Decision-making support apparatus, and decision-making support method

Abstract

To efficiently support decision making of an administrator in logistic business.SOLUTION: A decision-making support apparatus according to an aspect of an embodiment has a control unit. The control unit collects actual data relating to logistic works from a logistic work system to accumulate them in a database. The control unit provides a user with a decision-making support UI including a UI for guiding a decision-making process in the logistic works. The control unit analyzes and visualizes, based on the actual data, decision-making support information as information responding to a request from the user through the decision-making support UI. The control unit provides the user with the decision-making support information analyzed and visualized.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The disclosed embodiments relate to a decision support device and a decision support method. [Background technology]

[0002] BACKGROUND ART Conventionally, a system that supports decision-making for inventory planning in a supply chain is known as a decision-making support system (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-136290 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned conventional techniques have room for further improvement in terms of more efficiently supporting decision-making by managers in logistics operations.

[0005] In conventional logistics operations, there was a problem of a heavy burden on managers due to the heavy workload of collecting data necessary for decision-making, such as the performance of equipment and personnel in warehouse operations.Furthermore, the decision-making process was heavily dependent on the manager's experience, which tended to be highly personal.

[0006] In this regard, the technology disclosed in Patent Document 1 supports decision-making for inventory planning in a supply chain, and cannot be applied directly to solve the above-mentioned problems, i.e., problems common to all logistics operations.

[0007] One aspect of the embodiment has been made in consideration of the above, and aims to provide a decision-making support device and a decision-making support method that can more efficiently support managerial decision-making in logistics operations. [Means for solving the problem]

[0008] A decision support device according to one aspect of the embodiment includes a control unit. The control unit collects performance data related to logistics operations from a logistics operation system and stores the data in a database. The control unit also provides a user with a decision support UI including a UI that guides the decision-making process in the logistics operations. The control unit also analyzes and visualizes decision support information, which is information requested by the user via the decision support UI, based on the performance data. The control unit also provides the analyzed and visualized decision support information to the user. [Effects of the Invention]

[0009] According to one aspect of the embodiment, it is possible to more efficiently support decision-making by managers in logistics operations. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an outline of a decision support method according to an embodiment. [Figure 2] FIG. 2 is a block diagram of a decision support device according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a usage scene according to the embodiment. [Figure 4] FIG. 4 shows an example of a menu display that steps out the decision-making process in the application scenario "worker allocation plan." [Figure 5] FIG. 5 is a diagram showing an example of the display of decision support information when viewing "Shipping Work Progress." [Figure 6] FIG. 6 is a diagram showing an example of the display of decision support information when viewing the "staff shortage / surplus situation by process." [Figure 7]FIG. 7 is a diagram showing an example of the display of decision support information when viewing the "Personnel Redeployment Simulation (Processing Volume)." [Figure 8] FIG. 8 is a diagram showing an example of display of decision support information relating to equipment availability. [Figure 9] FIG. 9 is a diagram showing a display example of the factor analysis result display area. [Figure 10] FIG. 10 is an explanatory diagram of the procedure for estimating the cause possibility. [Figure 11] FIG. 11 is a hardware configuration diagram showing an example of a computer that realizes the functions of the decision-making support device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of a decision-making support device and a decision-making support method disclosed herein will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the embodiments of the present disclosure described below (hereinafter referred to as "present embodiments" as appropriate).

[0012] In the following, an example will be given in which the target of decision-making support is warehouse management in logistics operations. The decision-making support device according to this embodiment is assumed to be a decision-making support device 10 included in a warehouse management system 1 shown in FIG.

[0013] <Outline of this embodiment> First, an overview of the decision support method according to this embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram illustrating the overview of the decision support method according to this embodiment.

[0014] 1, the warehouse operation system 1 includes a warehouse system 5, a decision-making support device 10, and a terminal device 100. The warehouse system 5, the decision-making support device 10, and the terminal device 100 are connected to each other so as to be able to communicate with each other via a network N1 such as an intranet, the Internet, or a mobile phone network.

