KR20240050596A - Server, method and computer program for managing release of goods - Google Patents

Abstract

A server that manages the shipment of goods, comprising: an order reception unit that receives orders for goods loaded in a logistics warehouse; a calculation unit that calculates the picking time required to pick products corresponding to the received order from the warehouse; a picking selection unit that selects a picking method for shipping the product from the warehouse to the outside based on the calculated picking time; and an order processing unit that processes the order based on the selected picking method, wherein the calculation unit calculates the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data for each delivery destination. .

Description

Server, method, and computer program for managing the shipment of goods {SERVER, METHOD AND COMPUTER PROGRAM FOR MANAGING RELEASE OF GOODS}

The present invention relates to a server, method, and computer program for managing the shipment of goods.

A logistics center is a logistics facility that seeks to improve the efficiency of transportation and delivery in the physical distribution process where multiple suppliers and consumers exist. In other words, the logistics center is a base located between suppliers and consumers, has the concept of passing through products, and performs the function of replenishing products for the delivery center.

Figure 1 is a diagram to explain the process of shipping goods from a logistics center. Referring to FIG. 1, generally, a distribution center stores goods 100 received from a supplier by stacking them 110 on a shelf 120 within the center. When the logistics center receives an order from a consumer, the goods corresponding to the order are picked (130) from the goods stored (110) on the shelf (120), packed (140), and shipped to the outside (150).

Logistics centers repeatedly perform this process for physical distribution between suppliers and consumers. At this time, the most time is spent picking (130) the products corresponding to the consumer's order within the center. For example, in the entire process of shipping products (100%), the time to find products corresponding to the customer's order within the center is about 20%, the time to pick the found products (130) is about 15%, and the time to pack (140%) is about 15%. ), etc., takes about 15% of the time. That is, during the shipping process, about 50% of the time, or half of the time, is used for picking (130) and packing (140).

Generally, methods for picking items 130 within a center include order picking and batch picking. Order picking is a method of picking (130) products within the center according to the consumer's order and packing (140) for each delivery destination. Batch picking is a method where multiple orders contain the same product. This is a method in which center staff picks (130) matching products all at once. For example, batch picking is a pickup method used when there is sufficient order volume for a specific consumer (e.g., a specific store).

There are methods for picking items 130 within a center, such as order picking or batch picking, but each picking method has different characteristics, so a method of appropriate use according to the situation is needed. In other words, there is a need to find a way to most efficiently perform the picking (130) task within the logistics center.

Korean Patent Publication No. 10-2014-0141349 (published on December 10, 2014) Korean Patent Publication No. 10-1783586 (registered on September 25, 2017)

The present invention is intended to solve the problems of the prior art described above, and seeks to provide a product shipping management server, method, and computer program that can shorten the time required for picking and packing in the process of shipping products. Through this, we aim to provide a product delivery management server, method, and computer program that can efficiently perform the physical distribution process between suppliers and consumers.

However, the technical challenges that this embodiment aims to achieve are not limited to the technical challenges described above, and other technical challenges may exist.

As a means for achieving the above-described technical problem, an embodiment of the present invention provides a server for managing the shipment of goods, comprising: an order reception unit for accepting orders for goods loaded in a logistics warehouse; a calculation unit that calculates the picking time required to pick products corresponding to the received order from the warehouse; a picking selection unit that selects a picking method for shipping the product from the warehouse to the outside based on the calculated picking time; and an order processing unit that processes the order based on the selected picking method, wherein the calculation unit calculates the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data by delivery destination. In fact, a shipping management server can be provided.

Another embodiment of the present invention provides a method for managing the shipment of goods, comprising: receiving an order for goods loaded in a logistics warehouse; calculating a picking time required to pick an item corresponding to the received order from the warehouse; selecting a picking method for shipping the product from the warehouse to the outside based on the calculated picking time; and processing the order based on the selected picking method, wherein the step of calculating the picking time comprises the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data for each delivery destination. A delivery management method that calculates time can be provided.

Another embodiment of the present invention is a computer program stored in a computer-readable recording medium containing a sequence of instructions for managing the shipment of goods, wherein when the computer program is executed by a computing device, the goods loaded in the warehouse Receive an order for, calculate the picking time required to pick the product corresponding to the received order from the warehouse, and ship the product from the warehouse to the outside based on the calculated picking time. A command for selecting a picking method for shipping, processing the order based on the selected picking method, and calculating the picking time based on at least one of daily inventory data and stock keeping data (SKU: Stock Keeping Unit) for each delivery destination. It is possible to provide a computer program stored on a computer-readable recording medium, including a sequence of the above.

