Information processing device and information processing method

Abstract

To perform state monitoring of a project that is not closed substantially because updating of a work closing time is determined.SOLUTION: The present invention is an information processing device 100 including a generation unit 1 that generates a prediction model trained with monitoring information of a first project that has been closed, a prediction unit 2 that inputs an explanatory variable of a second project that is in process into the prediction model to output a prediction value of an estimated excess of an estimated cost of the second project and an explanatory variable that is a basis for the prediction value, and a cutting unit 5 that cuts out a portion of a work time period of a continued project whose work closing time is updated to generate one or more partial projects. If a work closing time of the partial project is prior to a time point of prediction, the partial project is handled as the first project, and if a work time period of the partial project includes the time point of prediction, the partial project is handled as the second project.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] In recent years, there has been active development of technologies to promote Digital Transformation (DX), and as part of this, importance is being placed on status monitoring of projects such as software development. Status monitoring can be divided into two categories: focused monitoring and wide-area monitoring. Focused monitoring is monitoring of projects by the management department. Wide-area monitoring is monitoring of all projects using specified tools. In status monitoring, first, projects that are judged to have a high possibility of failure or a large impact in the event of failure in wide-area monitoring based on data such as costs entered into the tool are targeted for focused monitoring. Next, with support from the management department, efforts are made to prevent deterioration or improve the projects that are the subject of focused monitoring.

[0003] Conventional wide-area monitoring would identify projects after problems, such as costs exceeding estimates, become apparent. In light of the goal of leading a project to success, it is preferable to detect signs of project deterioration at an early stage before problems become apparent. However, conventional wide-area monitoring had the problem of making it difficult to detect signs of project deterioration at an early stage before problems become apparent.

[0004] Furthermore, in conventional wide-area monitoring, investigations are conducted to determine whether or not a project of concern should really be the subject of priority monitoring. The investigations include, for example, investigating the scale of the project and the amount of overruns in estimated costs. However, such investigations require a great deal of human resources. For this reason, projects with large order amounts and other projects that would be greatly affected if the situation were to worsen are given priority, and monitoring of all projects, including small-scale projects, is not conducted. In other words, conventional wide-area monitoring had the problem of requiring a large human resources cost to determine which projects should be the subject of priority monitoring.

[0005] Furthermore, there are projects for which the end date of work is determined to be updated, and which are never actually completed. For example, there is a project related to social insurance premiums. Since the social insurance premium burden rate is updated every year due to legal changes, the project is managed based on the updated burden rate. In conventional technology, unlike projects with a determined end date, there is no established method for wide-area monitoring of such projects.

[0006] Patent Document 1 discloses a project management method and a project management program that can grasp and manage the progress of a project more accurately so that delays in the project can be dealt with quickly. According to the invention of Patent Document 1, it is determined whether a task is delayed based on the scheduled end date and time of the task that shows an achievement rate equal to or higher than the change management standard, and if it is predicted that the task will not be completed by the scheduled end date and time, the scheduled end date and time are updated. The projects that are handled by the invention of Patent Document 1 are projects in which task delays are not normally planned, and task delays that require updating the scheduled end date and time are not regarded as a problem in the present invention. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2005-284385 A Summary of the Invention [Problem to be solved by the invention]

[0008] In view of the above circumstances, an object of the present invention is to realize status monitoring of a project in which it has been decided to update the completion date of the work and which is in effect never completed. [Means for solving the problem]

[0009] The present invention solves the above problems, a generation unit for generating a predictive model trained on monitoring information of a completed first project; a prediction unit that inputs explanatory variables of a second project in process into the prediction model and outputs a predicted value of an estimated cost overrun of the second project and explanatory variables that are the basis of the predicted value; an extracting unit that extracts a part of the work period of the continuing project whose work end time is updated, and generates one or more partial projects; If the completion time of the partial project is before the predicted time, the partial project is set as the first project; When the work period of the partial project includes a prediction time point, the information processing device sets the partial project as the second project.

[0010] The present invention also provides a method for producing a semiconductor device comprising the steps of: An information processing device, generating a predictive model trained on monitoring information of the completed first project; inputting explanatory variables of a second project in process into the prediction model, and outputting a predicted value of an estimated cost overrun of the second project and explanatory variables that are the basis of the predicted value; extracting a part of the work period of the ongoing project whose work end time is to be updated, and generating one or more partial projects; If the completion time of the partial project is before the predicted time, the partial project is set as the first project; In the information processing method, when the work period of the partial project includes a prediction time point, the partial project is regarded as the second project. Effect of the Invention

[0011] According to the present invention, it is possible to realize status monitoring of a project in which the completion date of the work is decided to be updated and which is in effect never completed. [Brief description of the drawings]

