Decision-making support device, decision-making support system and decision-making support method

Abstract

To support an administrator's decision-making in a logistics operation while optimizing data placement and achieving cost efficiency.SOLUTION: A decision-making support device according to one aspect of an embodiment comprises a data infrastructure and a control unit. The data infrastructure is configured such that an RDB is placed in the first layer and a DWH is placed in the second layer. The control unit accumulates real-time data for the current business day from logistics operations collected via the logistics operations system into the RDB, while accumulating historical data into the DWH. Furthermore, the control unit links the RDB and DWH via batch processing. Furthermore, the control unit links the most recent specified period of historical data from the DWH to the RDB during the aforementioned batch processing. Additionally, the control unit analyzes and visualizes decision-making support information data corresponding to user requests for decision-making support in logistics operations based on the data in the first layer. Moreover, the control unit provides the user with analyzed and visualized decision-making support information.SELECTED DRAWING: Figure 7

Description

[Technical Field]

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

[0002] Conventionally, decision support systems equipped with data warehouses have been known that extract data from relational databases, perform aggregation and analysis processing, and display the results in order to support the decision-making of managers and executives (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-183182 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 supporting decision-making by managers in logistics operations while optimizing data placement and optimizing costs.

[0005] For example, data warehouses have the advantage of being able to store large amounts of data and are well suited to data accumulation and analysis. On the other hand, data warehouses generally have a pay-per-use system based on time and resources used, and the costs can become enormous if startup times increase. Data warehouses also have the disadvantage of not being suitable for real-time update processing of results displayed on digital dashboards, etc.

[0006] In contrast, relational databases have the advantage of being strong at real-time update processing, but they also have the disadvantage of being able to store a small amount of data and their response time decreasing as the amount of data increases.

[0007] Patent Document 1 does not disclose the advantages and disadvantages of such data warehouses and relational databases. In other words, when using the above-mentioned conventional technology, it is not possible to support the decision-making of managers in logistics operations while optimizing data placement and optimizing costs.

[0008] One aspect of the embodiment has been made in consideration of the above, and aims to provide a decision-making support device, decision-making support system, and decision-making support method that can support decision-making by managers in logistics operations while optimizing data placement and rationalizing costs. [Means for solving the problem]

[0009] A decision-making support device according to one aspect of the embodiment includes a data infrastructure and a control unit. The data infrastructure is configured such that an RDB is located on a first layer and a DWH is located on a second layer. The control unit stores real-time data for the current day of logistics operations collected from a logistics operation system in the RDB and stores past data in the DWH. The control unit also links the RDB and the DWH through batch processing. The control unit also links past data from the DWH for a specified period of time to the RDB through the batch processing. The control unit also analyzes and visualizes decision-making support information, which is information requested by a user regarding decision-making support in logistics operations, based on the data on the first layer. The control unit also provides the analyzed and visualized decision-making support information to the user. [Effects of the Invention]

[0010] According to one aspect of the embodiment, it is possible to support the decision-making of managers in logistics operations while optimizing data placement and optimizing costs. [Brief explanation of the drawings]

[0011] [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 diagram showing an outline of a data flow in the warehouse management system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a decision-making scene according to the embodiment. [Figure 4] FIG. 4 is a diagram showing an example of when to use the dashboard. [Figure 5] FIG. 5 is an explanatory diagram of the data architecture in the DWH layer. [Figure 6] FIG. 6 is a diagram illustrating an example of the roles of the upper and lower layers in a DWH layer. [Figure 7] FIG. 7 is a block diagram of a decision-making support device according to the embodiment. [Figure 8] FIG. 8 is a block diagram of a data infrastructure control unit according to the embodiment. [Figure 9] FIG. 9 is a diagram showing an example of a menu display that steps out the decision-making process in the application scenario "worker allocation plan." [Figure 10] FIG. 10 is a diagram showing an example of display of decision support information when viewing "Shipping Work Progress." [Figure 11] FIG. 11 is a diagram showing an example of the display of decision support information when viewing the "staff shortage / surplus situation by process." [Figure 12] FIG. 12 is a diagram showing an example of display of decision support information when viewing "Personnel Redeployment Simulation (Processing Volume)." [Figure 13] FIG. 13 is a diagram showing an example of display of decision support information when viewing "equipment availability rate." [Figure 14] FIG. 14 is a diagram showing a display example of the factor analysis result display section. [Figure 15] FIG. 15 is an explanatory diagram of the procedure for estimating the cause possibility. [Figure 16] FIG. 16 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

[0012] Hereinafter, with reference to the accompanying drawings, embodiments of a decision-making support device, a decision-making support system, and a decision-making support method disclosed herein will be described in detail. 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).