The above-described means for solving the problem are merely illustrative and should not be construed as intended to limit the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description of the invention.

According to any one of the means for solving the problems of the present invention described above, the method of picking items within the center can be selected according to the situation. Therefore, it is possible to provide a product shipping management server, method, and computer program that can efficiently shorten the time required for picking and packing in the process of shipping products.

Through this, it is possible to provide a product delivery management server, method, and computer program that can more efficiently perform the physical distribution process between suppliers and consumers.

Figure 1 is a diagram to explain the process of shipping goods from a logistics center.
Figure 2 is a configuration diagram of a product delivery management server.
Figure 3 is an example diagram for explaining variables related to order picking time.
Figure 4 is an example diagram for explaining variables related to batch picking time.
5A and 5B are exemplary diagrams of an example of average unit work time.
6A and 6B are exemplary diagrams of order picking times calculated by day.
7A and 7B are exemplary diagrams of batch picking times calculated by day.
Figure 8 is an exemplary diagram of order picking time and batch picking time calculated for each temperature range.
9A and 9B are exemplary diagrams comparing order picking time and batch picking time.
Figure 10 is an exemplary diagram of correlation analysis between daily inventory data and inventory data by delivery destination and order picking time and batch picking time.
Figure 11 is an exemplary diagram for explaining productivity for picking operations.
FIG. 12 is an exemplary diagram illustrating a process for assigning a rank to an order picking task based on urgency.
Figure 13 is a flowchart of a method for managing the shipment of goods.

Below, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts unrelated to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected," but also the case where it is "electrically connected" with another element in between. . In addition, when a part is said to "include" a certain component, this means that it does not exclude other components, but may further include other components, unless specifically stated to the contrary, and one or more other features. It should be understood that it does not exclude in advance the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

In this specification, 'part' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more pieces of hardware, and two or more units may be realized using one piece of hardware.

In this specification, some of the operations or functions described as being performed by a terminal or device may instead be performed on a server connected to the terminal or device. Likewise, some of the operations or functions described as being performed by the server may also be performed on a terminal or device connected to the server.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the attached drawings.

Figure 2 is a configuration diagram of a product delivery management server according to an embodiment of the present invention. Referring to FIG. 2 , the product delivery management server 200 may include an order reception unit 210, a calculation unit 220, a picking selection unit 230, and an order processing unit 240. Additionally, the calculation unit 220 may include a first calculation unit 221, a second calculation unit 222, and a third calculation unit 223. However, the above components 210 to 240 are merely exemplary illustrations of components that can be controlled by the product delivery management server 200.

Each component of the product delivery management server 200 of FIG. 2 is generally connected through a network. For example, as shown in FIG. 2, the order reception unit 210, the calculation unit 220, the picking selection unit 230, and the order processing unit 240 may be connected simultaneously or at time intervals.

Network refers to a connection structure that allows information exchange between nodes such as terminals and servers, including Local Area Network (LAN), Wide Area Network (WAN), and World Wide Area Network (WWW). Wide Web), wired and wireless data communication networks, telephone networks, wired and wireless television communication networks, etc. Examples of wireless data communication networks include 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth communication, infrared communication, and ultrasound. This includes, but is not limited to, communication, Visible Light Communication (VLC), LiFi, etc.

The product delivery management server 200 can select a method of picking products within the center according to the situation. For example, the product shipment management server 200 may select a picking method by comparing the total work time (pick activity duration time) required when order picking is selected and the total work time required when batch picking is selected. .

Through this, the product shipping management server 200 can optimize the movement path in the process of shipping products and minimize the user's order processing time. Therefore, the time required for picking and packing in the process of shipping products can be efficiently shortened. Hereinafter, we will look at each configuration of the product delivery management server 200.

The order reception unit 210 can receive orders for products loaded in the warehouse, and the calculation unit 220 can calculate the picking time required to pick products corresponding to the received order from the warehouse. You can. Here, the picking time may include at least one of the time to find the location where the product is stored within the center, the time to remove the product from the stored location, and the time to package the product for each delivery destination.

The calculation unit 220 may calculate the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data for each delivery destination. Here, the daily inventory data is the total number of orders received by date, and the inventory data by delivery destination is the total number of orders received by delivery destination.

