Work process modeling method and work process modeling system

Abstract

This approach aims to reduce the man-hours required for modeling work processes, eliminate reliance on individual expertise, and improve reproducibility. [Solution] The work process modeling method obtains work standard data that defines at least the required time for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order, from the warehouse operation management system. Based on the work standard data and work instruction data, it generates work list data to be processed in a simulation that predicts the required time for each task in the target warehouse, and performs the settings to use the work list data in the simulation.

Description

Technical Field

[0001] The present disclosure relates to a work process modeling method and a work process modeling system.

Background Art

[0002] Patent Document 1 discloses a simulation model generation method for automatically generating a simulation model for the management of warehouse operations according to the warehouse to be simulated. This simulation model generation method receives the designation of the warehouse to be simulated by user operation, and acquires slot information including the positions and sizes of slots for storing articles held by the warehouse to be simulated from a warehouse management system according to the designation. Further, the simulation model generation method generates a simulation model for managing the operations in the warehouse to be simulated based on the slot information.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] To construct a simulation model for managing warehouse operations in a target warehouse, it is necessary to model (in other words, define) the actual work processes performed by workers in that warehouse and how long those processes take. In the following explanation, a work process is defined as at least one work process performed by workers in a target warehouse. However, work processes tend to be complex and differ in detail for each target warehouse. For this reason, conventional methods such as interviewing workers about their work content have been used to model work processes. Therefore, ensuring the amount of work required for modeling and its reproducibility (i.e., whether the simulation results for warehouse operations will have a certain level of reliability) has been a challenge. Patent Document 1 also does not mention the generation of a simulation model targeting work processes performed by workers in a target warehouse.

[0005] This disclosure was devised in light of the aforementioned conventional circumstances and aims to achieve both a reduction in the man-hours required for modeling work processes, elimination of reliance on individual expertise, and improvement of reproducibility. [Means for solving the problem]

[0006] This disclosure is, A work process modeling method performed by a computer device, The system obtains work standard data from the warehouse operation management system, which defines at least the time required for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order. Based on the work standard data and the work instruction data, it generates a list of tasks to be processed in a simulation that predicts the time required for each task in the target warehouse, and uses the list of tasks data in the simulation. like Setting Determine This provides a method for modeling work processes.

[0007] Furthermore, this disclosure includes a processor and memory, the processor, in cooperation with the memory, obtains from a warehouse operation management system work standard data that defines at least the time required for each standard task performed in the target warehouse, and work instruction data that instructs the sequence of multiple tasks specified for each order, generates a list of tasks to be processed in a simulation that predicts the time required for each task in the target warehouse based on the work standard data and the work instruction data, and uses the list of tasks to be processed in the simulation. like Setting Determine We provide a work process modeling system.

[0008] These comprehensive or specific embodiments may be implemented as systems, devices, methods, integrated circuits, computer programs, or recording media, or as any combination of systems, devices, methods, integrated circuits, computer programs, and recording media. [Effects of the Invention]

[0009] According to this disclosure, it is possible to achieve both a reduction in the man-hours required for modeling work processes, elimination of reliance on individual expertise, and improvement of reproducibility. [Brief explanation of the drawing]

[0010] [Figure 1] This figure shows an example of the system configuration of the work process modeling system according to this embodiment. [Figure 2] A flowchart showing an example of the overall operation procedure of the work process modeling method according to this embodiment, in chronological order. [Figure 3] Table showing an example of task list data. [Figure 4] A flowchart showing a time-series example of the operation procedure for a simulation based on a task list. [Figure 5] Table showing an example of work standard data. [Figure 6] Table showing an example of work instruction data. [Figure 7]A flowchart showing an example of the operation procedure of work based on work instructions in chronological order [Figure 8] A table showing an example of work location data [Figure 9] A table showing an example of passageway data [Figure 10] A table showing an example of work area data [Figure 11] A plan view of an example layout inside the target warehouse [Figure 12] A table showing an example of work performance data [Figure 13] A table showing an example of slot position data [Figure 14] A table showing an example of passageway position data [Figure 15] A flowchart showing an example of the generation procedure of passageway position data in chronological order [Figure 16] A diagram showing an example of state transition related to the generation of passageway position data [Figure 17] A diagram showing an example of state transition related to the generation of passageway position data following Figure 16 [Figure 18] A diagram showing an example of state transition related to the generation of passageway position data following Figure 17 [Figure 19] A diagram showing an example of state transition related to the generation of passageway position data following Figure 18 [Figure 20] A diagram showing an example of state transition related to the generation of passageway position data following Figure 19 [Figure 21] A diagram showing an example of state transition related to the generation of passageway position data following Figure 20

Embodiments for Carrying Out the Invention

[0011] Hereinafter, with appropriate reference to the drawings, embodiments specifically disclosing the work process modeling method and work process modeling system relating to this disclosure will be described in detail. However, unnecessarily detailed explanations may be omitted. For example, detailed explanations of already well-known matters and redundant explanations of substantially identical configurations may be omitted. This is to avoid the following explanation becoming unnecessarily verbose and to facilitate understanding by those skilled in the art. The accompanying drawings and the following explanation are provided to enable those skilled in the art to fully understand this disclosure and are not intended to limit the subject matter of the claims. Furthermore, in the following explanation, the same elements may be assigned the same reference numerals to simplify or omit explanations.

