JP2019148973A - Project evaluation device, project evaluation method, and project evaluation program - Google Patents

Abstract

To allow for appropriate evaluation of a project being executed.SOLUTION: A project evaluation server 100 which evaluates an executing project comprises: a prediction processing unit 212 which predicts evaluation results corresponding to one or more evaluation items of the executing project on the basis of project data of an executed project, evaluation results corresponding to one or more evaluation items for the executed project, and project data of the executing project; and a display processing unit 213 which displays evaluation results for one or more evaluation items of the executing project predicted by the prediction processing unit 212.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a project evaluation apparatus for evaluating a project.

  For example, various apparatuses for managing projects such as software development and system development are known. For example, Patent Document 1 discloses an apparatus that analyzes a text included in project information, extracts an expression pattern, and predicts a trouble occurrence probability of the project based on the expression pattern. Further, Patent Document 2 discloses a system that proposes measures when a project work delay occurs.

JP 2009-230351 A JP 2006-126898 A

  In general, it is difficult to recognize whether or not a project is malfunctioning in the middle of a project. For example, there is a process meeting as an opportunity for an expert who has the know-how of past projects to give advice to a project manager with little experience, for example, a process meeting is held once a month, for example. It is difficult to provide timely advice to project managers because of the limited frequency.

  In addition, the cause of the malfunction of the project may be investigated and analyzed for a project completed in that period every predetermined period (for example, quarter), and the result is sent to individual project managers. Feedback is also costly and can be difficult to enforce. In addition, since a completed project is targeted, it is not possible to give advice in a timely manner to a project that is being executed.

  This invention is made | formed in view of the said situation, The objective is to provide the technique which can evaluate the project in execution appropriately.

  In order to achieve the above object, a project evaluation apparatus according to one aspect is a project evaluation apparatus that evaluates an ongoing project, and that corresponds to project data of an executed project and one or more evaluation items for the executed project. Based on the result and the project data of the project being executed, an evaluation estimation unit for estimating an evaluation result corresponding to one or more evaluation items of the project being executed, and an evaluation of the project being executed estimated by the evaluation estimation unit An evaluation result display processing unit for displaying an evaluation result for one or more evaluation items.

  According to the present invention, a running project can be appropriately evaluated.

FIG. 1 is an overall configuration diagram of a project prediction server according to an embodiment. FIG. 2 is a configuration diagram of a project data table according to an embodiment. FIG. 3 is a configuration diagram of project basic information according to an embodiment. FIG. 4 is a configuration diagram of profit / loss information according to an embodiment. FIG. 5 is a configuration diagram of progress information according to an embodiment. FIG. 6 is a configuration diagram of quality information according to an embodiment. FIG. 7 is a configuration diagram of a malfunction investigation data table according to an embodiment. FIG. 8 is a configuration diagram of an AI project data table according to an embodiment. FIG. 9 is a configuration diagram of evaluation information according to an embodiment. FIG. 10 is a configuration diagram of an AI malfunction investigation data table according to an embodiment. FIG. 11 is a configuration diagram of factor pattern information according to an embodiment. FIG. 12 is a configuration diagram of a risk data table according to an embodiment. FIG. 13 is a configuration diagram of risk data according to an embodiment. FIG. 14 is a diagram showing a project evaluation result screen according to an embodiment. FIG. 15 is a flowchart of an evaluation estimation model learning process according to an embodiment. FIG. 16 is a flowchart of a malfunction factor estimation model learning process according to an embodiment. FIG. 17 is a flowchart of a project evaluation process according to an embodiment.

  Embodiments will be described with reference to the drawings. The embodiments described below do not limit the invention according to the claims, and all the elements and combinations described in the embodiments are essential for the solution of the invention. Is not limited.

  In the following description, information may be described using the expression “AAA table”, but the information may be expressed in any data structure. That is, the “AAA table” can be referred to as “AAA information” to indicate that the information does not depend on the data structure.

  The processor may include a hardware circuit that performs a part or all of the processing. The program may be installed in a computer-like device from a program source. The program source may be, for example, a non-volatile storage medium that can be read by a program distribution server or a computer. In the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

  FIG. 1 is an overall configuration diagram of a project prediction server according to an embodiment.