[0013] In the following, the case where the target of decision-making support is warehouse management in logistics operations will be taken as an example. The decision-making support system according to this embodiment is a warehouse management system 1 (see FIG. 1) that supports the decision-making of a manager in this warehouse management. The decision-making support device according to this embodiment is a decision-making support device 10 (see FIG. 1) included in the warehouse management system 1.

[0014] In the following, relational databases will be abbreviated as "RDB," data warehouses will be abbreviated as "DWH," and digital dashboards will be abbreviated as "dashboards."

[0015] In the following, "real-time" does not always mean "simultaneous" but broadly includes cases where there is an interval of several minutes to 15 minutes. "Real-time" may also be read as "instantaneous."

[0016] <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.

[0017] 1, the warehouse operation system 1 includes a warehouse system 5, a decision-making support device 10, a DWH 30, and a terminal device 100. The warehouse system 5, the decision-making support device 10, the DWH 30, 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.

[0018] 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.

[0019] 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.

[0020] 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).

[0021] 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.

[0022] 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 public cloud. Note that the decision-making support device 10 may also be realized as a private cloud. The decision-making support device 10 also includes an RDB 12b.

[0023] The decision support device 10 also provides a 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 (corresponding to an example of a "web application") running on the terminal device 100.

[0024] 1, the decision support device 10 is shown as one device, but the decision support device 10 may be realized by multiple devices. Also, in Fig. 1, the RDB 12b is shown as one RDB, but it is an abstract representation of an RDB managed by the decision support device 10 as an RDBMS (Relational Database Management System), and may include multiple RDBs.

[0025] The DWH 30 is a computer system that accumulates and analyzes various data collected from the warehouse system 5. The DWH 30 is realized, for example, as a public cloud. The DWH 30 may also be realized as a private cloud. The DWH 30 is managed and operated by a DWH operator.

[0026] 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.

[0027] 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.

[0028] As already mentioned, the DWH 30 and the RDB 12b each have their own advantages and disadvantages. For example, the DWH 30 has the advantage of being able to store a large amount of data and being strong at accumulating and analyzing data. On the other hand, the DWH 30 generally has a pay-as-you-go system based on time and resources used, and the cost becomes enormous as the startup time increases. Another disadvantage of the DWH 30 is that it is not suitable for real-time update processing of results displayed on dashboards, etc.

[0029] In contrast, the RDB 12b has the advantage of being strong in real-time update processing, but has the disadvantage that the amount of data that can be stored is small and response time decreases as the amount of data increases.

[0030] In particular, logistics operations are known to have a relatively large number of transactions and process patterns compared to various operations in other industries and fields. For this reason, in logistics-related systems, if operations are performed using only the RDB 12b, the RDB 12b may quickly become bloated, so it is desirable to use a DWH 30. However, DWH 30 generally operates on a pay-as-you-go basis. Therefore, the decision-making support method according to the embodiment incorporates measures to optimize data placement and optimize costs while still using the DWH 30.

[0031] 1, the warehouse operation system 1 has a data infrastructure P1. The data infrastructure P1 includes an RDB 12b and a DWH 30. The decision support device 10 executes a data infrastructure control process to control the data infrastructure P1, and links the RDB 12b and the DWH 30 to optimize data placement and rationalize costs (step S1).

[0032] Specifically, the decision-making support device 10 collects various data indicating the operating status in the warehouse from the warehouse system 5 (step S2). The decision-making support device 10 also processes the collected data as needed and stores the data in the RDB 12b and the DWH 30 (step S3).

