Project plan optimization tool and project plan optimization method

Abstract

This project plan optimization tool comprises: a plan value parameter input unit into which is input a first plan value parameter for each phase resulting from the division of the project into a plurality of phases; a past performance result information storage unit that accumulates performance result value parameters and result evaluation values for each phase of a plurality of past projects; a past performance result information analysis unit that associates and analyzes the performance result value parameters and the result evaluation values of each phase of the past project, and generates a project prediction model; a project result prediction unit that uses the prediction model to predict result evaluation values from the first plan value parameters; and a plan value parameter optimization unit that redistributes the first plan value parameters among the phases and outputs optimized second plan value parameters.

Description

Project planning optimization tools and project planning optimization methods Incorporation by Reference

[0001] This application claims priority from Japanese Patent Application No. 2023-108268, filed on June 30, 2023, the contents of which are incorporated herein by reference.

[0002] The present invention relates to a project planning optimization tool that optimizes planned value parameters based on project outcome predictions when planning a project.

[0003] In software development projects, data utilization is being carried out to analyze and make decisions based on the actual values ​​of accumulated project management data, which allows for efficient promotion of project plans and early detection of risks through predictive technology.

[0004] When planning a project, project outcome prediction technology has been developed that compares the planned values ​​with accumulated actual values ​​and predicts the results. There are various prediction methods, but one method searches accumulated project management data for past projects that are similar to or have similar trends as the new project to be predicted, and predicts the project's evaluation value based on the management data of the past projects found.

[0005] The accumulated data includes explanatory variables used to find similarities and a target variable, which is the item to be predicted. The explanatory variables are values ​​based on the nature of the project and the development content, such as the product category and the scale of development. On the other hand, the target variable is an evaluation value that indicates the results of the development project, such as the man-hours and costs determined at the end of past projects.

[0006] The relationship between explanatory variables and dependent variables can be viewed as a rule-based causal relationship, and in project outcome prediction technology, the relationship between explanatory variables and dependent variables is treated as a prediction model. In project outcome prediction technology, a prediction model receives input of the explanatory variables of the project to be predicted and outputs a predicted outcome evaluation value as the dependent variable. Prediction technology can be applied at the start of a project, but there are explanatory variables that are difficult to determine at the start of a project, and some are input as planned values.

[0007] The following prior art is included as background art in this technical field: Patent Literature 1 (JP 2022-32115 A) discloses a project sign detection device comprising: a storage device that stores information on the content and profit and loss of each past project; a process that extracts the information on each of the past projects from the storage device, and generates a decision tree for each of the explanatory variables by machine learning, using the values ​​of one or more predetermined items in the content of each piece of information as explanatory variables and the profit and loss situation of each piece of information as a target variable; and a computing device that extracts values ​​of items corresponding to the explanatory variables of the decision tree from the information on the content of a target project, inputs them into the corresponding decision trees, and calculates the failure probability of the target project using a random forest algorithm.

[0008] The above project outcome prediction technology is used in the formulation of project plans. By planning a project based on appropriate planned values ​​that will produce favorable results using project outcome prediction technology, the validity of the plan can be confirmed. For example, results based on planned values ​​created by humans can be checked using project outcome prediction technology to see if the plan is unrealistic. Furthermore, if necessary, the planned values ​​can be updated and predicted again, allowing adjustments to be made to obtain better predicted results.

[0009] Meanwhile, development projects are often managed in multiple phases separated by chronological development and release periods. When planning a project, a new requirement arises: allocation of pre-estimated planned values ​​for development scale, duration, budget, personnel, etc. to each phase. Project outcome prediction technology can be applied to each phase. In this case, explanatory variables for each phase are input, and a predicted outcome evaluation value can be output as the objective variable for each phase. Looking at the project plan as a whole for one objective variable, it is possible to confirm the progress of each phase throughout the project. For example, prediction results can be obtained from the perspective of which phases are likely to incur a burden. Based on these results, it is necessary to adjust the planned values ​​and take measures such as smoothing out the burden between phases and dealing with risks on a phase-by-phase basis.