[0012] 1. Overview of the Work Process Modeling System First, let's explain the overview of the work process modeling system 100 according to this embodiment. Conventionally, as mentioned above, personal methods such as interviewing workers about their work content have been used for modeling work processes. For example, in an interview, the engineer in charge of simulation might ask a warehouse worker, "What specific tasks do workers who pick items perform?" The warehouse worker might reply, "The details and location of the items are displayed on each person's terminal, so we pick them according to that." The engineer might then ask, "For example, do you use any equipment necessary for picking, such as pallet jacks or carts?" The warehouse worker might reply, "Yes. The ID of the necessary equipment is displayed on the terminal, so we go to the equipment storage area to get it." This kind of question-and-answer session is repeated endlessly. In other words, not only is it necessary to exchange information multiple times, but the answers from warehouse workers can vary depending on how the engineer asks the questions.

[0013] Therefore, the work process modeling system 100 according to this embodiment uses the warehouse operation management system 10 (commonly known as WES), described later, to model each work process of multiple tasks performed in the target warehouse, and configures the system to use the modeling results for simulation. The engineer in charge of the simulation confirms with the warehouse staff, "Does this simulation seem correct?" The warehouse staff replies, "It seems correct." In this way, the interaction between the engineer and the warehouse staff is limited to final confirmation, so that stable (i.e., highly reproducible) simulation results can be obtained regardless of the skill level of the engineer in charge.

[0014] 2. Configuration of the work process modeling system First, the system configuration of the work process modeling system 100 according to this embodiment will be described with reference to Figure 1. Figure 1 is a diagram showing an example of the system configuration of the work process modeling system 100 according to this embodiment. The work process modeling system 100 includes at least a warehouse operation management system 10, an automatic work process modeling device 20, and a warehouse SIM device 50. In the work process modeling system 100, the automatic work process modeling device 20 and the warehouse SIM device 50 are configured as separate units, but they may be configured as an integrated unit. Also, in the following explanation, the worker will be described as a "person," but the worker may be a robot such as a "transportation device."

[0015] The warehouse operation management system 10, the work process automatic modeling device 20, and the warehouse SIM device 50 are each connected to each other via a wired or wireless network, enabling data communication between them. The wireless network may be a network such as a Wide Area Network (WAN), Local Area Network (LAN), Long Term Evolution (LTE), 4G, 5G, or other mobile communication network, power line communication, short-range wireless communication (e.g., Bluetooth® communication), or mobile phone communication network. The wired network may be a wired LAN or a wired WAN, for example.

[0016] The warehouse operation management system 10 is also called a Warehouse Execution System (WES). The warehouse operation management system 10 is a computer system that has the function of giving work instructions to workers or managing the progress of work performed in the warehouse for each of the multiple tasks specified for each order entering the warehouse. The warehouse operation management system 10 includes at least a data storage unit 11.

[0017] The data storage unit 11 stores work standard data D1 (see Figure 5), work instruction data D2 (see Figure 6), work location data D3 (see Figure 8), aisle data D4 (see Figure 9), and work performance data D5 (see Figure 12). Note that aisle data D4 is not stored in the data storage unit 11 and may be generated by a method described later. Work standard data D1, work instruction data D2, work location data D3, aisle data D4, and work performance data D5 will be described later with reference to Figures 5, 6, 8, 9, and 12.

[0018] The automated work process modeling device 20 is configured using, for example, a personal computer (PC) or a server computer. The automated work process modeling device 20 uses various data acquired from the warehouse operation management system 10 to perform setting (modeling) for each work process of multiple tasks performed in the target warehouse, which will be used for simulations performed by the warehouse SIM device 50. Using the results of the modeling, the automated work process modeling device 20 instructs the warehouse SIM device 50 to perform a simulation of the required time for each of the multiple tasks defined for each order entering the target warehouse. The work processes to be modeled are defined by the work list data D6 and aisle position data D7, which will be described later. The automated work process modeling device 20 includes a processor 21, a memory 22, and an input / output I / F 23. The automated work process modeling device 20 is connected to enable input and output of data signals between an input device 30 such as a mouse that can accept user operations and a display device 40 such as a display that can output various display screens.

[0019] The processor 21 is composed of at least one of the following: a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or a Graphical Processing Unit (GPU). The processor 21 functions as a controller that manages the overall operation of the automated work process modeling device 20. The processor 21 performs control processing to coordinate the operation of each part of the automated work process modeling device 20, data input / output processing between each part of the automated work process modeling device 20, data calculation processing, and data storage processing. The processor 21 operates according to the program stored in the memory 22. During operation, the processor 21 uses the memory 22 to cooperate in executing various processes and temporarily stores data generated or acquired by the processor 21 in the memory 22. By cooperating with the memory 22, the processor 21 realizes the functions of the automated work process modeling unit 21a and the automated work process modification unit 21b.

[0020] The automated work process modeling unit 21a uses various data received from the warehouse operation management system 10 to generate various data to be processed in a simulation that predicts the time required for each type of work performed in the target warehouse (i.e., various setting data to be used when running the simulation). This generated setting data includes, for example, work list data D6 (see Figure 3), which is the work to be processed in a simulation that predicts the time required for each type of work performed in the target warehouse, and aisle location data D7 (see Figure 14), which defines the location of the aisles through which workers pass within the target warehouse. Details of the work list data D6 and aisle location data D7 will be described later with reference to Figures 3 and 14. The automated work process modeling unit 21a sends simulation setting instructions (i.e., setting instructions for using the work list data D6 and aisle location data D7 during the simulation) including the various data to be processed in the simulation to the warehouse SIM device 50 via the input / output I / F 23.