[0033] The decision support device 10 stores, in the RDB 12b, real-time data for the current business day that is required for real-time analysis, for example. The decision support device 10 also stores, in the DWH 30, data for the past few years (for example, five years) that is required for analyses other than real-time analysis, such as daily analysis or weekly or later analysis.

[0034] The decision support device 10 then performs, for example, real-time analysis using the data in the RDB 12b of the data base P1, and causes the DWH 30 to perform various analyses other than real-time analysis using the data in the DWH 30 (step S4). The decision support device 10 also links the RDB 12b and the DWH 30 daily by batch processing during a predetermined time period, such as at night.

[0035] A more specific description will be given using Figures 2 to 6. Figure 2 is a diagram showing an outline of a data flow in the warehouse management system 1 according to the embodiment. Figure 3 is a diagram showing an example of a decision-making scene according to the embodiment.

[0036] Fig. 4 is a diagram showing an example of when to use the dashboard. Fig. 5 is an explanatory diagram of the data architecture in the DWH layer L3. Fig. 6 is a diagram showing an example of the roles of the upper and lower layers in the DWH layer L3.

[0037] As shown in FIG. 2, the data infrastructure P1 is configured to have three layers: a collection layer L1, an ETL (Extract / Transform / Load) layer L2, and a DWH layer L3.

[0038] In the collection layer L1, the decision support device 10 collects various data from "transactions," "masters," "aggregated data," etc. of the warehouse system 5, and extracts and stores data necessary for decision support. In the collection layer L1, data is stored in file units as shown in Figure 2. The files are, for example, flat files.

[0039] In the ETL layer L2, the decision support device 10 cleanses, converts, and normalizes the data stored in file units as necessary, and stores the data in storage 12a. The storage 12a corresponds to a so-called data lake. In the ETL layer L2, the decision support device 10 loads the data stored in storage 12a into the DWH 30.

[0040] In the DWH layer L3, the decision support device 10 links data accumulated in the storage 12a to the RDB 12b in real time while combining data and aggregating it for each purpose as needed. Also, in the DWH layer L3, the decision support device 10 allows the terminal device 100 to utilize the data via the RDB 12b.

[0041] At this time, in the DWH layer L3, the decision-making support device 10 extracts data from the RDB 12b and the DWH 30 as necessary. Also, in the DWH layer L3, the decision-making support device 10 links the RDB 12b and the DWH 30 daily using the batch processing described above. Details of the data architecture in the DWH layer L3 will be described later using Figures 5 and 6. Also, data generated during data utilization in the terminal device 100 is stored in the storage 12a as appropriate.

[0042] In the warehouse management system 1 according to this embodiment, for example, seven types of decision-making scenarios in warehouse management are defined, as shown in Fig. 3. As part of the dashboard service for the terminal device 100, the decision-making support device 10 provides the terminal device 100 with a decision-making support UI (User Interface) including a menu procedurally defining the decision-making process for each of the seven types of decision-making scenarios shown in Fig. 3.

[0043] Of the seven types of decision-making scenarios, for example, "worker allocation planning," "determining the work order and work start timing," and "detecting and taking measures against work delays and backlogs," require real-time analysis results using real-time data, as shown in Figure 3. On the other hand, "worker productivity evaluation and training planning," "optimal inventory allocation and movement planning," "management of work defects and measures," and "measurement of equipment effectiveness and measures" require analysis results based on retrospective analysis using past data on a daily or weekly basis.

[0044] Taking this into consideration, the warehouse management system 1 according to this embodiment provides a dashboard service in which the dashboard is broadly composed of functions for three usage timings: "real time," "daily," and "weekly or later," as shown in FIG. 4.

[0045] When the timing of utilization is "real time," it means that the main purpose is to check progress and personnel allocation status, and to appropriately manage the operational status of the site. In this case, management actions in warehouse management can be progress management, personnel redeployment, etc. In the warehouse management system 1 according to this embodiment, dashboards corresponding to such management actions are provided, such as "Shipping Work Progress," "Personnel Over-and-Shortage Status by Process," and "Personnel Redeployment Simulation (Processing Volume)." Specific examples of these dashboards will be described later using FIGS. 10 to 12.