[0012] [Figure 1] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing device according to the present embodiment. [Diagram 2] 13 is a screen example of output information from a prediction unit. [Diagram 3] 4 is an example of a flowchart illustrating a process according to the present embodiment. [Figure 4] FIG. 13 is an explanatory diagram of segmentation of an ongoing project. [Diagram 5] This is a concrete example of cutting out an ongoing project. [Figure 6] 13 is an example of a flowchart illustrating a continuing project editing process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] First Embodiment [composition] The information processing device 100 shown in FIG. 1 is a computer that detects signs of deterioration in a project in progress through wide-area monitoring. The information processing device 100 includes hardware such as an input unit, an output unit, a control unit, and a storage unit. For example, when the control unit is configured with a CPU (Central Processing Unit), information processing by a computer including the control unit is realized by program execution processing by the CPU. Furthermore, the storage unit included in the computer stores various programs for realizing the functions of the computer according to instructions from the CPU. This realizes collaboration between software and hardware. The programs can be provided by recording them on a recording medium or via a network. The output unit may include a function of a display unit that displays on a screen.

[0014] A project is a task to achieve a specific purpose. Projects can be classified into past projects that have been completed and projects that are in progress. In this embodiment, past projects are called "first projects" and projects that are in progress are called "second projects." In addition, when there is no need to distinguish between past projects and projects that are in progress, they are simply called "projects."

[0015] 1, the information processing device 100 includes a generation unit 1, a prediction unit 2, and a cut-out unit 5. The information processing device 100 also stores a first project DB 3 and a second project DB 4. The cut-out unit 5 will be described later.

[0016] The generation unit 1 generates a prediction model for predicting the actual estimated cost of the second project by machine learning. The actual estimated cost is an estimated value of the cost that will occur by the end of the second project. There are multiple types of cost, such as manufacturing cost and sales cost, but in this embodiment, the cost is defined as an economic resource consumed to achieve a specific purpose, measured in monetary terms, and is described as a term that includes manufacturing cost, sales cost, etc. The prediction unit 2 uses the prediction model generated by the generation unit 1 to predict the actual estimated costs of the second project. The first project DB3 is a database that stores the monitoring information of the first project for each first project. The second project DB4 is a database that stores the monitoring information of the second project for each second project.

[0017] <Project monitoring information> The project monitoring information is information for monitoring the status of the project. The first project monitoring information can be composed of, for example, production number information, explanatory variables, and objective variables.

[0018] The production number information is information for identifying the first project. For example, the production number information includes, but is not limited to, a production number, a progress rate (%), and a production number name. The production number is an identifier for the first project and can be expressed, for example, as an alphanumeric string. The progress rate is a parameter that quantitatively indicates the progress of the first project. The project name is the name of the first project and can be expressed in a conceivable word, for example.

[0019] The explanatory variables are variables that express the state of the first project. There are multiple types of explanatory variables. Examples of explanatory variables include, but are not limited to, the start time of work, the end time of work, actual cost, forecast cost, estimated cost, person in charge, work time, and actual man-hours. For example, Boruta can be used to extract explanatory variable candidates, and the optimal explanatory variable can be selected from them, but the method of selecting explanatory variables is not limited to this. The work start time is the start time (year, month, day) of the first project. The work completion date is the completion date (year, month, and date) of the first project. The actual cost is the cost incurred from the start of the work to a specified time. Projected costs are costs that are expected to occur from a specified time until the end of the work. The estimated cost is the cost expected to be incurred from the start of the work to the end of the work. The person in charge is the person(s) in charge of the first project. The working time is the time each person in charge spent working on the first project from the start of the work to a specified time. The actual man-hours are the man-hours that have been completed from the start of work to a specified time out of the total man-hours that make up the first project. The predetermined time is any time between the start time and the end time of the work.

[0020] The objective variable is a variable that depends on the explanatory variables. The objective variable can be, for example, the final actual cost, which is the actual cost at the end of the first project, but is not limited to this.

[0021] (Progress rate) The progress rate can be calculated, for example, based on a time standard. For example, if the period from when work on a project starts to when it ends is 30 days and the target period is the 15th day from when work starts, the progress rate is 50%. Since the first project is a past project that has been completed, the progress rate at the current time is 100%. Here, the explanatory variables of the first project can be values ​​that change depending on the progress rate. The monitoring information of the first project can be configured as a collection of explanatory variables for each progress rate.

[0022] For example, the actual cost as an explanatory variable is a set of actual costs at all progress rates between 0% and 100%. The actual cost at a progress rate of X% is the cost incurred between the start of work and the time corresponding to the progress rate of X%. Furthermore, the forecast cost as an explanatory variable is a set of forecast costs at all progress rates between 0% and 100%. The forecast cost at a progress rate of X% is the cost expected to be incurred between the time corresponding to the progress rate of X% and the time the work is completed. Note that there are explanatory variables that do not change depending on the progress rate, such as the start and end of work, but it is preferable to treat such explanatory variables as constants that take the same value depending on the progress rate.

[0023] On the other hand, the monitoring information of the second project can be composed of, for example, production number information and explanatory variables.