[0021] The work process modification unit 21b modifies at least a portion of the work list data D6 or aisle position data D7, or both, related to the current work process, based on the data signal from the input device 30 resulting from user operation when the SIM result D8 sent from the warehouse SIM device 50 is displayed on the display device 40. The work process modification unit 21b sends a simulation setting instruction (see above) including the modified work list data D6 or aisle position data D7 to the warehouse SIM device 50 via the input / output I / F 23.

[0022] Memory 22 is configured, for example, using Random Access Memory (RAM) and Read Only Memory (ROM), and temporarily stores programs necessary for the operation of the automated work process modeling device 20, as well as data acquired or generated during operation. RAM is, for example, work memory used during the operation of the automated work process modeling device 20. ROM stores, for example, programs for controlling the automated work process modeling device 20 in advance. Memory 22 temporarily stores various data generated by the processor 21, or various data received and acquired from the warehouse operation management system 10 or the warehouse SIM device 50.

[0023] The input / output interface 23 is configured using interface circuits that enable input and output of data signals to and from the warehouse operation management system 10, the input device 30, the display device 40, and the warehouse SIM device 50. The input / output interface 23 receives data signals from the input device 30 via user operation and sends them to the processor 21, and sends data signals from the processor 21 to the display device 40. The input / output interface 23 also receives data from the warehouse operation management system 10 and transmits and receives data with the warehouse SIM device 50.

[0024] The warehouse SIM device 50 is configured using, for example, a personal computer (PC) or a server computer. Based on instructions from the automated work process modeling device 20, the warehouse SIM device 50 performs simulations of the time required for each of several tasks defined for each order entering the target warehouse. The warehouse SIM device 50 sends the SIM result D8, which is the result of the simulation, to the automated work process modeling device 20. This SIM result D8 may include not only data on the processing results of the simulation of the time required for each task, but also data indicating whether the processing results meet predetermined pass / fail criteria.

[0025] The processor 51 is composed of at least one of the following: a CPU, DSP, FPGA, or GPU. The processor 51 functions as a controller that oversees the overall operation of the warehouse SIM device 50. The processor 51 performs control processing to coordinate the operation of each part of the warehouse SIM device 50, data input / output processing between each part of the warehouse SIM device 50, data calculation processing, and data storage processing. The processor 51 operates according to the program stored in the memory 52. ​​During operation, the processor 51 uses the memory 52 to perform various processes in cooperation with it and temporarily stores data generated or acquired by the processor 51 in the memory 52. ​​By cooperating with the memory 52, the processor 51 realizes the functions of the work simulation unit 51a and the simulation evaluation unit 51b.

[0026] The work simulation unit 51a performs a simulation of the time required for each of several tasks defined for each order entering the target warehouse, based on instructions from the work process automatic modeling device 20. The method for processing this simulation may be implemented using already known means (for example, calculations using an evaluation function predetermined for simulation), or by other methods.

[0027] The simulation evaluation unit 51b reads from memory 52 the threshold values ​​that determine the pass / fail status of the required time for each type of work, which are pre-registered in memory 52, and evaluates whether the processing result of the work simulation unit 51a is less than or equal to the threshold value for the required time of the corresponding work. The simulation evaluation unit 51b generates a SIM result D8 that includes the processing result (e.g., required time) from the work simulation unit 51a and the evaluation result corresponding to that processing result, and sends it to the work process automatic modeling device 20.

[0028] Memory 52 is configured, for example, using RAM and ROM, and temporarily stores programs necessary for the operation of the warehouse SIM device 50, as well as data acquired or generated during operation. RAM is, for example, work memory used during the operation of the warehouse SIM device 50. ROM stores, for example, programs for controlling the warehouse SIM device 50 in advance. Memory 22 temporarily stores various data generated by the processor 51, or various data received and acquired from the warehouse operation management system 10 or the work process automatic modeling device 20.

[0029] Although not shown in Figure 1, the warehouse SIM device 50 has an input / output interface (I / F) with the same configuration as the I / F 23 of the automated work process modeling device 20. Data signals are transmitted and received between the warehouse operation management system 10 and the automated work process modeling device 20 via this I / F.

[0030] 3. Modeling Procedure for the Work Process Next, with reference to Figure 2, the modeling operation procedure of the work process modeling system 100 according to this embodiment will be described. Figure 2 is a flowchart showing an example of the overall operation procedure of the work process modeling method according to this embodiment in chronological order. The series of processes shown in Figure 2 are realized by the cooperation of the processor 21 and memory 22 of the work process automatic modeling device 20, and the cooperation of the processor 51 and memory 52 of the warehouse SIM device 50, respectively.

[0031] In Figure 2, the processor 21 acquires and extracts a set of data necessary for modeling the work process from the warehouse operation management system 10 (St1). This set of necessary data includes, for example, work standard data D1 (see Figure 5), work instruction data D2 (see Figure 6), work location data D3 (see Figure 8), and aisle data D4 (see Figure 9).