[0046] Furthermore, when the timing of utilization is "daily," the main purpose is to check the progress, productivity, workload, and other performance data, and to identify problems and use them for daily improvements. In this case, possible management actions in warehouse operations include productivity management (by process or by individual) and personnel allocation. When the timing of utilization is "weekly or later," the main purpose is to identify areas for improvement and use them to improve work methods, location, and other aspects. In this case, possible management actions in warehouse operations include productivity management and analysis. The warehouse management system 1 according to this embodiment provides a dashboard such as "equipment utilization rate" that supports such retrospective analysis using past data. Specific examples will be described later using FIGS. 13 to 15.

[0047] In the decision-making support method according to the embodiment, taking into account the contents explained using Figures 3 and 4, the data infrastructure P1 is configured to have a three-tier x two-tier data architecture within the DWH layer L3 in order to optimize data placement and rationalize costs.

[0048] Specifically, as shown in Figure 5, the DWH layer L3 is configured to have three layers: a storage layer L31, an integration layer L32, and a usage layer L33. The DWH layer L3 is further configured to have two layers, one above the other, across the storage layer L31, the integration layer L32, and the usage layer L33. The upper layer corresponds to an example of a "first layer." The lower layer corresponds to an example of a "second layer."

[0049] An RDB 12b is disposed above each of the storage layer L31, integration layer L32, and utilization layer L33, and stores data structured according to the respective schemas of the storage layer L31, integration layer L32, and utilization layer L33. A DWH 30 is disposed below each of the integration layer L32 and utilization layer L33.

[0050] In the accumulation layer L31, real-time data for the day's work is accumulated in the RDB 12b from the storage 12a. In the integration layer L32, data from the RDB 12b and the DWH 30 is integrated. The usage layer L33 is the front-end layer for users. In the usage layer L33, data structured according to the purpose of use, for example, the dashboard service provided to the user, is stored in each RDB 12b.

[0051] The RDBs 12b in the storage layer L31 and the integration layer L32 are linked in real time. The RDBs 12b in the integration layer L32 and the utilization layer L33 are also linked in real time.

[0052] In the integration layer L32, the RDB 12b and the DWH 30 are linked daily by batch processing during a predetermined time period. In the utilization layer L33, the RDB 12b and the DWH 30 are also linked daily by batch processing.

[0053] In this daily linkage, the RDB 12b stores real-time data accumulated on the day of the business in the DWH 30. Meanwhile, in this daily linkage, the DWH 30 links the formatted data (i.e., past data) for the most recent predetermined period (e.g., one year) to the RDB 12b. The formatted data includes the results of analysis of the past data by the DWH 30.

[0054] As a result, as shown in Figure 5, the upper-level RDB 12b stores real-time data for the current business day and formatted data for the most recent specified period (here, one year). The formatted data includes, for example, aggregated data such as productivity data compiled by the DWH 30. Because the formatted data is small in volume, it is unlikely to overwhelm the capacity of the RDB 12b. Furthermore, the lower-level DWH 30 stores accumulated data for, for example, the most recent past few years (here, five years).

[0055] Based on this data arrangement, as shown in FIG. 6, for example, real-time analysis and retrospective analysis using past data for a specified period of time (here, one year) are performed by the decision support device 10 using data in the RDB 12b in the upper layer of the utilization layer L33.

[0056] Furthermore, for example, retrospective analysis using past data exceeding a specified period of time, big data analysis, advanced analysis, and optional analysis optionally specified by the user are performed by the DWH 30 using data from the lower-level DWH 30. These analyses using the data from the lower-level DWH 30 only require the DWH 30 to be used when necessary, and do not require the DWH 30 to be running all the time. Furthermore, the daily linkage between the RDB 12b and the DWH 30 described above is also performed by batch processing during a specified time period, so the DWH 30 only requires the DWH 30 to be used when necessary, and does not require the DWH 30 to be running all the time.

[0057] In other words, by having the DWH layer L3 of such a data architecture in the data infrastructure P1, it becomes possible to optimize data placement and optimize costs. Note that although the RDB 12b and the DWH 30 are linked daily, they may be linked weekly or at other predetermined intervals depending on the operation mode and the specifications of the RDB 12b.

