Warehouse system, storage bin division method, and storage bin integration method

Abstract

This warehouse system is connected to a database and comprises: an acquisition unit that acquires, from the database, work plan data related to product picking work in a warehouse and including order information related to a product; a derivation unit that derives, on the basis of the order information, a picking frequency indicating the frequency of the product picking work in a period of a predetermined range; and a determination unit that determines whether or not to divide storage bins used for the product picking work in the warehouse into at least two on the basis of the derived picking frequency.

Description

Warehouse system, storage bin division method, and storage bin integration method

[0001] The present disclosure relates to a warehouse system, a storage bin division method, and a storage bin consolidation method.

[0002] In recent years, automated warehouses for receiving and shipping packaged goods have become widespread. For example, Patent Literature 1 discloses a picking system that realizes a function for sorting pick instructions to workers and a function for dividing pick instructions.

[0003] Japanese Patent Application Laid-Open No. 2014-205552

[0004] The present disclosure has been devised in consideration of the above-described conventional situation, and aims to improve the operational efficiency of storage bins by adaptively dividing or merging storage bins that are transported to work sites within a warehouse.

[0005] The present disclosure provides a warehouse system connected to a database, the warehouse system including: an acquisition unit that acquires, from the database, work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a predetermined range of time based on the order information; and a decision unit that determines whether to divide a storage bin used for product picking work within the warehouse into at least two based on the derived picking frequency.

[0006] The present disclosure also provides a warehouse system connected to a database, the warehouse system including: an acquisition unit that acquires, from the database, work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a predetermined period based on the order information; and a decision unit that decides whether to consolidate storage bins used for product picking work within the warehouse based on the derived picking frequency.

[0007] The present disclosure also provides a storage bin division method executed by a processor and a memory working together, the storage bin division method including the steps of: acquiring, from a database, work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; deriving a picking frequency indicating the frequency of product picking work within a specified range of time based on the order information; and determining, based on the derived picking frequency, whether to divide a storage bin used for product picking work within the warehouse into at least two bins.

[0008] The present disclosure also provides a storage bin consolidation method executed by a processor and a memory working together, the storage bin consolidation method including the steps of: acquiring, from a database, work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; deriving a picking frequency indicating the frequency of the picking work within a predetermined period based on the order information; and determining, based on the derived picking frequency, whether to consolidate storage bins used for product picking work within the warehouse.

[0009] Any combination of the above components, and conversion of the expression of the present disclosure into a method, device, system, storage medium, computer program, etc., are also valid aspects of the present disclosure.

[0010] According to the present disclosure, the storage bins to be transported to the locations of the main work subjects within the warehouse can be divided or combined adaptively to improve the operational efficiency of the storage bins.

[0011] a block diagram showing an overall overview of a warehouse system according to the present embodiment; a block diagram showing an example of the hardware configuration of an information processing device according to the present embodiment; a schematic diagram showing an example of the configuration inside a warehouse according to the present embodiment; a data table showing an example of work plan data; a diagram schematically showing an example of storage bin integration processing; a flowchart chronologically showing an example of the operation procedure for generating an integrated bin list of storage bins before operation; a flowchart chronologically showing an example of the storage bin integration processing during operation; a diagram schematically showing a first example of the storage bin division processing; a diagram schematically showing a second example of the storage bin division processing; a flowchart chronologically showing an example of the operation procedure for generating a bin division list of storage bins before operation; a flowchart chronologically showing an example of the storage bin division processing during operation; a diagram schematically showing an example of the storage bin picking work after the division processing; a diagram schematically showing a first example of the division processing and integration processing when multiple types of products are stored in one storage bin;

[0012] (Background to the Disclosure) In warehouses where tasks such as receiving, storing, packaging, and shipping of goods are performed, automation using robots and the like is required to reduce the manual workload. With the recent increase in the volume of goods, further efficiency improvements in a series of tasks are required. In such warehouses, items (hereinafter referred to as "goods") with various shapes and attributes are handled, so it is necessary for humans or robots to share tasks and perform them in consideration of the characteristics of the goods. One such task is picking, based on order information indicating order information regarding shipment from the warehouse, from a storage box (hereinafter referred to as a "storage bin") in which goods are stored in the warehouse to a storage box (hereinafter referred to as a "shipping bin") that contains one or more goods to be shipped. Picking is typically performed at a location called a picking station. In this case, storage bins and shipping bins are arranged around the picking station, and a worker moves the corresponding goods from the storage bin to the shipping bin at the picking station. Note that in the following description, a shipping bin may contain not only one type of product, but also multiple different types of products.

[0013] However, when a picking operation is being performed at a picking station and the same product is to be picked at another picking station, the following problem occurs. That is, in the above-described use case, after the first picking operation is completed, the worker (e.g., a robot) at the next picking station must wait until the storage bin containing the product to be picked arrives at the next picking station. More specifically, suppose a first robot at a first picking station picks several products (e.g., apples) from a storage bin, and a second robot at a second picking station picks several of the same products (i.e., apples) from the storage bin. In this case, while the first robot is picking apples at the first picking station, the second robot at the second picking station cannot perform picking operations until the storage bin arrives from the first picking station, resulting in wasted wait time.

[0014] Therefore, in the following embodiments, examples of a warehouse system, a storage bin division method, and a storage bin consolidation method are described that adaptively divide or consolidate storage bins to be transported to the work site within the warehouse to improve the operational efficiency of the storage bins.

[0015] Hereinafter, with reference to the drawings as appropriate, detailed descriptions of embodiments that specifically disclose a warehouse system, a storage bin division method, and a storage bin integration method according to the present disclosure will be provided. However, unnecessary detailed descriptions may be omitted. For example, detailed descriptions of well-known matters or redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter recited in the claims.

[0016] (Embodiment) [System Configuration] First, with reference to FIG. 1, an example configuration of a warehouse system 1 according to this embodiment will be described. FIG. 1 is a block diagram showing an overview of the warehouse system 1 according to this embodiment. The system configuration shown in FIG. 1 is an example, and one system may be divided into multiple systems, or multiple systems may be integrated into one system. Furthermore, one system or device shown in FIG. 1 may be provided multiple times. Furthermore, the processing entities shown below are an example, and some of the functions of one system may be realized as functions of another system.

[0017] The warehouse system 1 includes a warehouse management system 100, a warehouse operations management system 200, warehouse control systems 300, 400, and 500, a robot 600, an automated warehouse 700, an automated guided vehicle 800, and an operation terminal 900. Each component of the warehouse system 1 is configured to be able to communicate with each other via a network.

[0018] The warehouse management system 100 is a system that manages and controls logistics within a warehouse, and is positioned at the highest level in the example configuration shown in FIG. 1 . The warehouse management system 100, for example, manages inventory management of items within the warehouse, as well as the inflow and outflow (receiving and shipping) and movement of items. The warehouse management system is sometimes referred to as a Warehouse Management System (WMS). The warehouse management system 100 may be configured to be able to communicate with external systems not shown in FIG. 1 and may acquire and manage instructions related to the inflow and outflow of goods, product information, information about warehouse equipment such as robots, information about workers, and the like.

