the warehouse with the fastest delivery can be obtained quickly and accurately.

Further, if the first delivery time of the first warehouse in the step S1 exceeds the e-commerce preset time limit, the time limit for obtaining other warehouses in the step S2 is replaced by the e-commerce preset time limit with the first e-commerce time limit. A delivery time plus the preset number of days.

From the above description, it can be seen that when the overall logistics pressure is high or there is a shortage of some goods, so that the delivery time cannot meet the e-commerce preset time limit, it is necessary to add the preset number of days to the shortest first delivery time Screen other warehouses to minimize delivery delays caused by the above phenomena.

Further, the order to be processed is a collection of a preset number of individual orders whose delivery address is the same logistics node.

Further, the order to be processed is a collection of all individual orders whose delivery address is the same logistics node within a preset time interval.

From the above description, it can be seen that according to different situations, choose time or quantity as the restriction condition, so as to collect the individual orders whose delivery address is the same logistics node as pending orders, so as to reduce the calculation pressure.

Further, both the outbound pressure and the logistics pressure of the candidate warehouse are lower than the corresponding pressure upper limit, and when the inventory quantity of a certain warehouse is 0 and item scheduling is required, the The time of item scheduling is added to the delivery time of the warehouse.

Among them, the pressure upper limit is the maximum processing capacity of the warehouse.

From the above description, it can be known that when the inventory quantity is 0, it is necessary to wait for the delivery time of the item dispatch, and the increased time on this basis can easily lead to exceeding the preset time limit of the e-commerce, thereby reducing the number of dispatches between warehouses as much as possible In order to reduce the pressure of outbound and logistics at the same time.

Further, before the step S1, it also includes:

During the preset peak period, obtain in advance the historical total sales data of the past years in the same peak period, the historical category sales data of each category, the hot-selling merchants corresponding to each historical hot-selling product, and each The historical preferential strength of the above-mentioned hot-selling merchants and the historical product reputation of each of the historical hot-selling products before the corresponding peak period;

Predict the current total sales data in the current peak period based on the total sales data in the past years, predict the current category sales data in the current peak period based on the historical category sales data in the past years, and predict the current category sales data in the current peak period according to the historical discount strength and The current preferential strength is calculated in proportion to obtain the preferential coefficient, and according to the current total sales data, the current category sales data and the preferential coefficient of each of the hot-selling merchants, the conversion of each of the hot-selling merchants in the current peak period is obtained. Forecast merchant sales data;

Taking the iterative product corresponding to the historical hot-selling product of each hot-selling merchant in the same peak period in the past years as the predicted hot-selling product, and according to the time before the current peak period of the preset hot-selling product According to the predicted sales data of merchants and the proportional coefficient between the current product reputation and the historical product reputation, the predicted product sales data of the predicted hot-selling products are obtained, and the judgment of the Whether the predicted product sales data exceeds the corresponding preset hot-selling threshold, if so, adding the predicted hot-selling product into the preset hot-selling set, otherwise ignoring the predicted hot-selling product;

Performing regional clustering analysis on the historical hot-selling products that are iteratively related to the predicted hot-selling products in the past years, so as to obtain the region corresponding to each of the predicted hot-selling products in each of the logistics nodes The historical sales ratio of the predicted hot-selling product is converted by the predicted product sales data of the predicted hot-selling product and the historical sales ratio of each of the logistics nodes, and the predicted hot-selling product corresponds to each of the said logistics nodes during the current peak period. The preset sales volume of logistics nodes;

Based on the relationship between the warehouse and the logistics node, the optimal warehouse corresponding to each logistics node is obtained according to the fastest delivery principle, and each warehouse is obtained for each of the predicted hot spots in the current peak period. The stocking quantity of sales products;

Based on the difference between the real-time inventory of each of the predicted hot-selling products in each warehouse and the required stocking quantity, advance cargo scheduling is performed, so that each of the predicted hot-selling products in each warehouse The real-time inventory of is within the allowable fluctuation range of the stocking quantity.