[0058] Returning to the explanation of Fig. 1, the decision-making support device 10 provides the terminal device 100 with a decision-making support UI as part of the dashboard service as described above, and the user requests desired information via this decision-making support UI (step S5).

[0059] Then, the decision support device 10 visualizes the decision support information, which is information in response to a request from the user, based on the analysis results of step S4 using each data in the data base P1 (step S6).

[0060] Then, the decision support device 10 provides the visualized decision support information to the user (step S7). The user makes various decisions in warehouse management based on the provided decision support information.

[0061] The decision-making support UI includes a UI that guides the decision-making process in warehouse operations. For example, as described above, the decision-making support UI includes a menu that procedurally explains the decision-making process for each decision-making scenario shown in Figure 3. In the following, the decision-making scenarios will be referred to as "utilization scenarios" where appropriate.

[0062] The user can make an efficient decision while checking the decision support information required for decision-making from the menu that steps out the decision-making process for each use scenario. A specific example of using this menu will be described later with reference to Figures 9 to 12, taking the worker allocation planning as an example of a use scenario.

[0063] Furthermore, for example, the decision support UI includes a UI that enables estimation of the operation status of equipment and the factors of that operation status. A specific example of using such a UI will be described later with reference to FIGS. 13 to 15, taking the dashboard "Equipment Availability" as an example.

[0064] As described above, in the decision-making support method according to the embodiment, the decision-making support device 10 includes a data infrastructure P1. The data infrastructure P1 is configured such that an RDB 12b is arranged on an upper layer and a DWH 30 is arranged on a lower layer. The decision-making support device 10 accumulates real-time data for the current business day, among data related to warehouse operation collected from the warehouse system 5, in the RDB 12b, and accumulates past data in the DWH 30. The decision-making support device 10 also links the RDB 12b and the DWH 30 through batch processing. The decision-making support device 10 also links past data from the DWH 30 for a specified period of time to the RDB 12b through the batch processing. The decision-making support device 10 also analyzes and visualizes decision-making support information, which is information related to decision-making support in warehouse operation in response to a user request, based on the data on the upper layer. The decision-making support device 10 also provides the analyzed and visualized decision-making support information to the user.

[0065] 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.

[0066] <Configuration of decision support device> Next, a configuration example of the decision-making support device 10 will be described. Fig. 7 is a block diagram of the decision-making support device 10 according to the embodiment. Fig. 8 is a block diagram of the data infrastructure control unit 13a according to the embodiment. Note that in Figs. 7 and 8, components necessary for explaining the features of this embodiment are represented by functional blocks, and descriptions of general components are omitted.

[0067] 7 and 8 are functional concepts and do 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 them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0068] In the description using FIGS. 7 and 8, the description of the components that have already been described may be simplified or omitted.

[0069] As shown in FIG. 7, the decision support device 10 according to the embodiment includes a communication unit 11, a storage unit 12, and a control unit 13.

[0070] 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, the DWH 30, and the terminal device 100.

[0071] 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. 7, the storage unit 12 stores a storage 12a, an RDB 12b, an analytical model 12c, and UI information 12d.

[0072] The storage 12a and the RDB 12b have already been described, and therefore their description will be omitted here. The analytical model 12c is model information that the decision support device 10 uses to analyze data in the RDB 12b in order to generate various types of decision support information. The analytical model 12c is a mathematical model that includes, for example, mathematical formulas for calculating actual values ​​and predicted values ​​included in the decision support information based on the data in the RDB 12b.

[0073] Furthermore, the analytical model 12c is a learning model that is trained to output a predicted value included in the decision support information when data from the RDB 12b is input. If the analytical model 12c is a learning model, the analytical model 12c is trained using a machine learning algorithm such as deep learning.

[0074] The UI information 12d is information relating to various UIs to be provided to the terminal device 100. The UI information 12d includes, for example, definition information relating to the decision support UI described above.

[0075] 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).

[0076] The control unit 13 has a data infrastructure control unit 13a, an analysis unit 13b, a visualization unit 13c, and a provision unit 13d, and realizes or executes the functions and actions of information processing described below.