[0015] The warehouse system 5 includes a WMS (Warehouse Management System) 5a, a WES (Warehouse Execution System) 5b, a WCS (Warehouse Control System) and an RCS (Robot Control System) 5c, and other systems 5d.

[0016] WMS5a is a computer system with functions such as inventory management and warehousing management for cargo, materials, and products in warehouses at logistics centers, etc. WES5b is a computer system that manages automated equipment linkages and work in warehouses.

[0017] The WCS / RCS5c is a computer system that controls various facilities in the warehouse, such as material handling equipment, based on operational instructions from the WES5b. Material handling equipment includes, for example, AS / RS (Automated Storage and Retrieval Systems), robots, and AGVs (Automated Guided Vehicles).

[0018] WMS5a, WES5b, and WCS / RCS5c operate in cooperation with each other under the management of WMS5a. Other systems 5d are computer systems related to warehouse operations other than WMS5a, WES5b, and WCS / RCS5c. Other systems 5d include a TMS (Transport Management System), an attendance management system, etc.

[0019] The decision-making support device 10 is a computer that executes the decision-making support method according to this embodiment. The decision-making support device 10 is realized, for example, as a private cloud. Note that the decision-making support device 10 may also be realized as a public cloud. The decision-making support device 10 also has a storage DB (Database) 12a.

[0020] The decision support device 10 also provides a digital dashboard (hereinafter abbreviated as "dashboard") service to the terminal device 100. For example, the decision support device 10 functions as a web server and provides decision support information to the terminal device 100 via a web browser (web application) running on the terminal device 100.

[0021] Although the decision support device 10 is shown as one device in FIG. 1, the decision support device 10 may be realized as a plurality of devices.

[0022] The terminal device 100 is a computer used by a user. The user is a manager or the like who makes decisions in the warehouse operation system 1. The terminal device 100 is realized by various types of personal computers (PCs) including desktop and tablet types, mobile computers such as smartphones, etc. The terminal device 100 may also be realized by wearable devices such as head-mounted displays (HMDs) and smart glasses.

[0023] The decision-making support method according to the embodiment is realized by the decision-making support device 10 in the warehouse management system 1 configured as above.

[0024] Specifically, the decision-making support device 10 collects various performance data indicating the operating status and the like in the warehouse from the warehouse system 5 in real time or at a predetermined timing (step S1). In addition, the decision-making support device 10 performs necessary processing and the like on the collected performance data and stores the data in the storage DB 12a (step S2).

[0025] Meanwhile, the decision support device 10 provides a decision support UI (User Interface) to the terminal device 100 as part of a dashboard service for the terminal device 100, and the user requests desired information via this decision support UI (step S3).

[0026] The decision support device 10 analyzes and visualizes decision support information, which is information requested by a user via the decision support UI, based on the performance data stored in the storage DB 12a (step S4). Then, the decision support device 10 provides the analyzed and visualized decision support information to the user (step S5). The user makes various decisions regarding warehouse management based on the provided decision support information.

[0027] The decision-making support UI includes a UI that guides the decision-making process in warehouse operations. For example, the decision-making support UI includes a menu that procedurally outlines the decision-making process for each predetermined decision-making scenario in warehouse operations. An example of a decision-making scenario is when planning the allocation of workers. In the following, the decision-making scenario will be referred to as a "use scenario" where appropriate.

[0028] The user can make an efficient decision while checking the decision support information required for decision-making in order from the menu that procedurally describes the decision-making process for each use scenario. A specific example of using this menu will be described later with reference to Figures 4 to 7, taking the worker allocation planning as an example use scenario.

[0029] Furthermore, for example, the decision support UI includes a UI that enables estimation of the equipment operation status and the factors behind that operation status. Such a UI is provided, for example, as an equipment operation rate screen that shows whether the equipment is producing the effects expected at the time of installation based on the difference between the equipment operation rate and machine capacity (which can also be rephrased as "throughput") relative to a standard. The equipment operation rate screen estimates the factors behind the lack of effect by comparing the aforementioned differences, and displays the results of a factor analysis that promotes efforts toward effective use of the equipment. Specific examples of this equipment operation rate screen will be described later using Figures 8 to 10.