From the above description, it can be known that according to the sales data of the past years and the classification of different products, the predicted sales data of the corresponding categories can be obtained more accurately. On this basis, since merchants are constantly launching new products for iteration, the iterative series of products often have a strong correlation. The preset sales data of the product, and at the same time add the comparison of wind reviews between different generations of products in the iterative process, so as to adapt to the sales surge and slump caused by the change of word-of-mouth of different generations of products in the iterative process, so that the final product The preset sales quantity is more accurate and real; in this way, based on the more accurate preset sales quantity, according to the regional sales situation in the past years, it is possible to predict the delivery quantity of each warehouse in the upcoming current peak period for advance stocking , so that you can make better use of the outbound and logistics idleness before the peak period to reduce the outbound and logistics pressure during the peak period, and avoid the need for deployment or the need for farther warehouses for distribution due to inventory issues. The resulting delivery delays to reduce the occurrence of the phenomenon that the delivery time of goods in the current peak period exceeds the e-commerce preset time limit.

Further, the lower limit value of the allowable fluctuation range of the stocking quantity is [90%, 110%], and the upper limit value of the allowable fluctuation range of the stocking quantity is greater than 120%.

Please refer to Fig. 1, embodiment one of the present invention is:

Step S1. Obtain the pending order, obtain the first warehouse to be delivered to the delivery address of the pending order the fastest, and determine whether the output pressure, logistics pressure, and inventory quantity corresponding to the pending order of the first warehouse are all less than The corresponding preset threshold value, if yes, send the pending order to the first warehouse for outbound delivery, otherwise execute step S2;

In this embodiment, the order to be processed is a collection of individual orders whose delivery addresses are the same logistics node. In this case, the order to be processed can be a preset number of individuals whose delivery addresses are the same logistics node. The collection of orders can also be the collection of all individual orders whose delivery address is the same logistics node within a preset time interval. Or a combination of the two: that is, when the number of individual orders whose delivery address is the same logistics node reaches the preset number or the current duration has reached the preset time interval, they will be aggregated into a pending order. For example, if the preset quantity is 10 and the preset time interval is 1 minute, the time and quantity will be accumulated in real time. When the number of personal orders whose delivery address is the same logistics node reaches 10 or has passed 1 minutes, these 10 individual orders or all individual orders within this 1 minute will be regarded as a pending order.

Wherein, it should be noted that the preset threshold is a positive integer, and compared with directly using the inventory quantity as 0 as a judgment basis, a smaller quantity of inventory is reserved. At this time, when performing weighted calculations, the previous first warehouse is also taken into consideration. In some extreme cases, there is no other warehouse that can deliver to the delivery address to be delivered within the e-commerce preset time limit Or the overall outbound distribution of other warehouses is less optimistic than that of the first warehouse. At this time, it is necessary to return to the first warehouse for outbound delivery. In extreme cases, it cannot be delivered within the e-commerce preset time limit, thereby reducing the delay in delivery of goods in extreme cases. For example, if the inventory quantity of an item in the first warehouse is 3, which is just the preset threshold, the delivery will not be made first. If other warehouses can deliver within the e-commerce preset time limit, other warehouses will be allowed to deliver. When there is no other warehouse. If the time limit is met, the delivery can be made from the first warehouse. Therefore, the preset threshold corresponds to how many extreme situations can be eliminated.

In this embodiment, step S1 specifically includes the following:

Step S11, obtain the pending order, and set all the items of the pending order as a collection of items to be shipped;

Step S12, obtaining the first warehouse that is the fastest to deliver to the delivery address of the pending order;

Step S13, classify the items whose inventory quantity in the first warehouse exceeds the corresponding preset threshold in the set of items to be shipped as the first sub-order, and classify other items in the set of items to be shipped as the second sub-order;

Step S14. Determine whether the outbound pressure and logistics pressure of the first warehouse are both lower than the corresponding preset thresholds. If so, send the first sub-order to the first warehouse for outbound delivery, and take the second sub-order as pending Order to execute step S2, otherwise directly execute step S2.

Wherein, when step S2 is executed with the second sub-order as an order to be processed, the final score of the first warehouse in step S2 is obtained after the product of the weighted calculation score of the first warehouse and a preset coefficient greater than 1 get the score.

For example, if the default 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 by the optimal warehouse or the first warehouse. It is almost the same, but according to the value, it is handed over to the optimal warehouse for outbound, logistics and delivery, which is a waste of one outbound, logistics and delivery resources. As a result, the consumer’s material experience is not good. Therefore, it is more appropriate for the dismantled sub-orders of the same order to be delivered by the same warehouse when the distribution of other warehouses is not much different. On the one hand, it saves the cost of one delivery, logistics and delivery Resources, on the other hand, improve the logistics experience of consumers.