[0077] The data infrastructure control unit 13a executes a data infrastructure control process for controlling the data infrastructure P1 described with reference to Figures 2 and 5, appropriately via the communication unit 11. As shown in Figure 8, the data infrastructure control unit 13a includes a collection layer processing unit 13aa, an ETL layer processing unit 13ab, and a DWH layer processing unit 13ac.

[0078] The collection layer processing unit 13aa executes collection layer processing corresponding to the above-mentioned collection layer L1. The ETL layer processing unit 13ab executes ETL layer processing corresponding to the above-mentioned ETL layer L2. The DWH layer processing unit 13ac executes DWH layer processing corresponding to the above-mentioned DWH layer L3.

[0079] Returning to the explanation of Figure 7, the analysis unit 13b extracts data from the RDB 12b in response to a user request acquired by the provision unit 13d (described later) and performs an analysis to generate decision support information desired by the user, using the analytical model 12c. Furthermore, if the data requested by the user is not available in the RDB 12b, the analysis unit 13b causes the DWH 30 to perform an analysis using the data in the DWH 30 via the communication unit 11. Furthermore, the analysis unit 13b acquires the analysis results from the DWH 30 via the communication unit 11.

[0080] The visualization unit 13c visualizes the analysis results obtained by the analysis unit 13b using the UI information 12d, and generates decision support information to be provided to the user. The provision unit 13d provides a dashboard service to the terminal device 100 via the communication unit 11. The provision unit 13d also provides a decision support UI based on the UI information 12d to the terminal device 100 via the communication unit 11.

[0081] For example, the providing unit 13d provides the terminal device 100 with 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, based on the UI information 12d.

[0082] The providing unit 13d also acquires a request from a user via the decision support UI and notifies the analyzing unit 13b. The providing unit 13d also provides the decision support information generated by the visualizing unit 13c to the terminal device 100 via the communication unit 11.

[0083] <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.

[0084] Fig. 9 is a diagram showing an example of a menu display that steps out the decision-making process in the "worker allocation plan" application scenario. Fig. 10 is a diagram showing an example of the decision-making support information displayed when viewing "shipping work progress." Fig. 11 is a diagram showing an example of the decision-making support information displayed when viewing "staffing shortage / surplus status by process." Fig. 12 is a diagram showing an example of the decision-making support information displayed when viewing "staffing reassignment simulation (throughput)."

[0085] First, when the user selects the "Use scenario" tab shown in Fig. 9, a list corresponding to each decision-making scenario shown in Fig. 3 is displayed, although not shown here. When the user selects the use scenario "Worker allocation plan" from this list, a menu is displayed, as shown in Fig. 9, that steps out the decision-making process for this "Worker allocation plan."

[0086] 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.

[0087] By clicking the "View" button for each menu item, you can move to a dashboard that displays the decision-making 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 9 shows an example in which "Shipping Work Progress" and "Inbound / Outbound Work Progress Management by Time and Process" have been specified as favorites.

[0088] Here, let us assume that the user first selects "Shipping Work Progress" from the menu in Figure 9. Then, the dashboard "Shipping Work Progress" is displayed, as shown in Figure 10. In the dashboard "Shipping Work Progress", the user can check the progress status of the entire shipping work and the estimated time of completion.

[0089] As shown in FIG. 10, the dashboard "Shipping Work Progress" includes, for example, a header information section 51, a filter section 52, a first graph section 53, and a second graph section .

[0090] 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.

[0091] 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 .

[0092] 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.

[0093] 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.

[0094] Next, let us assume that the user selects "Personnel Shortage / Overage Status by Process" from the menu in Fig. 9. Then, the dashboard "Personnel Shortage / Overage Status by Process" is displayed, as shown in Fig. 11. In the dashboard "Personnel Shortage / Overage Status by Process," the user can understand the required man-hours and productivity for each process, as well as the skills of the workers, and can confirm the personnel who should be reassigned.