[0030] As described above, in the decision-making support method according to the embodiment, the decision-making support device 10 collects performance data related to warehouse operation from the warehouse operation system 1 and stores the data in the storage DB 12a. The decision-making support device 10 also provides a user with a decision-making support UI that includes a UI that guides the decision-making process in warehouse operation. The decision-making support device 10 also analyzes and visualizes decision-making support information, which is information requested by the user via the decision-making support UI, based on the performance data and provides the information to the user. Therefore, the decision-making support method according to the embodiment can more efficiently support the decision-making of a manager in warehouse operation.

[0031] The configuration of the decision support device 10 to which the decision support method according to the above-described embodiment is applied will be described in more detail below.

[0032] <Configuration of decision support device> Next, Fig. 2 is a block diagram of a decision-making support device 10 according to an embodiment. In Fig. 2, components necessary for explaining the features of this embodiment are shown as functional blocks, and descriptions of general components are omitted.

[0033] In other words, each component shown in Figure 2 is a functional concept and does not necessarily have to be physically configured as shown. For example, the specific form of distribution and integration of each functional block is not limited to that shown, and all or part of it can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0034] In the description using FIG. 2, the description of the components that have already been described may be simplified or omitted.

[0035] As shown in FIG. 2, the decision support device 10 according to the embodiment includes a communication unit 11, a storage unit 12, and a control unit 13.

[0036] The communication unit 11 is realized by, for example, a network adapter, etc. The communication unit 11 is connected to the network N1 by wire or wirelessly, and transmits and receives information to and from the warehouse system 5 and the terminal device 100.

[0037] The storage unit 12 is realized by a storage device such as a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk drive, an optical disk drive, etc. In the example shown in Fig. 2, the storage unit 12 stores an accumulation DB 12a, an analysis model 12b, and UI information 12c.

[0038] As already mentioned, the accumulation DB 12a is a database that accumulates various performance data collected from the warehouse system 5 and processed as needed. The analysis model 12b is model information that analyzes the performance data to generate various decision support information. The analysis model 12b is, for example, a mathematical model that includes mathematical formulas for calculating actual values ​​and predicted values ​​included in the decision support information based on the performance data.

[0039] Furthermore, the analytical model 12b is, for example, a learning model that is trained to output a predicted value included in the decision support information when performance data is input. When the analytical model 12b is a learning model, the analytical model 12b is trained using a machine learning algorithm such as deep learning.

[0040] The UI information 12c is information relating to various UIs to be provided to the terminal device 100. The UI information 12c includes, for example, definition information relating to the decision support UI described above.

[0041] The control unit 13 corresponds to a so-called processor or controller. The control unit 13 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a GPU (Graphical Processing Unit). The functions of the control unit 13 are realized by executing a decision support program according to an embodiment (not shown) stored in the storage unit 12 using RAM as a work area. The control unit 13 can also be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0042] The control unit 13 has a collection unit 13a, a storage unit 13b, an analysis unit 13c, a visualization unit 13d, and a provision unit 13e, and realizes or executes the functions and actions of information processing described below.

[0043] The collection unit 13a collects various types of performance data indicating the operating status and the like in the warehouse from the warehouse system 5 in real time or at predetermined timing via the communication unit 11. The accumulation unit 13b performs necessary processing and the like on the collected performance data and accumulates the performance data in the accumulation DB 12a.

[0044] In response to a request from a user acquired by the providing unit 13e (described later), the analysis unit 13c extracts performance data accumulated in the accumulation DB 12a and performs analysis to generate decision support information desired by the user, using the analysis model 12b.