  The project prediction server 100 as an example of a project evaluation apparatus is configured by a computer such as a server or a PC (Personal Computer), for example, and includes a processor 210, a program memory 220, a display device 230, a keyboard 240, and pointing. A device 250, a storage unit 255 as an example of an estimated malfunction factor storage unit, and a data memory 280 are included.

  The processor 210 is, for example, a CPU (Central Processing Unit), and executes various processes according to a program stored in the program memory 220. In the present embodiment, the processor 210 executes a program stored in the program memory 220 to thereby execute a learning processing unit 211, a prediction processing unit 212 as an example of an evaluation estimation unit, a malfunction factor estimation unit, and a risk identification unit. And an evaluation result display processing unit, a malfunction factor display processing unit, and a display processing unit 213 as an example of a risk display processing unit.

  The program memory 220 is, for example, a RAM (Random Access Memory) and stores various programs executed by the processor 210. In the present embodiment, the program memory 220 stores a project evaluation program constituting the learning processing unit 211, the prediction processing unit 212, and the display processing unit 213 by being executed by the processor 210.

  The storage unit 255 is, for example, a hard disk drive or an SSD (Solid State Drive), and stores programs executed by the processor 210 and various data used by the processor 210. In the present embodiment, the storage unit 255 includes a project data table 60, a malfunction investigation data table 120, an AI (Artificial Intelligence) project data table 260, an AI malfunction investigation data table 270, a risk data table 290, The conference material data 70 and the log file 300 are stored. In the conference material data 70, project data relating to the project being executed (the project being executed) is stored. The evaluation result log 300 stores a malfunction factor that is an evaluation result related to the ongoing project, countermeasures, and the like.

  The data memory 280 is, for example, a RAM, and stores data used in a program executed by the processor 210. In the present embodiment, the data memory 280 stores AI project data 261, AI malfunction investigation data 271, risk data 291 and the like read from the storage unit 255 as the program is executed. In this embodiment, as shown in FIG. 1, the table and data stored in the data memory 280 and the storage unit 255 are for prediction of project evaluation items (evaluation prediction), and for predicting malfunction factors. It can be broadly divided into things to do.

  The display device 230 is, for example, a liquid crystal display, and displays various information (for example, a project evaluation result screen (see FIG. 14) and the like). The keyboard 240 and the pointing device 250 accept various inputs from the user of the project prediction server 100.

  Next, the project data table 60 will be described.

  FIG. 2 is a configuration diagram of a project data table according to an embodiment.

  The project data table 60 stores an entry (project data 61) for each executed project. The project data 61 includes project basic information 281 (see FIG. 3), which is basic information about the project, profit / loss information 282 (see FIG. 4) regarding the profit and loss of the project, and progress information 283 (see FIG. 5) regarding the progress of the project. ) And quality information 284 regarding the quality of the project (see FIG. 6). The project data of the ongoing project included in the conference material data 70 has the same configuration as the project data 61 of the executed project.

  Next, the project basic information 281 will be described.

  FIG. 3 is a configuration diagram of project basic information according to an embodiment. Note that “{}” in FIG. 3 (the same applies to FIGS. 6 and 11) indicates that any one of the numerical values in {} is selected and set.

  The basic project information 281 is basic information about a project, and includes fields (members) such as an introduction form 281-1, a business form 281-2, a commercial distribution form 281-3, and a management level 281-4. The introduction form 281-1 stores the introduction form of the project. The business form 281-2 stores the business form of the project. The commercial distribution form 281-3 stores the distribution form of the product of the project. The management level 281-4 stores the level of management for the project.

  Next, the profit / loss information 282 will be described.

  FIG. 4 is a configuration diagram of profit / loss information according to an embodiment. “9” in the field in FIG. 4 (the same applies to FIGS. 6, 8, 9, and 10) means that a numerical value is set.

  The profit / loss information 282 is information related to the profit / loss of the project, and has fields (members) such as sales 282-1, manufacturing cost 282-2, gross profit 282-3, and sales profit / loss 282-4. The sales 282-1 stores the sales of the project. The manufacturing cost of the project is stored in the manufacturing cost 282-2. The gross profit 282-3 stores the gross profit of the project. In the sales profit / loss 282-4, the sales profit / loss of the project is stored.