In this embodiment, step S12 specifically includes the following steps:

Determine the final logistics node of the order to be processed based on the delivery address of the order to be processed;

The warehouse with the shortest delivery time among all delivery times corresponding to the final logistics node is taken as the first warehouse.

Therefore, the positions of warehouses and logistics nodes are fixed, and the delivery time between each logistics node and warehouse is known in advance, so the delivery time can be directly and quickly obtained by determining the final logistics node of the order to be processed according to the delivery address The shortest warehouse in the warehouse, so as to get the fastest delivery warehouse quickly and accurately. For example, from warehouse A to the logistics outlet in city B, the shortest logistics time is obtained according to the existing route specification, so that before calculation, the time data of these warehouses and logistics outlets are known, and then the final logistics outlet can be used to know which warehouse is recent.

Step S2. Obtain other warehouses that can be delivered to the receiving address within the e-commerce preset time limit, use other warehouses and the first warehouse as candidate warehouses, and use all candidate warehouses according to the delivery pressure, logistics pressure and The inventory quantity corresponding to the order to be processed is weighted according to the proportional relationship with the corresponding preset threshold to obtain the final scores of all candidate warehouses, and the pending order is sent to the candidate warehouse with the highest final score for outbound delivery.

Among them, the preset time limit for e-commerce is the promised same-day delivery, next-day delivery, and so on. When the logistics pressure is small, the existing logistics system can basically deliver according to the e-commerce preset time limit.

Among them, if the first delivery time of the first warehouse in step S1 exceeds the e-commerce preset time limit, then in step S2, the time limit for obtaining other warehouses is replaced by the e-commerce preset time limit with the first delivery time plus the preset number of days .

At this time, it means that the overall outbound logistics pressure is high, which makes it impossible to deliver within the preset time limit of e-commerce. Naturally, it is necessary to increase the preset number of days for screening other warehouses, otherwise there will be no other warehouses. The preset number of days may be 1 day or 0.5 days.

Wherein, the outbound pressure and logistics pressure of the candidate warehouses including the first warehouse are both lower than the corresponding pressure upper limit, so the preset threshold referred to in step S1 is smaller than the pressure upper limit. And when the inventory quantity of a warehouse is 0 and item scheduling is required, the time for item scheduling is added to the delivery time of the warehouse. After all, for customers, the time span from placing an order to receiving the goods is the entire logistics time, and the time delay caused at this point in time will aggravate the logistics time, which needs to be overcome and considered by the merchant when delivering.

Please refer to Fig. 1, embodiment two of the present invention is:

A delivery method for e-commerce smart warehousing, on the basis of the first or second embodiment above, further includes before step S1:

Step S01. During the preset peak period, obtain in advance the historical total sales data of the past years in the same peak period, the historical category sales data of each category, the hot-selling merchants corresponding to each historical hot-selling product, each The historical preferential strength of the hot-selling merchants and the historical product reviews of each historical hot-selling product before the corresponding peak period;

Among them, the peak period of e-commerce, such as the existing "Double 11" and "618", is essentially because of greater discounts. Therefore, the discounts of various merchants have a direct impact on users' shopping desires. On this basis, the homogeneity of existing products is relatively serious, and the main functions of different products are often the same, and the reputation of iterative products will affect the choice of customers. One of the data sources for forecasting.

In this embodiment, the past years can be three to five years, and the hot-selling products can be judged by the number of the top ones in the list, or the number of sales can be considered as hot-selling products.

Step S02. Predict the current total sales data of the current peak period based on the total sales data of previous years, predict the current category sales data of the current peak period based on the historical category sales data of previous years, and predict the current discount intensity according to the historical and current discount intensity of the hot sellers Calculate the discount coefficient according to the current total sales data, the current category sales data and the discount coefficient of each hot-selling merchant to obtain the predicted sales data of each hot-selling merchant in the current peak period;

Therefore, according to the sales data of the past years and the classification of different products, the predicted sales data of the corresponding categories can be obtained more accurately. And balance the value according to the preferential coefficient to ensure the accuracy of predicting the sales data of the merchant.