[0045] The visualization unit 13d visualizes the analysis results obtained by the analysis unit 13c using the UI information 12c, and generates decision support information to be provided to the user. The provision unit 13e provides a dashboard service to the terminal device 100 via the communication unit 11. The provision unit 13e also provides a decision support UI based on the UI information 12c to the terminal device 100 via the communication unit 11.

[0046] For example, the providing unit 13e provides, based on the UI information 12c, a menu in which the decision-making process for each of the above-described utilization scenarios is organized into procedures, as a decision-making support UI, to the terminal device 100. Fig. 3 is a diagram showing an example of a utilization scenario according to the embodiment.

[0047] The providing unit 13e according to this embodiment provides, as a decision-making support UI, a menu in which the decision-making process for each of the seven types of utilization scenarios shown in Fig. 3 is organized into procedures to the terminal device 100. Note that Fig. 3 is merely an example and does not limit the utilization scenarios that the decision-making support device 10 provides to the user.

[0048] The providing unit 13e also acquires a request from a user via the decision support UI and notifies the analyzing unit 13c. The providing unit 13e also provides the decision support information generated by the visualizing unit 13d to the terminal device 100 via the communication unit 11.

[0049] <Example of usage scenario: "Worker allocation plan"> Next, a specific example of providing a menu in which the decision-making process for each utilization scenario is organized into procedures to the terminal device 100 as a decision-making support UI will be described, taking the utilization scenario of "worker allocation plan" as an example.

[0050] Fig. 4 is a diagram showing an example of a menu display that steps out the decision-making process in the application scenario "Worker Allocation Plan." Fig. 5 is a diagram showing an example of the decision-making support information displayed when viewing "Shipping Work Progress." Fig. 6 is a diagram showing an example of the decision-making support information displayed when viewing "Personnel Surplus / Shortage Status by Process." Fig. 7 is a diagram showing an example of the decision-making support information displayed when viewing "Personnel Allocation Simulation (Processing Volume)."

[0051] First, when the user selects the "Use scenarios" tab shown in Fig. 4, a list corresponding to each use scenario shown in Fig. 3 is displayed (not shown here). When the user selects the use scenario "Worker allocation plan" from this list, a menu is displayed that steps out the decision-making process for this "Worker allocation plan," as shown in Fig. 4.

[0052] The user can efficiently make decisions regarding the "worker allocation plan" by viewing and checking each piece of decision-making support information corresponding to this menu in the order shown by the arrow aw1. In addition, since decisions can be made using the same decision-making process regardless of the user, it is possible to eliminate dependency on individuals in decision-making.

[0053] By clicking the "View" button for each menu item, you can move to the display screen for the decision support information corresponding to that item. Also, by clicking the favorite button indicated by a heart mark for each menu item, you can group the corresponding menu items under the "Favorites" tab. Figure 4 shows an example in which "Shipping Work Progress" and "Inbound / Outbound Work Progress Management by Time and Process" have been designated as favorites.

[0054] Here, let us assume that the user first selects "Shipping Work Progress" from the menu in Figure 4. This will display the Shipping Work Progress screen, as shown in Figure 5. On the Shipping Work Progress screen, the user can check the progress of the entire shipping work and the estimated time of completion.

[0055] As shown in FIG. 5, the shipping work progress screen has, for example, a header information section 51, a filter section 52, a first graph section 53, and a second graph section .

[0056] The header information section 51 displays information about the entire shipping work for that day, including the scheduled completion time of the shipping work, the target completion time, the remaining time required to complete the shipping work, the number of remaining processes, and the average productivity per person per hour for the entire shipping work for that day.

[0057] A filter for selecting the type and unit of shipping work is displayed in the filter section 52. The filter results selected by the user in this filter section 52 are reflected in the first graph section 53 and the second graph section .

[0058] The first graph section 53 displays a graph that allows the user to compare the total shipping work schedule for the day with the cumulative work results and the estimated number of tasks, and to grasp whether there are any delays. The estimated number of shipping tasks is displayed with two types of predicted values ​​based on the accumulation of current productivity and the accumulation of pre-registered individual productivity.