[0024] The production number information is information for identifying the second project. For example, the production number information includes, but is not limited to, a production number, a progress rate (%), and a production number name. The production number is an identifier of the second project and can be expressed, for example, as an alphanumeric string. The progress rate is a parameter that quantitatively indicates the progress of the second project. The project number is the name of the second project and can be expressed, for example, in a conceivable word.

[0025] The explanatory variables are variables that express the state of the second project. There are multiple types of explanatory variables. Examples of explanatory variables include, but are not limited to, the start time of work, the end time of work, actual cost, forecast cost, estimated cost, person in charge, work hours, and actual man-hours. For example, it is possible to extract explanatory variable candidates using Boruta and select the optimal explanatory variable from among them, but the method of selecting explanatory variables is not limited to this. The work start date is the start date of the second project. The work completion date is the completion date (year, month, and date) of the second project. The actual cost is the cost incurred from the start of the work to a specified time. Projected costs are costs that are expected to occur from a specified time until the end of the work. The estimated cost is the cost expected to be incurred from the start of the work to the end of the work. The person in charge is the person(s) in charge of the second project. The working time is the time each person in charge spent working on the second project from the start of the work to the specified time. The actual man-hours are the man-hours that are completed from the start of work to a specified time out of the total man-hours that make up the second project. The predetermined time is the present time between the work start time and the work end time. As already explained, the actual estimated cost of the second project is the sum of the actual cost and the forecast cost of the second project.

[0026] The progress rate of the second project can be calculated as the ratio of the period from the start of work to the end of work to the period from the start of work to the present. The actual cost of a progress rate of X%, which corresponds to the present, is the cost incurred from the start of work to the present. Furthermore, the forecasted cost of a progress rate of X%, which corresponds to the present, is the cost expected to be incurred from the present to the end of work. Note that there are explanatory variables that do not change depending on the progress rate, such as the start and end of work, but it is preferable to treat such explanatory variables as constants that take the same value depending on the progress rate.

[0027] <Prediction model> (training) The generation unit 1 can generate a prediction model by combining, for example, multiple decision trees used in a random forest. A random forest is a machine learning algorithm, and is an ensemble learning algorithm that uses multiple decision trees and makes predictions by majority vote. The decision tree can be configured as a tree-like logic that combines, for example, judgment conditions using explanatory variables. The judgment conditions can be designed as appropriate, and can be, for example, whether or not the average daily working hours of a person in charge are 5 hours or more.

[0028] The generation unit 1 can train the prediction model using the monitoring information of the first project as training data. For example, for each first project, explanatory variables at a time point corresponding to a progress rate of 50% in the monitoring information of the first project can be used as input to the prediction model. Also, a value based on the objective variable of the monitoring information of the first project can be used as output of the prediction model. For example, the estimated cost overrun value obtained by subtracting the estimated cost from the final actual cost of the first project can be used as output of the prediction model. The generation unit 1 trains the prediction model using monitoring information of a predetermined number of first projects.

[0029] Here, the generating unit 1 prepares a plurality of prediction models, and can output the prediction models in a plurality of stages. For example, the generating unit 1 prepares a first prediction model and a second prediction model. The input of the first prediction model targets the monitoring information of all the first projects, and can be an explanatory variable of the monitoring information at a time when the progress rate is 50%. The output of the first prediction model can be whether or not there is an estimated cost overrun (greater than 0M (0 yen)) obtained by subtracting the estimated cost from the final actual cost of the first project. Next, the input of the second prediction model targets the monitoring information of the first project in which there is an estimated cost overrun in the output of the first prediction model, and can be an explanatory variable of the monitoring information at a time when the progress rate is 50%. The output of the second prediction model can be whether or not the estimated cost overrun is 1M (1 million yen) or more. As a result, the output of the prediction model can be classified into three values: the estimated cost overrun is 0M or less, greater than 0M and less than 1M, and 1M or more.

[0030] (prediction) The prediction unit 2 uses the trained prediction model to predict the actual estimated cost of the second project to be predicted. For example, the prediction unit 2 inputs explanatory variables from the monitoring information of the second project, that is, at the present time, i.e., at a time corresponding to a predetermined progress rate (preferably 50% or more, but may be less than 50%), into the prediction model. The prediction unit 2 can then obtain a value based on the actual estimated cost as the output of the prediction model. For example, the prediction unit 2 can obtain a value of the estimated cost overrun, which is obtained by subtracting the estimated cost from the actual estimated cost.

[0031] When the prediction model is the first prediction model or the second prediction model described above, the prediction unit 2 inputs the current explanatory variables from the monitoring information of the second project to be predicted into the first prediction model. Then, the prediction unit 2 can obtain a value indicating whether or not there has been an estimated cost overrun (greater than 0M (0 yen)) as the output of the first prediction model. If there has been an estimated cost overrun, the prediction unit 2 inputs the current explanatory variables from the monitoring information of the second project into the second prediction model. Then, the prediction unit 2 can obtain a value indicating whether or not the estimated cost overrun is 1M (1 million yen) or more as the output of the second prediction model. As a result, the estimated cost overrun can be classified into three values: 0M or less, greater than 0M and less than 1M, and 1M or more. Note that 1M and 0M are merely examples, and the value may be greater than 1M or greater than 0M, or may be less than 1M or less than 0M.

