Pick-list generating device, and pick-list generating method

Abstract

To provide a pick-list-generating device capable of generating a pick list that does not depend on the ability of a worker.SOLUTION: The pick-list generating device for generating one or more pick lists based on the order of goods includes: a setting unit that receives setting of the shipping date and time for goods; and a generation unit that generates, based on the orders, shipping dates and times, and past work performance, one or more pick lists so that the productivity of one or more pick list items satisfies the specified criteria. The past work performance includes the working time required for workers to pick goods in past picking operations. The pick list specifies the order in which the workers pick the good items. The work productivity means the picking quantity of goods based on the pick-list with respect to the total working time of one or more workers assigned in the pick-list.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a picklist generation device and a picklist generation method. [Background technology]

[0002] Patent Document 1 discloses a picking work management system that selects a picking work method in accordance with a shipping order in order to improve the efficiency of picking work at a distribution center. In this picking work management system, a planned worker extraction unit reads the work time and other data stored in a database and extracts the number of workers required for the picking work. In addition, in this picking work management system, a planned worker identification unit reads the work time for each individual worker using each picking method and identifies the worker from among the candidate workers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-239337 Summary of the Invention [Problem to be solved by the invention]

[0004] The picking work management system of Patent Document 1 takes into consideration the work time of picking workers when improving the efficiency of picking work. For example, in warehouse work, high labor fluidity and multitasking in warehouse work result in frequent changes in assigned workers. In such a situation where workers are replaced in a short period of time, it is difficult to collect individual information about workers to optimize warehouse work. Patent Document 1 does not take into consideration the above situation.

[0005] The present disclosure has been devised in view of the above-described conventional situation, and aims to generate a pick list that does not depend on the ability of a worker. [Means for solving the problem]

[0006] The present disclosure provides a picklist generation device that generates one or more picklists based on an order for an item, the picklist generation device including: a setting unit that accepts a setting of a shipping date and time for the item; and a generation unit that generates the one or more picklists based on the order, the shipping date and time, and past work performance so that the work productivity of the one or more picklists satisfies predetermined conditions, wherein the past work performance includes the work time required by workers to pick items in past picking operations, the picklist specifies the order in which the workers pick items, and the work productivity is the number of items picked based on the picklist relative to the total work time of one or more workers to whom the picklist is assigned.

[0007] The present disclosure also provides a picklist generation method for generating one or more picklists based on an order for an item, which receives a setting of a shipping date and time for the item, and generates the one or more picklists based on the order, the shipping date and time, and past work performance so that the work productivity of the one or more picklists satisfies a predetermined condition, wherein the past work performance includes the work time required by workers to pick items in past picking operations, the picklist specifies the order in which the workers pick items, and the work productivity is the number of items picked based on the picklist relative to the total work time of one or more workers to whom the picklist is assigned.

[0008] 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. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to generate a pick list that is not dependent on the ability of the worker and that allows smooth consolidation of items in subsequent work after picking. [Brief explanation of the drawings]

[0010] [Figure 1] A block diagram showing a configuration example of a warehouse system according to a first embodiment. [Figure 2] A block diagram for explaining a pick list generation function of the warehouse management system according to the first embodiment. [Figure 3] Schematic showing an example of a traditional warehouse operation and picklist [Figure 4] Sequence diagram to explain an example of the flow of conventional picklist generation [Figure 5] FIG. 1 is a schematic diagram illustrating an example of warehouse work and a pick list using a warehouse system according to a first embodiment. [Figure 6] A sequence diagram for explaining an example of a flow of pick list generation according to the first embodiment. [Figure 7] A sequence diagram for explaining an example of a flow of pick list generation according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, with reference to the drawings as appropriate, detailed descriptions will be given of embodiments that specifically disclose a picklist generation device and a picklist generation method according to the present disclosure. However, more detailed descriptions than necessary may be omitted. For example, detailed descriptions of already well-known matters and 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.

[0012] <First Embodiment> 1 is a block diagram showing an example of the configuration of a warehouse system 1 according to Embodiment 1. The warehouse system 1 includes a warehouse management system 2, a field personal computer (hereinafter referred to as a "PC") 60, a warehouse operations management system 70, an order receiving server 80, and a user terminal 90.

[0013] The warehouse management system 2 is a system that manages and controls logistics within a warehouse. The warehouse management system 2 also generates a pick list. The pick list specifies the order in which workers pick products. In this embodiment, it is assumed that picking is performed in a warehouse. The warehouse management system 2 may manage not only one warehouse, but also one or more warehouses. The warehouse management system 2 is configured using a general-purpose computer device such as a PC or a server computer. The warehouse management system 2 may also be referred to as a Warehouse Management System (hereinafter referred to as a "WMS"). Hereinafter, the warehouse management system 2 may also be referred to as a pick list generation device. The pick list generation function of the warehouse management system 2 will be described later with reference to FIG. 2.

[0014] The warehouse management system 2 includes a processor 10, a memory 20, an input device 30, a display device 40, and a communication interface device 50.

[0015] The processor 10 may be configured using, for example, a central processing unit (hereinafter referred to as "CPU"), a graphical processing unit (hereinafter referred to as "GPU"), a micro processing unit (hereinafter referred to as "MPU"), a digital signal processor (hereinafter referred to as "DSP"), or a field programmable gate array (hereinafter referred to as "FPGA"). The processor 10 realizes various functions of the warehouse management system 2, for example, by referencing various databases stored in the memory 20 or reading out programs. As will be described later with reference to FIG. 2, the processor 10 and the memory 20 cooperate to realize various functions of the setting unit 11, the allocation unit 12, and the generation unit 13.

[0016] The memory 20 is a storage unit for storing various data, programs, etc. The memory 20 may be composed of, for example, a volatile or non-volatile storage device such as a random access memory (hereinafter referred to as "RAM"), a read-only memory (hereinafter referred to as "ROM"), or a hard disk drive (hereinafter referred to as "HDD"). The memory 20 stores and maintains a past performance database 21, a calculation result database 22, and an inventory database 23. The past performance database 21 stores and maintains past work performance. The past work performance includes data such as the work time required by workers to pick items in past picking operations, delay information for past operations, and pick lists created in the past. The delay information for past operations is a log of operations (such as process name, product name, operation time, and quantity) that exceed the time set for each process. The time set for each process is specified, for example, by a service level agreement (hereinafter referred to as "SLA"). The calculation result database 22 stores and holds the work productivity and bundling rate (packing rate) calculated by the processor 10. The work productivity and bundling rate (packing rate) will be described later. The inventory database 23 stores and holds information on product inventory in the warehouse.

