WO2022157876A1 - Dispositif d'aide à la planification, programme et procédé d'aide à la planification - Google Patents

Dispositif d'aide à la planification, programme et procédé d'aide à la planification Download PDF

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Publication number
WO2022157876A1
WO2022157876A1 PCT/JP2021/001992 JP2021001992W WO2022157876A1 WO 2022157876 A1 WO2022157876 A1 WO 2022157876A1 JP 2021001992 W JP2021001992 W JP 2021001992W WO 2022157876 A1 WO2022157876 A1 WO 2022157876A1
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Prior art keywords
kpi
event
management
management information
prediction
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PCT/JP2021/001992
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English (en)
Japanese (ja)
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輝明 下田
知章 掛田
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株式会社日立製作所
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Priority to PCT/JP2021/001992 priority Critical patent/WO2022157876A1/fr
Publication of WO2022157876A1 publication Critical patent/WO2022157876A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention relates to a plan support device, a program, and a plan support method that support planning.
  • KPI Key Performance Indicator
  • Patent Document 1 discloses a management information display method realized by executing a program stored in the memory of a computer equipped with a CPU, a memory, and a storage device. wherein profit and loss data for each product stored in the storage device is read into the memory, each profit and loss amount obtained by sorting the products in descending order of profit amount is displayed on a display device, and one of the products is When specified, the profit and loss data and the profit rate for each region of the specified product stored in the storage device are read into the memory, and each profit and loss amount obtained by sorting the regions in descending order of profit amount is stored in the profit rate. Also, a method of displaying management information is described, which is characterized by displaying on the display device together with the management information.
  • Patent Document 1 analyzes the sales, profit and loss, profit rate, and growth rate for each product using the management information display method described above, and identifies products that currently contribute to the overall profit and loss, and products that are promising for future profit acquisition. , it is stated that it has the effect of identifying products whose future earnings are expected to be low and accurately reflecting the sales strategy.
  • conventional plan management systems have functions to display planned items to be managed as various indicators, and to predict the impact of the current plan and display changes in various indicators. be able to.
  • the present invention has been made in view of such a background, and aims to provide a plan support device, a program, and a plan support method that support planning including predictions that are difficult to formulate.
  • the planning support apparatus includes an event reflecting unit that changes management items constituting management information related to one or more management targets according to an event that changes according to a predetermined rule; a prediction unit that predicts a prediction target item related to the management target based on the management information changed by the event; and a KPI calculation unit that calculates a KPI based on the management information and the prediction target item.
  • the prediction unit predicts the past management information and the past results of the prediction target item using a trained machine learning model as teacher data.
  • FIG. 4 is a data configuration diagram of a management information database according to the embodiment;
  • FIG. 4 is a data configuration diagram of an event information database according to the embodiment;
  • FIG. 4 is a data configuration diagram of a KPI information database according to the embodiment;
  • FIG. 4 is a data configuration diagram of a supplier database according to the embodiment;
  • FIG. 3 is a data configuration diagram of a parts database according to the embodiment;
  • FIG. 4 is a data configuration diagram of a manufacturing management database according to the embodiment;
  • 4 is a data configuration diagram of management information according to the embodiment;
  • FIG. 4 is a data configuration diagram of a KPI calculation result table according to the embodiment;
  • FIG. 4 is a data configuration diagram of a KPI threshold determination result table according to the embodiment; It is a display example of KPI of "all products" in all events according to the present embodiment. It is a display example of KPIs of events in which the targets of all KPIs according to the present embodiment are achieved. It is an example of a display which specified KPI which concerns on this embodiment. It is a display example of specifying a KPI with a condition according to the present embodiment.
  • 1 is a diagram showing a recording medium according to this embodiment; FIG.
  • the planning support device of the present embodiment relates to planning of parts procurement in a manufacturer that manufactures a product that is a large machine such as a railway vehicle, and calculates and displays KPIs when parts suppliers and delivery dates are changed. .
  • the user of the planning support device sets an event to change the ordering information (management information) including the part supplier, delivery date, order quantity, etc. due to the change in the manufacturing deadline of the product.
  • the planning support device predicts the delivery date of parts when an event (plan change) is executed, and calculates KPI based on the prediction result.
  • KPIs include the rate of coincidence between the delivery date and arrival date of ordered parts, the number of occurrences of rescheduling at the manufacturing site due to delays in the arrival of ordered parts, the total manufacturing work time, the number of manufacturing delay days, manufacturing costs, and the like.