  Next, the progress information 283 will be described.

  FIG. 5 is a configuration diagram of progress information according to an embodiment.

  The progress information 283 is information relating to the progress of the project, and has fields (members) such as a plan 283-1, a basic design 283-2, a detailed design 283-3, and an operation support 283-4. The plan 283-1 stores data (days data) indicating the number of days indicating the difference between the planned date when the project plan ends and the actual date. The basic design 283-2 stores the number of days data indicating the difference between the scheduled date when the basic design of the project ends and the actual date. The detailed design 283-3 stores the number of days data indicating the difference between the scheduled date when the detailed design of the project ends and the actual date. The operation support 283-4 stores the number of days data indicating the difference between the scheduled date when the project operation support ends and the actual date.

  Next, the quality information 284 will be described.

  FIG. 6 is a configuration diagram of quality information according to an embodiment.

  The quality information 284 is information relating to the quality of the project, and includes fields (members) such as a development amount 284-1, a development language 284-2, a checklist number 284-3, and a bug number 284-4. The development amount 284-1 stores the development amount in the project. The development language 284-2 stores a code indicating the development language in the project. The number of checklists in the project is stored in the number of checklists 284-3. The number of bugs generated in the project is stored in the bug number 284-4.

  Next, the malfunction investigation data table 120 will be described.

  FIG. 7 is a configuration diagram of a malfunction investigation data table according to an embodiment.

  The malfunction survey data table 120 stores an entry (malfunction survey data) for each executed project. The malfunction investigation data includes fields of project basic information 281 which is basic information about the project, profit / loss information 282 regarding profit / loss of the project, factor 287, problem 288, and countermeasure 289. The basic project information 281 and the profit / loss information 282 are the same as those already described.

  The factor 287 stores, for a project that has failed, a factor that caused the project to malfunction (a malfunction factor). The malfunction factor is obtained, for example, by an expert who analyzes the project based on the project data. Examples of malfunction factors include low estimation accuracy, insufficient project planning, ambiguous specifications, lack of employees, and insufficient business knowledge. The problem 288 stores a problem when there is a malfunction factor of the factor 287. This problem is set by, for example, an expert on project analysis. In the measure 289, a possible measure for the malfunction factor 287 is stored. This measure is set by, for example, an expert for project analysis.

  Next, the AI project data table 260 will be described.

  FIG. 8 is a configuration diagram of the AI project data table 260 according to an embodiment.

  The AI project data table 260 stores an entry (AI project data 261) for each executed project. The AI project data 261 is used as teacher data for performing learning on an estimation model (evaluation estimation model) for evaluating an evaluation item by AI. The AI project data 261 includes project basic information 281, profit / loss information 282, progress information 283, quality information 284, and evaluation information 285 (profit / loss evaluation 285-1, progress evaluation 285-2, quality evaluation 285-3). And the field.

  The evaluation information 285 stores evaluation results for one or more evaluation items in the project (in the present embodiment, profit / loss, progress, and quality).

  Next, the evaluation information 285 will be described.

  FIG. 9 is a configuration diagram of evaluation information according to an embodiment.

  The evaluation information 285 includes fields (members) of profit / loss evaluation 285-1, progress evaluation 285-2, and quality evaluation 285-3. The profit / loss evaluation 285-1 stores an evaluation result relating to the profit / loss of the project. The evaluation result relating to profit / loss is, for example, a result of evaluation by an expert on the evaluation of the project based on the profit / loss information 282 of the project data. The progress evaluation 285-2 stores an evaluation result related to the progress of the project. The evaluation result regarding the progress is, for example, a result of evaluation by an expert on the evaluation of the project based on the progress information 283 of the project data. The quality evaluation 285-3 stores an evaluation result related to the quality of the project. The quality-related evaluation result is, for example, a result obtained by an expert with respect to project evaluation based on the quality information 284 of the project data.

  Next, the AI malfunction investigation data table 270 will be described.

  FIG. 10 is a configuration diagram of the AI malfunction investigation data table 270 according to an embodiment.