[0017] The input device 30 receives operations and instructions from users of the warehouse, such as a manager or a worker, etc. The input device 30 may be configured with a mouse, a keyboard, a touch panel display, etc.

[0018] The display device 40 displays various user interfaces to the user. The display device 40 may be configured as a liquid crystal display, a touch panel display, or the like.

[0019] The communication interface device 50 is an interface for communicating with external devices such as the field PC 60, warehouse operations management system 70, order server 80, and user terminal 90 via a network (not shown). There are no particular limitations on the communication standard that can be supported by the communication interface device 50, and the communication interface device 50 may be compatible with either a wired or wireless communication standard. Furthermore, the communication interface device 50 may be compatible with multiple communication standards.

[0020] The on-site PC 60 is installed in a warehouse (not shown) and is operated by a user of the warehouse. The on-site PC 60 stores the results of work such as picking performed in the warehouse.

[0021] The warehouse operations management system 70 is a system that manages and controls work within a warehouse. The warehouse operations management system may also be referred to as a Warehouse Execution System (hereinafter referred to as "WES"). The warehouse operations management system 70 stores the results of work such as picking that has been performed in the warehouse managed by the warehouse operations management system 70.

[0022] In this embodiment, the field PC 60 or the warehouse operations management system 70 transmits the stored picking work results to the warehouse management system 2. The picking work results transmitted to the warehouse management system 2 are stored in the past work results database 21 as past work results.

[0023] The order receiving server 80 receives product orders from, for example, companies and transmits the contents of the orders as an order list to the warehouse management system 2. The order receiving server 80 may be, for example, a system for supply chain management.

[0024] The user terminal 90 is an information processing device configured to be available to workers in the warehouse. The user terminal 90 may be, for example, a stationary information processing device such as a PC, or a mobile terminal such as a tablet terminal, handheld terminal, or smartphone. The user terminal 90 receives a pick list generated by the warehouse management system 2. Then, the worker can check the pick list using the user terminal 90.

[0025] FIG. 2 is a block diagram for explaining the pick list generation function of the warehouse management system 2 according to the first embodiment.

[0026] The processor 10 of the warehouse management system 2 implements the functions of a setting unit 11. The setting unit 11 has a manual parameter setting function 11A. The manual parameter setting function 11A accepts parameter settings by a user such as an operator. The parameters accepted by the manual parameter setting function 11A include location P1, list unit quantity P2, shipping date and time P3, and process path P4. The location P1, list unit quantity P2, shipping date and time P3, and process path P4 may be referred to as manually set parameters.

[0027] Location P1 is a parameter that indicates a storage area for a product in a warehouse. The storage area may be divided based on, for example, the shape of the shelves that store the product or the equipment arranged in the storage area. The product is stored in a storage area that is most efficient for the product.

[0028] List unit quantity P2 is a parameter that indicates the quantity of items in one pick list. For example, if two pick lists with different list unit quantities P2 are assigned to two workers, the working time for each of the two workers may differ. If the two pick lists each contain the same items, the time it takes to complete picking the same items may differ. In the subsequent picking process, consolidation, the picked items are grouped by shipping destination. Therefore, if there are not enough items in one picking list, it is necessary to wait until picking from both picking lists is complete. In other words, consolidation can become a bottleneck in the shipping process.

[0029] The shipping date and time P3 is a parameter that indicates the date and time when the product will be shipped. The shipping date and time P3 is calculated based on the customer's desired delivery date and time. The pick list is generated based on the shipping date and time P3. For example, the pick list is generated so that it includes products that will be shipped on the same day and excludes products that will be shipped the next day or later. Furthermore, for example, when picking of products to be shipped on the same day is completed, the shipping date and time P3 is also used when a pick list is generated by narrowing the date and time to products that will be shipped the next day or the day after that.

[0030] Process path P4 is a parameter set to associate with work authority. Users can set process path P4 as desired according to the work process or work area, etc. For example, if process path P4 is set to "pick only hazardous materials," a pick list containing only hazardous materials as products is generated. In this case, the warehouse management system 2 can automatically assign the pick list to a worker who can handle hazardous materials by granting the work authority to pick hazardous materials to that worker. Normally, pick lists are assigned randomly to workers, but setting process path P4 allows the pick list to be assigned to the most suitable worker.

[0031] In generating a pick list according to this embodiment, the settings of the location P1, the list unit quantity P2, and the process path P4 may be omitted.

[0032] The processor 10 of the warehouse management system 2 realizes the functions of an allocation unit 12. The allocation unit 12 has an inventory allocation function 12A that allocates inventory for ordered products. The inventory allocation function 12A also includes an automatic parameter setting function 12B.

[0033] The automatic parameter setting function 12B automatically sets parameters for maintaining logistics quality when allocating inventory. The parameters set by the automatic parameter setting function 12B may be referred to as automatic adjustment parameters. The automatic adjustment parameters include FIFOP5, LotP6, FlagP7, and Status Change P8.

[0034] FIFOP5 indicates a method for prioritizing received and released products. In this embodiment, the first-in, first-out (FIFO) method is assumed, in which products received first are shipped first. Therefore, parameter P5 is used, for example, as a flag indicating whether received and released products are prioritized based on FIFO. By setting FIFOP5, products with a close expiration date, such as a best-before date, are prioritized for picking. While FIFO is often used in typical warehouses, other prioritization methods are also known. For example, LIFO (Last-in, First-out), in which products received last are shipped first, may also be used. Therefore, P5 may be used as a flag indicating whether a method other than FIFO is to be used, or as a flag indicating whether a method other than FIFO or another method is to be used, based on the rules adopted in the warehouse.

[0035] LotP6 is a flag that indicates whether or not the product to be shipped should be selected according to the product lot number. By setting LotP6, among the same products, products with smaller product lot numbers (older products) are given priority in picking. Note that in cases where differences in product lots do not need to be taken into consideration (product lots do not matter as long as the products are the same), such as products that are shipped early, the P6 flag may be turned off or P6 itself may be omitted. Also, if you want to ship only products with limited product lot numbers, you may set the product lot number as P6.

[0036] When flag P7 is set, a pick list is generated that picks only the products associated with flag P7 by filtering with flag P7. For example, when flag P7 for hazardous materials is set, a pick list that includes only hazardous materials is generated by filtering with flag P7. This allows the warehouse management system 2 to route specific tasks, such as special packaging, to a different work line from the work line for non-hazardous materials, even if the subsequent picking task requires that task.