  • the planning support device uses a machine learning model trained with learning data (teacher data) whose input is past order information and whose output (correct label) is the arrival date. Predict.
  • the user can predict (simulate) how the KPI will change if the procurement plan (draft) is changed by changing the ordering party and delivery date of parts due to the change in the manufacturing deadline of the product. become.
  • By finding events (changes in plans/plan proposals) that satisfy important KPIs users will be able to formulate optimized plans. Moreover, even when the plan is changed during execution of the plan, the user can make the optimum change.
  • FIG. 1 is a functional block diagram of a planning support device 100 according to this embodiment.
  • the planning support device 100 is a computer and includes a control section 110 , a storage section 120 and an input/output section 180 .
  • User interface devices such as a display, a keyboard, and a mouse are connected to the input/output unit 180 .
  • the storage unit 120 is composed of storage devices such as ROM (Read Only Memory), RAM (Random Access Memory) and SSD (Solid State Drive).
  • the storage unit 120 stores a management information database 210, an event information database 230, a KPI information database 240, a customer database 250, a parts database 260, a manufacturing management database 270, a program 121, and a prediction model 122.
  • the management information database 210, the event information database 230, the KPI information database 240, the supplier database 250, the parts database 260, and the manufacturing management database 270 are respectively represented by a management information DB (database), an event information DB, and a KPI information database.
  • DB client DB
  • parts DB and manufacturing control DB.
  • the program 121 includes a description of the procedure for planning support processing (see FIG. 8, which will be described later).
  • the prediction model 122 is a machine learning model used to predict the arrival date 218 contained in the management information database 210 (see FIG. 2 described later).
  • FIG. 2 is a data configuration diagram of the management information database 210 according to this embodiment.
  • the management information database 210 in this embodiment stores order information for parts.
  • the management information database 210 is, for example, tabular data, one row (record) indicates ordering information for parts, and includes an order number 211, an order request date 212, a product 213, a part 214, a part 215, and a customer 216. , due date 217, arrival date 218, valid flag 219 (shown as valid F in FIG. 2), and supplementary information 220 columns (attributes).
  • attributes such as order quantity and price may be included.
  • the order number 211 is the identification information of the order.
  • the order placement date 212 is the date the order was placed.
  • the product 213 is the name or identification information of the product in which the ordered parts are used.
  • a part 214 is a part of the product 213 in which the ordered part is used.
  • the part 215 is the name or identification information of the ordered part.
  • Supplier 216 is the name or identification information of the supplier.
  • the delivery date 217 is the delivery date of the part.
  • the arrival date 218 is the date when the ordered parts were delivered. In FIG. 2, none of the parts have been delivered and the arrival date 218 is blank.
  • a valid flag 219 indicates whether the order is valid or invalid.
  • the validity flag 219 is valid at the time of ordering, but becomes invalid when the order is canceled or when the production of the part is finished.
  • Supplementary information 220 is additional information related to the order or parts.
  • FIG. 3 is a data configuration diagram of the event information database 230 according to this embodiment.
  • the event information database 230 includes information on events indicating changes in parts procurement plans, such as changes in product manufacturing plans.
  • the event information database 230 is data in, for example, a table format, one row (record) indicates an event, and columns of identification information 231 (shown as # in FIG. 3), event content 232, and event reflection content 233 ( attributes).
  • the identification information 231 is identification information of the event.
  • the event content 232 indicates the content of the plan change. Examples of changes include postponing or bringing forward the deadline for completion of manufacturing of products, and discontinuing manufacturing itself. In addition, there are changes in parts suppliers and confirmation of part delivery dates.
  • the event reflection content 233 indicates the change content (rule indicating change procedure) of the attribute (management item) included in the management information database 210 (see FIG. 2) when the event is executed. Examples of changes (examples of rules) include postponing or bringing forward the delivery date 217 in line with the product manufacturing completion deadline, changing the order request date 212 and supplier 216 due to a change in the supplier, and supplementing the confirmation of the parts delivery date. For example, the information 220 is updated to "confirmed one week ago".
  • FIG. 4 is a data configuration diagram of the KPI information database 240 according to this embodiment.
  • the KPI information database 240 includes KPI information that is an index for evaluating a plan.
  • the KPI information database 240 is, for example, tabular data, one row (record) indicates a KPI, and columns of identification information 241 (described as # in FIG. 4), KPI content 242, and KPI threshold setting 243 ( attributes).