[0059] The second graph section 54 displays a graph that allows the user to compare the hourly shipping schedule with the hourly shipping performance and to grasp whether there is a delay.

[0060] Next, let us assume that the user selects "Personnel Shortage / Overage Status by Process" from the menu in Figure 4. This will display the personnel shortage / overage status by process screen, as shown in Figure 6. On the personnel shortage / overage status by process screen, the user can understand the required man-hours and productivity for the process, as well as the worker skills, and can confirm the personnel who should be reassigned.

[0061] As shown in Figure 6, the process-specific personnel surplus / shortage status screen has, for example, a first filter section 61, a first text section 62, a graph section 63, a second filter section 64, a third filter section 65, a second text section 66, and a table section 67.

[0062] A filter for selecting a type of work is displayed in the first filter section 61. The filter results selected by the user in the first filter section 61 are reflected in the graph section 63, the second filter section 64, and the third filter section 65.

[0063] The first text section 62 sums up the surpluses and shortages displayed in the graph section 63 and displays a total number indicating the overall extent of surplus or shortage. The graph section 63 displays a graph for checking the surplus or shortage of personnel for each type of work and each process, and for managing the appropriate allocation of personnel.

[0064] A filter for selecting missing processes is displayed in the second filter section 64. The filter result selected by the user in the second filter section 64 is reflected in the second text section 66 and the table section 67.

[0065] A filter for selecting redundant processes and workers is displayed in the third filter section 65. The filter results selected by the user in the third filter section 65 are reflected in the second text section 66 and the table section 67.

[0066] The second text section 66 displays the aggregated results of the required man-hours and required productivity for processes with surpluses or shortages based on the filter results of the second filter section 64 and the third filter section 65. The table section 67 displays the remaining working hours (remaining time) for each worker, productivity in processes with shortages, and productivity in processes with surpluses based on the filter results of the second filter section 64 and the third filter section 65. The user can confirm which worker to use for adjusting personnel allocation based on the information displayed in the table section 67.

[0067] Next, let us assume that the user selects "Personnel Redeployment Simulation (Processing Volume)" from the menu in Figure 4. Then, the personnel redeployment simulation (processing volume) screen is displayed, as shown in Figure 7. On the personnel redeployment simulation (processing volume) screen, the user can check the shipping plan and deployment shown as a result of simulating personnel adjustments.

[0068] As shown in Figure 7, the personnel redeployment simulation (processing volume) screen has, for example, a first filter section 71, a first header information section 72, a first cross-tabulation table section 73, a second filter section 74, a second header information section 75, and a second cross-tabulation table section 76.

[0069] The first filter section 71 displays a filter for selecting surplus processes. The filter results selected by the user in this first filter section 71 are displayed in the first header information section 72. The first cross-tabulation table section 73 displays the planned and actual differences in the quantity and number of people for surplus processes, as well as the planned number of processes after personnel allocation changes. The planned number of processes can be calculated by adding up actual productivity and individual productivity, and can be adjusted by the user on the editing screen.

[0070] The second filter section 74 displays a filter for selecting processes that are lacking. The filter results selected by the user in this second filter section 74 are displayed in the second header information section 75. The second cross-tabulation table section 76 displays the planned and actual differences in the quantity and number of people for processes that are lacking, as well as the planned number of processes after changes in personnel allocation. The planned number of processes can be calculated by adding up actual productivity and individual productivity, and can be adjusted by the user on the editing screen.

[0071] As shown by the two dashed rectangles in Figure 7, on this personnel redeployment simulation (throughput) screen, the user can determine whether the work will be completed on time by transferring personnel from a surplus process to a shortage process.