[0032] Here, we will explain the work standard data D1, work instruction data D2, work location data D3, aisle data D4, and work area data D11 with reference to Figures 5, 6, 7, 8, 9, and 10, respectively. Figure 5 is a table showing an example of work standard data D1. Figure 6 is a table showing an example of work instruction data D2. Figure 7 is a flowchart showing an example of the operation procedure for work based on work instructions in chronological order. Figure 8 is a table showing an example of work location data D3. Figure 9 is a table showing an example of aisle data D4. Figure 10 is a table showing an example of work area data. Note that although Figures 5, 6, 8, 9, and 10 illustrate the data format using tables, the data may be stored in a format other than tables.

[0033] First, let's explain the work standard data D1 shown in Figure 5. Work standard data D1 is data that predefines, for each of the multiple tasks performed within the target warehouse, the location within the warehouse where the task will be performed and completed, how long it will take, and the speed of movement associated with that task.

[0034] Specifically, the work standard data D1 has a data structure (e.g., a record) for each task that includes the task identification information (work process ID), the location where the work is performed within the target warehouse (work area ID, see Figure 10), the standard work time required for the worker (standard work time [s]), the standard movement speed of the worker during the work (standard movement speed [m / s]), and the content of the work (work content) (see Figure 5). In the example in Figure 5, for each of the eight tasks, a record is defined that includes the work process ID, work area ID, standard work time [s], standard movement speed [m / s], and work content. Note that the work standard data D1 corresponding to the number of records is created in advance for tasks that are expected to be performed within the target warehouse, and the number of records may be increased or decreased as appropriate in consideration of the situation of the target warehouse.

[0035] Work area data D11 is data that shows a list of work areas for various tasks performed within the target warehouse. Specifically, work area data D11 has a data structure (e.g., a record) for each work area, which is a work area, that includes work area identification information (work area ID) and details of that work area (area details) (see Figure 10). In the example in Figure 10, a record containing the work area ID and area details is defined for each of the five work areas. Note that the number of work area data D11 corresponding to the number of records is created in advance for the work areas of tasks that are expected to be performed within the target warehouse, and the number of records may be increased or decreased as appropriate in consideration of the situation of the target warehouse.

[0036] Next, we will explain the work instruction data D2 shown in Figure 6. Work instruction data D2 is data that specifies detailed instructions for each of the multiple tasks defined for each order entering the target warehouse (for example, work process, person in charge, work location, order of tasks, etc.).

[0037] Specifically, the work instruction data D2 has a data structure (e.g., a record) for each work instruction that includes the work instruction identification information (work instruction ID), the work process ID, the identification information of the worker responsible for performing the work (worker ID), the identification information of the work location where the work is performed locally (work location ID), and the identification information of the next work instruction to be performed (next work instruction ID) (see Figure 6). In the example in Figure 6, for each of the 14 work instructions, a record is defined that includes the work instruction ID, the work process ID, the worker ID, the work location ID, and the next work instruction ID. According to the flowchart in Figure 7, the work instruction data D2 shown in Figure 6 shows that, for example, the work instructions "W0001" to "W0014," which represent a series of tasks performed by a worker in the target warehouse in one day, are performed in chronological order. The next work instruction ID "NULL" in the record for work instruction ID "W0014" in Figure 6 indicates that work instruction ID "W0014" is the final work instruction. The work instruction data D2 corresponding to the number of records is created in advance based on a series of tasks specified in the order entering the target warehouse, and the number of records may be increased or decreased as appropriate depending on the content of the order.

[0038] Next, we will explain the work location data D3 shown in Figure 8. Work location data D3 is data that shows the local work locations for various tasks performed within the target warehouse, as well as their relationship to the work area.

[0039] Specifically, the work location data D3 has a data structure (e.g., a record) for each work location that includes the work location identification information (work location ID), the work area identification information (work area ID) that includes that work location, and the orthogonal 3D coordinates of the work location (X coordinate, Y coordinate, Z coordinate) (see Figure 8). In the example in Figure 8, a record containing the work location ID, work area ID, and orthogonal 3D coordinates is defined for each of the eight work locations. Note that the number of work location data D3 corresponding to the number of records is created in advance for the work locations of the work that is expected to be performed within the target warehouse, and the number of records may be increased or decreased as appropriate in consideration of the situation of the target warehouse, etc.

[0040] Here, with reference to Figure 11, the correspondence between work areas and work locations will be explained. Figure 11 is a plan view of an example layout within the target warehouse. Note that, not limited to Figure 11, in this embodiment, the X and Y coordinates are mutually orthogonal and define a planar direction parallel to the floor surface of the target warehouse. The Z coordinate is orthogonal to the X and Y coordinates and defines a vertical direction perpendicular to the floor surface of the target warehouse.

[0041] A work area indicates a work area that contains one work location for work performed within the target warehouse. A work location indicates one work location for work performed within the target warehouse. In the example in Figure 11, the work area with work area ID "WZ0002" contains one work location with work location ID "POS0002". Similarly, the work area with work area ID "WZ9001" contains four work locations with work location IDs "POS0010, POS0011, POS0012, POS0013". The work area with work area ID "WZ0001" contains one work location with work location ID "POS0001". The work area with work area ID "WZ8001" contains one work location with work location ID "POS0050". The work area with work area ID "WZ0003" contains one work location with work location ID "POS0051". Furthermore, the passage with passage ID "A0001" represents a passage connecting two points, with the coordinates (30, 160, 0) of work location ID "POS0001" as the starting or ending point, and the coordinates (40, 160, 0) as the ending or starting point.