[0019] The warehouse operations management system 200 is a system that manages and controls work within a warehouse, and in the example configuration shown in FIG. 1 , is positioned below the warehouse management system 100. The warehouse operations management system 200 is sometimes referred to as a Warehouse Execution System (WES). The warehouse operations management system 200 comprehensively controls the work content of various work entities within the warehouse and the equipment within the warehouse. For example, the warehouse operations management system 200 divides and combines storage bins transported to picking stations within the warehouse based on a picking frequency, which indicates the frequency of picking work for the products stored in those storage bins. The process of dividing and combining storage bins will be described in detail below.

[0020] Warehouse control systems 300, 400, and 500 are provided corresponding to various pieces of equipment in a warehouse, and manage and control these pieces of equipment. Warehouse control systems are sometimes referred to as Warehouse Control Systems (WCS). In the configuration example shown in Figure 1, the warehouse control systems 300, 400, and 500 are responsible for managing and controlling a robot 600, an automated warehouse 700, and an automated guided vehicle 800, respectively.

[0021] The robot 600 is equipped with, for example, an orthogonal mechanism and a multi-axis arm, and is configured to be able to hold products stored in storage bins at the tip of the arm. The configuration of the tip of the robot 600 may be, for example, a suction type or a multi-fingered hand grip type, and is not particularly limited. Multiple types of robots 600 may be installed in a warehouse to accommodate picking operations for various products. For example, the robot 600 is placed in a picking station (see FIG. 3) installed in the warehouse. In addition, a camera may be installed around the robot 600 to capture images of the surrounding area, particularly images of the bins placed in the vicinity.

[0022] The automated warehouse 700 is configured to accommodate one or more storage bins capable of storing products, and transports the storage bins to a desired location using an automated guided vehicle 800 in response to a request from the warehouse operations management system 200. Note that the transportation of the storage bins is not limited to using the automated guided vehicle 800. For example, the storage bins may be transported using a belt conveyor or the like. The automated warehouse 700 is configured, for example, with a lattice-shaped frame so that multiple storage bins can be stored. The storage bins may include not only storage bins that store products, but also empty storage bins that do not store products and storage bins for which products to be stored have not yet been determined. Note that the bins used in the automated warehouse 700 may include not only the storage bins described above, but also shipping bins that store products together when shipping them from the warehouse according to order information. The storage bins and shipping bins may have different shapes and dimensions, but are configured to be transportable by the automated guided vehicle 800. In the following description, they are described as having the same rectangular box-shaped configuration.

[0023] The automated guided vehicle 800 is a vehicle for transporting bins (e.g., storage bins or output bins) to a predetermined position within the automated warehouse 700 designated by the warehouse operations management system 200, and automatically moves or waits within the frame (see above) that constitutes the automated warehouse 700 based on instructions from the warehouse operations management system 200. The automated guided vehicle 800 may also be referred to as an automatic guided vehicle (AGV), for example. The automated guided vehicle 800 may be configured to detachably mount bins on its upper portion, or may be configured to transport bins by pushing or pulling them. The automated guided vehicle 800 is configured to include, for example, a traveling unit for performing operations related to movement, sensors for acquiring peripheral information, and a communication unit for transmitting and receiving data to and from the warehouse control system 500.

[0024] The operation terminal 900 is an information processing device configured to be usable by workers (people) inside and outside the warehouse. The operation terminal 900 may be, for example, a stationary information processing device such as a personal computer (PC), or may be a mobile terminal such as a tablet terminal, a POS terminal, a handheld terminal, or a smartphone. The operation terminal 900 transmits and receives data to and from the warehouse management system 100, the warehouse operations management system 200, and the warehouse control systems 300, 400, and 500, and is used to receive notifications from each system and to configure and operate each system.

[0025] [Hardware Configuration Example] Next, with reference to FIG. 2 , an example of the hardware configuration of an information processing device constituting the warehouse system 1 according to the present embodiment will be described. FIG. 2 is a diagram showing an example of the hardware configuration of the information processing device according to the present embodiment. The information processing device of FIG. 2 can be applied to the configuration of each system shown in FIG. 1 (specifically, the warehouse management system 100, the warehouse operations management system 200, and the warehouse control systems 300, 400, and 500). Each system shown in FIG. 1 may be configured as an on-premise server device at a base where a warehouse is located, or may be configured as a cloud-based system on a network. Here, the configuration of the devices constituting each system will be described as being the same; however, some components may be omitted or other components may be added depending on the functions to be provided. An example of a component that may be added is a barcode reader used to acquire information from barcodes attached to products or bins in the warehouse.

[0026] The information processing device 10 includes a processing device 11, a storage device 12, a communication device 13, an input device 14, an image acquisition device 15, an external interface (I / F) 16, and a display device 17. Each component is configured to be able to communicate with each other via an internal interface 18.

[0027] The processing device 11 may be configured using, for example, a Central Processing Unit (CPU), a Graphical Processing Unit (GPU), a Micro Processing Unit (MPU), a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), etc. The processing device 11 realizes various functions described below by, for example, referring to various data stored in the storage device 12 or reading out programs.

[0028] The storage device 12 is a storage unit for storing various data, programs, etc., and may be composed of volatile / non-volatile storage devices such as Random Access Memory (RAM), Read Only Memory (ROM), Hard Disk Drive (HDD), Solid State Drive (SSD), etc.

[0029] The communication device 13 is an interface for communicating with external devices via the network 35. There are no particular limitations on the communication standards that can be supported by the communication device 13, and the communication standards may be wired or wireless. The communication device 13 may also be capable of supporting multiple communication standards. Therefore, the network 35 may be configured by combining networks based on multiple communication standards.

[0030] The input device 14 receives operations and instructions from users of the warehouse (for example, managers and workers). The input device 14 may be configured with a mouse, a keyboard, a touch panel display, and the like.

[0031] The image acquisition device 15 is an interface for acquiring images of the warehouse interior taken by cameras 30 placed throughout the warehouse, or images of storage bins or shipping bins, etc. taken by cameras 30 installed on robots placed at picking stations, etc.

[0032] The external interface 16 is an interface for communicating with an external system 20. The information processing device 10 may be communicably connected to the external system 20 via the communication device 13 and the network 35. The external system 20 is not limited to the systems shown in FIG. 1 (i.e., the warehouse management system 100, the warehouse operations management system 200, and the warehouse control systems 300, 400, and 500), but may be another system.

[0033] The display device 17 displays to the user various user interfaces mainly generated by the processing device 11. The display device 17 may be configured with a liquid crystal display, a touch panel display, a lamp, or the like.

[0034] [Warehouse Overview] Next, an overview of a warehouse in which the warehouse system 1 according to this embodiment is installed will be described with reference to Fig. 3. Fig. 3 is a conceptual diagram showing an example of the configuration inside the warehouse of the warehouse system 1 according to this embodiment. An automated warehouse 700 and one or more robots 600 are installed inside the warehouse. Although not shown in Fig. 3, multiple automated guided vehicles 800 are arranged in a drivable manner inside the warehouse or the automated warehouse 700 to transport bins such as storage bins to the positions of workers 850 or robots 600.