[0010] After predicting the results for each phase into which a project is divided, adjusting the input values ​​of the explanatory variables so that the predicted result evaluation values ​​for each phase are the values ​​desired by the user requires a large amount of trial and error, and technology is needed to perform this optimization automatically.

[0011] A representative example of the invention disclosed in the present application is as follows: A project plan optimization tool that outputs information related to improvement proposals for a project's business plan, the tool being configured by a computer having an arithmetic unit that executes arithmetic processing and a storage device accessible by the arithmetic unit, and comprising: a planned value parameter input unit to which first planned value parameters for each of a plurality of phases into which the project is divided are input; a past performance information storage unit that accumulates actual value parameters and result evaluation values ​​for each phase of a plurality of past projects; a past performance information analysis unit that associates and analyzes the actual value parameters and result evaluation values ​​for each phase of the past projects to generate a prediction model for the project; a project result prediction unit that predicts the result evaluation value from the first planned value parameters using the prediction model; and a planned value parameter optimization unit that reallocates the first planned value parameters among the phases and outputs optimized second planned value parameters.

[0012] According to one aspect of the present invention, it is possible to optimize the planned value parameters of a project. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments.

[0013] FIG. 1 is a diagram showing the overall configuration of a project plan optimization tool according to an embodiment of the present invention; FIG. 2 is a flowchart of optimization processing according to an embodiment of the present invention; FIG. 3 is a diagram showing multiple phases constituting a project according to an embodiment of the present invention; FIG. 4 is a diagram showing definitions of project plan values ​​and result predictions for each phase according to an embodiment of the present invention; FIG. 5 is a diagram showing an example of the configuration of a movement rule table according to an embodiment of the present invention; and FIG. 6 is a diagram showing an example of the configuration of a goal definition table according to an embodiment of the present invention.

[0014] First Embodiment FIG. 1 is a diagram showing the overall configuration of a project plan optimization tool 100 according to this embodiment.

[0015] The project plan optimization tool 100 includes a project result prediction unit 101, a plan value parameter optimization unit 102, a past performance information analysis unit 103, and a user interface unit 110. The user interface unit 110 has a function of presenting the execution results of the program to a user via a display device or an input device and receiving input from the user, and includes a plan value parameter input unit 111, a project prediction result output unit 112, a movement rule input unit 113, an optimization goal definition input unit 114, and an optimization result output unit 115. The past performance information analysis unit 103 is also connected to a past performance information accumulation unit 120, refers to project performance information 121, and holds a prediction model 104, which is the analysis result.

[0016] The project result prediction unit 101 has a function of predicting the results of a project by simulating the project, and predicts a prediction result evaluation value based on the planning value parameters input to the planning value parameter input unit 111, based on the prediction model 104. The predicted prediction result evaluation value is displayed in the project prediction result output unit 112.

[0017] The planned value parameter optimization unit 102 optimizes the prediction result by reallocating the planned value parameters to the prediction result by the project result prediction unit 101. The plan value parameter optimization unit 102 acquires constraint conditions for the planned value parameters in optimization from a movement rule input unit 113 and acquires goal conditions for optimization from an optimization goal definition input unit 114. The optimization results obtained by the planned value parameter optimization unit 102 are output from an optimization result output unit 115.

[0018] The past performance information analysis unit 103 analyzes the project performance information 121 stored in the past performance information accumulation unit 120, and generates and stores the forecast model 104.

[0019] To take a more specific example assuming a software development project, past performance information includes a wide range of information that can serve as explanatory variables for the prediction model, such as project parameters, such as the duration, development scale (number of lines of development, number of requirements), product category, distinction between new development and derivative development, safety / security level, and execution environment (OS, CPU). Additionally, final labor hours, cost (amount), quality (defect information), delivery date, and other result information that can serve as objective variables are stored. The past performance information analysis unit 103 models the causal relationships between these explanatory variables and objective variables to generate and store a prediction model 104. Machine learning technology can be used as one generation method. Generally, in such cases, supervised machine learning regression analysis is used, in which the explanatory variables and objective variables are used as training data to generate a prediction model.