  The AI malfunction investigation data table 270 stores an entry (AI malfunction investigation data 271) for each executed project. The AI malfunction investigation data 271 is used as teacher data for performing learning on an estimation model (a malfunction factor estimation model) for predicting malfunction factors of a project by AI. The AI malfunction investigation data 271 includes fields of project basic information 281, profit / loss information 282, and factor pattern information 286.

  The factor pattern information 286 stores a pattern (factor pattern 286-1) indicating a malfunction factor in the project.

  Next, the factor pattern information 286 will be described.

  FIG. 11 is a configuration diagram of factor pattern information according to an embodiment.

  The factor pattern information 286 has a field (member) of the factor pattern 286-1. The factor pattern 286-1 stores a pattern (cause pattern: here, a corresponding numerical value) corresponding to a malfunction factor (malfunction factor) that has occurred in the project. The factor pattern is a factor pattern corresponding to a malfunction factor evaluated with respect to project data by an expert for project evaluation.

  Next, the risk data table 290 will be described.

  FIG. 12 is a configuration diagram of a risk data table according to an embodiment. FIG. 13 is a configuration diagram of risk data according to an embodiment.

  The risk data table 290 is a table for managing risks, problems, countermeasures, and the like corresponding to the malfunction factor indicated by the factor pattern. The entry (risk data 291) for each malfunction factor includes the factor pattern 290-1, risk It has fields (members) of a problem (event) 290-2, a risk / problem (root cause) 290-3, and a countermeasure 290-4. The factor pattern 290-1 stores a factor pattern of a malfunction factor. The risk / problem (event) 290-2 stores a risk or a problem corresponding to the factor pattern corresponding to the risk data 291. The risk / problem (root cause) 290-3 stores the root cause for the risk or problem for the factor pattern corresponding to the risk data 291. The countermeasure 290-4 stores a countermeasure for the factor pattern corresponding to the risk data 291. Each risk data 291 in the risk data table 290 is set in advance by an expert on the risk / problem of the malfunction factor and countermeasures.

  Next, the project evaluation result screen will be described.

  FIG. 14 is a project evaluation result screen according to an embodiment.

  The project evaluation result screen 700 is a screen displayed on the display device 230 by the display processing unit 213. The project evaluation result screen 700 includes a profit / loss evaluation result area 701, a progress evaluation result area 702, a quality evaluation result area 703, a malfunction factor area 704, a risk / problem / measures display area 705, and malfunction factor statistical information. A display area 706 is displayed.

  In the profit / loss evaluation result area 701, the result of profit / loss evaluation of the project to be evaluated is displayed. In the progress evaluation result area 702, the result of progress evaluation of the project to be evaluated is displayed. The quality evaluation result area 703 displays the quality evaluation result of the project to be evaluated.

  In the malfunction factor area 704, when it is determined that the project to be evaluated is malfunctioning (for example, when the profit / loss evaluation, progress evaluation, and quality evaluation are all below the standard (below the predetermined standard value)). The estimated malfunction factor is displayed. In the risk / problem display area 705, the risk / problem, countermeasures, and the like due to the malfunction factor displayed in the malfunction factor area 704 are displayed. In the malfunction factor statistical information 706, statistical information regarding the malfunction factor displayed in the malfunction factor 704 is displayed. The statistical information is generated by the display processing unit 213 based on the evaluation result log of the log file 300. Specifically, the display processing unit 213 generates the number of projects in which the same malfunction factor as the malfunction factor displayed in the malfunction factor display area 704 is estimated, the ratio of the same malfunction factor in the whole, and the like.

  Next, the processing operation by the project prediction server 100 according to an embodiment will be described.

  First, an evaluation estimation model learning process in which an estimation model (evaluation estimation model) for evaluating each evaluation item for a running project is configured by learning will be described.

  FIG. 15 is a flowchart of an evaluation estimation model learning process according to an embodiment.

  The evaluation estimation model learning process is executed, for example, before executing the project evaluation process (see FIG. 17).

  First, the learning processing unit 211 acquires the project data 61 from the project data table 60 (S411). When the project data table 60 does not exist in the project prediction server 100, the project data table 60 may be acquired from another device that manages the project data table 60.