  • the identification information 241 is identification information of the KPI.
  • the KPI content 242 indicates the content (meaning) of the KPI. Examples of the contents include the rate of coincidence between the delivery date and the arrival date of the ordered parts, the total manufacturing work time, and the manufacturing cost.
  • the KPI threshold setting 243 indicates the target value of the KPI. The target value is not limited to the numerical value itself, and may be an increase/decrease value or an increase/decrease ratio compared to before the plan change.
  • FIG. 5 is a data configuration diagram of the customer database 250 according to this embodiment.
  • the supplier database 250 is a database containing information on suppliers of parts.
  • the supplier database 250 is, for example, data in tabular form, one row indicates a supplier, a supplier 251, the current number of orders placed 252, the current order amount 253, the cumulative number of orders placed in the past six months 254, Includes columns (attributes) for Cumulative Order Amount 255 for the past 6 months and Reliability 256 for the past 6 months.
  • the supplier 251 is the name or identification information of the parts supplier, and corresponds to the supplier 216 (see FIG. 2).
  • the current number of orders placed 252 and the current order amount 253 are the number of orders placed to the supplier 251 at the present time and the order amount, respectively.
  • the cumulative number of orders placed in the past 6 months 254 and the cumulative order amount 255 in the past 6 months are the number of orders placed and the amount of orders placed in the past 6 months, respectively.
  • the reliability of the past 6 months 256 is an index calculated from the rate of coincidence between delivery dates and arrival dates of parts in the past 6 months, the quality of parts delivered in the past 6 months, and the like.
  • FIG. 6 is a data configuration diagram of the parts database 260 according to this embodiment.
  • the parts database 260 is a database containing information on product parts.
  • the parts database 260 is, for example, data in tabular form, one row indicates a part, and columns of a part 261, a drawing 262, a design difficulty level 263, a manufacturing difficulty level 264, an estimated number of days for designing 265, and an estimated number of days to manufacture 266. (attributes).
  • the part 261 is the name or identification information of the part and corresponds to the part 215 (see FIG. 2).
  • Drawing 262 shows the drawing data of the part.
  • the design difficulty level 263 and the manufacturing difficulty level 264 indicate the difficulty level of designing the part and the difficulty level of manufacturing the part, respectively, and are indicated by levels from A to E.
  • the estimated design days 265 and estimated manufacturing days 266 indicate the number of days required for part design and the number of days required for part manufacturing, respectively.
  • FIG. 7 is a data configuration diagram of the manufacturing management database 270 according to this embodiment.
  • the manufacturing management database 270 contains information related to product manufacturing.
  • the manufacturing management database 270 is, for example, tabular data, one row indicates one product to be manufactured, and identification information 271 (shown as # in FIG. 7), product 272, orderer 273, manufacturing start date 274. contains the columns (attributes) of
  • the identification information 271 is identification information for each manufactured product.
  • Product 272 indicates the product to be manufactured and corresponds to product 213 (see FIG. 2).
  • the orderer 273 indicates the person who ordered the product 272 and is the delivery destination of the manufactured product 272 .
  • the manufacturing start date 274 indicates planned values and actual results for the manufacturing start date.
  • the manufacturing management database 270 may further include planned values and actual results such as manufacturing completion dates, shipping dates, periods of each process involved in manufacturing, personnel, and costs.
  • control unit 110 includes a CPU (Central Processing Unit) and includes a learning unit 111 , an event reflection unit 112 , a prediction unit 113 , a KPI calculation unit 114 and a result output unit 115 .
  • CPU Central Processing Unit
  • the learning unit 111 prepares learning data (teacher data) and trains and generates a prediction model 122, which is a machine learning model.
  • the learning data is the management information database 210 (see FIG. 2), which is order information for parts that have already been delivered. Specifically, data other than the arrival date 218 in the management information database 210 becomes the input data, and the arrival date 218 becomes the correct label (output data).
  • the input data may include the supplier database 250 (see FIG. 5) and the parts database 260 (see FIG. 6).
  • the prediction model 122 trained using such learning data and correct labels is machine learning used when predicting the arrival date 218 from information such as the parts 215, the order request date 212, the supplier 216, and the delivery date 217. Become a model.