[0020] The project plan optimization tool 100 of this embodiment is configured by a computer having a processor (CPU), a memory, an auxiliary storage device, a communication interface, an input interface, and an output interface.

[0021] The processor is a computing device that executes programs stored in memory. The processor executes various programs to realize the functions of each functional unit (e.g., project result prediction unit 101, planned value parameter optimization unit 102, past performance information analysis unit 103, etc.) of the project plan optimization tool 100. Note that some of the processing performed by the processor executing the programs may be executed by another computing device (e.g., hardware such as an ASIC or FPGA).

[0022] The memory includes ROM, which is a non-volatile storage element, and RAM, which is a volatile storage element. ROM stores unchanging programs (e.g., BIOS), etc. RAM is a high-speed, volatile storage element such as DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the processor and data used during program execution.

[0023] The auxiliary storage device is, for example, a large-capacity, non-volatile storage device such as a magnetic storage device (HDD) or a flash memory (SSD). The auxiliary storage device also stores data used by the processor when executing a program and the program executed by the processor. That is, the program is read from the auxiliary storage device, loaded into memory, and executed by the processor to realize each function of the project plan optimization tool 100.

[0024] The communication interface is a network interface device that controls communication with other devices according to a predetermined protocol.

[0025] The input interface is an interface that receives input from a user, such as a keyboard or a mouse. For example, the input interface receives input of a file in which cost data is recorded and stored in an auxiliary storage device. Alternatively, the input interface may provide a GUI and receive input of cost data from a user.

[0026] The output interface is an interface that outputs the execution results of a program in a format that can be viewed by a user, such as a display device or a printer. For example, the output interface outputs the execution results of a program. The output interface may also be a data output port that outputs the execution results of a program in a format that can be viewed by a user.

[0027] Note that a terminal connected to the computer via a network may provide the input interface and the output interface.

[0028] The programs executed by the processor are provided to the computer from removable media (such as CD-ROMs or flash memory) or via a network, and are stored in a non-volatile auxiliary storage device, which is a non-transitory storage medium. For this reason, the computer should have an interface for reading data from removable media.

[0029] The project plan optimization tool 100 is a computer system configured on one physical computer or on multiple logically or physically configured computers, and may operate on a virtual computer constructed on multiple physical computer resources. For example, the project result prediction unit 101, the plan value parameter optimization unit 102, and the past performance information analysis unit 103 may each operate on separate physical or logical computers, or multiple units may be combined to operate on a single physical or logical computer.

[0030] FIG. 2 is a flowchart of the optimization process of this embodiment.

[0031] The optimization process shown in FIG. 2 starts in a state where the prediction model 104, which is the premise of the process, has been generated by the past performance information analysis unit 103 (201).

[0032] When the process starts, in step 202, input of plan value parameters for each phase constituting the project is received from the plan value parameter input unit 111.

[0033] In step 203, the project result prediction unit 101 predicts the result evaluation value for the planned value parameters using the prediction model 104, thereby predicting the project result. The prediction of the project result is performed for each phase, and a series of predicted values ​​for one type of target variable to be predicted, the number of which is equal to the number of phases constituting the project, is output.

[0034] In step 204, the predicted result evaluation value is presented to the user to confirm whether or not it is necessary to continue the optimization process. If it is not necessary to continue the optimization process, the process ends (205).

[0035] In step 206, a planned value parameter movement rule is received from the movement rule input unit 113 as a user input, and an optimization goal definition is received from the optimization goal definition input unit 114.