[0035] A picking station 830 is configured around the automated warehouse 700 where a worker 850, who is the main operator of the picking work, works. A picking station is also provided where the robot 600 works. At the picking station, the main operator (specifically, the robot 600 or the worker 850) performs the work of transferring products from storage bins 810 to shipping bins 820 (i.e., picking work). From the perspective of improving the efficiency of the overall shipping work, if it is desirable to temporarily wait around the picking station instead of moving the bins into the automated warehouse 700, a waiting area for waiting the bins may be provided.

[0036] In the example of FIG. 3, one picking station where a human worker 850 works and one picking station where a robot 600 works are provided, but this is not limited to this. A warehouse may have multiple areas for each picking station. Alternatively, only one picking station where a human works and only one picking station where a robot works may be provided. Furthermore, although an example is shown in which a robot 600 equipped with an orthogonal mechanism is installed as the robot 600, a robot equipped with a multi-axis arm may also be installed.

[0037] [Work Plan Data] Next, the work plan data stored in the memory (see, for example, the storage device 12 in FIG. 2) of the warehouse operations management system 200 according to this embodiment will be described with reference to FIG. 4. FIG. 4 shows a data table TBL1 illustrating an example of the work plan data. The data in this data table TBL1 may be stored in the warehouse management system 100 and then temporarily stored in the storage device 12 of the warehouse operations management system 200 via data communication, or may be stored in advance in the storage device 12 of the warehouse operations management system 200.

[0038] Data table TBL1 is configured to have work plan data consisting of one record for each piece of order information identified by an order ID (i.e., various data for one piece of order information), and has work plan data for each of multiple pieces of order information. Each piece of order information is linked to one record of work plan data consisting of the following items: picking date and time, product, number of items picked, storage bin ID, shipping bin ID, and completion flag. Note that the items included in one record are not limited to those listed in FIG. 4; some of the items shown in FIG. 4 may be omitted, or other items may be added to the items shown in FIG. 4.

[0039] The order ID is identification information for one order registered in the warehouse management system 100. The picking date and time indicates the date and time when picking work for the order ID was actually performed or is scheduled to be performed in the future. The product indicates the product that will be the target of picking work for the order ID. The number of picks indicates the number of products specified by one order ID to be picked (i.e., the number of products to be transferred from the storage bin with the specified storage bin ID to the shipping bin with the specified shipping bin ID). The storage bin ID is identification information for the storage bin used in picking work for the order ID. The shipping bin ID is identification information for the shipping bin used in picking work for the order ID. The completion flag is information indicating whether picking work for the order ID has been completed, with "1" indicating "completed" and "0" indicating "incomplete." "Incomplete" can mean either "picking is currently being performed" or "picking work has not yet begun."

[0040] As shown in Figure 4, data table TBL1 includes work plan data for each order ID for a certain period of time in the past, based on the present, and work plan data for each order ID for a certain period of time in the future, based on the present. In the following description, the former data may be referred to as "work performance data." Note that in this specification, the past work performance data and future work plan data may be collectively referred to as "work plan data."

[0041] [Storage Bin Integration] Next, with reference to Figures 5 to 7, the process of integrating storage bins transported from the automated warehouse 700 to the picking station (see Figure 3) by the warehouse operations management system 200 according to this embodiment will be described. Figure 5 is a diagram schematically showing an example of the storage bin integration process. Figure 6 is a flowchart chronologically showing an example of the operational procedure for generating an integrated bin list of storage bins before operation. Figure 7 is a flowchart chronologically showing an example of the storage bin integration process during operation. Each process in Figures 6 and 7 is executed mainly by the processing device 11 (see Figure 2) of the warehouse operations management system 200 in cooperation with the storage device 12.

[0042] The processing device 11 acquires work plan data (see FIG. 4) related to the picking work of a product (e.g., apples shown in FIG. 5) in a warehouse for a predetermined period (e.g., in units of hours or days) immediately prior to the present. The processing device 11 derives the picking frequency of the product based on the acquired work plan data for the product. If the processing device 11 determines that the picking frequency is likely to decrease as a result of deriving the picking frequency, it decides to consolidate the storage bins used for picking work at the picking station for that product.

[0043] A case where the picking frequency of products (apples) is likely to decrease refers to a case where the derived value of the picking frequency (usage frequency) of the divided storage bins predicted from the work plan data decreases. Here, "decreasing" refers to a state where the picking frequency remains below a predetermined lower limit (usage frequency integration threshold) for a certain period of time, which determines whether or not to integrate the bins. In this case, the processing device 11 determines to integrate the two storage bins B2 and B3 into one storage bin B1. Based on this decision, the processing device 11 also generates a bin integration list that includes instructions to integrate the two products (apples) in storage bin B2 and the one product (apple) in storage bin B3, thereby storing three products (apples) in one storage bin B1. This bin integration list will be described later with reference to FIG. 6 .

[0044] The processing device 11 may also determine to consolidate the storage bins used for picking operations at the picking station for a particular product, not only when the picking frequency is likely to decrease as described above, but also when the number of products in the divided storage bins targeted for picking operations specified in the current (today's) order information becomes low, such as when storage bin B2 contains two products (apples) and storage bin B3 contains one product (apple) (see Figure 5). In this example, to determine whether the number of products is low, a predetermined lower limit for the number of products (product number consolidation threshold) that determines whether or not to consolidate may be set, and the necessity of consolidation may be determined based on this threshold. In this case, the processing device 11 determines whether or not to consolidate products by taking into account the arrival dates of each product contained in each storage bin to prevent the mixing of products with different expiration dates. This allows the processing device 11 to avoid consolidating products with different expiration dates and appropriately determine whether or not to consolidate storage bins.

[0045] The process shown in Figure 6 is a series of operational procedures for generating a bin integration list for integrating storage bins using the warehouse operations management system 200 from work plan data for the most recent specified period during the preparation stage before the warehouse system 1 begins actual operation.

[0046] 6, the processing device 11 acquires bin information, including the current storage bin usage status in the warehouse, based on various data input from the warehouse management system 100 and warehouse control systems 300, 400, and 500 (step St1). This bin information includes various data, such as the product currently being divided, the number of storage bins being used for division, and the upper limit of the number of storage bins that can be used for division. The processing device 11 references the storage device 12 or the warehouse management system 100 and acquires work plan data (see FIG. 4) related to product picking work in the warehouse for a predetermined period (e.g., in units of several hours or days) immediately preceding the present (step St2).

[0047] The processing device 11 references the warehouse management system 100 and acquires product information for the products to be picked (step St3). The product information here includes various data, such as the JAN code, the time of entry, the expiration date, the expiration date, physical parameters (e.g., size, weight, and shape), the number of products, and the storage bin ID of the storage bin in which the products are stored. The JAN code is a globally recognized product identification code that identifies which business owns which product. The processing device 11 references the warehouse management system 100 and acquires robot information for the robots deployed at the picking stations in the warehouse (step St4). The robot information here includes various data, such as the number of robots deployed at one or more picking stations in the warehouse, the specifications of each robot, and a picking score (see FIG. 12 ) indicating the robot's success rate in picking each product. The processing device 11 may omit steps St3 and St4.