[0032] The prediction unit 2 can output information including the output of the prediction model. For example, the display unit of the information processing device 100 can display the output information of the prediction unit 2 on a screen as shown in Fig. 2. As shown in Fig. 2, the output information of the prediction unit 2 can be in a table format with "prediction result", "production number information", and "explanatory variables and features of the prediction result" as columns, and the second project as rows.

[0033] The "prediction result" indicates the output content of the prediction model. The "prediction result" is composed of an "item number", a "predicted value", and a "confidence factor". "Item number" is the line number assigned to each second project. "Predicted value" is the result according to a three-value classification of estimated cost overruns when the forecast model is a combination of the first and second forecast models already explained. "1: Overrun by 1 million yen" corresponds to 1 million or more. "2: Overrun by less than 1 million yen" corresponds to greater than 0 million and less than 1 million. "3: No problem" corresponds to 0 million or less. "Confidence" is the reliability of the prediction and is indicated by a value between 0% and 100%. For example, the confidence can be calculated using bagging, but is not limited to this.

[0034] The "production number information" is the same as the production number information in the monitoring information of the second project. "Explanatory variables and features of prediction results" shows the explanatory variables that contribute to the "prediction results." "Explanatory variables and features of prediction results" consists of a "list of explanatory variables" and a "feature ranking." The "List of explanatory variables" is the same as the explanatory variables for the monitoring information of the second project. The "feature ranking" indicates the rank of explanatory variables that contribute to the "prediction result". The higher the rank, the greater the contribution rate of that explanatory variable to the output of the predicted value. For example, the contribution rate of each variable can be calculated using the Shapley Additive exPlanations (SHAP) algorithm, but is not limited to this.

[0035] According to the first embodiment, it is possible to extract second projects whose estimated cost exceeds the estimated cost by 1M or more. Therefore, it is possible to detect early signs of deterioration of a project through wide-area monitoring.

[0036] (Decision on whether to subject to intensive monitoring) The management department, having obtained the output information in Figure 2, decides whether or not to make the second project, which has an estimated cost overrun of 1M yen, a target for intensive monitoring. Conventionally, even if AI detected signs of deterioration in a project, the basis for the AI's prediction results was a black box. For this reason, it was not easy for the management department to track down the causes of the signs of deterioration in the project based on the AI's prediction results, and it required a large amount of human resources to decide whether or not to make it a target for intensive monitoring.

[0037] The "Feature Ranking" in Figure 2 can be said to show the reason why the estimated cost overrun became 1M or more. By referring to the "Feature Ranking", the management department can easily identify the explanatory variables that contributed greatly to the prediction that the estimated cost overrun became 1M or more. As a result, it becomes easier to decide whether or not to subject the second project to intensive monitoring, and the human cost of deciding whether or not to subject it to intensive monitoring can be reduced.

[0038] [process] The process executed by the information processing device 100 is as shown in Fig. 3. That is, first, the generating unit 1 generates a prediction model (step S1). Next, the generating unit 1 trains the prediction model using monitoring information of the first project at a predetermined progress rate (step S2). Next, the predicting unit 2 uses the prediction model to output a predicted value of the estimated cost overrun of the target second project and explanatory variables contributing to the predicted value as prediction grounds (step S3). The management department judges, based on the prediction grounds, whether or not to target the second project showing signs of deterioration as a target for intensive monitoring.

[0039] Second Embodiment In explaining the second embodiment, differences from the first embodiment will be explained, and overlapping points will be omitted. In the first embodiment, the explanatory variables of the monitoring information of the first project, which is the training data, were explanatory variables at a time when the progress rate was equivalent to 50%. In the second embodiment, the time of the explanatory variables of the first project used for the training data is periodic.

[0040] For example, if the duration of the first project, i.e., the period from the start of work to the end of work, is several months, the period of the explanatory variables used in the training data, i.e., the training date (learning date), is set to the 25th of each month. Therefore, if work on the first project of work number A starts on 4 / 15 and ends on 6 / 30, the training dates for the first project of work number A will be 4 / 25, 5 / 25, and 6 / 25. In other words, from the monitoring information for the first project of work number A, the explanatory variables on 4 / 25 (explanatory variables with a progress rate equivalent to 4 / 25), the explanatory variables on 5 / 25 (explanatory variables with a progress rate equivalent to 5 / 25), and the explanatory variables on 6 / 25 (explanatory variables with a progress rate equivalent to 6 / 25) for a total of three times will be input into the prediction model. Also, if the work start date for the first project of work number B is 5 / 1 and the work end date is 9 / 15, the training dates for the first project of work number B will be 5 / 25, 6 / 25, 7 / 25, and 8 / 25. In other words, from the monitoring information for the first project of work number B, the explanatory variables on 5 / 25 (explanatory variables with a progress rate equivalent to 5 / 25), the explanatory variables on 6 / 25 (explanatory variables with a progress rate equivalent to 6 / 25), the explanatory variables on 7 / 25 (explanatory variables with a progress rate equivalent to 7 / 25), and the explanatory variables on 8 / 25 (explanatory variables with a progress rate equivalent to 8 / 25) for a total of four times will be input into the prediction model. In addition, if the work start date for the first project of work number C is 6 / 1 and the work end date is 7 / 10, the training date for the first project of work number C will be 6 / 25. In other words, the explanatory variables for 6 / 25 (explanatory variables with a progress rate equivalent to 6 / 25) from the monitoring information for the first project of work number C are used as input for the prediction model for one session.