[0036] In step 207, the degree of deviation of the series of predicted values ​​generated in step 203 or step 210 (described later) from the input goal definition of optimization is evaluated. For example, for a numerical sequence for which the goal definition can be changed, the deviation can be determined based on the difference from the corresponding predicted value. In this case, the phase with the largest deviation is selected and stored as the selected phase for the subsequent step.

[0037] In step 208, the possibility of moving the planned value parameters of the selected phase is evaluated according to the plan value parameter moving rules between phases, and candidates for the moved planned value parameters are prepared. This process is performed for all applicable moving rules, and groups of planned value parameters obtained by combining the planned value parameter candidates are listed as patterns and stored.

[0038] In step 209, the project result prediction unit 101 predicts the result evaluation values ​​for all patterns of the set of planned value parameters in order, and a series of predicted result evaluation values ​​for all patterns is obtained.

[0039] In step 210, the result that is closest to the optimization goal definition is recorded from all predicted result scores.

[0040] In step 211, it is determined whether sufficient optimization has been performed. This determination may be made by determining the proximity to the optimization goal definition in the comparison in step 210 using a predetermined threshold, or by determining whether sufficiency has been met based on the number of repetitions since step 207. If the result shows that the optimization is insufficient, the process returns to step 207 and repeats the process.

[0041] If it is determined in step 211 that the optimization is sufficient, the optimized planned value parameters, which are the results of the optimization, are presented to the user in step 212, and the process ends (213).

[0042] 3 and 4, a specific example of the processing performed by the project result prediction unit 101 will be described. This processing is executed in steps 203 and 209.

[0043] Figure 3 shows the multiple phases that make up a project. The horizontal axis represents the project's timeline, with four phases defined along the timeline. While each phase exists by dividing the project along the timeline, the duration of each phase is not clearly delimited by a specific date; there may be overlaps between adjacent phases, or there may be gaps between phases.

[0044] FIG. 4 is a diagram showing definitions of project plan values ​​and result forecasts for each phase shown in FIG.

[0045] In step 202 , the planned value parameters of each phase, which are composed of the input value table 401 and the planned value parameter table 402 , are input to the planned value parameter input unit 111 .

[0046] In step 209, multiple patterns of planned value parameter groups corresponding to the input value table 401 and the planned value parameter table 402 are created in accordance with the movement rule. These become candidates for optimization. The results of predictions made using these are stored in the same data structure as the result prediction table 403.

[0047] In step 210, the input value table 401, the planned value parameter table 402, and the result prediction table 403 are stored for use in subsequent processing using the set of planned value parameters that have resulted in the appropriate result at that time. If optimization is to continue, the values ​​in the stored tables are used in steps 207 and 208. If the process proceeds to step 212, the values ​​in the stored tables are used.

[0048] The input value table 401 holds planned value parameters for each phase. Each planned value parameter is structured like the planned value parameter table 402, with planned values ​​stored in definition items. A different instance of the planned value parameter table 402 is created for each row of the input value table 401, and holds information for each phase.

[0049] The project result prediction unit 101 acquires information stored in the input value table 401 and the planned value parameter table 402 for each phase, predicts the project results (man-hours, quality) using the prediction model 104, and stores the predicted values ​​in the result prediction table 403. When there are multiple types of targets to be predicted, a predicted value is stored for each prediction target. Figure 4 shows an example of prediction for two types of prediction targets, man-hours and quality.

[0050] Referring to FIG. 5, the movement rule table 500 will be described.

[0051] The transfer rule table 500, in which the transfer rules for planned value parameters are defined, includes planned value parameters 501 and their transfer rules 502, and the transfer rules input by the user to the transfer rule input unit 113 are recorded for each type of planned value parameter as shown in the following example rules. The transfer rule table 500 is stored in the memory of the computer in which the project plan optimization tool 100 is implemented.