[0072] <Example of equipment utilization rate screen> As described above, the decision support UI according to this embodiment includes a UI that enables estimation of the operation status of equipment and the factors behind that operation status. For example, this UI is provided as an equipment availability screen that shows whether the equipment is producing the effects expected at the time of installation based on the difference between the equipment's availability rate and machine capacity relative to a standard. The equipment availability screen estimates the factors behind the lack of effect based on a comparison of the aforementioned differences, and displays the results of a factor analysis that promotes efforts toward effective utilization of the equipment.

[0073] Next, this equipment availability screen will be described. Fig. 8 is a diagram showing a display example of decision support information related to equipment availability. Fig. 9 is a diagram showing a display example of the factor analysis result display section 83. Fig. 10 is an explanatory diagram of the procedure for estimating the factor possibility.

[0074] As shown in FIG. 8, the equipment availability screen has a filter section 81, a graph section 82, and a factor analysis result display section 83.

[0075] The filter section 81 displays filters for selecting the date, facility type, facility classification, time, and day of the week to be displayed. The filter results selected by the user in the filter section 81 are reflected in the graph section 82.

[0076] A graph comparing the trends in the average availability rate and standard availability rate of the equipment is displayed in the upper part of the graph section 82. This graph allows the user to confirm to what extent the equipment is actually operating relative to the standard availability rate (planned value).

[0077] A graph showing the progress of the machine capacity standard achievement rate, which is the achievement rate of the actual throughput (actual machine capacity) relative to the standard throughput (standard machine capacity) of the equipment, is displayed in the lower part of the graph section 82. Using this graph, the user can confirm how much the equipment is actually processing relative to the standard throughput (planned value).

[0078] The factor analysis result display unit 83 displays the result of the factor analysis of the operating status of the equipment shown in the graph unit 82. Specifically, as shown in Fig. 9, the factor analysis result display unit 83 displays a factor possibility screen. The factor possibility screen displays the deviation rate and the factor possibility.

[0079] The deviation rate is the degree of deviation between the planned value and the actual value for the equipment operation status. The possible causes are displayed as factors estimated from the correlation between the operation rate and machine capacity. The possible causes are estimated as shown in Figure 10.

[0080] Specifically, the analysis unit 13c calculates the operation rate by dividing the average operation rate by the reference operation rate based on the equipment data included in the performance data of the accumulation DB 12a (step S11-1). Similarly, the analysis unit 13c calculates the machine capacity by dividing the actual throughput by the reference throughput based on the equipment data (step S11-2).

[0081] The analysis unit 13c then compares the calculated availability rate and machine capacity (step S12). The analysis unit 13c then calculates a deviation rate (step S13) and estimates the cause possibility based on the comparison result of step S12. The analysis unit 13c estimates the cause possibility for each of the cases where the availability rate is greater than machine capacity, where the availability rate is less than machine capacity, and where both the availability rate and machine capacity are smaller than a threshold (step S14).

[0082] At this time, the analysis unit 13c may estimate the cause possibility using a preset lookup table, etc. Furthermore, the analysis unit 13c may use the analytical model 12b that has been trained so as to be able to estimate the deviation rate and the cause possibility based on the equipment data.

[0083] Fig. 9 shows an example display where the "2nd floor automated warehouse" has an "operation rate > machine capacity" relationship, and "insufficient work in progress," "back-end process congestion," and "traffic congestion" are estimated as possible causes. Also, Fig. 9 shows an example display where the "Autostore" has an "operation rate < machine capacity" relationship, and "batch generation issue" and "insufficient work in progress" are estimated as possible causes.

[0084] By checking such a facility availability screen, the user can efficiently make decisions to promote efforts to make effective use of the facility.

[0085] <Hardware configuration> The warehouse system 5, decision-making support device 10, and terminal device 100 according to the above-described embodiments are realized by one or more computers 1000 configured as shown in Fig. 11. The decision-making support device 10 will be taken as an example for explanation.

[0086] 11 is a hardware configuration diagram showing an example of a computer that realizes the functions of the decision-making support device 10. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, a hard disk drive (HDD) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0087] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0088] The HDD 1400 stores programs executed by the CPU 1100 and data used by the programs. The communication interface 1500 receives data from other devices via a communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the communication network.