[0048] Based on the order information included in the work plan data acquired in step St2 (e.g., work plan data associated with the order ID shown in FIG. 4 ), the processing device 11 derives a picking frequency, which indicates the frequency of product picking operations in the warehouse over a predetermined period (e.g., several hours or days) relative to the present (step St5). The processing device 11 also references the warehouse management system 100 to acquire work performance data for a certain period in the past relative to the present, or work plan data for a certain period in the future (step St6), which is different from the predetermined period (e.g., several hours or days). The processing device 11 then predicts the future picking frequency of the products to be picked based on the picking frequency derived in step St5 and the work performance data or work plan data acquired in step St6 (step St7). The future prediction here refers to, for example, a time-series distribution of the picking frequency, which indicates the future trend (e.g., whether the picking frequency will increase or decrease) in light of the past work performance data or future work plan data. It should be noted that future prediction does not have to be limited to the time series distribution of this picking frequency.

[0049] The processing device 11 determines whether to consolidate storage bins based on a comparison between the results of the picking frequency prediction performed in step St7 (e.g., the time-series distribution of picking frequencies) and a pre-specified consolidation threshold indicating the need for consolidation (see the above-mentioned use frequency consolidation threshold) (step St8). For example, if the processing device 11 determines that the time-series distribution indicating the change in picking frequency is less than the use frequency consolidation threshold (step St8, YES), the processing device 11 determines to consolidate storage bins used for picking operations at picking stations installed in the warehouse. Based on this determination result (i.e., the result of consolidating multiple storage bins containing the same product to be picked into a single storage bin), the processing device 11 adds various data, such as product information, storage bin IDs of the storage bins before consolidation, and storage bin IDs of the storage bins after consolidation, to the order ID, and updates the bin consolidation list and stores it in the storage device 12 (step St9). On the other hand, if the processing device 11 determines that the time series distribution showing the change in picking frequency is equal to or greater than the threshold value for whether or not to consolidate the usage frequency (step St8, NO), it determines that there is no need to consolidate the storage bins and terminates the processing of Figure 6.

[0050] The process shown in FIG. 7 is an operational procedure in which the warehouse operations management system 200 consolidates storage bins in accordance with the bin consolidation list generated by the process shown in FIG. 6 during actual operation of the warehouse system 1.

[0051] 7, the processing device 11 refers to the storage device 12 and acquires the bin consolidation list stored in the storage device 12 in step St9 shown in FIG. 6 (step St11). Using the bin consolidation list acquired in step St11, the processing device 11 generates bin consolidation plan data for a predetermined period of time based on the work plan data used to generate the bin consolidation list (step St12). This bin consolidation plan includes information suggesting, for example, when to consolidate storage bins, specifically, information such as when to consolidate storage bins during off-peak hours, such as nighttime or daytime hours, and when to interrupt the current picking operation to consolidate storage bins if this is effective. Using the bin consolidation plan generated in step St12, the processing device 11 instructs each warehouse control system 300, 400, 500 to execute processing (e.g., consolidating storage bins and picking operations targeting the consolidated storage bins) in accordance with the bin consolidation plan (step St13).

[0052] [Storage Bin Division] Next, with reference to Figures 8A to 10, the division process of storage bins transported from the automated warehouse 700 to the picking station by the warehouse operations management system 200 according to this embodiment will be described. Figure 8A is a diagram schematically showing a first example of the storage bin division process. Figure 8B is a diagram schematically showing a second example of the storage bin division process. Figure 9 is a flowchart chronologically showing an example of the operational procedure for generating a bin division list for storage bins before operation. Figure 10 is a flowchart chronologically showing an example of the storage bin division process during operation. Each of the processes in Figures 9 and 10 is executed mainly by the processing device 11 (see Figure 2) of the warehouse operations management system 200 in cooperation with the storage device 12.

[0053] The processing device 11 acquires work plan data (see FIG. 4 ) related to the picking work of a product (e.g., apples shown in FIGS. 8A and 8B ) in a warehouse for a predetermined period (e.g., in units of hours or days) immediately prior to the present. The processing device 11 derives the picking frequency of the product based on the acquired work plan data for the product. If the processing device 11 determines that the picking frequency will be high as a result of deriving the picking frequency, it decides to divide the storage bins used for picking work at the picking station for that product into at least two.

[0054] An increase in the picking frequency of a product (apples) refers to an increase in the derived value of the picking frequency (usage frequency) predicted from the work plan data. Here, "increase" refers to, for example, a state in which the picking frequency exceeds a predetermined threshold (division necessity threshold) that determines whether or not division is necessary for a certain period of time. In this case, the processing device 11 determines to divide one storage bin B1 into two storage bins B2a (B2b) and B3a (B3b). In response to this determination, the processing device 11 generates a division / consolidation list that includes instructions to divide the nine products (apples) in storage bin B1 into storage bin B2a (B2b) containing five or seven products (apples) and storage bin B3a (B3b) containing four or two products (apples). This division / consolidation list will be described later with reference to FIG. 9 . This enables the warehouse operations management system 200 to have operators perform picking work for the same product at the same time at different picking stations during actual operation of the warehouse system 1, thereby improving the efficiency of picking work.

[0055] Furthermore, the processing device 11 may determine to divide the storage bin to be used for the picking operation at the picking station for that product not only when the picking frequency described above increases, but also when the number of products in the storage bin to be picked as specified in the current (today's) order information is large, such as when storage bin B1 contains nine products (apples) as shown in Figures 8A or 8B. In this example, in order to determine whether the number of products is large or not, for example, a predetermined threshold value for the number of products (product number division necessity threshold) that determines whether division is necessary may be set separately, and whether division is necessary may be determined based on the threshold value.

[0056] Furthermore, if the processor 11 of the warehouse operations management system 200 determines that a product has a consistently high picking frequency calculated for a certain period of time in the past or a certain period of time in the future (see above) relative to the present, the processor 11 determines to divide the product in equal proportions. "Continuously high" here refers to a state in which the picking frequency calculated for a predetermined period of time is equal to or greater than a predetermined threshold. This period of time may be the same as the most recent predetermined range of time described above, or may be a predetermined period shorter than that period. For example, as shown in FIG. 8A , the processor 11 of the warehouse operations management system 200 divides nine products (apples) stored in one storage bin B1 into two storage bins B2a, each containing five products (apples), and another storage bin B3a, each containing four products (apples). While this example illustrates division into two storage bins, the processor 11 of the warehouse operations management system 200 may divide the product into three or more storage bins if there are any empty storage bins available for division throughout the warehouse. As a result, the warehouse operations management system 200 can improve the efficiency of picking operations throughout the warehouse by updating the work plan so that picking operations for products that have been ordered continuously for more than a certain period of time can be performed at the same time at different picking stations.