  • the event reflection unit 112 changes the management information database 210 according to the selected event (see the event information database 230 shown in FIG. 3). Specifically, as will be described later, the management information 210A (see FIG. 9 described later), which is equivalent data to the management information database 210, is changed (see steps S12 and S13 in FIG. 8 described later). For example, when an event whose identification information 231 is “event 1” is selected, the event reflection unit 112 changes the delivery date 217 of the record whose product 213 is “product A” in the management information database 210 to 3 postpone for a month.
  • the prediction unit 113 predicts the arrival date 218 using the prediction model 122 .
  • the arrival date 218 is predicted using the prediction model 122 with the management information database 210 (see management information 210A shown in FIG. 9 to be described later) as input data.
  • the input data may include the supplier database 250 (see FIG. 5) and the parts database 260 (see FIG. 6).
  • the KPI calculation unit 114 calculates KPIs for individual products and all products according to the KPI contents 242 for the KPIs in the KPI information database 240 (see FIG. 4). For example, for the KPI whose identification information 241 is "KPI1", the KPI calculation unit 114 calculates the matching rate between the delivery date 217 and the arrival date 218 in the management information database 210 for individual products and all products. Further, for example, the KPI calculation unit 114 calculates the KPIs whose identification information 241 is “KPI3” to “KPI5” by referring to the manufacturing management database 270 (see FIG. 7). The KPI calculation unit 114 may also refer to the supplier database 250 and the parts database 260 .
  • the result output unit 115 outputs (displays) the KPI calculated by the KPI calculation unit 114 to a display connected to the input/output unit 180 (see FIG. 1).
  • the result output unit 115 does not always output all KPIs, and may output KPIs specified based on user's designation.
  • FIG. 8 is a flow chart of planning support processing according to the present embodiment.
  • control unit 110 starts processing to repeat steps S 12 to S 16 for each of the unchanged event and the event included in event information database 230 .
  • a no-change event is an event in which the management information database 210 (see FIG. 2) is not changed. Note that in the repeated processing of steps S12 to S16, events without change are processed first.
  • step S ⁇ b>12 the event reflecting unit 112 reads the content of the management information database 210 into a different area from the management information database 210 in the storage unit 120 .
  • this read data is referred to as management information 210A (see FIG. 9 described later).
  • step S13 the event reflecting unit 112 changes the management information 210A according to the event.
  • step S14 the prediction unit 113 uses the management information 210A as input data, predicts the arrival date using the prediction model 122, and stores the arrival date in the management information 210A.
  • FIG. 9 is a data configuration diagram of the management information 210A according to this embodiment.
  • the management information 210A has the same configuration as the management information database 210 (see FIG. 2), and the columns (attributes) are assigned the same reference numerals as those of the management information database 210.
  • FIG. 10 is a data configuration diagram of the KPI calculation result table 310 according to this embodiment.
  • the KPI calculation result table 310 stores KPIs calculated by the KPI calculator 114 .
  • the KPI calculation result table 310 is data in tabular form, for example, one row (record) indicates the KPI after the event is reflected, and includes columns (attributes) of a product 311, an event 312, and KPIs 313-317.
  • a product 311 indicates a product to be calculated, and includes “all products” which are all products in addition to individual products.
  • An event 312 indicates an event that caused the management information database 210 to be changed.
  • a record whose event 312 is "-" is a KPI in a no-change event and indicates a reference value.
  • Other records show KPIs based on the management information 210A (see FIG. 9) changed based on the event indicated by the event 312, and include increase/decrease values/ratios from the reference value.
  • KPIs 313-317 indicate calculated KPIs.
  • KPI1 of the KPI 313 of the record in which the product 311 is “all products” and the event 312 is “event 1” has a matching rate of 77% for the due date and the arrival date of ordered parts (see FIG. 4), which is the reference value. It shows that it increased by 7 from “KPI3" of KPI 315 indicates that the total manufacturing work time is 1100 hours, which is 110% of the reference value.
  • step S16 the KPI calculation unit 114 determines whether or not the calculated KPI satisfies the target value (see KPI threshold setting 243 shown in FIG. 4).
  • the determination result table 320 Stored in the determination result table 320 (see FIG. 11 described later).
  • FIG. 11 is a data configuration diagram of the KPI threshold determination result table 320 according to this embodiment.
  • the KPI threshold determination result table 320 stores whether or not the KPI determined by the KPI calculation unit 114 has achieved the target value.
  • the KPI threshold determination result table 320 is, for example, tabular data. One row (record) indicates whether or not the KPI target value has been achieved after the event is reflected. )including.