[0089] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0090] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0091] For example, when the computer 1000 functions as the decision support device 10 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 13. The HDD 1400 also stores data in the storage unit 12. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a communication network.

[0092] Furthermore, in the explanation using FIG. 11, the decision support device 10 was used as an example, but it goes without saying that a terminal device 100 on which a Web application runs can also be realized by a computer 1000 configured as shown in FIG. 11.

[0093] <Conclusion> As described above, the decision-making support device 10 according to the embodiment includes the control unit 13. The control unit 13 collects performance data related to warehouse operation (corresponding to an example of a "logistics operation") from the warehouse system 5 (corresponding to an example of a "logistics operation system") and stores the data in the accumulation DB 12a (corresponding to an example of a "database"). The control unit 13 also provides a user with a decision-making support UI including a UI that guides the decision-making process in warehouse operation. The control unit 13 also analyzes and visualizes decision-making support information, which is information requested by the user via the decision-making support UI, based on the performance data. The control unit 13 also provides the user with the analyzed and visualized decision-making support information.

[0094] Therefore, the decision-making support device 10 according to the embodiment can more efficiently support the decision-making of the manager in warehouse management.

[0095] 4 to 9 in the above-described embodiment, various display examples are shown, but they are merely examples and do not limit the display layout in actual operation. For example, in the menu in which the decision-making process for each utilization scenario is proceduralized as shown in FIG. 6, it is sufficient that the viewing order of each menu item is clear, and the layout position of each item may be adjusted as appropriate.

[0096] Furthermore, in the above-described embodiment, warehouse management is taken as an example of a logistics business, but this embodiment can be applied to decision-making in logistics business in general.

[0097] Further advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents. [Explanation of symbols]

[0098] 1 Warehouse Management System 5. Warehouse System 5a WMS 5b WES 5c WCS RCS 5d Other Systems 10 Decision support equipment 11 Communications Department 12 Storage section 12a Accumulation DB 12b Analytical Model 12c UI information 13 Control Unit 13a Collection Department 13b Storage section 13c Analysis Department 13d visualization section 13e supply department

Claims

1. We collect performance data on logistics operations from the logistics operations system and store it in a database. Providing a user with a decision-making support UI including a UI that guides the decision-making process in logistics operations; analyzing and visualizing decision support information, which is information in response to a request from a user via the decision support UI, based on the performance data; a control unit that provides the analyzed and visualized decision support information to a user; A decision support device comprising:

2. The decision support UI includes: It includes a menu that steps out the decision-making process for each specific decision-making scenario in logistics operations. The decision support device of claim 1 .

3. The decision-making scene includes at least a worker allocation planning time.

3. The decision support device of claim 2.

4. The decision support UI includes: a display screen showing the operational status of the equipment in the logistics business system, which is analyzed and visualized based on the equipment data included in the performance data; The decision support device of claim 1 .

5. the display screen includes a display of possible causes of the operating status estimated from a comparison of the availability rate and machine capacity of the equipment based on the equipment data, 5. The decision support device of claim 4.

6. The control unit An average operation rate relative to a standard operation rate of the equipment is calculated as the operation rate, and an actual processing amount relative to a standard processing amount of the equipment is calculated as the machine capacity.

6. The decision support device of claim 5.

7. The control unit estimating the factor possibility for each of the cases where the availability rate is greater than the machine capacity, where the availability rate is smaller than the machine capacity, and where both the availability rate and the machine capacity are smaller than a threshold value; 7. The decision support device according to claim 5 or 6.

8. 1. A computer-implemented decision support method comprising: Collecting performance data on logistics operations from a logistics operation system and storing it in a database; Providing a user with a decision support UI including a UI that guides the decision-making process in logistics operations; analyzing and visualizing decision support information, which is information in response to a request from a user via the decision support UI, based on the performance data; providing the analyzed and visualized decision support information to a user; A decision support method comprising:

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