[0052] In the example shown in the first row, the planned value parameter "LOC" has two rules: "It can be moved up to 30% from the initial state" and "The distance of the moving phase is limited to one line forward or backward." LOC is an abbreviation for Line of Code and represents the number of lines. The former rule specifies the maximum amount by which the planned value parameter LOC can be moved in step 208. For example, if the planned value initially input in step 202 is 1000 lines, it is possible to move up to 30% forward or backward, i.e., 300 lines, through optimization.

[0053] The latter is a rule regarding the range of destination phases that allows the planned value parameters to be moved only to phases adjacent to the selected phase before and after it when considering the movement of the planned value parameters in step 208, targeting the selected phase in step 207.

[0054] In the example shown in the second row, the planned value parameter "End Date" is not movable, and is not subject to the consideration of movableness in step 208. Among the project planned values, there are also planned value parameters that cannot be changed depending on their nature or organizational circumstances. Such rules can also be defined.

[0055] In the example shown on the third line, a rule is defined for the planned value parameter "number of tests" that "it moves in proportion to LOC." The planned value parameters are not independent and may have a correlation with each other. In such cases, it is desirable to maintain the relationship between the planned value parameters when considering the amount of movement in step 208. In this example, LOC and the number of tests are assumed to be proportional, and the rule is to consider the possibility of moving both the LOC value and the number of tests while maintaining their proportional relationship. Such interrelationships may be three or more.

[0056] In the example shown in the fourth line, a numerical range specific to the planned value parameter is defined. In the example shown in Fig. 3, the upper limit of the number of development personnel per unit time is set to N people, and in step 208, a movement of the planned value that does not exceed this number is considered.

[0057] As described above, four examples have been shown in the transfer rule table 500, and in this format, rules for transferring planned value parameters between phases can be specified in step 206.

[0058] In the example shown in Figure 5, the rules are written in natural language, but it is advisable to prepare such rule templates in advance and implement them in a format where the user selects a template and specifies the details using numbers. The template shows the rule definition in a format such as "Can move by X% from the initial state" or "Move in proportion to X," and after selecting a template on the user interface, the user inputs the value of X and the target. This template can be prepared without depending on specific planning value parameters.

[0059] The user generates a migration rule table 500 in which migration rules are defined by sequentially assigning rules to each planned value parameter. Note that it is also possible for rules to be applied in a somewhat fixed manner depending on the organization. In such cases, migration rules defined in a pre-prepared table may be loaded, and the loaded migration rules may be customized for use.

[0060] In step 209, parameter movement patterns are generated based on the definitions in the movement rule table 500 shown in FIG. 5 . For example, if the movement rule states that "movement from the initial state up to 30% is possible," candidate movement amounts within the 30% range are selected. While multiple approaches are possible, if the movement is performed collectively within a range, the range of possible movement amounts is divided into equal intervals (e.g., 10 equal parts) to create a group of candidates. Furthermore, according to the rule that "movement phases can be limited to one forward or backward," combinations of forward and backward movement amounts are created as a group of candidates. In step 209, candidates are similarly created for subsequent movement rules, and predictions are made using the prediction model 104 for all patterns of planned value parameter groups derived from the candidate combinations, and appropriate planned value parameters are found in step 210. While it is desirable to evaluate all combinations, the computational complexity is expected to increase as the number of combinations increases. Therefore, it is advisable to suppress the increase in computational complexity by selectively evaluating patterns using random numbers, or by prioritizing candidate groups with the smallest differences from the initial state as patterns. Furthermore, the information on causal relationships in the prediction model 104 may be used to determine the priority of explanatory variables that have an impact based on the target goal definition, and patterns may be created in accordance with that order.

[0061] The goal definition table 600 will be described with reference to FIG.

[0062] The goal definition table 600 records the content of the goal definition input by the user in step 206. The goal definition table 600 includes a goal definition name 601, which is a name for identifying the goal definition, and its content 602. In step 210, the goal definition is compared with the prediction result to evaluate the degree of achievement of optimization.