[0037] The status change P8 is a parameter for changing the status of a product in accordance with the allocation of inventory. Examples of product status include "available for allocation," "allocated," and "reserved."

[0038] In generating a picklist according to this embodiment, the automatic parameter setting function 12B by the inventory allocation function 12A may be omitted.

[0039] The processor 10 of the warehouse management system 2 realizes the functions of the generation unit 13. The generation unit 13 has a pick list generation function 13A that generates a pick list. The pick list generation function 13A also includes a parameter extraction function 13B and a load matching optimization function 13C.

[0040] The parameter extraction function 13B extracts parameters to be considered in generating a pick list from the manually set parameters. Specifically, the parameter extraction function 13B extracts the section P9, the story P10, and the shelf width P11 from the location P1. The parameter extraction function 13B also extracts the process path P4 set by the parameter manual setting function 11A.

[0041] Section P9 is a more detailed division of the storage area, such as a refrigerated section or a hazardous material storage section. Level P10 is a parameter used to indicate a specific level when picking is performed on a multi-story warehouse. Shelf opening P11 is a parameter used to indicate a specific shelf opening in a warehouse. Shelf opening is the smallest unit of space for storing goods, and for example, one shelf may have multiple shelf openings. Shelf openings are sometimes called slots.

[0042] In generating a pick list according to this embodiment, the parameter extraction function 13B performed by the pick list generation function 13A may be omitted.

[0043] The packing optimization function 13C optimizes, in other words, adjusts, the work productivity P12 or the bundling rate (packing rate) P13 using the mathematical optimization model M1 to generate a pick list.

[0044] Work productivity is the number of items picked based on a picklist relative to the total work time of one or more workers assigned to the picklist. A picklist is typically assigned to one worker. However, a worker assigned to a picklist may be replaced by another worker, for example, due to lack of work authorization. The number of items picked is the number of items picked. For example, if one item on a picklist is picked, the picking quantity for that item is one. Here, the number of items picked may be counted in units that are easy for the warehouse to manage. For example, if a warehouse manages items by picklist row, the picking quantity may be counted by row. In this case, if 100 items with five items per picklist row are picked, i.e., if the items are picked by picking 20 rows, the picking quantity for that item is counted as 20 rows. Work productivity for picking work can be obtained, for example, by dividing the total picking quantity by the total work time. For example, if the work time required to pick 20 items is 1 hour, the work productivity is 20 items / hour. For example, if the work time required to pick 20 items is 2 hours, the work productivity is 10 items / hour. Note that the number of workers engaged in the picking work is not taken into account when calculating work productivity.

[0045] In the past, pick lists were created based on the individual capabilities of each worker. However, frequent changes in workers can make it difficult to collect the information necessary to create a pick list. Furthermore, because pick lists do not reflect information other than the worker's capabilities, such as the size of the transport jig or the item, factors other than the worker's capabilities can lead to inefficient pick lists. For example, if a pick list contains only large items, even a highly skilled worker may need to change the transport jig. This requires additional time, which can lead to inefficient picking. Work productivity can be used as an evaluation index that does not take into account differences in worker capabilities or the effects of transport jig changes. For example, work productivity is the same when one person takes two hours to pick a certain number of items as when two people take one hour to pick the same number of items. Furthermore, work productivity calculated from past work performance also incorporates past delays that occurred during the picking process, such as changing the transport jig. Therefore, by generating a pick list based on work productivity and independent of the individual abilities of workers, it is possible to improve work efficiency even in an environment with frequent worker turnover. Furthermore, various delay information that occurs in actual work can be incorporated when generating the pick list, so work efficiency can be improved by reflecting the delay information. Note that work productivity is an index that focuses on the results that reflect all delay information, so by creating a pick list based on work productivity, it is possible to improve work efficiency by reflecting delay information due to factors not recognized by workers or managers, not limited to the replacement of transport jigs as described above.

[0046] The bundling rate (packing rate) is a value indicating the number of products per destination. In this embodiment, the bundling rate uses the average number of products per destination, but other statistical values ​​such as the median may also be used. For example, an order will be described with a total order quantity of 300 units. In this case, if 100 units of the product are destined for the same destination, the bundling rate for that order is 3 units per order. Furthermore, if 30 units of the product are destined for the same destination, the bundling rate is 300 units per order = 10 units per order. In the consolidation and packing tasks that follow the picking task, packages destined for the same destination, i.e., to a customer, are combined. Because consolidation involves combining packages destined for the same destination, the task cannot proceed until all packages destined for the same destination have arrived. Meanwhile, the packing task requires assembling packaging materials, such as shipping boxes, for each destination and placing products inside. Therefore, the more destinations there are, the longer it takes to assemble the packaging materials. When the bundling rate is high, the efficiency of bundling decreases because a large number of products are required for bundling, but the efficiency of packing work improves because multiple products can be packed into one shipping box. On the other hand, when the bundling rate is low, the number of products per destination is small, making it easier to collect the products needed for bundling in a short period of time, improving the efficiency of bundling. However, the efficiency of packing work decreases because shipping boxes must be assembled for each small number of products. Therefore, optimizing to increase the bundling rate can improve the efficiency of packing work. Note that, as described above, optimizing only with a focus on the bundling rate may reduce the efficiency of bundling work. Therefore, when optimizing to increase the bundling rate, it is recommended to use a method that improves the efficiency of bundling work. For example, the optimization that focuses on work productivity described above can improve the efficiency of bundling work by appropriately setting the objective function, as described below, and therefore should be used in combination with optimization to increase the bundling rate.

[0047] The mathematical optimization model M1 may adjust the work productivity P12 of each of the one or more pick lists to be generated based on the shipping date and time, the order list OL1 transmitted from the order server 80, and the past work performance stored in the past performance database 21, so that the work productivity of each of the one or more pick lists to be generated satisfies a predetermined condition. For example, the mathematical optimization model M1 may adjust the work productivity P12 so that the average value of the work productivity of each of the one or more pick lists to be generated is equal to or greater than a specified value. The specified value may be arbitrarily set in advance by a user such as an administrator or an operator. Furthermore, for example, the mathematical optimization model M1 may maximize an objective function, using the average value of the work productivity of each of the one or more pick lists to be generated. The one or more pick lists to be generated include products included in the order list OL1. The generation unit 13 calculates the work productivity of a certain product, the work productivity for each combination of multiple products, and the work productivity of past pick lists based on the past work performance. The calculation results are stored in the calculation result database 22. The mathematical optimization model M1 may maximize the objective function in consideration of the calculation results of work productivity based on past work performance, which are stored in the calculation result database 22.