  Next, the learning processing unit 211 performs a process of normalizing or converting to a predetermined value in the project data 61 (S412). For example, the learning processing unit 211 converts a value stored in the character form in the project data 61 into a numerical value corresponding to this value. If the project data 61 is composed only of values that do not need to be normalized or digitized, this step need not be executed.

  Next, the learning processing unit 211 receives an input of evaluation for each evaluation item of the project data 61 and sets it as AI project data 261 (S413). In the present embodiment, the evaluation items are profit / loss, progress, quality, and the like. The AI project data 261 becomes teacher data used for learning an evaluation estimation model described later. Next, the learning processing unit 211 sorts the AI project data 261 into data for each evaluation item (learning data) (S414). Specifically, the learning processing unit 211 classifies the AI project data 261 into learning information 282, progress information 283, and quality information 284 as learning data.

  Next, the learning processing unit 211 repeatedly executes the process of loop A (S415) for each evaluation item. Specifically, the learning processing unit 211 learns an evaluation estimation model that evaluates the evaluation item using the learning data sorted for the evaluation item for the evaluation item to be processed. The evaluation estimation model information (various set values) is stored in the storage unit 255 (S415). In the present embodiment, the evaluation estimation model is, for example, a multilayer neural network model, and the learning method is, for example, deep learning.

  The learning processing unit 211 executes the process of loop A for all evaluation items, then exits loop A and ends the process. According to this evaluation estimation model learning process, each evaluation estimation model for evaluating each evaluation item is created.

  Next, a prediction process model learning process in which a malfunction factor estimation model (prediction process model) for estimating a malfunction factor of project data is configured by learning will be described.

  FIG. 16 is a flowchart of a malfunction factor estimation model learning process according to an embodiment.

  The malfunction factor estimation model learning process is executed, for example, before executing the project evaluation process (see FIG. 17).

  First, the learning processing unit 211 acquires malfunction survey data from the malfunction survey data table 120 (S421). If the malfunction investigation data table 120 does not exist in the project prediction server 100, the malfunction investigation data table 120 may be acquired from another device that manages the malfunction investigation data table 120.

  Next, the learning processing unit 211 performs a process of normalizing or digitizing a predetermined value in the malfunction investigation data (S422). For example, the learning processing unit 211 converts a character string indicating a factor stored in the factor 287 of the malfunction investigation data into a numerical value of a factor pattern corresponding to the character string. It should be noted that this step need not be executed when the malfunction investigation data is composed only of values that do not need to be normalized or digitized.

  Next, the learning processing unit 211 generates AI malfunction investigation data 271 to which a factor pattern is added for each malfunction investigation data (S423). The AI malfunction investigation data 271 becomes teacher data used for learning a malfunction factor estimation model described later. Next, the learning processing unit 211 uses the AI malfunction investigation data 271 to learn the malfunction factor estimation model, and stores the learned malfunction factor estimation model information (various set values) in the storage unit 255 (S424). ), The malfunction factor estimation model learning process is terminated. In the present embodiment, the malfunction factor estimation model is, for example, a multilayer neural network model, and the learning method is, for example, deep learning.

  According to the malfunction factor estimation model learning process, a malfunction factor estimation model for evaluating the malfunction factor is created.

  Next, the project evaluation process for evaluating the evaluation items for the ongoing project will be described.

  FIG. 17 is a flowchart of a project evaluation process according to an embodiment.

  Before this project evaluation process is executed, the evaluation estimation model learning process and the malfunction factor estimation model learning process must be executed.

  First, the prediction processing unit 212 extracts project data of an ongoing project to be evaluated from the meeting material data 70 (S511). Note that the ongoing project to be evaluated may be a project designated by the user or all the ongoing projects included in the conference material data 70. When a plurality of running projects are to be evaluated, the project evaluation process shown in FIG. 17 is executed for each running project.

  Next, the prediction processing unit 212 performs a process of normalizing or digitizing a predetermined value in the acquired project data (S512). For example, the prediction processing unit 212 converts a value stored in the project data in a character format into a corresponding numerical value. If the project data is composed only of values that do not need to be normalized or digitized, this step need not be executed.