The calculation unit 220 may include a first calculation unit 221 and a second calculation unit 222. The first calculation unit 221 can calculate the order picking time required for order picking, which performs the picking task on an order-by-order basis. For example, order picking is a method of processing one order per person, which ensures high accuracy in order processing. Order picking has the advantage of being able to quickly process orders of many types, varieties, and small quantities.

The first calculation unit 221 may calculate the order picking time based on at least one of roll container movement time, location scan time, barcode scan time, picking time, and shipping dock movement time.

The second calculation unit 222 can calculate the batch picking time required for batch picking in which picking is performed on a per-item basis. For example, batch picking is a method of processing a certain amount of orders at once, and is a method of picking large quantities at once.

The second calculation unit 222 performs batch picking based on at least one of forklift movement time, location scan time, barcode scan time, picking time, sorting barcode scan time, sorting picking time, roll container barcode scan time, and shipping dock movement time. Time can be calculated.

Additionally, the calculation unit 220 may calculate productivity for the picking operation based on the ratio between daily inventory data and inventory data for each shipping destination. Here, inventory data may be highly correlated with order picking and batch picking. Accordingly, the calculation unit 220 can calculate productivity for the picking operation through the ratio between daily inventory data and inventory data for each shipping destination.

And, the calculation unit 220 may further include a third calculation unit 223. The third calculation unit 223 may calculate the urgency level for processing the received order based on a preset urgency weight. For example, when order picking is selected, the third calculation unit 223 may calculate a weight according to a preset urgency level.

The third calculation unit 223 may assign an urgency weight based on at least one of customer importance ranking, order reception time, picking error standard, and processing time.

The picking selection unit 230 may select a picking method for shipping products from the warehouse to the outside based on the calculated picking time. The picking selection unit 230 may select at least one of order picking and batch picking based on the calculated order picking time and the calculated batch picking time.

Additionally, the picking selection unit 230 may select batch picking when the calculated productivity for the picking task is lower than a preset threshold. For example, the picking selection unit 230 may select a picking method based on at least one of order picking and batch picking based on daily inventory data and inventory data by shipping destination that have a very high correlation with order picking and batch picking. .

Additionally, when order picking is selected, the picking selection unit 230 may assign a priority to the picking task based on the calculated urgency. For example, the picking selection unit 230 may select order picking when the calculated productivity for the picking task is greater than or equal to a preset threshold. For another example, the picking selection unit 230 may select order picking for other reasons regardless of the calculated picking work productivity. At this time, the picking selection unit 230 can assign a priority to set the order picking order, and perform order picking according to the set order.

The order processing unit 240 may process orders based on the selected picking method. For example, the order processing unit 240 may process orders received according to at least one picking method among order picking and batch picking. The order processing unit 240 can improve the efficiency of the product shipping process by processing orders received through order picking or batch picking selected according to the situation.

Figure 3 is an example diagram for explaining variables related to order picking time. Referring to FIG. 3, the shipping management server 200 may receive a shipping order 300 for processing products corresponding to the received order. When processing the delivery order 300 through order picking, the delivery management server 200 may calculate the work time 305 required to perform order picking.

For example, in order to calculate the total work time (pick activity duration time, second) required for order picking, the shipping management server 200 calculates the activity time (activity time, second) for the behavior variable for performing order picking. can be calculated individually and added up.

Specifically, the shipping management server 200 is a behavioral variable for performing order picking, including roll container movement time 310, location scanning time 320, barcode scanning time 330, picking time 340, and shipping dock. Travel time 350 can be defined.

As shown in FIG. 3, the delivery management server 200 calculates the roll container movement time 310 as '(average movement distance between racks * sum of number of stock data (SKU) by delivery destination per day) * walking speed' It can be calculated based on .

The shipping management server 200 can calculate the location scan time 320 based on '(storage) location scan time * sum of the number of inventory data (SKU) by delivery destination per day', and the product barcode scan time 330 It can be calculated based on ‘(product) barcode scanning time * sum of number of inventory data (SKU) by delivery destination per day’.

The shipping management server 200 can calculate the picking time 340 based on ‘(worker) picking time * the sum of the number of inventory data (SKU) by delivery destination per day’, and the shipping dock movement time 350 can be calculated based on ‘shipping dock travel time 350’. It can be calculated based on ‘dock travel time * (average travel distance from the final rack to the shipping dock * daily delivery destination) * walking speed.