[0041] As a result, explanatory variables at multiple types of progress rates are input to the prediction model for most of the first project. Using the prediction model trained in this way, the prediction unit 2 predicts the actual estimated cost of the second project. In this case, even if the progress rate of the second project is low (e.g., about 30%) and explanatory variables at a period equivalent to the low progress rate are input to the prediction model, it has been confirmed that the confidence level of the predicted value output by the prediction unit 2 (see FIG. 2) is sufficiently high.

[0042] According to the second embodiment, the training date can be set to a regular date, and explanatory variables for multiple types of progress rates for the same first project can be input to the prediction model. This allows for earlier prediction of the actual estimated cost of the second project. In addition, by regularizing the training dates, the input to the predictive model for all primary projects can be systematized, simplifying the processing required for training.

[0043] <Ongoing Project> Projects can be divided into continuing projects and normal projects other than continuing projects. A continuing project is a project for which it is decided to update the end time of work and which is not actually completed. The information processing device 100 can set a target project as a continuing project based on an input from a user. Specifically, a continuation flag is prepared for the project. If the continuation flag of the target project is set, the target project can be set as a continuing project.

[0044] An example of an ongoing project is a project related to social insurance premiums. The social insurance premium burden rate is updated every year due to legal changes. In this case, the project related to social insurance premiums can be an ongoing project with the end of the month preceding the renewal month as the work end time. For example, the information processing device 100 can set the work end time for the following year before the start of the renewal month based on input from the user. In other words, the work end time can be updated every year for one year. Note that multiple work end times may be set to be updated. For example, in addition to setting the work end time for the following year, the work end time for the year n years from now (n is a natural number equal to or greater than 2) may be set. In other words, multiple work end times may be set.

[0045] The work end time can be an explanatory variable or a target variable that constitutes the monitoring information of a project. As an ongoing project has a work end time set, just like a normal project, the data structure of the ongoing project is the same as the data structure of a normal project. However, since the work end time of an ongoing project is updated at a set timing and is not actually completed, the ongoing project will remain a second project no matter how much time passes and cannot fundamentally become a first project. Due to such characteristics of ongoing projects, it was not easy to use ongoing projects in the prediction model of this embodiment.

[0046] Therefore, in this embodiment, the information processing device 100 is provided with an extraction unit 5 (see FIG. 1). The extraction unit 5 extracts a part of the work period of the continuing project for which the work end time is updated, and generates one or more partial projects. FIG. 4 is an explanatory diagram of the extraction of a continuing project. As shown in FIG. 4, the continuing project 51 is expressed as a pentagonal arrow band extending horizontally along a time series. The left end of the continuing project 51 indicates the work start time, and the right end indicates the work end time. The broken line in the continuing project 51 indicates the updated and set work end time. The broken line to the left of the predicted time point (e.g., the current time point) indicates the set past work end time, and the broken line to the right of the predicted time point indicates the set future work end time.

[0047] As shown in Fig. 4, the extraction unit 5 prepares an extraction time point for an ongoing project 51, and can generate partial projects 52-54 by extracting a part of the work period of the ongoing project 51. The extraction time point can be prepared by input from the user of the information processing device 100. As shown in Fig. 4, the partial projects 52-54 are represented as pentagonal arrow bands extending horizontally in time series. The left ends of the partial projects 52-54 indicate the work start times, and the right ends indicate the work end times.

[0048] The information processing device 100 can store the monitoring information of the continuing project 51, but cannot store it in either the first project DB3 or the second project DB4. In contrast, the work end time of the partial projects 52 and 53 is earlier than the predicted time. Therefore, the information processing device 100 can treat the partial projects 52 and 53 as the completed first project. The information processing device 100 can store the monitoring information of the partial projects 52 and 53 in the first project DB3. The monitoring information of the partial projects 52 and 53 is obtained by extracting, from the monitoring information of the continuing project 51, monitoring information corresponding to the work period cut out from the continuing project 51.

[0049] The information processing device 100 can perform information processing on the partial projects 52 and 53 similar to that of the first project already described. Specifically, the generation unit 1 can generate a prediction model trained with monitoring information on the partial projects 52 and 53. In addition, a value based on a target variable of the monitoring information on the partial projects 52 and 53 can be output as the prediction model.