  Next, the prediction processing unit 212 sorts the project data into data for each evaluation item (evaluation data) in order to execute an inference process for each evaluation item (S513). Specifically, the prediction processing unit 212 relates to project data, such as profit / loss information (information similar to the profit / loss information 282), progress information (information similar to the progress information 283), and quality information (information similar to the quality information 284). ).

  Next, the prediction processing unit 212 repeatedly executes the process of loop B (S514) for each evaluation item. Specifically, the prediction processing unit 212 inputs the evaluation data sorted for the evaluation item into the evaluation estimation model that has already been learned in order to evaluate the evaluation item to be processed. Then, the evaluation item to be processed is evaluated, and the evaluation result (for example, evaluation score) is stored in the data memory (S514).

  The prediction processing unit 212 exits from the loop B after executing the processing of the loop B for all evaluation items. Next, the prediction processing unit 212 determines whether there are evaluation items less than a predetermined reference point in the evaluation results of the plurality of evaluation items (S515). As a result, when there is no evaluation item less than the reference point (S515: NO), it is considered that there is no malfunction factor in the project corresponding to the project data. The process proceeds to S519.

  On the other hand, if there is an evaluation item less than the reference point (S515: YES), it means that there is a malfunction factor in the project corresponding to the project data, so the prediction processing unit 212 has already learned the malfunction. By inputting information (project profit / loss information in the present embodiment) in the project data to the factor estimation model, the factor pattern is evaluated and the factor pattern of the malfunction factor for the project is determined (S516).

  Next, the prediction processing unit 212 refers to the risk data in the risk data table 290, selects risks / problems, countermeasures, etc. corresponding to the determined factor pattern (S517), and determines the determined factor pattern and the selected risk. An evaluation result log in which problems, countermeasures, and the like are associated is stored in the log file 300 of the storage unit 255 (S518), and the process proceeds to S519.

  In S519, the display processing unit 213 displays the project evaluation result screen 700 including the evaluation result on the display device 230, and ends the process. For example, in the case of NO in S515, the display processing unit 213 displays the evaluation result (evaluation score) of each evaluation item obtained in S514 as a profit / loss evaluation result area 701, a progress evaluation result area 702, and a quality evaluation result area. 703. On the other hand, when S518 is executed, the display processing unit 213 displays the evaluation result (evaluation score) of each evaluation item obtained in S514 as the profit / loss evaluation result area 701, the progress evaluation result area 702, and the quality evaluation result area. , The failure pattern corresponding to the factor pattern determined in the evaluation in S516 and the failure pattern are displayed in the failure factor area 704, and the risks / problems, countermeasures, etc. selected in S517 are identified as risks / problems. In the countermeasure display area 705, statistical information regarding the malfunction factor calculated based on the evaluation result log of the log file 300 is displayed in the malfunction factor statistical information display area 706.

  According to the project evaluation process described above, it is possible to display the evaluation result of each evaluation item for the ongoing project. In addition, it is possible to appropriately display the malfunction factors that are related to the ongoing projects that are assumed to have malfunction factors (in the above example, the ongoing projects whose evaluation results are all below the reference point) Risks / problems corresponding to malfunction factors, countermeasures corresponding to malfunction factors, and the like can also be displayed.

  In addition, this invention is not limited to the above-mentioned embodiment, In the range which does not deviate from the meaning of this invention, it can change suitably and can implement.

  For example, in the above-described embodiment, evaluation of evaluation items for a project and evaluation of malfunction factors in a project are performed using an estimation model (evaluation estimation model, malfunction factor estimation model) learned from teacher data. However, the present invention is not limited to this, and an evaluation item for the project or an evaluation of a malfunction factor in the project may be performed according to a predetermined algorithm without using the estimation model.

  In the above embodiment, when any of the plurality of evaluation items for the project is less than the reference point, the process of estimating the malfunction factor is reduced by causing the malfunction factor to be estimated and displayed. Although it is intended to prevent the display of information with low necessity for the user (such as information on a malfunction factor when there is no malfunction factor), the present invention is not limited to this, and all projects The malfunction factor may be estimated and displayed. Further, the condition for estimating and displaying the malfunction factor is not limited to the case where any of the plurality of evaluation items is less than the reference point, and may be an arbitrary condition as necessary.