  • the product 321, event 322, and KPIs 323-327 are the same as the product 311, event 312, and KPIs 313-317 of the KPI calculation result table 310 (see FIG. 10). However, the KPIs 323 to 327 store whether or not the KPI has achieved the target value (True/False).
  • step S17 the result output unit 115 inquires of the user and acquires the KPIs, their conditions, and the products to be displayed on the display (not shown) connected to the input/output unit 180 (see FIG. 1). do.
  • step S18 the result output unit 115 displays the KPI acquired in step S17, its conditions, and the KPI corresponding to the product on the display.
  • a display example will be described below.
  • FIG. 12 is a display example 330 of KPIs for "all products" in all events according to this embodiment. More specifically, it is a display example 330 when "all products" are selected as display target products in step S17 and all KPIs are selected. In the display example 330, the vertical direction is for each event, and the horizontal direction is for each KPI. By referring to the display example 330, the user can comprehend the impact of plan changes (differences in plan content) on KPIs.
  • FIG. 13 is a display example 340 of KPIs of events in which the goals of all KPIs according to this embodiment are achieved. More specifically, it is a display example 340 when "all products" is selected as the display target product in step S17 and "target value attainment for all KPIs" is selected as the KPI condition.
  • the result output unit 115 selects a record in which all the KPIs 323 to 327 are "True” indicating that the target value has been achieved, among the records in which the product 321 is "all products” in the KPI threshold determination result table 320 (see FIG. 11). to explore.
  • the result output unit 115 acquires records in which the event 322 is “event 5” as a search result, and displays the KPI of “event 5”.
  • FIG. 14 is a display example 350 specifying KPIs according to this embodiment. More specifically, it is a display example 350 when "all products", “product A” and “product B” are designated for “KPI1” and “all products” is selected for "KPI3" in step S17. .
  • FIG. 15 is a display example 360 in which KPIs are specified with conditions according to this embodiment. Specifically, in step S17, "all products", “product A”, and “product B” are selected for “KPI1”, “all products” are selected for "KPI3”, and "KPI1 and KPI3 are This is a display example 360 when the condition "target value achieved" is selected.
  • the result output unit 115 refers to the KPI threshold determination result table 320 (see FIG. 11) to search for a record that satisfies the conditions, and displays the KPI of the event corresponding to the search result record.
  • step S19 the result output unit 115 acquires the user operation, and if the operation is to end (step S20 ⁇ YES), the planning support process is terminated. NO) Return to step S17.
  • the plan support device 100 supports formulation of a plan for procurement of product parts. More specifically, the planning support device 100 predicts the delivery date of parts when an event is executed to change order information including an order destination, delivery date, order quantity, etc. of parts set by the user. Next, the planning support apparatus 100 calculates and displays KPIs, which are evaluation indices for planning, such as the coincidence rate between the delivery date and the arrival date of ordered parts and the total manufacturing work time. The planning support apparatus 100 selects and displays events, KPIs, and products designated by the user, or displays only events that satisfy the target values of KPIs set by the user.
  • KPIs evaluation indices for planning
  • a prediction model 122 (see Fig. 1), which is a machine learning model that uses past results as teacher data, is used to predict the arrival date. Even if there are no empirical rules or well-known theories that can be used to predict arrival dates, machine learning technology can be used to make predictions based on past performance.
  • the user of the planning support device 100 sets changes (differences, corrections) from the basic plan as events in addition to the basic plan that serves as a reference, and how the KPI changes when the event is executed (the plan is changed). It becomes possible to predict (simulate) whether A target value (threshold) can be set for the KPI, and it becomes possible to try how to change the plan in order to meet the target. Even if the goals of all KPIs cannot be achieved, it is possible to search for a plan that satisfies the goals by focusing on the important KPIs, and the user can formulate an optimized plan.
  • Event ⁇ In the above-described embodiment, events are set so that when the completion of manufacturing is postponed/advanced, the delivery of parts is also postponed/advanced by the same period (see “Event 1" and "Event 2" in FIG. 3). .
  • the event reflection unit 112 may refer to a PERT (Program Evaluation and Review Technique) diagram of product manufacturing and change the management information so that the postponement/advance period differs for each part.
  • PERT Program Evaluation and Review Technique
  • parts are subject to management, and related to planning of parts procurement, but other plans may be used.
  • you can set KPIs and target values for various management targets, and test how to change the plan (which plan to adopt) to achieve the target values. become.
  • machine learning technology can be used to make predictions based on past performance and calculate KPIs.