[0057] Furthermore, if the processor 11 of the warehouse operations management system 200 determines that a product has a uniquely high picking frequency derived for a certain period in the past or a certain period in the future (see above) relative to the present, the processor 11 determines to divide the product in a quantitatively biased manner. Here, "uniquely high" refers to a state in which the derived picking frequency is equal to or greater than a predetermined threshold value at some point during a recent, predetermined period. For example, as shown in FIG. 8B , the processor 11 of the warehouse operations management system 200 divides nine products (apples) stored in one storage bin B1 into a storage bin B2b containing seven products (apples) and a storage bin B3b containing two products (apples) in a quantitatively biased manner. Note that although an example of dividing the product into two storage bins is shown here, the processor 11 of the warehouse operations management system 200 may divide the product into three or more storage bins if there are any empty storage bins available for division throughout the warehouse. As a result, the warehouse operations management system 200 can improve the efficiency of picking operations throughout the warehouse by updating the work plan so that picking operations for items that have received a one-off order of more than a specified threshold at some point during the most recent specified period can be carried out quickly, for example, by biasing the picking operations to a picking station with a robot that is good at picking those items.

[0058] The process shown in Figure 9 is a series of operational procedures for generating a bin division list for dividing storage bins by the warehouse operations management system 200 from work plan data for the most recent specified range of time during the preparation stage before the actual operation of the warehouse system 1 begins.

[0059] In FIG. 9, the processing device 11 refers to the storage device 12 or the warehouse management system 100 and acquires work plan data (see FIG. 4) regarding product picking work in the warehouse for a specified period (e.g., in units of several hours or days) immediately prior to the present (step St21).

[0060] The processing device 11 references the warehouse management system 100 to acquire product information for the products to be picked (step St22). The product information here includes various data, such as the JAN code, the time of entry, the expiration date, the expiration date, physical parameters (e.g., size, weight, and shape), the number of products, and the storage bin ID of the storage bin in which the products are stored. The JAN code is a globally recognized product identification code that identifies the business and product of each company. The processing device 11 references the warehouse management system 100 to acquire worker information for the workers assigned to the picking stations in the warehouse and robot information for the robots (step St23). The worker information here includes various data, such as the number, name, gender, and age of the workers assigned to one or more picking stations in the warehouse, and the worker's picking proficiency level for each product. The robot information here also includes various data, such as the number of robots assigned to one or more picking stations in the warehouse, the specifications of each robot, and a picking score (see FIG. 12 ) indicating the robot's success rate for picking each product. The processing device 11 may omit the processes of steps St23 and St24.

[0061] Based on the order information included in the work plan data acquired in step St22 (e.g., the work plan data associated with the order ID shown in FIG. 4 ), the processing device 11 derives a picking frequency indicating the frequency of product picking operations in the warehouse within a predetermined period (e.g., in units of hours or days) immediately preceding the present (step St24). The processing device 11 determines whether the value of the picking frequency derived in step St24 is equal to or greater than the division necessity threshold (see above), and determines the number of divisions of the storage bin based on data table TBL2 (step St25). Note that if it is determined that the number of divisions of the storage bin is 1 (NO in step St25), the processing device 11 ends the process shown in FIG. 9 .

[0062] Here, the data table TBL2 will be described.

[0063] Data table TBL2 stores a threshold indicating the value or range of picking frequency and the number of storage bin divisions in association with each other. In other words, the processing device 11 of the warehouse operations management system 200 determines the number of storage bin divisions based on the derived value of picking frequency by referencing data table TBL2. Data table TBL2 may be stored in the warehouse management system 100 and then temporarily stored in the storage device 12 of the warehouse operations management system 200 via data communication, or may be stored in advance in the storage device 12 of the warehouse operations management system 200.

[0064] For example, if the picking frequency value is "0 to 100," the processing device 11 of the warehouse operations management system 200 determines that the number of divisions is "1," meaning that the storage bin will not be divided. Also, if the picking frequency value is "100 to 300," the processing device 11 of the warehouse operations management system 200 determines that the number of divisions is "2," meaning that the storage bin will be divided into two. Similarly, if the picking frequency value is "300 or more," the processing device 11 of the warehouse operations management system 200 determines that the number of divisions is "3," meaning that the storage bin will be divided into three.

[0065] On the other hand, if the processing device 11 determines that the number of storage bin divisions is greater than one (step St25, YES), it refers to the warehouse management system 100 and acquires work performance data for a certain period of time in the past relative to the present, which is different from the aforementioned predetermined period (see FIG. 2 ), or work plan data for a certain period of time in the future (step St26). The processing device 11 predicts the future picking frequency of the items to be picked based on the picking frequency derived in step St24 and the work performance data or work plan data acquired in step St26 (step St27). The future prediction here refers to, for example, a time series distribution of the picking frequency, which indicates the future trend (e.g., whether the picking frequency of items will increase or decrease) in light of the past work performance data or the future work plan data. Note that the future prediction does not have to be limited to the time series distribution of the picking frequency.

[0066] The processing device 11 determines the proportion of products (items) to be divided (in other words, the combination of products for each storage bin after division) using the division number determined in step St25 and the result of the future picking frequency prediction performed in step St27 (e.g., the time-series distribution of picking frequency) (step St28). The processing device 11 adds various data, such as the division number determination result in step St25 (i.e., the result that one storage bin containing the same product to be picked will be divided by the determined division number), information on the corresponding product, the number of picks of that product during the most recent predetermined period (see above), the predicted picking frequency obtained in step St27, the storage bin IDs of the storage bins before division, and the storage bin IDs of the storage bins after division, to the order ID and updates the bin division list data, and saves it in the storage device 12 (step St29).

[0067] It is preferable that the process shown in FIG. 6 (i.e., the process of adding a bin integration list related to the integration of storage bins) and the process shown in FIG. 7 (i.e., the process of integrating storage bins) be executed before the process shown in FIG. 9 (i.e., the process of adding a bin division list related to the division of storage bins) and the process shown in FIG. 10 (i.e., the process of dividing storage bins) are executed.

[0068] The process shown in FIG. 10 is an operational procedure in which the warehouse operations management system 200 divides storage bins in accordance with the bin division list generated by the process shown in FIG. 9 during actual operation of the warehouse system 1.

[0069] 10, the processing device 11 refers to the storage device 12 and acquires the bin division list stored in the storage device 12 in step St29 shown in FIG. 9 (step St31). The processing device 11 acquires bin information including the current storage bin usage status in the warehouse based on various data input from each of the warehouse management system 100 and the warehouse control systems 300, 400, and 500 (step St32). This bin information includes various data such as the currently divided products, the predicted future picking frequency of those products, the number of storage bins used for division, and the upper limit of the number of storage bins that can be used for division.

[0070] The processing device 11 uses the bin division list acquired in step St31 and the current bin information acquired in step St32 to organize (adjust) the number of divisions when dividing the storage bins scheduled in the bin division list (step St33). In step St33, since there is an upper limit to the number of storage bins that can be used for division in the entire warehouse, the processing device 11 determines which storage bins to divide preferentially based on the bin division list (for example, the current value and future predicted value of the picking frequency) and the storage bin IDs of storage bins that have already been divided, which are included in the bin information.