Step S03. Take the iterative product corresponding to the historical hot-selling product of each hot-selling merchant in the same peak period in the past years as the predicted hot-selling product, and according to the current Product reputation, based on the predicted merchant sales data and the ratio coefficient between the current product reputation and historical product reputation, the predicted product sales data of the predicted hot-selling products is obtained, and it is judged whether the predicted product sales data exceeds the corresponding preset hot sales threshold , if so, add the predicted best-selling product to the preset hot-selling set, otherwise ignore the predicted best-selling product;

Since merchants are constantly launching new products for iteration, and the iterative series of products often have a strong correlation, on this basis, predict the preset sales of the current product of its iteration based on the hot-selling products of the past years Data, and at the same time add the comparison of wind reviews between different generations of products in the iteration process, so as to adapt to the sales surge and plunge caused by the changes of word of mouth in the iteration process of different generations of products, so that the final preset sales volume More accurate and real.

Among them, iterative products such as the iPhone series, iPad series, and iMac series of Apple mobile phones, etc., the computer is iterated every year according to the update of the processor or graphics card, and the clothing is iterated according to the different popular elements every year.

Step S04, perform regional clustering analysis on the historical hot-selling products whose predicted hot-selling products have an iterative relationship in the past years, so as to obtain the historical sales ratio of each predicted hot-selling product in the region corresponding to each logistics node, and predict The predicted product sales data of hot-selling products are converted with the historical sales ratio of each logistics node to obtain the preset sales volume of predicted hot-selling products corresponding to each logistics node during the current peak period;

Step S05. Based on the relationship between the warehouse and the logistics node, the optimal warehouse corresponding to each logistics node is obtained according to the principle of fastest delivery, and the stocking quantity of each predicted hot-selling product in each warehouse during the current peak period is obtained. ;

Therefore, based on the more accurate preset sales quantity, according to the regional sales situation in the past years, it is possible to predict the delivery quantity of each warehouse in the upcoming current peak period for advance stocking, so as to make better use of the peak period To reduce the pressure of outbound and logistics during the peak period, and to avoid the need for deployment or delivery delays caused by the need for further warehouses for distribution due to inventory issues, In order to reduce the occurrence of the phenomenon that the delivery time limit of goods in the current peak period exceeds the e-commerce preset time limit.

Step S06, based on the difference between the real-time inventory of each predicted hot-selling product in each warehouse and the required stocking quantity, carry out advance cargo scheduling, so that the inventory of each predicted hot-selling product in each warehouse The real-time inventory is within the allowable fluctuation range of the stocking quantity.

In this embodiment, the lower limit value of the allowable fluctuation range of the stocking quantity is [90%, 110%], and the upper limit value of the allowable fluctuation range of the stocking quantity is greater than 120%.

Please refer to Fig. 1, embodiment three of the present invention is:

A delivery method for e-commerce smart warehousing. On the basis of the second embodiment above, the lower limit of the allowable fluctuation range of the stocking quantity is [100%, 110%]. At this time, step S06 specifically includes the following :

Step S061: Count the preset total amount of the preset hot-selling products that can be stored in the warehouse before the current peak period, obtain the ratio A between the preset total amount and the predicted product sales quantity in the predicted product sales data, and judge Whether the ratio A is greater than or equal to the upper limit of the allowable fluctuation range, if so, execute step S062, otherwise judge whether the ratio A is greater than or equal to the lower limit of the allowable fluctuation range, if so, execute step S063, Otherwise, execute step S064;

Step S062, based on the difference between the real-time inventory of each predicted hot-selling product and the required stocking quantity in each warehouse and the ratio A, perform advance cargo scheduling, so that the real-time inventory and the required stocking quantity Warehouses with a ratio greater than the ratio A*[1,1.2] will dispatch the predicted hot-selling products to other warehouses, so that the real-time inventory and stocking quantity for each predicted hot-selling product in each warehouse The ratio of is close to the ratio A;

Among them, step S062 refers to, for example, the ratio A is 1.3, the lower limit of the allowable fluctuation range is 120%, and the above-mentioned [1,1.1] is 1.1, then for some warehouses, the real-time inventory and the required stocking quantity If the ratio exceeds 1.3*1.1, that is, if it exceeds 1.43, it needs to be dispatched to the warehouse where the ratio between the real-time inventory and the required stocking quantity is lower than 1.3. Finally, the ratio between the real-time inventory and the required stocking quantity in all warehouses The ratios are all close to 1.3.