  • the planning support device 100 can be used for product purchase planning.
  • the management information (management information database 210) includes the amount of merchandise purchased by merchandise, by store, and by day, the weather in the area where the store is located, the supplier, the purchase price, the purchase amount, and the like.
  • the event is forecasted weather for each store and for each day.
  • the target of prediction is the sales volume for each product, each store, and each day. KPIs include gross profit, the amount of unsold items, and whether there are shortages.
  • the planning support device 100 can be used for campaign planning for members.
  • This member is a subscriber of a cellular phone service or a mail-order member.
  • the management information includes the subscription period for each member, the number of times the service is used, the amount of service used, the amount of money used, and the like.
  • An event is a campaign such as a charge discount or privilege.
  • the target of prediction is the number of times the service is used, the amount of service used, the amount of money used, etc. after the event is executed.
  • KPIs include a retention rate of members, a usage amount, a usage amount, and the like.
  • the planning support device 100 can be used for maintenance planning of manufacturing machines.
  • the management information includes introduction time for each manufacturing machine, cumulative operating time, operating time since the last maintenance inspection, failure rate, and the like.
  • the event is the next maintenance inspection time or the maintenance inspection interval.
  • the prediction target is the predicted date of failure occurrence.
  • KPIs include production volume by product, interval between failures, operating rate by manufacturing machine, average operating rate, and the like.
  • the planning support device 100 can be used for production planning of greenhouse-grown agricultural products.
  • the management information includes the planting time for each house, weather data, fertilizer data, growth data, and the like.
  • the event is the amount of adjustment of the environment in the house, the amount of fertilizer, and the like.
  • the target of prediction is harvest time, harvest amount, and the like.
  • KPIs include the amount of harvest, the amount of sales, and the amount of waste.
  • the program 121 is stored in the planning support device 100, which is a computer.
  • the program 121 in the recording medium 910 may be read, loaded into the storage unit 120 and executed, or may be installed from the recording medium 910 and executed.
  • FIG. 16 is a diagram showing a recording medium 910 according to this embodiment.
  • the arrival date is predicted using machine learning technology, but instead of machine learning, other methods such as a linear regression model may be used for prediction.
  • the management information database 210 and other databases are stored in the planning support apparatus 100, information stored in another apparatus may be accessed.
  • KPIs to be displayed, their conditions, and products are acquired. (see step S17).
  • the KPIs to be displayed and the KPIs necessary for confirming the conditions may be calculated after acquiring the KPIs to be displayed, their conditions, and the products.
  • the result output unit 115 identifies an event that has achieved the target value of the KPI and displays the KPI corresponding to the event. Events may be arranged and displayed in order. For example, the result output unit 115 may display the KPIs so that the events are arranged in descending order of achievement. In addition, the result output unit 115 may display KPIs such that events are arranged in descending order of the degree of achievement of preset KPIs of high importance.
  • Prediction model (machine learning model) 210 management information database (management information) 210A Management information 211 Order number (management item) 212 Order request date (control item) 213 Products (control items) 214 parts (control items) 215 parts (control items) 216 Business Partners (Management Items) 217 Delivery date (control item) 218 Arrival date (forecast target item) 219 valid flag (control item) 220 Supplementary Information (Control Items) 230 Event information database 233 Event reflection content (rule) 240 KPI information database 242 KPI threshold setting (target value, threshold)

Abstract

Un dispositif d'aide à la planification (100) est pourvu d'une unité de réflexion d'événement (112) qui change des éléments de gestion constituant des informations de gestion concernant une ou plusieurs cibles de gestion conformément à des événements devant être changés selon des règles prédéterminées, une unité de prédiction (113) qui prédit des éléments à prédire associés aux cibles de gestion sur la base des informations de gestion modifiées conformément à l'événement, et une unité de calcul de KPI (114) qui calcule des KPI sur la base des informations de gestion et des éléments à prédire. L'unité de prédiction (113) effectue une prédiction à l'aide d'un modèle de prédiction (122) formé par des données de formation constituées par des informations de gestion antérieures et des enregistrements antérieurs des éléments à prédire.
PCT/JP2021/001992 2021-01-21 2021-01-21 Dispositif d'aide à la planification, programme et procédé d'aide à la planification WO2022157876A1 (fr)

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CN115562948A (zh) * 2022-12-05 2023-01-03 成都索贝数码科技股份有限公司 大规模并行化的多kpi预测方法及系统

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