[0050] Furthermore, the work period of the partial project 54 includes the prediction time point. Therefore, the information processing device 100 can handle the partial project 54 as a second project in progress. The information processing device 100 can store the monitoring information of the partial project 54 in the second project DB4. The monitoring information of the partial project 54 is obtained by extracting, from the monitoring information of the continuing project 51, monitoring information corresponding to the work period cut out from the continuing project 51.

[0051] The information processing device 100 can perform the same information processing for the partial project 54 as for the second project already described. Specifically, the prediction unit 2 can predict the actual estimated cost of the partial project 54 to be predicted, using a trained prediction model. The prediction unit 2 can also input explanatory variables of the partial project 54 in progress to the prediction model, and output a predicted value of the estimated cost overrun of the partial project 54 and the explanatory variables that are the basis for the predicted value.

[0052] (Handling monitoring information for partial projects) The monitoring information of the continuing project is shown as an accumulation of monitoring information from the start of work. Therefore, the monitoring information of the partial project includes monitoring information of the continuing project for a work period prior to the work period of the partial project. However, depending on the type of explanatory variable or objective variable, it may be preferable not to include monitoring information of the continuing project for a work period prior to the work period of the partial project in the monitoring information of the partial project. In this case, the extraction unit 5 edits the values ​​of the explanatory variables or objective variables as appropriate.

[0053] FIG. 5 is a specific example of extracting an ongoing project. The upper part of FIG. 5 shows part of the monitoring information of an ongoing project with an identifier "Project No. A". In the following explanation, "Project No. A" will be used as a term to refer to an ongoing project. In FIG. 5, the explanatory variables of the monitoring information for Project No. A are exemplified as follows: estimated cost (shown as "Estimate" in FIG. 5), actual cost (shown as "Actual" in FIG. 5), and forecast cost (shown as "Forecast" in FIG. 5). Note that the explanatory variables of the monitoring information for Project No. A are not limited to these. Also, in FIG. 5, for Project No. A, The monthly explanatory variable values ​​(amounts) are shown for the work period from fiscal year 2020 to fiscal year 2022. Work number A started before 2020 and will continue after 2023.

[0054] Work No. A is managed on an annual basis. In other words, the estimated cost is set on an annual basis. The estimated cost is also reviewed every April, and a new estimated cost is set every April. Since values ​​are registered for Work No. A, the same estimated cost value is set every month in the same fiscal year. Actual cost values ​​are registered on a monthly basis according to work results. Projected cost is calculated as "Projected cost = Estimated cost - Actual cost", and values ​​are registered on a monthly basis. However, if the projected cost is negative, it is registered as 0M yen ("M" means 1 million yen).

[0055] According to Figure 5, the estimated cost is set at 100M yen in April of FY2020. The actual cost changes from 0M yen to 100M yen in FY2020. Therefore, the forecast cost changes from 100M yen to 0M yen in FY2020. Since the forecast cost did not become negative, it is determined that there will be no deterioration in FY2020.

[0056] A review will be conducted at the time of the fiscal year change in April of fiscal year 2021. As a result, the estimated cost will be set at 200M yen (100M yen increase compared to fiscal year 2020) in April of fiscal year 2021. The actual cost will change from 100M yen to 220M yen in fiscal year 2021. Therefore, the forecast cost will change from 100M yen to 0M yen in fiscal year 2021. In reality, since the forecast cost will be a negative value of -20M yen, it will be determined that there will be a deterioration in fiscal year 2021.

[0057] A review will be conducted at the time of the fiscal year changeover in April of FY2022. As a result, the estimated cost will be set at 320M yen (120M yen increase compared to FY2021) in April of FY2022. The actual cost will change from 220M yen to 320M yen in FY2022. Therefore, the forecast cost will change from 100M yen to 0M yen in FY2022. Since the forecast cost did not become negative, it is determined that there was no deterioration in FY2020.

[0058] The extraction unit 5 performs an extraction process on the production number A. As a result, as shown in the lower part of FIG. 5, partial projects such as "production number A_2020" extracted from the 2020 portion of the production number A, "production number A_2021" extracted from the 2021 portion of the production number A, and "production number A_2022" extracted from the 2022 portion of the production number A are generated. "Production number A_2020," "production number A_2021," and "production number A_2022" are identifiers for each partial project, but the explanation will continue as terms referring to each partial project.

[0059] The extraction unit 5 calculates the estimated cost of the partial project for the new fiscal year as "estimated cost of the partial project for the new fiscal year = estimated cost of the continuing project for the new fiscal year - actual cost of the continuing project at the end of the previous fiscal year". The extraction unit 5 also clears the actual cost of the partial project at the beginning of the new fiscal year (sets it to 0M￥). The extraction unit 5 also calculates the forecast cost of the partial project for the new fiscal year as "forecasted cost of the partial project for the new fiscal year = estimated cost of the partial project for the new fiscal year - actual cost of the partial project for the new fiscal year". In other words, the calculation formula for the forecast cost is the same for the continuing project and the partial project. This calculation makes it possible to process so that the monitoring information of the continuing project for the work period prior to the work period of the partial project is not included in the monitoring information of the partial project. As a result, the transition of the monitoring information during the work period of the target partial project can be made apparent.