[0071] After the adjustment in step St33, the processing device 11 determines whether there are any surplus storage bins available for dividing the storage bins based on the bin division list acquired in step St31 and the current bin information acquired in step St32 (step St34). If the processing device 11 determines that there are any surplus storage bins available for dividing the storage bins (step St34, YES), the processing device 11 performs a process to change the current picking frequency threshold (see the picking frequency column in data table TBL2 in FIG. 9) (step St35). Then, the processing device 11 recreates (i.e., updates) the bin division list (step St36). After step St36, the processing device 11 returns to step St33. That is, the processing device 11 repeats the series of processes from step St34 to step St36 until there are no more surplus storage bins available for dividing the storage bins. Furthermore, the processing device 11 may repeat the series of processes from step St34 to step St36 until the number of remaining storage bins reaches a predetermined certain number, rather than until there are no remaining storage bins. This allows the processing device 11 to send an instruction to the warehouse control system 300, 400, 500 to promptly use the remaining storage bins in preparation for, for example, a sudden arrival of new products in the warehouse.

[0072] For example, if there are surplus storage bins available for dividing the storage bins (in other words, a value equal to or greater than the remaining number threshold, indicating that there are sufficient storage bins), the processing device 11 lowers the current picking frequency threshold (see the picking frequency column in data table TBL2 in FIG. 9 ) and recreates (i.e., updates the bin division list by the downward adjustment). As an example, if the picking frequency is 100 or greater, the processing device 11 initially determines the number of divisions to be "2" according to data table TBL2 and generates the bin division list. However, if there are approximately "10" surplus storage bins available for dividing the storage bins, the processing device 11 can lower the picking frequency threshold from "100" to "50," and if the picking frequency is "50" or greater, the processing device 11 can determine the number of divisions to be "2." Note that the example described here is one in which the processing device 11 lowers the current picking frequency threshold when there are surplus storage bins (in other words, a value equal to or greater than the remaining number threshold, indicating that there are sufficient storage bins). However, if the processing device 11 determines that a shortage of storage bins in the warehouse is predicted based on the current bin information and the bin division list, the processing device 11 may adjust the current picking frequency threshold upward. This allows the processing device 11 to prevent unnecessary division and efficiently utilize the limited number of storage bins in the warehouse.

[0073] On the other hand, if the processing device 11 determines that there are no remaining storage bins available for dividing the storage bins (step St34, NO), it uses the bin division list acquired in step St31 to generate bin division plan data for a predetermined period of time in the work plan data from which the bin division list was generated (step St37). This bin division plan includes information suggesting, for example, when to divide the storage bins, specifically, information such as when it is best to divide the storage bins during off-peak hours such as nighttime or daytime, and information such as interrupting the current picking operation to divide the bins if it would be effective. Using the bin division plan generated in step St37, the processing device 11 instructs each warehouse control system 300, 400, 500 to execute processing (e.g., dividing the storage bins and picking the storage bins after division) in accordance with the bin division plan (step St38).

[0074] [Example of Use of Storage Bins After Division] Next, an example of picking work for storage bins after division will be described with reference to FIG. 11 . FIG. 11 is a diagram schematically illustrating an example of picking work for storage bins after division. The description of FIG. 11 assumes that, for example, two pieces of order information Od1 and Od2 have been sent from the warehouse management system 100 to the warehouse operations management system 200. The order information Od2 is assumed to have been generated after the order information Od1. The order information Od1 specifies that two apples contained in the storage bin be picked. The order information Od2 specifies that three apples contained in the storage bin be picked. The processing device 11 of the warehouse operations management system 200 instructs the warehouse control system 300 or the operation terminal 900 to perform picking work starting with the storage bin containing the largest number of products as a result of division.

[0075] <Example of t=t1> At time t=t1, which is the time before the picking operation corresponding to the order information Od1 is performed, the two divided storage bins A and B contain five and four apples, respectively. The processing device 11 of the warehouse operations management system 200 selects the storage bin with the largest number of products (storage bin A at time t1), generates an instruction to pick two apples from the selected storage bin A corresponding to the order information Od1, and sends the instruction to the warehouse control systems 300, 400, and 500. Based on this instruction, the warehouse control system 300 instructs the robot 600 at the picking station corresponding to the order information Od1 to pick two apples from storage bin A. Based on this instruction, the warehouse control system 500 also instructs the automated guided vehicle 800 to place storage bin A on the automated guided vehicle and proceed to the target picking station and return from the picking station to its original position (see above). As a result, the automated guided vehicle 800 can move storage bin A toward the desired picking station in response to instructions from the warehouse control system 500, and after two apples have been removed from storage bin A at the picking station, it can return to its previous position within the automated warehouse 700 (e.g., a designated waiting area) with storage bin A still inside.

[0076] <Example at t=t2> At time t=t2, which is the time before the picking operation corresponding to the order information Od2 is performed, the two divided storage bins A and B contain three and four apples, respectively, which are the same product. The reason there are only three apples in storage bin A is because two apples were picked in response to the order information Od1. The processing device 11 of the warehouse operations management system 200 selects the storage bin with the largest number of products (storage bin B at time t2) from the two divided storage bins A and B, generates an instruction to pick three apples from the selected storage bin B in response to the order information Od2, and sends the instruction to the warehouse control systems 300, 400, and 500. Based on this instruction, the warehouse control system 300 instructs the robot 600 at the picking station corresponding to the order information Od2 to pick three apples from storage bin B. Based on this instruction, the warehouse control system 400 also assists the automated warehouse 700 in moving the automated guided vehicle 800 from its position before the start of its movement (e.g., a predetermined waiting area) to the target picking station, and in moving it back from the picking station to its position before the start of its movement (see above). Based on this instruction, the warehouse control system 500 also instructs the automated guided vehicle 800 to move forward to the target picking station with storage bin B on it, and to return from the picking station to its position before the start of its movement (see above). This allows the automated guided vehicle 800 to move storage bin B toward the target picking station in accordance with the instructions from the warehouse control system 500, and after three apples are removed from storage bin B at the picking station, return to its position in the automated warehouse 700 before the start of its movement (e.g., a predetermined waiting area) with storage bin B still on it.

[0077] <Example at t=t3> After the picking work corresponding to the two pieces of order information Od1 and Od2 is completed, storage bins A and B are returned to the predetermined waiting locations described above. Since the number of products in storage bin A was (5) at time t=t1, two apples have been picked in accordance with order information Od1, resulting in a product count of (2) at time t=t3. Similarly, since the number of products in storage bin B was (4) at time t=t2, three apples have been picked in accordance with order information Od1, resulting in a product count of (1) at time t=t3. It should be noted that times t=t2 and t=t3 may be the same time, and picking work may be performed simultaneously for order information Od1 and order information Od2.