  In the above embodiment, the project data table 60 is stored in the project prediction server 100, and the project prediction server 100 creates the AI project data 261 by associating the evaluation result with the project data 61. However, the present invention is not limited to this, and AI project data in which an evaluation result is associated with project data may be acquired from another device.

  In the above embodiment, the evaluation result of the project is displayed as the project evaluation result screen 700. However, the display form of the evaluation result is not limited to this. For example, the evaluation result is displayed together with the project data related to the project. You may make it do.

  In the above embodiment, the risk / problem and countermeasure for the malfunction factor are selected using the risk data table 290. However, the present invention is not limited to this, for example, the risk / problem for the malfunction factor in advance. Points, models for evaluating countermeasures, and the like may be learned, and risks / problems and countermeasures against malfunction factors may be selected using the model.

DESCRIPTION OF SYMBOLS 100 ... Project prediction server, 210 ... Processor, 211 ... Learning processing part, 212 ... Prediction processing part, 213 ... Display processing part, 220 ... Program memory, 255 ... Storage part, 280 ... Data memory

Claims (10)

A project evaluation device for evaluating an ongoing project,
Corresponding to the one or more evaluation items of the running project based on the project data of the executed project, the evaluation result corresponding to one or more evaluation items for the executed project, and the project data of the executing project. An evaluation estimation unit for estimating an evaluation result;
An evaluation result display processing unit for displaying an evaluation result for the one or more evaluation items of the running project estimated by the evaluation estimation unit;
A project evaluation device comprising: The project evaluation apparatus according to claim 1, wherein the one or more evaluation items include at least one of project profit / loss, project progress, and project quality. The evaluation estimation unit is based on an evaluation estimation model learned using, as teacher data, project data of the executed project and the evaluation result corresponding to the one or more evaluation items for the executed project. The project evaluation apparatus according to claim 1, wherein the evaluation result corresponding to the one or more evaluation items of the project is estimated. A malfunction factor estimation unit that estimates a malfunction factor of the running project based on project data of the executed project, a malfunction factor of the executed project, and project data of the running project;
The project evaluation apparatus according to any one of claims 1 to 3, further comprising: a malfunction factor display processing unit that displays information on the malfunction factor estimated by the malfunction factor estimation unit. The project evaluation device according to claim 4, wherein the malfunction factor estimation unit estimates a malfunction factor of the ongoing project when all the evaluation results evaluated by the evaluation estimation unit are below a predetermined standard. A risk identifying unit that identifies at least one of a risk, a problem, or a countermeasure corresponding to the malfunction factor;
A risk display processing unit for displaying the identified risk, problem, or countermeasure;
The project evaluation device according to claim 4 or 5, further comprising: An estimated malfunction factor storage unit for storing a log of the malfunction factor of the running project estimated by the malfunction factor estimation unit;
The project according to any one of claims 4 to 6, wherein the malfunction factor display processing unit displays statistical information based on the log regarding the estimated malfunction factor together with information on the estimated malfunction factor. Evaluation device. The malfunction factor estimation unit calculates the malfunction factor of the project being executed based on a malfunction factor estimation model learned using the project data of the executed project and the malfunction factor for the executed project as teacher data. The project evaluation apparatus according to any one of claims 4 to 7, which is estimated. A project evaluation method by a project evaluation device for evaluating an ongoing project,
Based on the project data of the executed project, the evaluation result corresponding to one or more evaluation items for the executed project, and the project data of the executing project, the evaluation corresponding to the one or more evaluation items of the executing project Estimate the results,
A project evaluation method for displaying an evaluation result for the one or more evaluation items of the estimated ongoing project. A project evaluation program to be executed by a computer constituting a project evaluation apparatus that evaluates an ongoing project,
The computer,
Based on the project data of the executed project, the evaluation result corresponding to one or more evaluation items for the executed project, and the project data of the executing project, the evaluation corresponding to the one or more evaluation items of the executing project An evaluation estimator for estimating the result;
A display result display unit for displaying an evaluation result for the one or more evaluation items of the running project estimated by the evaluation estimation unit;
Project evaluation program