[0042] Next, we will explain the aisle data D4 shown in Figure 9. Aisle data D4 is data that indicates the location of each of the multiple aisles provided within the target warehouse and whether or not they are passable to each other.

[0043] Specifically, the aisle data D4 has a data structure (e.g., a record) for each aisle that includes aisle identification information (aisle ID), the orthogonal 3D coordinates (X, Y, and Z coordinates) of the start and end points of the aisle, and a flag indicating whether or not it is possible to pass through both aisles (passability). (See Figure 9). In the example in Figure 9, a record containing the aisle ID, orthogonal 3D coordinates, and passability is defined for each of the 10 work locations. In the aisle in Figure 10, the passability is set to "TRUE" for all aisles, indicating that two-way passage is possible. Note that the aisle data D4 corresponding to the number of records is created in advance for the work locations of the work expected to be performed within the target warehouse, and the number of records may be increased or decreased as appropriate in light of the situation of the target warehouse. Alternatively, aisle data D4 may not be created in advance. In this case, the automated work process modeling device 20 generates aisle position data D7 (see Figure 14) using the method described later (see Figures 13 to 21).

[0044] Let's return to the explanation of Figure 2.

[0045] Processor 21 uses the set of data acquired in step St1 to perform a process (St2) that models the work process for each of the multiple tasks performed in the target warehouse. Specifically, Processor 21 uses the set of data acquired in step St1 to generate work list data D6 (see Figure 3) and aisle location data D7 (see Figure 14). For example, Processor 21 generates work list data D6 (see Figure 3) based on work standard data D1 (see Figure 5), work instruction data D2 (see Figure 6), and work location data D3. Processor 21 generates aisle location data D7 (see Figure 14) based on work location data D3 (see Figure 8) and aisle data D4 (see Figure 9).

[0046] The processor 21 generates setting instructions to be used when processing the simulation in the warehouse SIM device 50, along with the work list data D6 and aisle position data D7 generated in step St2, and requests the warehouse SIM device 50 to do so (St3). In other words, the processor 21 requests the warehouse SIM device 50 to apply the data generated in step St2 (in other words, the work process model) to the simulation in the warehouse SIM device 50.

[0047] In the warehouse SIM device 50, the processor 51 performs simulation processing using work list data D6 and aisle location data D7 based on the setting instructions sent from the work process automatic modeling device 20 in step St3 (see Figure 4). As mentioned above, aisle data D4 (see Figure 9) may not be stored in the warehouse operation management system 10 beforehand. In this case, the processor 21 generates aisle location data D7 (see Figure 14) according to the method described later (see Figures 13 to 21).

[0048] Here, with reference to Figures 3 and 4, the work list data D6 and the simulation processing procedure using work list data D6 and aisle position data D7 will be explained. Figure 3 is a table showing an example of work list data. Figure 4 is a flowchart showing an example of the operation procedure of a simulation based on the work list in chronological order. Work list data D6 is data that lists a series of tasks that are the subject of prediction (i.e., simulation) of the required time for each task defined in the work instruction data.

[0049] Specifically, the work list data D6 has a data structure (e.g., a record) for each work that includes a work ID corresponding to the work instruction ID, identification information of the person in charge of the work (person in charge ID), identification information of the work location where the work is performed (work location ID), work time [s] which is the predicted or expected time required for the work, and the movement speed [m / s] during the work (see Figure 3). In the example in Figure 3, for each of the 14 work items, corresponding to the number of work process IDs, the work ID, person in charge ID, work location ID, work time [s], and movement speed [m / s] are shown. Note that the number of records in the work list data D6 is generated in accordance with the number of work processes defined in the work instruction data D2, and may be adaptively increased or decreased in accordance with increases or decreases in the number of work processes in the work instruction data D2.

[0050] The warehouse SIM device 50 uses the work list data D6 and aisle position data D7 to calculate the time required for the work corresponding to the work ID of each record constituting the work list data D6, according to the processing procedure shown in Figure 4. In other words, as shown in Figure 4, the processor 51 determines whether or not the record in the work list data D6 is empty (St11). If it is determined that the record in the work list data D6 is empty (St11, TRUE), the series of processes by the processor 51 shown in Figure 4 is terminated.

[0051] If the processor 51 determines that the records in the work list data D6 are not empty (St11, FALSE), it retrieves the data of the first non-empty record (St12). The processor 51 then refers to the data of the retrieved record and runs a simulation to have the worker corresponding to the person in charge ID move to the specified work location at the specified speed (St13), and further runs a simulation to have the worker perform the work process corresponding to the work ID at the destination work location (St14). After step St14, the processor 51 returns to step St11. In this way, the processor 51 can calculate the time required for the work corresponding to a single record through simulation by the series of processes from step St12 to step St14.

[0052] Based on the simulation processing (see Figure 4) based on the setting instructions in step St3, the processor 51 compares and evaluates the simulation processing result for the required time for each relevant task with the required time specified in the task list data D6 (in other words, the standard work time for each task) (St4). In step St4, the processor 51 refers to the work performance data D5 acquired by the warehouse SIM device 50 from the warehouse operation management system 10. If the processor 51 determines that the simulation processing result is less than or equal to the standard work time (St5, pass), it generates a message indicating that it is granting a pass as a simulation processing result and sends it to the work process automatic modeling device 20.