Figure 4 is an example diagram for explaining variables related to batch picking time. Referring to FIG. 4, the shipping management server 200 may receive a shipping order 400 for processing products corresponding to the received order. When processing the delivery order 400 through batch picking, the shipping management server 200 may calculate the work time 405 required to perform batch picking.

For example, the shipping management server 200 calculates the total work time (pick activity duration time, second) required for batch picking, and calculates the activity time (activity time, second) for the behavior variable for performing batch picking. can be calculated individually and added up.

Specifically, the shipping management server 200 may define behavioral variables for performing batch picking by broadly dividing them into total quantity picking (410) and sorting (420). Here, total quantity picking 410 may include forklift movement time 411, location scan time 412, barcode scan time 413, and picking time 414, and sorting 420 may include classification barcode scan time 421. ), sorting picking time (422), roll container movement time (423), and shipping dock movement time (424) may be included.

As shown in FIG. 4, the delivery management server 200 defines the forklift movement time 411 as '(average movement distance between racks * sum of number of stock data (SKU) by delivery destination per day) * forklift movement speed.' It can be calculated based on , and the location scan time 412 can be calculated based on '(storage) location scan time * sum of the number of inventory data (SKU) by delivery destination per day.'

The shipping management server 200 can calculate the barcode scanning time 413 based on '(product) barcode scanning time * the sum of the number of inventory data (SKU) by delivery destination per day', and the picking time 414 can be calculated as '( Worker) It can be calculated based on the sum of picking time * number of daily inventory data (SKU).

In addition, the shipping management server 200 can calculate the classification barcode scan time 421 based on '(classification) barcode scan time * sum of the number of inventory data (SKU) by delivery destination', and the classification picking time 422 can be calculated based on '(average sorting picking moving distance at the sorting station * sum of inventory data (SKU) number by delivery destination) * walking speed.'

The shipping management server 200 can calculate the rolltainer barcode scan time 423 based on ‘(rolltainer) location scan time * the sum of the number of inventory data (SKU) by delivery destination’, and the shipping dock time 424 can be calculated based on '(Average moving distance from shipping dock for each sorting station roll container*delivery destination/2)*walking speed.'

5A and 5B are exemplary diagrams of an example of average unit work time. Referring to FIGS. 5A and 5B, the shipping management server 200 may define the unit time of each behavioral variable to calculate order picking and batch picking.

First, referring to FIG. 5A, the delivery management server 200 may define the roll container movement time 500 as '3.0 per second'. This refers to the fact that the average route finding speed is 3km per hour.

In addition, the shipping management server 200 sets the location scan time 501 to '16.7 per second', the product barcode scan time 502 to '8.3 per second', and the picking time 503 to '8.3 per second', and sets the shipping dock to '8.3 per second'. The movement time 504 can be defined as '1.7 per second'.

Referring to Figure 5b, the shipping management server 200 may define the assumed moving distance 510 between shelves as '2.0' and the average moving distance 511 from the shelf to the shipping dock as '11.7'. there is.

In addition, the shipping management server 200 sets the average moving distance 512 of the roll container from the sorting yard to the shipping destination to '1.7' and the average moving distance 513 of the roll container from the sorting yard to the shipping dock to '3.3'. It can be defined as:

6A and 6B are exemplary diagrams of the average time 600 for each task and the order picking time 610 calculated for each day. Referring to FIGS. 6A and 6B, a table for the order picking time 610 calculated from April 1 to April 9 can be created.

For example, the shipping management server 200 can calculate the order picking time using Equation 1 below.

<Equation 1>

Order picking time =

In Equation 1, 'R' is the roll container movement time, and 'L' is the location scan time. And, 'B' is the product barcode scanning time, 'PK' is the picking time, and 'D' is the shipping dock movement time.

For example, the shipping management server 200 calculates the order picking time 610 by summing up the roll container movement time, location scanning time, product barcode scanning time, picking time, and shipping dock moving time required while performing order picking. It can be calculated.

Figures 7a and 7b are exemplary diagrams of daily order total data (700, 720) and batch picking times (710, 730) calculated on a daily basis. Referring to FIGS. 7A and 7B , a table for batch picking times 710 and 730 calculated from April 1 to April 9 can be created.

For example, the shipping management server 200 may calculate the batch picking time using Equation 2 below.

<Equation 2>

Batch picking time =

+

In Equation 2, 'F' is the forklift movement time, 'L' is the location scanning time, 'B' is the product barcode scanning time, and 'PK' is the picking time. And, 'SB' is the sorting barcode scanning time, 'SK' is the sorting picking time, 'RB' is the roll container barcode scanning time, and 'SD' is the moving time from the sorting yard to the shipping dock.