[0060] For example, the extraction unit 5 calculates the estimated cost of production number A_2021 as "estimated cost of production number A_2021 = estimated cost of production number A for fiscal year 2021 - actual cost of production number A in March of fiscal year 2020". The extraction unit 5 also sets the actual cost of production number A_2021 for April to 0M yen. From May onwards, the actual costs of each month are added. The extraction unit 5 does not change the forecast cost of production number A_2021. In other words, the calculation is made as "forecast cost of production number A_2021 = estimated cost of production number A_2021 - actual cost of production number A_2021". By these processes, when setting the estimated cost, actual cost, and forecast cost of production number A_2021, the estimated cost, actual cost, and forecast cost of production number A_2020 can be excluded. As a result, the transition of the monitoring information for production number A_2021 can be made apparent. In particular, by clearing the actual cost of production number A_2021, it is possible to clearly determine whether there is any deterioration focusing on fiscal year 2021. According to Figure 5, in reality, the forecast cost will be a negative value such as -20M yen (the value will be registered as 0M yen), so it will be determined that there is deterioration in fiscal year 2021.

[0061] Also, for example, the extraction unit 5 calculates the estimated cost of production number A_2022 as "estimated cost of production number A_2022 = estimated cost of production number A for fiscal year 2022 - actual cost of production number A in March of fiscal year 2020". The extraction unit 5 also sets the actual cost of production number A_2022 for April to 0M yen. From May onwards, the actual costs of each month are added. The extraction unit 5 does not change the forecast cost of production number A_2022. In other words, the calculation is made as "forecast cost of production number A_2022 = estimated cost of production number A_2022 - actual cost of production number A_2022". By these processes, when setting the estimated cost, actual cost, and forecast cost of production number A_2022, the estimated cost, actual cost, and forecast cost of production number A_2021 can be excluded. As a result, the transition of the monitoring information for production number A_2022 can be made apparent. In particular, by clearing the actual cost of production number A_2022, it is possible to clearly determine whether there is any deterioration focusing on fiscal year 2022. According to Figure 5, since the forecast cost was not negative, it is determined that there is no deterioration in fiscal year 2020.

[0062] It is advisable to include the identifier of the continuing project, "Production No. A," and the fiscal years of the management units, "2020," "2021," and "2022," in the identifier of the partial project, such as "Production No. A_2020," "Production No. A_2021," and "Production No. A_2022." This makes it easy to grasp the relationship between the continuing project and the partial project, even when referring to data after it has been divided into management units, improving the convenience of project management.

[0063] [process] The following describes the continuing project editing process performed by the cut-out unit 5. Fig. 6 is an example of a flowchart showing the continuing project editing process. The information processing device 100 can acquire monitoring information of the continuing project by referring to, for example, a continuation flag based on an input from the user.

[0064] First, the extraction unit 5 extracts a work period from the monitoring information of the continuing project (step S1). Specifically, when a work start time and a work end time are specified by input from the user, the extraction unit 5 identifies the work period from the specified work start time to the specified work end time among the work periods from the work start time to the work end time defined in the monitoring information of the continuing project. The extraction unit 5 also acquires monitoring information included in the identified work period from the monitoring information of the continuing project. The extraction unit 5 also treats the acquired monitoring information as monitoring information of a partial project. Note that, for example, the work period to be extracted may be a predetermined management unit (e.g., one year starting in April), and the continuing project may be divided into one or more partial projects.

[0065] Next, the extraction unit 5 updates the monitoring information of the partial project (step S2). Specifically, the extraction unit 5 appropriately edits the values ​​of the explanatory variables or the objective variables according to the types of the explanatory variables or the objective variables so that the monitoring information of the partial project does not include the monitoring information of the continuing project in a work period prior to the work period of the partial project. The extraction unit 5 also gives an identifier to the partial project and assigns a production number to the partial project.

[0066] Next, the extracting unit 5 judges whether the prediction time is included in the work period of the partial project (step S3). Specifically, the extracting unit 5 judges whether the work start time of the partial project is earlier than the prediction time (e.g., the current time) and the work end time of the partial project is later than the prediction time. If the prediction time is not included (No in step S3), that is, if the work end time is earlier than the prediction time, the extracting unit 5 treats the partial project as a completed first project and stores the monitoring information of the partial project in the first project DB3 (step S4). This ends the processing of FIG. 6. On the other hand, if the prediction time is included (Yes in step S3), the extracting unit 5 treats the partial project as a second project in progress and stores the monitoring information of the partial project in the second project DB4 (step S5). This ends the processing of FIG. 6.