[0048] Furthermore, the mathematical optimization model M1 may adjust the packing rate P13 so that the packing rate of products included in each of the one or more pick lists to be generated satisfies a predetermined condition based on the shipping date and time, the order list OL1 transmitted from the order server 80, and the past work results stored in the past work results database 21. For example, the mathematical optimization model M1 may adjust the packing rate P13 so that the packing rate of products included in each of the one or more pick lists to be equal to or greater than a predetermined value. The predetermined value may be arbitrarily set in advance by a user such as an administrator or an operator. For example, the mathematical optimization model M1 may maximize an objective function using the average value of the packing rates of products included in each of the one or more pick lists to be generated. The one or more pick lists to be generated include products included in the order list OL1. The generation unit 13 calculates the packing rate of products in past pick lists based on the past work results. The calculation results are stored in the calculation result database 22. The mathematical optimization model M1 may maximize the objective function by taking into account the calculation results of the packing rates of products in past pick lists stored in the calculation result database 22.

[0049] The mathematical optimization model M1 may adjust both the work productivity P12 and the bundling rate P13, or may adjust either one of them. Priorities may be set for each of the work productivity P12 and the bundling rate P13. For example, when the warehouse management system 2 generates a picklist, it may be set to prioritize maximizing the work productivity of the picklist over the bundling rate of the items included in the picklist. Conversely, it may be set to prioritize maximizing the bundling rate over work productivity. Furthermore, for example, the mathematical optimization model M1 may maximize the greater of the average work productivity for each item in one or more picklists and the average bundling rate for each item in one or more picklists. The objective function in this case is expressed as follows:

[0050]

number

[0051] Here, the left term in the parentheses on the right side of equation (1) represents the average value of work productivity, and the right term represents the average value of the bundling rate. n and m in equation (1) each correspond to the number of picklists. Because the number of picklists that optimizes (e.g., maximizes) work productivity and the number of picklists that optimizes (e.g., maximizes) the bundling rate may differ, they are each expressed as separate variables. The various parameters used by the mathematical optimization model M1 are as follows: P i :Picking quantity of product i T i : The work time required to pick product i N i : Total order quantity of product i O i : Number of orders for product i W i :Weight of product i S i :Product size W max : Maximum load weight of transport cart S max : Maximum loading size of transport cart T max :Total work time limit M: Number of available workers TS i :Timestamp or sequence number of item i

[0052] A transport cart is used to load and transport products picked by a worker. Note that the equipment used for loading and transporting products is not limited to a cart, and a transport cart may be interpreted as any transport jig. A product's timestamp indicates when the product was stored in the warehouse, or more precisely, at the shelf entrance. A product's sequence number is synonymous with a product's timestamp.

[0053] Furthermore, the constraints for adjusting the work productivity P12 or the bundling rate P13 using the mathematical optimization model M1 are as shown in the following formulas (2) to (6).

[0054]

number

[0055]

number

[0056]

number

[0057]

number

[0058]

number

[0059] Equation (2) constrains that the total weight of the items to be picked does not exceed the maximum load weight of the transport cart. Equation (3) constrains that the total size of the items to be picked does not exceed the maximum load size of the transport cart. Equation (4) constrains that the work time required for picking does not exceed the total work time limit. Equation (5) constrains that the number of workers performing the picking work does not exceed the number of available workers. Equation (6) shows the first-in, first-out constraint.

[0060] The processor 10 of the warehouse management system 2 implements a work authority granting function 14. The work authority granting function 14 checks the qualifications held by each worker. Information including the qualifications held by each worker may be stored and maintained, for example, in memory 20. The qualifications held by a worker refer to, more precisely, the qualifications held by the worker to perform work at the work site. The work authority granting function 14 also grants work authority to a worker based on manual operation by a user. For example, a user grants a worker who has a license to operate a forklift the authority to operate a forklift, in other words, the authority to use a forklift. In this way, a user grants work authority to a worker who has the license to operate a forklift in order to have the worker perform work that requires the license.

[0061] In generating a picklist according to this embodiment, if no work requires special qualifications, the work authority granting function 14 may be omitted. Furthermore, the generation unit 13 may have each of the work authority granting function 14, qualification confirmation function 15, and manual assignment function 16. That is, when generating a picklist, the granting of work authority and the assignment of the picklist to a worker may be performed automatically.

[0062] The processor 10 of the warehouse management system 2 implements a qualification confirmation function 15 and a manual assignment function 16. The qualification confirmation function 15 confirms the qualifications held by each worker. Information on the qualifications held by each worker confirmed by the qualification confirmation function 15 is used when the pick list generation function 13A generates a pick list. The manual assignment function 16 confirms the qualifications held by each worker. In addition, the manual assignment function 16 accepts the assignment of the generated pick list to a worker through manual operation by the user. To assign a pick list to a worker, either the qualification confirmation function 15 or the manual assignment function 16 is executed.

[0063] Next, generation of a conventional picklist and warehouse operations based on a picklist generated by a conventional method will be described with reference to Figures 3 and 4. Figure 3 is a schematic diagram showing an example of conventional warehouse operations and a picklist.

[0064] When the warehouse management system 2 receives an order, in other words, a shipping request, it accepts manual parameter settings, allocates inventory, and generates a pick list. The manual parameter settings are accepted by a manual parameter setting function 11A. The manual parameter setting function 11A accepts the settings of manually set parameters for location P1, list unit quantity P2, shipping date and time P3, and process path P4. The inventory allocation function 12A allocates inventory. When allocating inventory, the automatic parameter setting function 12B automatically sets the automatic adjustment parameters for FIFOP5, LotP6, Flag P7, and Status Change P8. The pick list is generated by a pick list generation function 13A. When generating the pick list, the parameters for section P9, level P10, shelf width P11, and process path P4 extracted by the parameter extraction function 13B are taken into consideration. The work authorization assignment function 14 also assigns work authorization to the worker. If the work authority is required for the picking work of the generated picklist, the picklist is assigned to a worker who has been granted the work authority.