For example, the shipping management server 200 records the forklift movement time, location scan time, product barcode scan time, picking time, sorting barcode scan time, sorting picking time, roll container barcode scan time, and sorting time required while performing batch picking. Batch picking time can be calculated by adding up the travel time from each warehouse to the shipping dock.

Figure 8 is an exemplary diagram of order picking time and batch picking time calculated for each temperature range. Referring to FIG. 8, the shipping management server 200 can calculate the order picking time 800 and batch picking time 810 for 'frozen, refrigerated, and room temperature'.

Looking at the order picking time (800) and batch picking time (810) calculated by temperature range, the roll container movement time (820) accounts for the most in the order picking time (800), and the forklift movement takes up the largest portion in the batch picking time (810). Time (820) occupies the most.

And, when comparing the roll container movement time (820) of order picking and the forklift movement time (820) of batch picking, it can be seen that the roll container movement time of order picking takes about 3.5 times longer than the forklift movement time of batch picking. there is.

In other words, it can be seen that order picking takes more time than batch picking at both freezing, refrigeration, and room temperature.

Meanwhile, looking at the ratio of time spent on behavioral variables in each temperature range (830) in the order picking time (800) and batch picking time (810), it can be seen that similar patterns are observed at frozen, refrigerated, and room temperature. Therefore, it can be seen that classification according to temperature zone does not have a significant impact on the time required for order picking and the time required for batch picking.

9A and 9B are exemplary diagrams comparing order picking time and batch picking time. Referring to FIGS. 9A and 9B, it can be confirmed that there is a correlation between daily inventory data and inventory data by shipping destination and order picking time and batch picking time.

First, looking at the case (910) in which the ratio between daily inventory data and inventory data by shipping destination is highest in Figure 9a, if the date (900) is April 1 and the storage temperature (901) is refrigerated, the daily inventory data ( It can be seen that the sum of the number of SKUs (902) is 64, the sum of the number of inventory data by delivery destination (SKU) (903) is 500, and the ratio between inventory data by delivery destination and daily inventory data (904) is the largest at 7.8 times. there is. In addition, the ratio 905 between order picking time and batch picking time is also about 2.73 times, so it can be seen that there is a difference of about 2.73 times in work productivity between order picking and batch picking.

Meanwhile, looking at the case (920) where the ratio between daily inventory data and inventory data by shipping destination is lowest, when the date (900) is April 3 and the storage temperature (901) is room temperature, the sum of the number of daily inventory data ( 902) is 115, the sum of the number of inventory data by delivery destination (903) is 257, and the ratio between inventory data by delivery destination and daily inventory data (904) is the lowest at 2.23 times. In addition, the ratio 905 between order picking time and batch picking time is also about 1.44 times, so it can be seen that there is a difference of about 1.44 times in work productivity between order picking and batch picking.

Through this, it can be seen that there is a correlation between daily inventory data and inventory data by shipping destination with order picking and batch picking. Specifically, it can be determined that the productivity of each task shows a difference when order picking or batch picking is selected according to daily inventory data and inventory data by shipping destination.

That is, as shown in Figure 9a, it can be confirmed that daily inventory data and inventory data by delivery destination are factors that affect work productivity between order picking and batch picking.

In addition, looking at the case (930) in which the ratio between daily inventory data and inventory data by shipping destination is highest in Figure 9b, when the date (900) is April 8 and the storage temperature (901) is refrigerated, the daily inventory data ( It can be seen that the sum of the number of SKUs (902) is 73, the sum of the number of inventory data by shipping destination (SKU) (903) is 486, and the ratio between inventory data by shipping destination and daily inventory data (904) is the largest at 6.65 times. there is. In addition, the ratio 905 between order picking time and batch picking time is also about 2.60 times, so it can be seen that there is a difference in work productivity of about 2.60 times between order picking and batch picking.

Meanwhile, looking at the case where the ratio between daily inventory data and inventory data by shipping destination is lowest (940), when the date (900) is April 9 and the storage temperature (901) is refrigerated, the sum of the number of daily inventory data ( 902) is 39, the sum of the number of inventory data by delivery destination (903) is 91, and the ratio between inventory data by delivery destination and daily inventory data (904) is the lowest at 2.33 times. In addition, the ratio 905 between order picking time and batch picking time is also about 1.60 times, so it can be seen that there is a difference in work productivity of about 1.60 times between order picking and batch picking.