Step S063, based on the difference between the real-time inventory of each predicted hot-selling product in each warehouse and the required stocking quantity, carry out advance cargo scheduling, so that the inventory of each predicted hot-selling product in each warehouse The real-time inventory is within the allowable fluctuation range of the stocking quantity;

Step S064, generate early warning information, limit the shopping quantity of the predicted hot-selling product in the area corresponding to the logistics network corresponding to each warehouse, and based on the real-time inventory and the total amount of each predicted hot-selling product in each warehouse. The difference between the required stocking quantity and the ratio A is used to pre-schedule goods, so that the ratio between the real-time inventory and the required stocking quantity is greater than the ratio A*[1,1.1] Warehouses will predict hot-selling products Scheduling to other warehouses, so that the ratio between the real-time inventory and the stocking quantity of each predicted hot-selling product in each warehouse is close to the ratio A;

Among them, step S064 refers to, for example, 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, the real-time inventory and the required stocking quantity If the ratio between the ratio exceeds 0.8*1.05, that is, if it exceeds 0.84, it needs to be dispatched to the warehouse whose ratio between the real-time inventory and the required stocking quantity is lower than 0.8. Finally, the ratio between the real-time inventory and the required stocking quantity in all warehouses The ratio between them is close to 0.8.

Therefore, through the above-mentioned goods scheduling, in the case of sufficient goods, each warehouse is guaranteed to have redundant capacity to reduce the situation of goods scheduling in the current peak period, and in the case of insufficient goods, the value coefficient Reduce the number of goods scheduling and ensure that each warehouse has appropriate goods for sale to balance each area, so as to better adapt to various situations during the current peak period.

To sum up, the present invention provides a delivery method for e-commerce smart warehousing, which predicts the delivery quantity of each warehouse in the upcoming peak period before the arrival of the peak period, and according to the quantity of goods According to the actual situation, different degrees of advance stocking are carried out to make better use of the outbound and logistics idleness before the peak period, to reduce the outbound and logistics pressure during the peak period, and to avoid the need for deployment or allocation due to inventory problems. Delivery delays caused by the need for further warehouses for distribution, in order to reduce the occurrence of the phenomenon that the delivery time of goods in the current peak period exceeds the e-commerce preset time limit. During the peak period, the orders to be processed are allocated to the first warehouse for outbound delivery to ensure their timeliness, and when the first warehouse exceeds the preset threshold in one of the indicators, it will be delivered at the e-commerce preset time The weighted calculation is performed on the candidate warehouses, and the candidate warehouse with the highest final score is obtained for outbound delivery, so as to ensure that the delivery can be delivered within the e-commerce preset time limit, and it can avoid the occurrence of a certain warehouse's high outbound pressure and a certain warehouse. Insufficient inventory in the warehouse and high logistics pressure caused by too concentrated receiving addresses to minimize the occurrence of delivery delays. At the same time, an order is disassembled into multiple sub-orders according to the inventory quantity for outbound distribution in different warehouses, so as to ensure that all items can be delivered to customers in time.

The above description is only an embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent transformations made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in related technical fields, are all included in the same principle. Within the scope of patent protection of the present invention.

Claims

A delivery method for e-commerce intelligent warehousing, characterized in that it includes:

Step S1. Obtain the order to be processed, obtain the first warehouse that can be delivered to the delivery address of the order to be processed the fastest, and judge the delivery pressure and logistics pressure of the first warehouse and the corresponding order Whether the inventory quantity of each is less than the corresponding preset threshold, if so, send the pending order to the first warehouse for outbound delivery, otherwise execute step S2;

Step S2. Obtain other warehouses that can deliver to the delivery address within the e-commerce preset time limit, use the other warehouses and the first warehouse as candidate warehouses, and store all the candidate warehouses According to the outbound pressure, the logistics pressure and the inventory quantity corresponding to the order to be processed according to the proportional relationship with the corresponding preset threshold, the final scores of all the candidate warehouses are obtained, and the order to be processed is calculated. Send to the candidate warehouse with the highest final score for outbound delivery.
The outbound distribution method for e-commerce smart warehousing according to claim 1, wherein the step S1 specifically includes the following steps:

Obtain pending orders, and set all items of the pending orders as a collection of items to be shipped;

Obtain the first warehouse to be delivered to the delivery address of the order to be processed the fastest;

Classify the items in the set of items to be shipped whose inventory quantity in the first warehouse exceeds the corresponding preset threshold as the first sub-order, and classify other items in the set of items to be shipped as the second sub-order child order;

Judging whether the outbound pressure and logistics pressure of the first warehouse are both lower than the corresponding preset thresholds, if so, sending the first sub-order to the first warehouse for outbound delivery, and sending the second sub-order to the first warehouse for outbound delivery. The sub-order is used as the order to be processed to perform the step S2, otherwise the step S2 is directly performed;