[0078] [Dividing and Merging Cases in Which Multiple Types of Products are Stored in a Single Storage Bin] The above explanation has been given of an example in which a single storage bin stores one type of product. However, in this embodiment, multiple types of products may be stored in a storage bin. Below, division and merging of a storage bin when multiple types of products are stored in a single storage bin will be explained. Below, an example of dividing a storage bin will be explained with reference to FIGS. 12 and 13 . FIG. 12 is a diagram schematically showing a first example of the division and merging process when multiple types of products are stored in a single storage bin. FIG. 13 is a diagram schematically showing a second example of the division and merging process when multiple types of products are stored in a single storage bin. In this example, two apples, two bananas, and two avocados are stored in one storage bin B11 as multiple types of products (e.g., three types).

[0079] The storage device 12 of the warehouse management system 100 or the storage device 12 of the warehouse operations management system 200 stores a data table TBL3 shown in Fig. 12 in advance. The data table TBL3 shows the success rate of grasping an item (i.e., picking score) for each robot 600. In Fig. 12, the success rate of grasping an apple, banana, and avocado (i.e., whether it is easy to grasp) for robot AA and the success rate of grasping an apple, banana, and avocado (i.e., whether it is easy to grasp) for robot BB are shown as values ​​between 0 and 1.0.

[0080] Assume that the processing device 11 of the warehouse operations management system 200 determines, based on the picking frequency values ​​derived in step St24 of FIG. 9 , that the picking frequencies of the apples and bananas stored in storage bin B11 are equal to or greater than the division threshold, while the picking frequency of the avocados is less than the division threshold (see FIG. 12 ). In this case, the processing device 11 refers to data table TBL3 and determines a combination of products to be picked by the same robot when dividing the storage bins. For example, because robot AA has a higher success rate for grasping apples and avocados than a predetermined threshold (e.g., 0.5), the processing device 11 determines to divide the products in storage bin B11 (two apples, two bananas, and two avocados) into products in storage bin B12a (two apples and two avocados) and products in storage bin B13a (two bananas). As a result, if apples and avocados need to be picked at the same time, they are stored together in one storage bin B12a, so the robot AA can perform the picking work at the same time without moving the storage bin B12a. Note that the above explanation is an example of the operation of dividing the storage bin B11 into two storage bins B12a and B13a, but the processing device 11 may also decide to combine the two storage bins B12a and B13a into one storage bin B11 if a condition for combination, such as a decrease in picking frequency, is met.

[0081] Assume that the processing device 11 of the warehouse operations management system 200 determines, based on the picking frequency values ​​derived in step St24 of FIG. 9 , that the picking frequencies of the apples, bananas, and avocados stored in storage bin B11 are equal to or greater than the division threshold (see FIG. 13 ). In this case, the processing device 11 refers to data table TBL3 and determines a combination of items to be picked by the same robot with a high picking success rate exceeding a predetermined threshold (e.g., 0.5) when dividing the storage bins. For example, the processing device 11 determines to divide the items in storage bin B11 (two apples, two bananas, and two avocados) into items in storage bin B12b (one apple, one banana, and one avocado) and items in storage bin B13b (one apple, one banana, and one avocado). This enables picking operations to be performed efficiently at the same time at different picking stations. Note that the above explanation is an example of the operation of dividing storage bin B11 into two storage bins B12b and B13b, but the processing device 11 may also decide to consolidate the two storage bins B12b and B13b into one storage bin B11 if the conditions for consolidation, such as a decrease in picking frequency, are met.

[0082] (Additional Notes) The above description of the embodiments discloses the technical ideas described in the following items.

[0083] (Item 1) A warehouse system connected to a database, comprising: an acquisition unit that acquires, from the database, work plan data related to product picking work within the warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a predetermined period based on the order information; and a decision unit that determines whether to divide a storage bin used for product picking work within the warehouse into at least two based on the derived picking frequency.

[0084] This configuration allows the storage bins to be transported to the locations of the main work tasks within the warehouse to be divided adaptively, improving the efficiency of storage bin operation.

[0085] (Item 2) The warehouse system according to Item 1, wherein an upper limit number of the storage bins that can be used for division in the entire warehouse is set in advance, and the determination unit determines the number of divisions of the storage bins so that the number is equal to or less than the upper limit number.

[0086] This configuration makes it possible to avoid an operation in which storage bins are divided unlimitedly, and to efficiently utilize the limited number of storage bins available for division in the warehouse.

[0087] (Item 3) The warehouse system according to Item 1 or 2, wherein the acquisition unit acquires work performance data relating to the picking work of the products for a certain period of time in the past from the database, the derivation unit predicts a picking frequency of the products for the predetermined range of time based on the work performance data for the certain period, and the determination unit determines a proportion of the number of the products to be stored in each of at least two storage bins based on the predicted picking frequency.

[0088] With this configuration, the warehouse system can improve the efficiency of picking operations within the warehouse by using past work performance data to respond to shipping demand specific to events that occur around the same time.

[0089] (Item 4) The warehouse system according to Item 1 or 2, wherein the acquisition unit acquires work plan data relating to the picking work of the products for a certain period from the present from the database, the derivation unit predicts a picking frequency of the products based on the work plan data for the certain period from the present, and the determination unit determines a proportion of the number of the products to be stored in each of at least two storage bins based on the predicted picking frequency.

[0090] This configuration enables the warehouse system to improve the efficiency of picking operations within the warehouse in accordance with shipping demand specific to an event or the like that is known in advance using future work plan data.

[0091] (Item 5) The warehouse system according to any one of Items 1 to 4, wherein the determination unit determines to divide the multiple products stored in each of the at least two storage bins in equal proportions when the predicted picking frequency is continuously higher than a certain value.

[0092] With this configuration, the warehouse system can improve picking efficiency throughout the warehouse by updating work plans so that picking work for products that have been ordered continuously for more than a certain period of time can be carried out at the same time at different picking stations.

[0093] (Item 6) The warehouse system according to any one of Items 1 to 4, wherein the determination unit determines to divide the multiple products stored in each of the at least two storage bins in a manner that biases the quantities of the products when the predicted picking frequency is higher than a certain value for a certain period of time.

[0094] With this configuration, the efficiency of picking work throughout the warehouse can be improved by updating the work plan so that picking work for products that have received a one-off order of more than a specified threshold at some point during the most recent specified period can be carried out quickly, for example, by biasing it to a picking station with a robot that is good at picking that product.

[0095] (Item 7) The warehouse system according to any one of items 1 to 6, wherein when multiple types of products are stored in the storage bins before being transported to a picking station in the warehouse and the picking frequency of at least two types of products among the multiple types of products is equal to or greater than a predetermined value, the determination unit determines a combination of the multiple types of products to be stored in each of the at least two storage bins based on a product-specific grasping success rate for each robot arranged in the warehouse.

[0096] With this configuration, if the robot needs to pick multiple types of products at the same time, the products are stored together in the same storage bin, so the picking work can be performed efficiently at the same time without moving the storage bin.