[0067] If there are multiple cuts in step S1, the process of FIG. 6 is executed for each of the multiple partial projects that are generated. The monitoring information of the partial projects stored in the first project DB 3 can be used as training data for training the prediction model created by the generation unit 1. In addition, the monitoring information of the partial projects stored in the second project DB 4 can be a prediction target of the prediction unit 2 that uses the prediction model. The output of the prediction model can include predicted values ​​of explanatory variables required by the partial projects and explanatory variables that are the basis for the predicted values.

[0068] [Effects of ongoing projects] According to this embodiment, for an ongoing project in which it has been decided to update the work end time and which is not substantially completed, a work period is extracted and one or more partial projects are generated. This makes it possible to apply partial projects to the prediction model of this embodiment and to realize status monitoring of ongoing projects. In addition, since the prediction model can be trained not only on regular projects but also on partial projects, the prediction accuracy of the prediction model can be improved. As a result, the prediction accuracy for regular projects and ongoing projects can be improved. Also, processing can be performed so that monitoring information of the continuing project during a work period prior to the work period of the partial project is not included in the monitoring information of the partial project. In particular, calculations can be implemented for estimated costs, actual costs and forecast costs such that monitoring information of continuing projects for work periods prior to the work period of the partial projects is not included in the monitoring information of the partial projects.

[0069] [Variations] (a): In the first and second embodiments, the progress rate is calculated based on a time standard using the period of the project. However, for example, the progress rate may be calculated based on the degree of accomplishment of the work to be done in the project. (b): In the first embodiment, explanatory variables at a time point corresponding to a progress rate of 50% in the monitoring information of the first project were input to the prediction model for each first project. However, for example, explanatory variables at a time point corresponding to any same progress rate other than 50% may be input to the prediction model for each first project. Also, explanatory variables at a time point corresponding to a different progress rate for each first project may be input to the prediction model. (c): In the second embodiment, by regularizing the training days, multiple types of progress rates are substantially selected for the same first project, and explanatory variables at the selected progress rates are input to the prediction model. However, for example, a user of the information processing device 100 may operate the input unit to select multiple types of arbitrary progress rates for the same first project, and input explanatory variables at the selected progress rates to the prediction model. (d): In the present embodiment, a case has been described in which a plurality of partial projects are created for an ongoing project with a management unit of one year. However, when the extracting unit 5 extracts a work period for an ongoing project, the work start time and the work end time may be any time. In other words, the management unit is not limited to one year. (e): In this embodiment, the calculation of the estimated cost, actual cost, and forecast cost has been described for the handling of monitoring information of partial projects. However, the calculation objects are not limited to the estimated cost, actual cost, and forecast cost, and may be other types of prices other than the cost, or other numerical values. It is preferable that the values ​​be values ​​that can be explanatory variables or objective variables.

[0070] (f): It is also possible to realize a technology that appropriately combines the various technologies described in this embodiment. (g) The software described in the present embodiment can be realized as hardware, and vice versa. (h) In addition, the hardware, software, flow charts, etc. may be modified as appropriate without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0071] 100 Information processing device 1 Generation part 2. Prediction Department 3. First Project DB 4. Second Project DB 5 Cut-out section

Claims

1. a generation unit for generating a predictive model trained on monitoring information of a completed first project; a prediction unit that inputs explanatory variables of a second project in process into the prediction model and outputs a predicted value of an estimated cost overrun of the second project and explanatory variables that are the basis of the predicted value; an extracting unit that extracts a part of the work period of an ongoing project whose work end time is updated, and generates one or more partial projects; If the work completion time of the partial project is before the predicted time, the partial project is set as the first project; When the work period of the partial project includes a prediction time point, the information processing device sets the partial project as the second project.

2. The information processing apparatus according to claim 1 , wherein the monitoring information of the partial project is information obtained by subtracting monitoring information of the continuing project in a work period prior to the work period of the partial project.

3. In the monitoring information of the partial project, The estimated cost of the work period of the partial project is calculated by subtracting the actual cost of the continuing project at the end of the work period prior to the work period of the partial project from the estimated cost of the continuing project during the work period of the partial project; The actual cost at the start of the work period of the partial project is set to 0, 3 . The information processing apparatus according to claim 2 , wherein the forecast cost of the work period of the partial project is set to a forecast cost obtained by subtracting an actual cost of the work period of the partial project from an estimated cost of the work period of the partial project.

4. An information processing device, generating a predictive model trained on monitoring information of the completed first project; inputting explanatory variables of a second project in process into the forecasting model, and outputting a forecast value of an estimated cost overrun of the second project and explanatory variables that are the basis of the forecast value; extracting a part of the work period of the ongoing project whose work end time is to be updated, and generating one or more partial projects; If the work completion time of the partial project is before the predicted time, the partial project is set as the first project; an information processing method for determining, when a work period of the partial project includes a forecast time point, the partial project as the second project;

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