[0063] In the example of effort smoothing shown in the first row, the goal is to equalize the effort predicted for each phase as the objective variable throughout the project. When the user inputs the goal definition, it is preferable that it be specified using natural language, as shown in content 602, or a template similar to the movement rules described above. During evaluation in step 210, the objective variable specified in the goal definition content 602 is re-expressed as a numerical value based on the results of the sequence for each phase. In effort smoothing, the goal is reached when the predicted result of the effort for each phase falls within a predetermined error range with the average effort for each phase. In step 210, the predicted result that minimizes the difference in effort for each phase and the planned value parameters after movement at that time are selected.

[0064] In the example of quality improvement shown in the second line, the goal is to improve quality the later the phase in the chronological order. In this goal definition, unlike the example of effort mentioned above, a goal numerical value is defined that slopes according to the position of each phase on the time axis. The slope of the numerical value can be an implicitly specified value or can be specified by the user. When using bug density or number of bugs as quality indicators, it is desirable for the quality indicators to decrease over time toward completion. It is advisable to calculate the quality indicators of each phase as goal numerical values ​​based on the expected quality indicators (bug density, number of bugs) for the entire project and the specified slope.

[0065] As shown in Fig. 6, goal definitions are individually defined for multiple objective variables. Therefore, in the determination of step 211, the closeness of the calculated objective variable to each goal definition shown in the goal definition table 600 is evaluated. Most simply, it is advisable to select as the solution the case with the smallest average difference between the calculated objective variable and each goal definition value. The goal definition may be defined by a combination of multiple conditions.

[0066] Alternatively, a priority may be assigned to each goal definition, and a weighted average value based on the priority may be used, thereby allowing step 210 to select a solution that prioritizes goal definitions with higher priorities.

[0067] As described above, the project plan optimization tool according to the embodiment of the present invention allows for the reallocation of plan parameter values ​​between the phases by transferring plan values ​​between the multiple phases that make up the project, thereby optimizing the plan parameter values. Furthermore, the rules for transferring plan values ​​allow for obtaining appropriate plan parameter values ​​that ensure the validity of the plan values. Furthermore, by inputting the desired shape of the plan parameter transition as a target goal and approaching the target goal within the range permitted by the transfer rules, the tool obtains plan parameter values ​​that derive the project results desired by the user.

[0068] In the first embodiment, a group of candidates for reallocation of planned parameters was generated using the movement rule shown in FIG. 3 as a constraint, and the search for planned parameters that would lead to an appropriate prediction result was limited to the group of candidates under this constraint. However, calculated parameters that lead to an appropriate prediction result may exist even when some of the constraints are released. In this case, if the released constraints are acceptable, the optimized calculated parameters obtained therefrom can also be used. In this embodiment, when listing possible parameter movement patterns in step 208, intentionally deviating from the movement rule. For example, searching for a combination of parameters that are reallocated within a 50% rule deviation range for the movement rule in the "30% from the initial state" range of FIG. 5 may result in a more appropriate result than if the movement rule were followed.

[0069] If a proper result is found by deviating from such a movement rule, the result display in step 212 may display the deviated movement rule together with a comparison of the result of deviating from the movement rule and the result that complies with the movement rule, allowing the user to decide whether or not to deviate from the movement rule.

[0070] Note that among the movement rules, there are strict rules that do not allow deviation, and rules that allow deviation to a certain extent. By adding a specification to the definition of the movement rules shown in Figure 3 as to whether or not to allow attempts that deviate from the movement rules, it becomes possible to search for solutions efficiently.

[0071] The present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit and scope of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to configurations including all of the described configurations. Furthermore, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Furthermore, the configuration of another embodiment may be added to the configuration of one embodiment. Furthermore, part of the configuration of each embodiment may be added, deleted, or replaced with other configurations.

[0072] Furthermore, the aforementioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits, or may be realized in software by having a processor interpret and execute a program that realizes each function.

[0073] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, hard disk, or SSD (Solid State Drive), or in a recording medium such as an IC card, SD card, or DVD.