[0097] (Item 8) The warehouse system according to any one of Items 1 to 7, wherein the determination unit uniformly determines a combination of the multiple types of products to be stored in each of the at least two storage bins when the storage bins contain multiple types of products before transporting them to a picking station in the warehouse and the picking frequency of each of the multiple types of products is equal to or greater than a predetermined value.

[0098] This configuration enables picking operations to be efficiently performed at the same time at different picking stations.

[0099] (Item 9) The warehouse system according to any one of items 1 to 8, further comprising a threshold change unit that changes the threshold for the number of divisions according to the picking frequency when the storage bins available for division are equal to or greater than a predetermined value, and the determination unit determines the number of divisions for the storage bins based on the changed threshold.

[0100] This configuration improves the operational efficiency of storage bins that can be used for limited division within the warehouse, thereby improving the picking work efficiency of the warehouse as a whole.

[0101] (Item 10) A warehouse system connected to a database, comprising: an acquisition unit that acquires, from the database, work plan data related to product picking work within the warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a predetermined period based on the order information; and a decision unit that decides whether to consolidate storage bins used for product picking work within the warehouse based on the derived picking frequency.

[0102] This configuration allows adaptive consolidation of storage bins to be transported to locations within the warehouse where work is primarily performed, thereby improving the efficiency of storage bin operation.

[0103] (Item 11) A storage bin division method executed by a processor and a memory in cooperation with each other, comprising the steps of: retrieving from a database work plan data relating to product picking work within a warehouse, the work plan data including order information relating to the products; deriving a picking frequency indicating the frequency of product picking work within a predetermined range of time based on the order information; and determining, based on the derived picking frequency, whether to divide the storage bins used for product picking work within the warehouse into at least two.

[0104] This method allows adaptive division of storage bins to be transported to locations within the warehouse where work is performed, thereby improving the efficiency of storage bin operation.

[0105] (Item 12) A storage bin consolidation method executed by a processor and a memory in cooperation with each other, comprising the steps of: retrieving from a database work plan data relating to product picking work within a warehouse, the work plan data including order information relating to the products; deriving a picking frequency indicating the frequency of product picking work within a specified range of time based on the order information; and determining whether to consolidate storage bins used for product picking work within the warehouse based on the derived picking frequency.

[0106] This method allows adaptive consolidation of storage bins to be transported to work-focused locations within the warehouse, improving the efficiency of storage bin operation.

[0107] The present disclosure also applies to programs and storage media that realize the functions of the devices of the above-mentioned embodiments, which are supplied to the device via a network or various storage media and that are read and executed by a computer within the device.

[0108] Although various embodiments have been described above with reference to the drawings, it goes without saying that the present disclosure is not limited to these examples. It is clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents may be made within the scope of the claims, and it is understood that these also fall within the technical scope of the present disclosure. Furthermore, the components of the various embodiments described above may be combined in any manner without departing from the spirit of the invention.

[0109] The present disclosure is useful as a warehouse system, a storage bin division method, and a storage bin consolidation method that adaptively divides or consolidates storage bins to be transported to work sites within a warehouse to improve the operational efficiency of the storage bins.

[0110] REFERENCE SIGNS LIST 1...Warehouse system 10...Information processing device 11...Processing device 12...Storage device 13...Communication device 14...Input device 15...Image acquisition device 16...External interface 17...Display device 18...Internal interface 20...External system 30...Camera 35...Network 100...Warehouse management system 200...Warehouse operation management system 300, 400, 500...Warehouse control system 600...Robot 700...Automated warehouse 800...Automated guided vehicle 900...Operation terminal

Claims

1. A warehouse system connected to a database, comprising: an acquisition unit that acquires from the database work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a specified period based on the order information; and a decision unit that decides whether to divide a storage bin used for product picking work within the warehouse into at least two based on the derived picking frequency.

2. The warehouse system according to claim 1, wherein an upper limit of the storage bins that can be used for division in the entire warehouse is set in advance, and the determination unit determines the number of divisions of the storage bins so that the number is equal to or less than the upper limit.

3. The warehouse system described in claim 1, wherein the acquisition unit acquires work performance data for a certain period of time in the past regarding the picking work of the product from the database, the derivation unit predicts the picking frequency of the product for the specified range of time based on the work performance data for the certain period, and the determination unit determines the proportion of the number of the products to be stored in each of at least two storage bins based on the predicted picking frequency.

4. The warehouse system described in claim 1, wherein the acquisition unit acquires work plan data for the picking work of the product for a certain period from the present from the database, the derivation unit predicts the picking frequency of the product based on the work plan data for the certain period from the present, and the determination unit determines the proportion of the number of the product to be stored in each of at least two storage bins based on the predicted picking frequency.

5. The warehouse system according to claim 3 or 4, wherein the determination unit determines to divide the multiple products stored in each of the at least two storage bins in equal proportions when the predicted picking frequency is continuously higher than a certain value.

6. The warehouse system according to claim 3 or 4, wherein the determination unit determines to divide the multiple products stored in each of the at least two storage bins in a manner that biases the quantities when the predicted picking frequency is higher than a certain value for a certain period of time.

7. The warehouse system described in claim 1, wherein, when multiple types of products are stored in the storage bins before being transported to a picking station within the warehouse and the picking frequency of at least two types of products among the multiple types of products is equal to or greater than a predetermined value, the determination unit determines a combination of the multiple types of products to be stored in each of the at least two storage bins based on the product-specific grasping success rate of each robot located within the warehouse.

8. The warehouse system described in claim 1, wherein the determination unit uniformly determines the combination of the multiple types of products to be stored in each of the at least two storage bins when the storage bins contain multiple types of products before transporting them to a picking station within the warehouse and the picking frequency of each of the multiple types of products is equal to or greater than a predetermined value.

9. The warehouse system of claim 1, further comprising a threshold change unit that changes the threshold for the number of divisions according to the picking frequency when the number of storage bins available for division is equal to or greater than a predetermined value, and the determination unit determines the number of divisions for the storage bins based on the changed threshold.

10. A warehouse system connected to a database, comprising: an acquisition unit that acquires from the database work plan data related to product picking work within a warehouse, the work plan data including order information related to the products; a derivation unit that derives a picking frequency indicating the frequency of product picking work within a specified period based on the order information; and a decision unit that decides whether to consolidate storage bins used for product picking work within the warehouse based on the derived picking frequency.

11. A storage bin division method executed by a processor and a memory in cooperation with each other, comprising the steps of: retrieving from a database work plan data relating to product picking work within a warehouse, the work plan data including order information relating to the products; deriving a picking frequency indicating the frequency of product picking work within a specified range of time based on the order information; and determining whether to divide the storage bins used for product picking work within the warehouse into at least two bins based on the derived picking frequency.

12. A storage bin consolidation method executed by a processor and a memory in cooperation with each other, comprising the steps of: retrieving from a database work plan data relating to product picking work within a warehouse, the work plan data including order information relating to the products; deriving a picking frequency indicating the frequency of product picking work within a specified range of time based on the order information; and determining whether or not to consolidate storage bins used for product picking work within the warehouse based on the derived picking frequency.

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