[0095] As shown in Figure 11, the dashboard "Personnel shortage / surplus status by process" 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Next, let us assume that the user selects "Personnel Redeployment Simulation (Throughput)" from the menu in Figure 9. Then, the dashboard "Personnel Redeployment Simulation (Throughput)" is displayed, as shown in Figure 12. In the dashboard "Personnel Redeployment Simulation (Throughput)", the user can check the shipping plan and allocation shown as the results of simulating personnel adjustments.

[0102] As shown in Figure 12, the dashboard "Personnel Redeployment Simulation (Processing Volume)" 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.

[0103] 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.

[0104] 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.

[0105] As shown by the two dashed rectangles in Figure 12, this dashboard, "Personnel Redeployment Simulation (Processing Volume)," allows users to determine whether they can meet the work schedule by transferring personnel from a surplus process to a shortage process.

[0106] Note that the decision support device 10 performs real-time analysis using data from each RDB 12b in the DWH layer L3 in the data infrastructure P1, and visualizes each dashboard shown in FIGS. 10 to 12 based on the analysis results.

[0107] <Example of the "Capacity Utilization Rate" dashboard> As described above, the decision support UI according to this embodiment includes a UI that enables estimation of the equipment operation status and factors behind that operation status. For example, this UI is provided as a dashboard called "equipment operation rate" that indicates whether the equipment is producing the expected results at the time of installation based on the difference between the equipment operation rate and machine capacity relative to a standard. The dashboard called "equipment operation rate" estimates factors behind the lack of effectiveness 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. The dashboard called "equipment operation rate" is displayed as the analysis result of a retrospective analysis using past data.

[0108] This dashboard "Facility Availability" will now be described in detail. Fig. 13 is a diagram showing an example of the display of decision support information when viewing "Facility Availability." Fig. 14 is a diagram showing an example of the display of the factor analysis result display unit 83. Fig. 15 is an explanatory diagram of the procedure for estimating the factor possibility.

[0109] As shown in FIG. 13, the dashboard “Facility Availability” has a filter section 81, a graph section 82, and a factor analysis result display section 83.

[0110] 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.

[0111] 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).

[0112] 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).

[0113] 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. 14, the factor analysis result display unit 83 displays a factor possibility screen. The factor possibility screen displays the deviation rate and the factor possibility.

[0114] 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 15.

[0115] Specifically, the analysis unit 13b calculates the availability rate by dividing the average availability rate by the standard availability rate based on the past equipment data included in the RDB 12b (step S11-1). Similarly, the analysis unit 13b calculates the machine capacity by dividing the actual throughput by the standard throughput based on the past equipment data (step S11-2).

[0116] The analysis unit 13b then compares the calculated availability rate and machine capacity (step S12). The analysis unit 13b then calculates a deviation rate (step S13) and estimates the cause possibility based on the comparison result of step S12. The analysis unit 13b 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).

[0117] At this time, the analysis unit 13b may estimate the cause possibility using a preset lookup table, etc. Furthermore, the analysis unit 13b may use the analytical model 12c that has been trained so as to be able to estimate the deviation rate and the cause possibility based on the equipment data.

[0118] Fig. 14 shows an example of a 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 shown is an example of a display where the "AutoStore" (AS) has an "operation rate < machine capacity" relationship, and "batch generation issue" and "insufficient work in progress" are estimated as possible causes.

[0119] By checking this dashboard "Facility Availability Rate," users can make efficient decisions to promote efforts to make effective use of facilities.

[0120] As mentioned above, the past data contained in RDB12b is for the most recent specified period (for example, one year), so if a date beyond this specified period is specified in the filter unit 81, the analysis unit 13b extracts past data corresponding to the specified date, for example, from DWH30.

[0121] <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. 16. The decision-making support device 10 will be described as an example.

[0122] 16 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Furthermore, in the explanation using FIG. 16, 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. 16.