[0053] Now, referring to Figure 12, we will explain the work performance data D5. Work performance data D5 is data that stores the time required to process each of the work instructions corresponding to multiple tasks that have been performed in the past at the target warehouse, as work performance data.

[0054] Specifically, the work performance data D5 has a data structure (e.g., a record) for each work instruction that includes the work instruction identification information (work instruction ID), the identification information of the worker responsible for performing the work (worker ID), the identification information of the work location where the work is performed locally (work location ID), the identification information of the next work instruction to be performed (next work instruction ID), the work start date and time, and the work end date and time (see Figure 12). In the example in Figure 12, for each of the 14 work instructions, a record is defined that includes the work instruction ID, the worker ID, the work location ID, the next work instruction ID, the work start date and time, and the work end date and time. Note that the work performance data D5 corresponding to the number of these records is created in advance based on a series of tasks specified in an order entering the target warehouse, and the number of records may be increased or decreased as appropriate depending on the content of the order.

[0055] On the other hand, if the processor 51 determines that the simulation processing result exceeds the standard work time as a comparison result (St5, failure), it analyzes and identifies the cause of the failure (St6). The processor 51 generates a SIM result D8 that includes the result identified in step St6 and a statement indicating that the simulation processing result is a failure, and sends it to the automated work process modeling device 20. In the automated work process modeling device 20, the processor 21 corrects the data of the part that caused the failure based on the SIM result D8 sent from the warehouse SIM device 50, in response to the data signal from the input device 30 operated by the user (St7). For example, if the cause was that the standard work time was too short, the processor 21 corrects it to slightly increase the standard work time in response to the user operation. After step St7, the processor 21 uses the work list data D6 corrected in step St7 to generate setting instructions to be used when processing the simulation in the warehouse SIM device 50 and requests them from the warehouse SIM device 50 (St3). In other words, the work process model is repeatedly modified in step St5 until all work processes pass the test.

[0056] 4. Overview of slot position data and aisle position data Next, an overview of slot location data and aisle location data will be explained with reference to Figures 13 and 14. Figure 13 is a table showing an example of slot location data D12. Figure 14 is a table showing an example of aisle location data D7. Slot location data D12 is data that indicates the location of slots, which are the smallest unit of space in the target warehouse.

[0057] Specifically, the slot location data D12 has a data structure (e.g., a record) for each slot that includes the slot identification information (slot ID) and the orthogonal two-dimensional coordinates (X coordinate, Y coordinate) indicating the location of that slot (see Figure 13). In the example in Figure 13, a record containing the slot ID and orthogonal two-dimensional coordinates (X coordinate, Y coordinate) is defined for each of the 12 slots. Note that the number of slot location data D12 corresponding to the number of records is created in advance for the smallest unit of space in the target warehouse, and the number of records may be increased or decreased as appropriate in consideration of changes in the layout of the target warehouse, etc.

[0058] Aisle location data D7 is data indicating the location of each of the multiple aisles provided within the target warehouse. Aisle location data D7 may be generated based on aisle data D4, or it may be generated based on slot location data D12 by the method described later. An aisle is defined as a passable path connecting one starting point and one ending point.

[0059] Specifically, the aisle location data D7 has a data structure (e.g., a record) for each aisle that includes aisle identification information (aisle ID), orthogonal 2D coordinates (X coordinate, Y coordinate) indicating the position of the starting point of the aisle, and orthogonal 2D coordinates (X coordinate, Y coordinate) indicating the position of the ending point of the aisle (see Figure 14). In the example in Figure 14, a record is defined for each of the 17 aisles, including the aisle ID and the orthogonal 2D coordinates (X coordinate, Y coordinate) of the starting and ending points. Note that the aisle location data D7 corresponding to the number of records is created in advance for the aisles in the target warehouse, and the number of records may be increased or decreased as appropriate in consideration of changes in the layout of the target warehouse, etc.

[0060] 5. Procedure for generating aisle position data using slot position data Next, with reference to Figures 15 to 21, the procedure and specific examples for generating aisle position data D7 using slot position data D12 will be explained. Figure 15 is a flowchart showing an example of the aisle position data generation procedure in chronological order. Figure 16 is a diagram showing an example of state transitions related to the generation of aisle position data. Figure 17 is a diagram showing an example of state transitions related to the generation of aisle position data following Figure 16. Figure 18 is a diagram showing an example of state transitions related to the generation of aisle position data following Figure 17. Figure 19 is a diagram showing an example of state transitions related to the generation of aisle position data following Figure 18. Figure 20 is a diagram showing an example of state transitions related to the generation of aisle position data following Figure 19. Figure 21 is a diagram showing an example of state transitions related to the generation of aisle position data following Figure 20. The series of processes shown in Figure 15 are executed by the processor 21 of the automated work process modeling device 20.

[0061] In Figure 15, the processor 21 acquires slot position data D12 (see Figure 13) that can identify the location of each slot within the target warehouse, which has been created in advance based on some data source (for example, a drawing layout of the target warehouse) (St21). Note that the slot position data D12 is not limited to those created in advance based on the drawing layout, but may also be acquired in advance by the automated work process modeling device 20 by data communication with a warehouse management system (WMS) that manages the layout data of the target warehouse.