Likewise, in Figure 9b, it can be seen that the work productivity between order picking and batch picking varies depending on daily inventory data and inventory data by shipping destination.

In this way, through Figures 9a and 9b, the larger the ratio between inventory data by shipping destination and daily inventory data, the larger the ratio between order picking time and batch picking time, and the smaller the ratio between inventory data by shipping destination and daily inventory data, the greater the order picking time. It can be seen that the ratio between time and batch picking time is small. In other words, it can be statistically confirmed that the greater the number of inventory data by shipping destination compared to daily inventory data, the higher the work productivity efficiency in batch picking.

Figure 10 is an exemplary diagram of correlation analysis between daily inventory data and inventory data by shipping destination and order picking time and batch picking time. Referring to FIG. 10, as a result of performing a correlation analysis (1010) between daily inventory data and order picking time (1000) and batch picking time (1001), the correlation between daily inventory data and order picking time (1000) is approximately 49%, and the correlation between daily inventory data and batch picking time (1001) was approximately 86%.

In addition, as a result of performing a correlation analysis (1020) between inventory data by shipping destination and order picking time (1000) and batch picking time (1001), the correlation between inventory data by shipping destination and order picking time (1000) was approximately 99%. , the correlation between inventory data by shipping destination and batch picking time (1001) was approximately 75%. Meanwhile, as shown in Figure 10, it can be seen that the total work time (1000) of order picking is overall higher than the total work time (1001) of batch picking with respect to daily inventory data and inventory data by delivery destination (Figure see 1020 of 10).

Figure 11 is an exemplary diagram for explaining productivity for picking operations. Referring to FIG. 11, the order picking work time (1100) and the batch picking work time (1110) according to the ratio between daily inventory data and inventory data by shipping destination can be confirmed, respectively.

In Figure 11, the horizontal axis is the ratio between daily inventory data and inventory data by delivery destination, that is, productivity for the picking operation, and the vertical axis is the total work time required for the picking operation.

For example, the shipping management server 200 can calculate productivity for the picking task using Equation 3 below.

<Equation 3>

In Equation 3, P is the productivity for the picking operation. The shipping management server 200 may calculate productivity for the picking task based on the ratio between daily inventory data and inventory data by delivery destination.

As shown in FIG. 11, when the ratio between daily inventory data and inventory data by delivery destination is less than 30% (1120), it can be seen that the batch picking time is relatively short (1130). At this time, the shipping management server 200 may preset a ratio of 30% between daily inventory data and inventory data for each delivery destination as a threshold for selecting a batch picking method.

Through this, the shipping management server 200 selects the batch picking method when the ratio between daily inventory data and inventory data by shipping destination, that is, productivity for the picking task is less than 30%, thereby efficiently reducing the time required for the picking task. It can be shortened.

Meanwhile, the shipping management server 200 can select order picking as the picking method when the productivity for the picking task is 30% or more, and can strategically select order picking even when the productivity for the picking task is less than 30%. In this case, the shipping management server 200 can promote efficiency in the picking task by allocating a priority for performing order picking.

FIG. 12 is an exemplary diagram illustrating a process for assigning a rank to an order picking task based on urgency. Referring to FIG. 12, the delivery management server 200 may suggest order picking to be performed according to the priority assigned based on urgency.

Looking at the work efficiency of order picking according to priority through a total of three inspection/packing workers (1200, 1210, and 1220), when order picking is performed according to priority, the relative throughput rate of the picking work is about 19%. It can be seen that the time required for the picking operation is reduced by about 25% (1230).

Specifically, as shown in FIG. 12, the first inspection/packing worker 1200, the second inspection/packing worker 1210, and the third inspection/packing worker 1220 are all in the order of the time the order was received at the distribution center. It can be seen that the throughput rate of the picking work increases and the time required for the picking work is reduced when it is proposed to perform order picking according to priority rather than when order picking is assigned (FCFS: First-Come, First-Served). there is.

For example, the shipping management server 200 may calculate the urgency level based on a weight given to at least one of a preset urgency weight, for example, customer importance ranking, order reception time, picking error standard, and processing time. there is. The shipping management server 200 can calculate the urgency level using Equation 4 below.