[0065] The generated picklist is output to a user terminal 90, and the worker confirms the received picklist and begins warehouse work. The generated picklist may be printed, for example. The worker may then confirm the printed picklist and begin warehouse work. Warehouse work involves picking ordered items based on the generated picklist, assembling the picked items for each customer, or in other words, the shipping destination, and then packing and shipping them. In conventional methods, picklists are set based on numerous manually configured parameters, resulting in inconsistencies in picking efficiency depending on the ability and errors of the parameter setter. As a result, even if the parameter setter believes they have set optimal parameters, a bottleneck can occur between the picking process and the assembling process during warehouse work based on the generated picklist. One cause of this bottleneck is a large discrepancy in work productivity between picklists. Because delays in picking can occur for a variety of reasons, it is difficult to predict all of these factors when setting parameters. For example, if the parameter settings are not adjusted to take into account the loading capacity of a transport cart, the loading capacity of the transport cart may be exceeded during the picking process, requiring the transport cart to be replaced. As a result, the work productivity of the picklist will be lower than expected by the person who set the parameters, resulting in a difference in work productivity from other picklists. If the products required for the assembling process are divided into multiple picklists, the assembling process cannot be carried out until the picking work for those multiple picklists is completed. Therefore, if the work productivity of one picklist is high and picking work based on that picklist is completed in a short time, while the work productivity of a different picklist is low, it will take a long time to complete all picking work, which can cause a bottleneck.

[0066] Furthermore, in warehouse operations based on picklists generated using conventional methods, bottlenecks can occur between the consolidation process and the packing process. One example of a bottleneck is when products to be shipped to the same customer are distributed across multiple picklists, resulting in a long wait time for the total quantity ordered by that customer to be shipped. In other words, a low rate of products being bundled together (consolidation rate) in the picklists can also be cited.

[0067] Picklist PL1 and picklist PL2 are examples of picklists generated by the warehouse management system 2 in a conventional manner after receiving order list OL2. The warehouse management system 2 generates a picklist so that 50 units of item A, 25 units of item B, 10 units of item C, and 20 units of item D are to be picked. For the sake of explanation, it is assumed that there are two orders. In other words, there are two shipping destinations. While the example in Figure 3 shows one order list OL2, the number of order lists may be the same as the number of orders. It is also assumed that one of the two companies has ordered 50 units of item A and 20 units of item D, and the other has ordered 25 units of item B and 10 units of item C.

[0068] Picklist PL1 specifies that 50 units of item A and 25 units of item B are to be picked. A worker assigned to picklist PL1 picks 50 units of item A and 25 units of item B. The work productivity of picklist PL1 is 10 units / hour. Picklist PL2 specifies that 10 units of item C and 20 units of item D are to be picked. A worker assigned to picklist PL2 picks 10 units of item C and 20 units of item D. The work productivity of picklist PL2 is 30 units / hour. In this case, the overall work productivity, that is, the average work productivity of picklist PL1 and picklist PL2, is 20 units / hour.

[0069] When picking work is performed based on picklist PL1 and picklist PL2, a bottleneck may occur between the picking process and the assembling process because the work productivity of picklist PL1 and picklist PL2 differs. Also, because products to be shipped to the same customer are distributed across picklist PL1 and picklist PL2, a bottleneck may occur between the assembling process and the packing process.

[0070] Figure 4 is a sequence diagram illustrating an example of a conventional picklist generation flow. Each process shown in Figure 4 is executed by various functions of the processor 10 of the warehouse management system 2. It is assumed that the user operating the warehouse management system 2 is aware of the content of the order at the start of the sequence diagram shown in Figure 4.

[0071] The parameter manual setting function 11A of the setting unit 11 accepts input of manual setting parameters manually operated by the user (step S200). The setting unit 11 accepts input of a location P1, a list unit quantity P2, a shipping date and time P3, and a process path P4.

[0072] The setting unit 11 sets the manually set parameters based on the input of the manually set parameters received in step S200 (step S201). The manually set parameters set in step S201 are used by the generating unit 13.

[0073] The allocation unit 12 receives the order list from the order receiving server 80 (step S202).

[0074] The allocation unit 12 allocates inventory for the ordered product based on the order list received in step S202 and the inventory database 23 (step S203). If the allocation unit 12 is unable to allocate inventory, it notifies the user that there is no inventory by displaying a message on the display device 40. In this case, the processor 10 stops the subsequent processing.

[0075] When inventory is allocated to the ordered product in step S203, the parameter automatic setting function 12B of the allocation unit 12 sets automatic adjustment parameters (step S204). The order list received by the allocation unit 12 in step S202 and the automatic adjustment parameters set in step S204 are used by the generation unit 13.

[0076] The parameter extraction function 13B of the pick list generation function 13A of the generation unit 13 extracts parameters of the section P9, the tier P10, the shelf opening P11, and the process path P4 based on the manually set parameters set in step S201 (step S205). The process path P4 extracted in step S205 is used by the work authority granting function 14.

[0077] The pick list generation function 13A generates a pick list based on the order list, the automatic adjustment parameters set in step S204, and the various parameters extracted in step S205 (step S206).

[0078] The work authority granting function 14 checks the qualifications held by each worker (step S207). At this time, the work authority granting function 14 may display the check results as a list on the display device 40. At this time, the work authority granting function 14 may also display a list of workers who hold qualifications related to the process path P4 on the display device 40.

[0079] The work authority granting function 14 accepts the granting of work authority to the worker through manual operation by the user (step S208). Information on the work authority granted to the worker is used by the generation unit 13.

[0080] The pick list generation function 13A of the generation unit 13 determines the allocation of the pick list generated in step S206 to the workers based on the work authority granted to the workers in step S208 (step S209).

[0081] The generation unit 13 outputs the pick list assigned to the worker in step S209 to the user terminal 90 of the worker (step S210).

[0082] The worker then performs the picking work based on the pick list. This is the conventional method for generating a pick list.

[0083] Next, generation of a picklist according to this embodiment and warehouse work based on a picklist generated by a method according to this embodiment will be described with reference to Figures 5, 6, and 7. Figure 5 is a schematic diagram showing an example of warehouse work and a picklist using warehouse system 1 according to embodiment 1.

[0084] When the warehouse management system 2 receives an order, in other words, a shipping request, it accepts manual parameter settings, allocates inventory, and generates a pick list. The manual parameter settings are accepted by a manual parameter setting function 11A. The manual parameter setting function 11A accepts at least the setting of a shipping date and time P3. The inventory allocation function 12A allocates inventory. The pick list is generated by a pick list generation function 13A. When generating a pick list, the packing optimization function 13C adjusts the work productivity and bundling rate of the generated pick list. The qualification confirmation function 15 also verifies the qualifications of each worker. The generated pick list is assigned to an appropriate worker based on the qualifications of each worker confirmed by the qualification confirmation function 15. Although not shown in FIG. 5, a manual assignment function 16 may confirm the qualifications of each worker instead of the qualification confirmation function 15. In this case, the manual assignment function 16 accepts the assignment of the pick list to a worker manually operated by the user.