[0074] In addition, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily represent all the control lines and information lines that are necessary for implementation. In reality, it can be assumed that almost all components are interconnected.

Claims

1. A project plan optimization tool that outputs information related to improvement proposals for a project's business plan, comprising a computer having a calculation device that executes calculation processing and a storage device accessible by the calculation device, and comprising: a planned value parameter input unit to which a first planned value parameter for each of a plurality of phases into which the project is divided is input; a past performance information accumulation unit that accumulates actual value parameters and result evaluation values ​​of each phase of a plurality of past projects; a past performance information analysis unit that associates and analyzes the actual value parameters and result evaluation values ​​of each phase of the past projects and generates a prediction model for the project; a project result prediction unit that predicts the result evaluation value from the first planned value parameters using the prediction model; and a planned value parameter optimization unit that reallocates the first planned value parameters between the phases and outputs optimized second planned value parameters.

2. A project plan optimization tool as described in claim 1, characterized in that the past performance information analysis unit analyzes the performance value parameters and the result evaluation values ​​stored in the past performance information storage unit for each phase using machine learning, and generates a predictive model that learns the causal relationship between the performance value parameters and the result evaluation values.

3. A project plan optimization tool as described in claim 1, characterized in that the project result prediction unit calculates multiple result evaluation values ​​by simulating multiple projects using multiple planned value parameters, and the planned value parameter optimization unit selects a result evaluation value that is closest to a goal definition set in accordance with the project's objectives, and outputs a second planned value parameter corresponding to the selected result evaluation value.

4. A project plan optimization tool as described in claim 3, characterized in that the planned value parameter optimization unit determines that optimization is completed by selecting a result evaluation value within the goal definition and a preset error range, and outputs a second planned value parameter corresponding to the selected result evaluation value.

5. A project plan optimization tool as described in claim 3, characterized in that the goal definition is defined by a combination of a plurality of conditions, and priorities are assigned to the plurality of conditions.

6. A project plan optimization tool as described in claim 3, characterized in that the goal definition is a transition of evaluation values ​​depending on the position of each phase on the time axis within the project.

7. A project plan optimization tool as described in claim 1, characterized in that the planned value parameter optimization unit searches for a combination of planned value parameter movements between phases that derives an appropriate result evaluation value based on movement rules previously set for each type of planned value parameter, and outputs a second planned value parameter based on the combination found.

8. A project plan optimization tool as described in claim 7, characterized in that the movement rule can specify at least one of whether or not a planned value parameter can be moved, the amount by which the planned value parameter can be moved, the range of phases to which the planned value parameter is to be moved, and the numerical range of the planned value parameter after the movement.

9. A project plan optimization tool according to claim 7, characterized in that said movement rules include rules for maintaining the relationships between a plurality of correlated planned value parameters.

10. A project plan optimization tool as described in claim 7, characterized in that the planned value parameter optimization unit searches for a combination of redistribution of planned value parameters in a range that deviates from the movement rule, and derives a second planned value parameter to which a movement rule deviation condition is attached.

11. A project plan optimization method executed by a project plan optimization tool that outputs information related to improvement proposals for a project's business plan, wherein the project plan optimization tool is composed of a computer having a calculation device that executes calculation processing and a storage device accessible by the calculation device, and the storage device has a past performance information storage unit that accumulates performance value parameters and result evaluation values ​​of each phase of a plurality of past projects, and the project plan optimization method comprises: a planned value parameter input step in which a first planned value parameter for each of a plurality of phases into which the project is divided is input; a past performance information analysis step in which the actual value parameters and result evaluation values ​​of each phase of the past project are associated with each other and analyzed to generate a prediction model for the project; a project result prediction step in which the prediction model is used to predict a result evaluation value from the first planned value parameters; and a planned value parameter optimization step in which the first planned value parameters are reallocated between the phases and optimized second planned value parameters are output.

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