<Equation 4>

In Equation 4, U _ik is the total urgency when the worker (k) picks the received order (i), and l is the index of the preset standard (e.g. l = 1, 2, 3, 4, 5). And, U _lik is the urgency level with respect to the preset standard (l) when the worker (k) picks the received order (i), that is, partial urgency. , Wi is the weight given to the preset criterion (l)

For example, the shipping management server 200 may set preset standards for customer importance ranking, order reception time, picking error standard, and processing time, for example, processing time standard (W ₁ ), residence time standard (W ₂ ), Each weight can be assigned according to the inspection/packing waiting standard (W ₃ ), picker error standard (W ₄ ), and customer importance standard (W ₅ ).

Specifically, the delivery management server 200 may assign weight to the customer importance ranking as 'W ₁ = 0.2, W ₂ = 0.1, W ₃ = 0.1, W ₄ = 0.1, W ₅ = 0.5', and order Regarding the reception time, weights can be assigned as 'W ₁ = 0.2, W ₂ = 0.4, W ₃ = 0.2, W ₄ = 0.1, W ₅ = 0.1'. In addition, the shipping management server 200 may assign weights to the picking error criteria as 'W ₁ = 0.3, W ₂ = 0.1, W ₃ = 0.1, W ₄ = 0.4, W ₅ = 0.1', and the processing time Weights can be assigned as 'W ₁ = 0.4, W ₂ = 0.3, W ₃ = 0.1, W ₄ = 0.1, W ₅ = 0.1'.

For example, looking at the process of allocating order picking to a worker based on priority according to urgency, the shipping management server 200 determines that when the worker completes order picking for the first order, the worker Order picking for the second order can be assigned to . At this time, the shipping management server 200 can assign a priority to each order based on the above-described urgency, and sequentially assign order picking according to the priority assigned to the worker.

Meanwhile, when there are multiple workers, order picking can be assigned to the worker who completed the picking task first based on priority. At this time, the shipping management server 200 may adjust the number of workers when the number of idle workers increases.

Specifically, the shipping management server 200 can identify the remaining orders at the time the worker completes the order picking task. The shipping management server 200 can calculate partial urgency for each remaining order.

The shipping management server 200 may calculate the total urgency for the remaining orders based on the calculated partial urgency. When the shipping management server 200 calculates the total urgency of all remaining orders, it can assign the order with the highest total urgency to the worker. The shipping management server 200 may repeatedly perform the above-described process until all remaining orders are assigned.

Figure 13 is a flowchart of a method for managing the shipment of goods. The product shipment management method shown in FIG. 13 includes steps processed in time series according to the embodiments shown in FIGS. 2 to 12. Therefore, even if the content is omitted below, it also applies to the method of managing the shipment of products in the product shipment management server according to the embodiment shown in FIGS. 2 to 12.

In step S1310, the product shipment management server may receive orders for products loaded in the logistics warehouse.

In step S1320, the product delivery management server may calculate the picking time required to pick the product corresponding to the received order from the warehouse.

In step S1330, the product shipping management server may select a picking method for shipping the product from the warehouse to the outside based on the calculated picking time.

In step S1340, the product shipment management server may process the order based on the selected picking method.

In the above description, steps S1310 to S1340 may be further divided into additional steps or combined into fewer steps, depending on the implementation example of the present invention. Additionally, some steps may be omitted or the order between steps may be switched as needed.

The method of managing the shipment of products in the product shipment management server described with reference to FIGS. 2 to 13 is a computer program stored in a computer-readable recording medium executed by a computer or in the form of a recording medium containing instructions executable by a computer. It can also be implemented as: In addition, the method of managing the shipment of products in the product shipment management server described with reference to FIGS. 2 to 13 may also be implemented in the form of a computer program stored in a computer-readable recording medium that is executed by a computer.

Computer-readable recording media can be any available media that can be accessed by a computer and includes both volatile and non-volatile media, removable and non-removable media. Additionally, computer-readable recording media may include computer storage media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.

The description of the present invention described above is for illustrative purposes, and those skilled in the art will understand that the present invention can be easily modified into other specific forms without changing the technical idea or essential features of the present invention. will be. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. For example, each component described as single may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. do.