[0129] <Conclusion> As described above, the decision-making support device 10 according to the embodiment includes a data infrastructure P1 and a control unit 13. The data infrastructure P1 is configured such that the RDB 12b is arranged in an upper layer (corresponding to an example of the "first layer") and the DWH 30 is arranged in a lower layer (corresponding to an example of the "second layer"). The control unit 13 accumulates real-time data for the current day of warehouse operation (corresponding to an example of the "logistics operation") collected from the warehouse system 5 (corresponding to an example of the "logistics operation system") in the RDB 12b and accumulates past data in the DWH 30. The control unit 13 also links the RDB 12b and the DWH 30 through batch processing. The control unit 13 also links past data from the DWH 30 for a specified period of time to the RDB 12b through the batch processing. The control unit 13 also analyzes and visualizes decision-making support information, which is information requested by a user regarding decision-making support for warehouse operation, based on the data in the upper layer. The control unit 13 also provides the analyzed and visualized decision-making support information to the user.

[0130] Therefore, the decision-making support device 10 according to the embodiment can support the decision-making of a manager in a logistics operation while optimizing data allocation and optimizing costs.

[0131] 9 to 14 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. 11, 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.

[0132] 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.

[0133] 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]

[0134] 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 Storage 12b RDB 12c Analysis Model 12d UI information 13 Control Unit 13a Data Infrastructure Control Unit 13aa Collection layer processing section 13ab ETL layer processing section 13ac DWH layer processing section 13b Analysis Department 13c Visualization Department 13d Supply Department 30 DWH 100 terminal device

Claims

1. A data infrastructure configured such that an RDB is placed on a first layer and a DWH is placed on a second layer; Among the data related to logistics operations collected from the logistics operation system, real-time data for the day of operation is stored in the RDB, and past data is stored in the DWH, The RDB and the DWH are linked by batch processing; In the batch processing, past data for a predetermined period of time in the DWH is linked to the RDB; Analyzing and visualizing decision support information, which is information in response to a request from a user regarding decision support in logistics operations, based on the data in the first layer; a control unit that provides the analyzed and visualized decision support information to a user; A decision support device comprising:

2. The past data for the most recent predetermined period includes shaped data that is the analysis result of the past data by the DWH. The decision support device of claim 1 .

3. The shaped data includes aggregated data compiled by the DWH.

3. The decision support device of claim 2.

4. The control unit providing the decision support information to a user via a dashboard service; The decision support device of claim 1 .

5. The control unit providing said dashboard service such that the dashboard comprises at least real-time, daily, and weekly or later functionality; 5. The decision support device of claim 4.

6. The dashboard, which supports real-time functionality, includes at least the following: progress management and personnel reassignment in logistics operations; 6. The decision support device of claim 5.

7. The control unit performing a real-time analysis using the real-time data of the first layer and a retrospective analysis using the historical data of the first layer; The decision support device of claim 1 .

8. The control unit If the first layer does not contain data corresponding to the request from the user, the DWH performs an analysis using the second layer data to obtain an analysis result. The decision support device of claim 1 .

9. The data infrastructure has three layers: a storage layer, an integration layer, and a utilization layer, and is configured to have two layers above and below the three layers, the first layer corresponds to the upper layer; The second layer corresponds to the lower layer. The decision support device according to any one of claims 1 to 8.

10. A DWH and a decision support device, The decision support device comprises: A data infrastructure configured such that an RDB is placed in a first layer and the DWH is placed in a second layer; Among the data related to logistics operations collected from the logistics operation system, real-time data for the day of operation is stored in the RDB, and past data is stored in the DWH, The RDB and the DWH are linked by batch processing; In the batch processing, past data for a predetermined period of time in the DWH is linked to the RDB; Analyzing and visualizing decision support information, which is information in response to a request from a user regarding decision support in logistics operations, based on the data in the first layer; providing the analyzed and visualized decision support information to a user; A decision support system characterized by:

11. A decision support method executed by a computer having a data infrastructure configured such that an RDB is arranged in a first layer and a DWH is arranged in a second layer, Among data relating to logistics operations collected from a logistics operation system, real-time data for the day of the operation is stored in the RDB, and past data is stored in the DWH; Linking the RDB and the DWH through batch processing; In the batch processing, past data for a predetermined period of time in the DWH is linked to the RDB; Analyzing and visualizing decision support information, which is information in response to a request from a user regarding decision support in logistics operations, based on the data of the first layer; providing the analyzed and visualized decision support information to a user; A decision support method comprising:

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