[0062] Here, the slot position data D12 obtained in step St21 consists of a data structure (e.g., a record) that includes the identification information of a total of 12 slots S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, and S12, and the orthogonal two-dimensional coordinates (X coordinate, Y coordinate) of each slot, as shown in Figure 16. Note that in Figures 16 to 21, for example, slot ID "S0001" is conveniently written as "S1", slot ID "S0002" is conveniently written as "S2", and the same applies to the remaining slots. Similarly, for example, aisle ID "A0001" is conveniently written as "A1", aisle ID "A0002" is conveniently written as "A2", and the same applies to the remaining aisles.

[0063] The processor 21 uses the slot position data D12 obtained in step St21 to group multiple slots that are or may be located in the same column (for example, in the X direction) (St22, see Figure 17). In the example in Figure 17, the processor 21 groups slots S1-S3, S4-S6, S7-S9, and S10-S12, which are located in the X direction column and whose Y coordinate values ​​are sufficiently close. Slots S1-S3 are set to group Gr1, slots S4-S6 to group Gr2, slots S7-S9 to group Gr3, and slots S10-S12 to group Gr4.

[0064] In step St22, the processor 21 determines the orientation of the slots constituting each group (St23) based on the assumption that the multiple groups Gr1 to Gr4 obtained through the grouping process are back-to-back in pairs of two groups. In the example in Figure 18, the processor 21 determines that in groups Gr1 and Gr2, which are adjacent in the Y direction (in other words, back-to-back), group Gr1 faces the negative Y direction and group Gr2 faces the positive Y direction. Similarly, the processor 21 determines that in groups Gr3 and Gr4, which are adjacent in the Y direction (in other words, back-to-back), group Gr3 faces the negative Y direction and group Gr4 faces the positive Y direction.

[0065] Based on the orientation of the groups determined in step St23, the processor 21 identifies pairs of groups that face each other and groups that do not face each other (St24). In the example in Figure 19, the groups that face each other are the pair of groups Gr2 and Gr3, and the groups that do not face each other are groups Gr1 and Gr4, respectively. Using the identification results and slot position data D12, the processor 21 generates pathways A6, A7, and A8 between pairs of groups that face each other (for example, the pair of groups Gr2 and Gr3 Pr1 in Figure 19) (St24). This completes the records for pathways A6, A7, and A8 in the pathway position data D7.

[0066] Furthermore, the processor 21 generates pathways (St24) that allow access to each slot in groups Gr1 and Gr4, which do not face each other. At this time, the processor 21 generates pathways according to the orientation of the slots determined in step St23. In the example in Figure 20, the orientation of group Gr1 is in the negative Y direction, so pathways A2 and A3 are generated in the negative Y direction of group Gr1. Similarly, the orientation of group Gr4 is in the positive Y direction, so pathways A11 and A12 are generated in the positive Y direction of group Gr4. This completes the records for pathways A2, A3, A11, and A12 in the pathway position data D7.

[0067] Processor 21 generates pathways connecting the pathways generated in step St24 (St25). Specifically, processor 21 generates pathways that directly connect from the endpoint of each group to the endpoint of a pathway in an adjacent group. If a direct connection is not possible, processor 21 generates a pathway that extends the endpoint of one of the groups to create the connection. In the example in Figure 21, pathways A1, A14, A4, A15, and A9 are each generated as pathways that surround and connect groups Gr1 and Gr2 in an accessible manner. Similarly, pathways A5, A16, A17, A10, and A13 are each generated as pathways that surround and connect groups Gr3 and Gr4 in an accessible manner.

[0068] (Summary of this disclosure) The above description of embodiments discloses the technical concepts corresponding to the following items.

[0069] (Item 1) The work process modeling method relating to this disclosure is: Work standard data (D1) that defines at least the time required for each standard task performed in the target warehouse, and work instruction data (D2) that specifies the sequence of multiple tasks defined for each order are obtained from the warehouse operation management system (10). Based on the aforementioned work standard data and work instruction data, a list of tasks (D6) to be processed in a simulation predicting the time required for each task at the target warehouse is generated. The settings are executed to use the aforementioned work list data for the simulation. This allows the work process modeling method to set up simulations using work list data generated from work standard data and work instruction data acquired from the warehouse operation management system, thereby achieving both a reduction in the man-hours required for work process modeling, elimination of reliance on individual expertise, and improved reproducibility.

[0070] (Item 2) In the work process modeling method described in item 1, The work performance data (D5) of the multiple operations previously performed at the target warehouse is further obtained from the warehouse operation management system. Based on the comparison results between the processing results of the simulation based on the aforementioned work list data and the actual work data, the settings for using the work list data in the simulation are executed. As a result, according to the work process modeling method, it becomes possible to acquire more work performance data from the warehouse operation management system, and to set up simulations based on a comparison between work list data and work performance data, which is expected to improve the accuracy of the simulations.

[0071] (Item 3) In the work process modeling method described in item 1 or 2, Work location data defining the work location for each of the multiple tasks in the target warehouse is further obtained from the warehouse operation management system, The settings for using the aforementioned work location data in the simulation are executed. This means that, according to the work process modeling method, work location data can be further acquired from the warehouse operations management system and used for modeling the work process.

[0072] (Item 4) In the work process modeling method described in item 3, Based on the aforementioned work location data, aisle location data is generated to define the location of aisles in the target warehouse. The settings for using the aforementioned passage position data in the simulation are executed. As a result, according to the work process modeling method, aisle location data can be dynamically generated even if it is not pre-stored in the warehouse operation management system, and this aisle location data can be used for modeling work processes.