[0085] The generated picklist is output to the user terminal 90, and the worker checks the picklist received by the user terminal 90 and starts warehouse work. The generated picklist may be printed, for example. The worker may then check the printed picklist and start warehouse work. Warehouse work involves picking ordered items based on the generated picklist, assembling the picked items for each customer, in other words, according to the shipping destination, packing, and shipping. Warehouse work based on a picklist generated by the method according to this embodiment can prevent bottlenecks from occurring between the picking process and the assembling process, and between the assembling process and the packing process. This is because the picklist generated by the method according to this embodiment has high work productivity or a bundling rate.

[0086] Picklist PL3 ​​and picklist PL4 are examples of picklists generated by the warehouse management system 2 after receiving order list OL2 using the method according to this embodiment. The warehouse management system 2 generates a picklist so that 50 units of item A, 25 units of item B, 10 units of item C, and 20 units of item D are to be picked. As with the explanation with reference to FIG. 3, it is assumed that there are two orders. It is also assumed that one of the two companies has ordered 50 units of item A and 20 units of item D, and the other company has ordered 25 units of item B and 10 units of item C.

[0087] Picklist PL3 ​​specifies that 50 units of item A and 20 units of item D are to be picked. The worker assigned to picklist PL3 ​​picks 50 units of item A and 20 units of item D. The work productivity of picklist PL3 ​​is 25 units / hour. Picklist PL4 specifies that 25 units of item B and 10 units of item C are to be picked. The worker assigned to picklist PL4 picks 25 units of item B and 10 units of item C. The work productivity of picklist PL4 is also 25 units / hour. In this case, the overall work productivity, that is, the average work productivity of picklist PL3 ​​and picklist PL4, is 25 units / hour.

[0088] In the example of FIG. 5, the average work productivity of each of picklists PL3 and PL4 is 25 pieces / hour. In the example of FIG. 3, the average work productivity of each of picklists PL1 and PL2 is 20 pieces / hour. Therefore, the picking work in the example of FIG. 5 is more efficient than the picking work in the example of FIG. 3. Because the work productivity of picklist PL1 generated by the conventional method is 10 pieces / hour, picking work based on picklist PL1 is more efficient and likely to be completed faster than picking work based on picklist PL3 ​​or picklist PL4. However, the overall work productivity is 20 pieces / hour in the example of FIG. 3 and 25 pieces / hour in the example of FIG. 5. Therefore, the overall picking work is more efficient and likely to be completed faster when performed based on a picklist generated by the method of this embodiment than when performed based on a picklist generated by the conventional method.

[0089] In the example of Figure 5, 50 units of item A and 20 units of item D need to be shipped to one company. The 50 units of item A and 20 units of item D are picked based on picklist PL3, so they are not distributed across multiple picklists. In other words, the packing rate for items A and D in picklist PL3 ​​is high. In the example of Figure 5, 25 units of item B and 10 units of item C need to be shipped to one company. The 25 units of item B and 10 units of item C are picked based on picklist PL4, so they are not distributed across multiple picklists. In other words, the packing rate for items B and C in picklist PL4 is high. Therefore, bottlenecks between the consolidation process and the packing process can be reduced.

[0090] Fig. 6 is a sequence diagram illustrating an example of the flow of picklist generation according to the first embodiment. Each process shown in Fig. 6 is executed by various functions of the processor 10 of the warehouse management system 2. It is assumed that the user operating the warehouse management system 2 is aware of the content of the order at the start of the sequence diagram shown in Fig. 6.

[0091] The parameter manual setting function 11A of the setting unit 11 accepts input of manually set parameters manually operated by the user (step S300). The setting unit 11 accepts input of at least a shipping date and time P3. The setting unit 11 may also accept input of a location P1, a list unit quantity P2, and a process path P4. In this case, these manually set parameters may be added to the constraints of the mathematical optimization model M1 together with the shipping date and time P3 in step S304 described below.

[0092] The setting unit 11 sets the manually set parameters based on the input of the manually set parameters received in step S300 (step S301). The manually set parameters set in step S301 are used by the generating unit 13.

[0093] The allocation unit 12 receives the order list from the order receiving server 80 (step S302).

[0094] The allocation unit 12 allocates inventory for the ordered product based on the order list received in step S302 and the inventory database 23 (step S303). At this time, if the allocation unit 12 is unable to allocate inventory, it notifies the user that there is no inventory by displaying a message on the display device 40 that there is no inventory. In this case, the processor 10 cancels subsequent processing. The order list received by the allocation unit 12 in step S302 is used by the generation unit 13.

[0095] The packing optimization function 13C of the pick list generation function 13A of the generation unit 13 adds the manually set parameters set in step S301 to the constraints of the mathematical optimization model M1 (step S304). As a result, a pick list is generated based on the shipping date and time P3 set in step S301. For example, if a location P1 is set in addition to the shipping date and time P3 in step S301, the set location P1 is added to the constraints, and a pick list is generated that includes products stored in a specific section, floor, or shelf opening.

[0096] The generation unit 13 acquires the past work results from the past results database 21 (step S305).

[0097] The packing optimization function 13C calculates the work productivity of a certain product, the work productivity for each combination of multiple products, the work productivity of past pick lists, and the product bundling rate in past pick lists based on the past work performance obtained in step S305 (step S306).

[0098] The qualification confirmation function 15 confirms the qualifications held by each worker (step S307). The qualification information of each worker confirmed by the qualification confirmation function 15 is used by the generation unit 13 to assign workers to the generated pick list.

[0099] The packing optimization function 13C generates one or more pick lists based on the shipping date and time P3 added to the constraints of the mathematical optimization model M1 in step S304, the order list, and the work productivity and packing rate calculated in step S306, and assigns the generated one or more pick lists to suitable workers based on the qualification information of each worker (step S308).

[0100] The generation unit 13 outputs the pick list generated in step S308 and assigned to the worker to the user terminal 90 of the worker (step S309).

[0101] Then, the workers perform picking work based on the pick list. In the example of FIG. 6, the qualification verification function 15 verifies the qualifications of each worker, and the warehouse management system 2 automatically allocates pick lists to each worker. However, the qualification verification of each worker may be performed by the manual allocation function 16, and the user may manually allocate pick lists to workers. Referring to FIG. 7, an example of the functioning of the manual allocation function 16 is shown. Note that the same reference numerals are used for the same contents as those in the sequence diagram shown in FIG. 6, and the description will be omitted as appropriate.