When the second sub-order is used as the order to be processed to execute the step S2, the final score of the first warehouse in the step S2 is the score after the weighted calculation of the first warehouse and a score greater than 1 The score obtained after the product between the preset coefficients.
According to claim 2, an outbound distribution method for e-commerce intelligent storage, characterized in that, in the step S1, the first delivery address to be delivered to the order to be processed is obtained the fastest The warehouse specifically includes the following steps:

Calculate the delivery time from each warehouse to each logistics node in advance;

determining the final logistics node of the order to be processed based on the delivery address of the order to be processed;

The warehouse with the shortest delivery time among all the delivery times corresponding to the final logistics node is used as the first warehouse.
According to claim 1, an outbound distribution method for e-commerce smart warehousing, characterized in that if the first delivery time of the first warehouse in the step S1 exceeds the e-commerce preset time limit, then the The time limit for obtaining other warehouses in step S2 is replaced by the e-commerce preset time limit with the first delivery time plus the preset number of days.
The outbound distribution method for e-commerce smart warehousing according to claim 1, wherein the order to be processed is a collection of a preset number of individual orders whose delivery address is the same logistics node.
The outbound distribution method for e-commerce intelligent warehousing according to claim 1, wherein the order to be processed is all personal orders whose delivery address is the same logistics node within a preset time interval gather.
The outbound distribution method for e-commerce intelligent storage according to claim 1, wherein the outbound pressure and the logistics pressure of the candidate warehouse are both lower than the corresponding upper pressure limit value, and when the inventory quantity of a certain warehouse is 0 and item scheduling is required, the time for item scheduling is added to the delivery time of the warehouse.
The outbound distribution method for e-commerce smart warehousing according to claim 7, characterized in that before the step S1, it also includes:

During the preset peak period, obtain in advance the historical total sales data of the past years in the same peak period, the historical category sales data of each category, the hot-selling merchants corresponding to each historical hot-selling product, and each The historical preferential strength of the above-mentioned hot-selling merchants and the historical product reputation of each of the historical hot-selling products before the corresponding peak period;

Predict the current total sales data in the current peak period based on the total sales data in the past years, predict the current category sales data in the current peak period based on the historical category sales data in the past years, and predict the current category sales data in the current peak period according to the historical discount strength and The current discount intensity is calculated in proportion to obtain the discount coefficient, and according to the current total sales data, the current category sales data and the discount coefficient of each of the hot-selling merchants, the conversion of each of the hot-selling merchants in the current peak period is obtained. Forecast merchant sales data;

Taking the iterative product corresponding to the historical hot-selling product of each hot-selling merchant in the same peak period in the past years as the predicted hot-selling product, and according to the time before the current peak period of the preset hot-selling product According to the predicted sales data of merchants and the proportional coefficient between the current product reputation and the historical product reputation, the predicted product sales data of the predicted hot-selling products are obtained, and the judgment of the Whether the predicted product sales data exceeds the corresponding preset hot-selling threshold, if so, adding the predicted hot-selling product into the preset hot-selling set, otherwise ignoring the predicted hot-selling product;

Performing regional clustering analysis on the historical hot-selling products that are iteratively related to the predicted hot-selling products in the past years, so as to obtain the region corresponding to each of the predicted hot-selling products in each of the logistics nodes The historical sales ratio of the predicted hot-selling product is converted by the predicted product sales data of the predicted hot-selling product and the historical sales ratio of each of the logistics nodes, and the predicted hot-selling product corresponds to each of the said logistics nodes during the current peak period. The preset sales volume of logistics nodes;

Based on the relationship between the warehouse and the logistics node, the optimal warehouse corresponding to each logistics node is obtained according to the fastest delivery principle, and each warehouse is obtained for each of the predicted hot spots in the current peak period. The stocking quantity of sales products;

Based on the difference between the real-time inventory of each of the predicted hot-selling products in each warehouse and the required stocking quantity, advance cargo scheduling is performed, so that each of the predicted hot-selling products in each warehouse The real-time inventory of is within the allowable fluctuation range of the stocking quantity.
According to claim 8, an outbound distribution method for e-commerce intelligent warehousing, characterized in that, the lower limit of the allowable fluctuation range of the stocking quantity is [90%, 110%], and the stocking quantity The upper limit of the allowable fluctuation range is greater than 120%.