200: Goods delivery management server
210: Order reception department
220: Calculation unit
230: Picking selection unit
240: Order processing department

Claims (20)

In the server that manages the shipment of goods,
An order reception department that accepts orders for goods loaded in a logistics warehouse;
a calculation unit that calculates the picking time required to pick products corresponding to the received order from the warehouse;
a picking selection unit that selects a picking method for shipping the product from the warehouse to the outside based on the calculated picking time; and
An order processing unit that processes the order based on the selected picking method
Including,
The calculation unit,
A shipping management server that calculates the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data for each delivery destination.
According to claim 1,
The daily inventory data is the total number of orders received by date, and the inventory data by shipping address is the total number of orders received by shipping address. A shipping management server.
According to claim 2,
The calculation unit,
A first calculation unit that calculates the order picking time required for order picking, which performs picking on an order basis; and
A second calculation unit that calculates the batch picking time required for batch picking where picking is performed on a per-item basis.
A shipping management server that includes a.
According to claim 3,
The first calculation unit is,
A shipping management server that calculates the order picking time based on at least one of roll container moving time, location scanning time, barcode scanning time, picking time, and shipping dock moving time.
According to claim 3,
The second calculation unit is,
Calculating the batch picking time based on at least one of forklift movement time, location scan time, barcode scan time, picking time, sorting barcode scan time, sorting picking time, roll container barcode scan time, and shipping dock movement time, Shipping management server.
According to claim 3,
The picking selection unit,
A shipping management server that selects at least one picking method of the order picking and the batch picking based on the calculated order picking time and the calculated batch picking time.
According to claim 2,
The calculation unit,
A shipping management server that calculates productivity for picking work based on the ratio between the daily inventory data and the inventory data for each shipping destination.
According to claim 7,
The picking selection unit,
If the calculated productivity for the picking task is lower than a preset threshold, the batch picking is selected.
According to claim 3,
The calculation unit,
A third calculation unit that calculates the urgency level for processing the received order based on a preset urgency weight.
Including more,
The third calculation unit is,
A shipment management server that assigns the urgency weight based on at least one of customer importance ranking, order reception time, picking error standard, and processing time.
According to clause 9,
The picking selection unit,
When selecting the order picking, the shipping management server assigns a rank to the picking task based on the calculated urgency.
In the method of managing the shipment of goods,
Receiving orders for goods loaded in a logistics warehouse;
calculating a picking time required to pick an item corresponding to the received order from the warehouse;
selecting a picking method for shipping the product from the warehouse to the outside based on the calculated picking time; and
Processing the order based on the selected picking method
Including,
The step of calculating the picking time is,
A shipment management method that calculates the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data for each delivery destination.
According to claim 11,
The step of calculating the picking time is,
Calculating the order picking time required for order picking, where picking work is performed on an order basis; and
Step of calculating the batch picking time required for batch picking where picking is performed on a per-item basis
A shipping management method comprising:
According to claim 12,
The step of calculating the order picking time is,
A shipping management method comprising calculating the order picking time based on at least one of roll container moving time, location scanning time, barcode scanning time, picking time, and shipping dock moving time.
According to claim 12,
The step of calculating the batch picking time is,
Comprising the step of calculating the batch picking time based on at least one of forklift movement time, location scan time, barcode scan time, picking time, sorting barcode scan time, sorting picking time, roll container barcode scan time, and shipping dock movement time. This is how to manage shipments.
According to claim 12,
The step of selecting the picking method is,
A shipping management method comprising selecting at least one picking method of the order picking and the batch picking based on the calculated order picking time and the calculated batch picking time.
According to claim 12,
The step of calculating the picking time is,
A shipment management method further comprising calculating productivity for a picking operation based on a ratio between the daily inventory data and the inventory data for each shipping destination.
According to claim 16,
The step of selecting the picking method is,
If the calculated productivity for the picking operation is lower than a preset threshold, the shipment management method further includes selecting the batch picking.
According to claim 12,
The step of calculating the picking time is,
Further comprising calculating the urgency for processing the received order based on a preset urgency weight,
The step of calculating the urgency is,
A shipment management method comprising assigning the urgency weight based on at least one of customer importance ranking, order reception time, picking error standard, and processing time.
According to claim 18,
The step of selecting the picking method is,
When selecting the order picking, the shipment management method further includes assigning a rank to the picking task based on the calculated urgency.
A computer program stored in a computer-readable recording medium containing a sequence of instructions for managing the shipment of goods, comprising:
When the computer program is executed by a computing device,
Receive orders for goods loaded in the warehouse,
Calculate the picking time required to pick products corresponding to the received order from the warehouse,
Selecting a picking method for shipping the product from the warehouse to the outside based on the calculated picking time,
Process the order based on the selected picking method,
A computer program stored in a computer-readable recording medium, comprising a sequence of instructions for calculating the picking time based on at least one of daily inventory data and stock keeping unit (SKU) data.