[0102] Fig. 7 is a sequence diagram illustrating an example of the flow of picklist generation according to the first embodiment. Each process shown in Fig. 7 is executed by various functions of the processor 10 of the warehouse management system 2. It is assumed that the user operating the warehouse management system 2 is aware of the content of the order at the start of the sequence diagram shown in Fig. 7.

[0103] The packing optimization function 13C of the generation unit 13 generates one or more pick lists based on the shipping date and time P3 added to the constraints of the mathematical optimization model M1 in step S304, the order list, and the work productivity and bundling rate calculated in step S306, and determines the provisional allocation of the one or more generated pick lists to workers (step S310).The one or more generated pick lists are used by the manual allocation function 16 for official worker allocation through manual operation by the user.

[0104] The manual allocation function 16 checks the qualifications held by each worker (step S311). Then, the manual allocation function 16 accepts the official allocation of the pick list generated in step S310 to the workers through manual operation by the user (step S312). At this time, the user may decide on the official allocation by referring to the provisional allocation in step S310 and the qualifications held by each worker checked in step S311.

[0105] The manual allocation function 16 outputs the pick list allocated to the worker in step S312 to the user terminal 90 of the worker (step S313). Then, the worker performs the picking work based on the pick list.

[0106] The qualification confirmation function 15 and the manual allocation function 16 may be used as follows. For example, a user of the warehouse management system 2 may use the qualification confirmation function 15 immediately after starting work for the day. In this case, processing is performed according to the sequence shown in Figure 6, and the generated pick list is automatically assigned to the worker. Because the user does not manually assign the pick list to the worker, the assignment may not be optimal, but a good assignment is performed quickly.

[0107] Furthermore, users of the warehouse management system 2 may use the manual allocation function 16, for example, during the late hours of the day or when the number of orders decreases. This is based on the following assumption: Shipping instructions are issued, for example, at intervals of several times throughout the day. The later in the day it is, the fewer orders there tend to be. The later in the day it is, the higher the likelihood of an emergency shipment. Under these assumptions, during the late hours of the day or when the number of orders is decreasing, experienced warehouse workers have more free time. Therefore, even if it takes a skilled worker some time to manually allocate the picklist, the overall efficiency of warehouse operations can be improved. In this case, the process is performed according to the sequence shown in Figure 7, and the generated picklist is manually allocated to a worker. When a picklist is manually allocated to a worker, it is likely to be allocated to the most suitable worker.

[0108] As described above, the warehouse management system 2 includes a setting unit 11 that accepts the setting of the shipping date and time P3 of the product. The warehouse management system 2 also includes an allocation unit 12 that allocates inventory for the ordered product. The warehouse management system 2 also includes a generation unit 13 that generates one or more picklists based on the order, shipping date and time P3, and past work performance when the allocation unit 12 allocates inventory for the ordered product. The past work performance includes the work time required by workers to pick products in past picking operations and previously generated picklists. The picklist specifies the order in which workers pick products. The one or more picklists are generated so that the work productivity, the bundling rate, or both are as follows: That is, the one or more picklists are generated so that the average work productivity of the one or more picklists is equal to or greater than a specified value. Alternatively, the one or more picklists are generated so that the bundling rate of the products included in each of the one or more picklists is equal to or greater than a specified value. Note that work productivity is the number of products picked based on the picklist relative to the total work time of one or more workers assigned to the picklist. The bundling rate of products in a picklist is the number of destinations for the products included in the picklist relative to the number of orders for the products. Picklists generated by the warehouse management system 2 do not depend on the capabilities of workers, which improves the efficiency of picking work. Furthermore, a high bundling rate of products in the picklists generated by the warehouse management system 2 improves the efficiency of consolidation, which is a subsequent task after picking work.

[0109] (Other variations) In the above embodiment, the generation of a pick list for a product has been described. However, the generation of a pick list based on the concept of the above embodiment can be applied to all goods, including items that are not commercially distributed, such as novelty goods.

[0110] In the above-described embodiment, optimization was performed so that the average value of work productivity of the picklist was equal to or greater than a specified value. However, optimization may also be performed so that the average value of work productivity is maximized or so that the variation in the average value of work productivity is minimized (leveled). Optimization may also be performed using statistical values ​​other than the average, such as the median. Furthermore, the value or condition of work productivity targeted for optimization may be changed depending on the balance between the user's desired picking efficiency and the waiting time for item matching. For example, if the goal is to maximize the average value, picking efficiency as a whole may be improved. However, in this case, there is a possibility that variations in work productivity may occur between individual picklists, making it easier for waiting times to occur during the matching process. Furthermore, if the goal is to level the average value, waiting times for item matching are less likely to occur. However, in this case, the efficiency of the picking operation may be reduced. Furthermore, if the goal is to set the average value, etc., equal to or greater than a specified value, the overall efficiency of the picking operation can be improved while reducing the possibility of generating a picking list with extremely low work productivity that significantly reduces the average value, etc. However, this may not provide optimal results in terms of both picking and waiting time for assembly.

[0111] In the above-described embodiment, optimization was performed so that the bundling rate was equal to or greater than a specified value. However, optimization may also be performed so that the bundling rate is maximized or so that variations in the bundling rate are minimized (leveled out).

[0112] In the above-described embodiment, optimization may be performed taking into account only either work productivity or the bundling rate. When optimizing the efficiency of the consolidation process, only work productivity needs to be considered. When optimizing the efficiency of the packing process, only the bundling rate needs to be considered. However, when improving the overall efficiency of the shipping process, a partially optimal solution obtained by optimizing only a portion of the process is not necessarily an appropriate solution. For example, even if the efficiency of the consolidation process is improved, if the efficiency of the packing process is low, the time required for shipping will ultimately be longer. Similarly, even if the efficiency of the packing process is high, if the efficiency of the consolidation process is low, there will be a wait time for the consolidation process to be completed. Therefore, when searching for an overall optimal solution for the shipping process, it is advisable to perform optimization taking into account both work productivity and the bundling rate. As described above, there is a trade-off between the efficiency of the consolidation process and the efficiency of the packing process. However, by performing mathematical optimization that deliberately takes into account the factor that creates the trade-off (the bundling rate), it is possible to obtain a solution that is optimal for the shipping process as a whole, even if it may not be optimal for either the consolidation process or the packing process.