[0073] (Item 5) In the work process modeling method described in item 4, The work area has one or more slots, which are the smallest unit of space in the target warehouse. The work location data includes slot location data that defines the location of the slot in the target warehouse, Based on the slot position data, one or more passages surrounding the slots are formed to generate the passage position data. This allows for the detailed generation of pathways that provide access to slots, according to the work process modeling method, thus contributing to the use of more accurate simulations.

[0074] (Item 6) In the work process modeling method described in any one of items 1 to 5, The aforementioned work standard data further includes the movement speed in the standard work. This allows the work process modeling method to generate work list data that takes into account standard movement speeds for tasks involving movement, and then model the work process.

[0075] (Item 7) In the work process modeling method described in any one of items 1 to 5, The aforementioned passage location data further includes data indicating whether or not mutual passage is permitted in the aforementioned passage. This allows the work process modeling method to further improve the accuracy of time simulations by using data that can distinguish between passages where only one-way traffic is permitted and passages where two-way traffic is permitted in the individual operations of the target warehouse.

[0076] (Item 8) The work process modeling system relating to this disclosure is It is equipped with a processor (21, 51) and memory (22, 52), The aforementioned processor, in cooperation with the memory, Obtain work standard data that defines at least the time required for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order, from the warehouse operations management system. Based on the aforementioned work standard data and work instruction data, a list of tasks to be processed in a simulation predicting the time required for each task at the target warehouse is generated. The settings are executed to use the aforementioned work list data for the simulation. As a result, the work process modeling system allows for the setting up of simulations using work list data generated from work standard data and work instruction data acquired from the warehouse operation management system. This makes it possible to simultaneously reduce the man-hours required for work process modeling, eliminate reliance on individual expertise, and improve reproducibility.

[0077] While embodiments have been described above with reference to the attached drawings, this disclosure is not limited to such examples. It is clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents can be conceived within the scope of the claims, and these are also understood to fall within the technical scope of this disclosure. Furthermore, the components of the embodiments described above can be combined in any way without departing from the spirit of the invention. [Industrial applicability]

[0078] The technology disclosed herein is useful as a work process modeling method, work process modeling apparatus, and program that simultaneously reduce the man-hours required for modeling work processes, eliminate reliance on individual expertise, and improve reproducibility. [Explanation of symbols]

[0079] 10 Warehouse Operations Management System 11 Data storage unit 20. Automated Modeling Device for Work Processes 21 processors 21a Work Process Automated Modeling Unit 21b Work Process Modification Section 22 memory 23 Input / Output Interfaces 30 Input Devices 40 Display Devices 50 Warehouse SIM device 51 processors 51a Work Simulation Department 51b Simulation Evaluation Department 52 memory 100 Work Process Modeling Systems D1 Standard Work Data D2 Work Instruction Data D3 Work location data D4 Aisle Data D5 Work Performance Data D6 Work List Data D7 Aisle position data D8 SIM result D11 Work Area Data D12 Slot Position Data

Claims

1. A work process modeling method performed by a computer device, Obtain work standard data that defines at least the time required for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order, from the warehouse operations management system. Based on the aforementioned work standard data and work instruction data, a list of tasks to be processed in a simulation predicting the time required for each task at the target warehouse is generated. Set the aforementioned work list data to be used in the simulation. Work process modeling method.

2. The work performance data of the multiple operations previously performed at the aforementioned target warehouse is further obtained from the warehouse operation management system. Based on the comparison results between the processing results of the simulation based on the work list data and the actual work data, the work list data is set to be used in the simulation. The work process modeling method according to claim 1.

3. Work location data defining the work location for each of the multiple tasks in the target warehouse is further obtained from the warehouse operation management system, Set the aforementioned work location data to be used in the simulation. The work process modeling method according to claim 1.

4. Based on the aforementioned work location data, aisle location data is generated to define the location of aisles in the target warehouse. The aforementioned passage position data is set to be used in the simulation. The work process modeling method according to claim 3.

5. The work area has one or more slots, which are the smallest unit of space in the target warehouse. The work location data includes slot location data that defines the location of the slot in the target warehouse, Based on the slot position data, one or more passages surrounding the slots are formed to generate the passage position data. The work process modeling method according to claim 4.

6. The aforementioned work standard data further includes the movement speed in the standard work, The work process modeling method according to claim 1.

7. The aforementioned passage location data further includes data indicating whether or not mutual passage is permitted in the aforementioned passage. The work process modeling method according to claim 4.

8. Equipped with a processor and memory, The aforementioned processor, in cooperation with the memory, Obtain work standard data that defines at least the time required for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order, from the warehouse operation management system. Based on the aforementioned work standard data and work instruction data, a list of tasks to be processed in a simulation predicting the time required for each task at the target warehouse is generated. Set the aforementioned work list data to be used in the simulation. A work process modeling system.

9. A computing device connected to a warehouse operation management system and capable of data communication, The warehouse operation management system is used to obtain work standard data that defines the time required for each standard task performed in the target warehouse, and work instruction data that specifies the sequence of multiple tasks defined for each order. Based on the aforementioned work standard data and work instruction data, a list of tasks to be processed in a simulation predicting the time required for each task at the target warehouse is generated. The task list data is set to be used in the simulation. A program for that purpose.

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