[0113] In the above-described embodiment, the generation of a pick list begins after inventory allocation. However, a pick list may be generated without inventory allocation. For example, when simulating the generation of a pick list assuming no inventory constraints, inventory allocation does not need to be taken into account.

[0114] (Summary of the first embodiment) The above description of the first embodiment discloses at least the following techniques. Note that the components corresponding to the first embodiment are shown in parentheses, but the present invention is not limited to these.

[0115] (Technology 1) A picklist generation device (e.g., warehouse management system 2) that generates one or more picklists based on an order for an item includes a setting unit (e.g., setting unit 11) that accepts the setting of the shipping date and time of the item, and a generation unit (e.g., generation unit 13) that generates one or more picklists based on the order, shipping date and time, and past work performance so that the work productivity of the one or more picklists meets specified conditions, where the past work performance includes the work time required by workers to pick items in past picking work, the picklist specifies the order in which the workers pick items, and the work productivity is the number of items picked based on the picklist relative to the total work time of one or more workers to whom the picklist is assigned.

[0116] This allows the pick list generation device to generate a pick list that is independent of the capabilities of the worker and that takes into account causes of delays that may occur at the picking site, thereby improving the efficiency of picking work and ultimately warehouse work as a whole.

[0117] (Technology 2) In the pick list generation device described in Technology 1, the generation unit generates one or more pick lists so that the packing rate of items included in each of the one or more pick lists is equal to or greater than a specified value, the past work performance further includes pick lists generated in the past, and the packing rate of items in the pick list is a value indicating the total order quantity of the items included in the pick list for each destination.

[0118] This increases the rate at which items are packed together in the pick list generated by the pick list generation device, making packing work more efficient.

[0119] (Technology 3) In the pick list generation device according to the first or second technique, the generation unit assigns the generated pick list to a worker based on information about the worker's work qualifications at the work site.

[0120] This allows the picklist generation device to assign the generated picklist to an appropriate worker.

[0121] (Technology 4) In the pick list generation device according to any one of the first to third techniques, the generation unit temporarily assigns the generated pick list to a worker, and accepts assignment of the pick list to the worker by a user operation.

[0122] This allows the picklist generation device to accept assignment of a picklist to a worker through a user operation, thereby realizing assignment of a picklist to a worker based on the user's experience, etc.

[0123] (Technology 5) In the pick list generation device described in any one of techniques 1 to 4, the pick list generation device further includes an allocation unit (e.g., allocation unit 12) that allocates inventory for ordered products, and the generation unit generates one or more pick lists when the allocation unit allocates inventory for the ordered products, and when the allocation unit cannot allocate inventory, the allocation unit notifies that there is no inventory.

[0124] This allows the pick list generation device to notify the user if inventory cannot be allocated.

[0125] (Technology 6) A picklist generation method for generating one or more picklists based on an order for an item receives a setting of a shipping date and time for the item, and generates one or more picklists based on the order, shipping date and time, and past work performance so that the work productivity of the one or more picklists satisfies specified conditions, where the past work performance includes the work time required by workers to pick items in past picking work, the picklist specifies the order in which the workers pick the items, and the work productivity is the number of items picked based on the picklist relative to the total work time of one or more workers to whom the picklist is assigned.

[0126] As a result, the picklist generation method can achieve the same effect as Technique 1.

[0127] The functions of the above-described embodiments can also be realized by supplying programs and applications for realizing the functions of the above-described embodiments to a system or device using a network or storage medium, etc., and having one or more processors in the computer of the system or device read and execute the programs.

[0128] Furthermore, the functions of the above-described embodiments may be realized by a circuit that realizes one or more functions (for example, an Application Specific Integrated Circuit (hereinafter referred to as "ASIC") or an FPGA).

[0129] Although the embodiments of the present disclosure have been described above with reference to the drawings, it goes without saying that the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, alterations, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure. Furthermore, the components of the above-described embodiments may be combined in any manner as long as they do not deviate from the spirit of the invention. [Industrial Applicability]

[0130] The present disclosure is useful for a picklist generation device and a picklist generation method. [Explanation of symbols]

[0131] 1. Warehouse System 2 Warehouse Management System 10 processors 11 Setting section 12. Provision Department 13 Generation part 20 memory 21 Past performance database 22 Calculation results database 23 Inventory Database 30 Input Devices 40 Display device 50 Communication interface device 60 On-site PC 70 Warehouse Operation Management System 80 Order Server 90 User terminals M1 Mathematical Optimization Model OL1, OL2 order list PL1,PL2,PL3,PL4 Picklist

Claims

1. A pick list generator that generates one or more pick lists based on an order for an item, the pick list generator comprising: a setting unit that accepts a setting of a shipping date and time of the item; a generation unit that generates the one or more pick lists based on the order received, the shipping date and time, and past work performance so that work productivity of the one or more pick lists satisfies a predetermined condition, The past work performance includes a work time required for a worker to pick an item in a past picking work, the pick list defines an order in which the items are to be picked by the workers; The work productivity is the number of items picked based on the pick list relative to the total work time of one or more workers to whom the pick list is assigned. Picklist generator.

2. the generation unit generates the one or more pick lists so that a packing rate of the items included in each of the one or more pick lists is equal to or greater than a specified value; The past work history further includes a pick list generated in the past; The bundling rate of the items in the picklist is a value indicating the total order quantity of the items for each destination of the items included in the picklist. The picklist generator of claim 1 .

3. the generation unit assigns the generated pick list to the worker based on information on the worker's work qualification at the work site. The picklist generator of claim 1 .

4. the generation unit temporarily assigns the generated pick list to the worker; Accepting an assignment of the picklist to the worker through a user operation; The picklist generator of claim 1 .

5. The system further includes an allocation unit that allocates inventory to ordered products, the generation unit generates the one or more pick lists when the allocation unit allocates inventory for the ordered product; If the allocation unit is unable to allocate inventory, it notifies the customer that there is no inventory. The picklist generator of claim 1 .

6. A pick list generation method for generating one or more pick lists based on an order for an item, the method comprising: receiving a setting of a shipping date and time for the item; allocating inventory for the ordered item; generating one or more pick lists based on the order, the shipping date and time, and past work performance data so that an average value of work productivity of the one or more pick lists is equal to or greater than a specified value when inventory is allocated to the ordered item, the past work performance data including work time required for workers to pick items in past picking work; the pick list defines an order in which the items are to be picked by the workers; The work productivity is the number of items picked based on the pick list relative to the total work time of one or more workers to whom the pick list is assigned. Picklist generation method.

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