Project success probability calculation system, project success probability calculation method, and project success probability calculation program

Abstract

Provided is a project success probability calculation system comprising: a performance influencing pre-element holding unit which holds performance influencing pre-elements that are pre elements influencing the performance of a project, the pre-elements being empirically known as influencing the success of the project; a questioning unit which assumes a specific project obtained by specifically specifying a project and urges an input by outputting a question for associating the performance influencing pre-elements with performance pre-state values such as numerical values that are values in a specific project; a probability distribution derivation function holding unit which holds a probability distribution derivation function that is a function for calculating a success probability distribution of the specific project by using the performance pre-state values associated with the performance influencing pre-element; a success probability distribution calculation unit which calculates a success probability distribution of the specific project for which the questioning unit receives the input by means of the performance pre-state values respectively associated with the performance influencing pre-elements obtained by the input to the questioning unit and the probability distribution derivation function held in the probability distribution derivation function holding unit; and a success probability distribution holding unit which holds the calculated success probability distribution.　

Description

[Technical field]

[0001] The present invention is a system, method, and program for determining the probability that a so-called project will succeed based on multifaceted and social empirical rules. [Background technology]

[0002] The idea of ​​diagnosing the success rate of a project has existed for some time. One example is Patent Document 1. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication 2016-170718 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 discloses the technical idea or problem of diagnosing the probability of project success.

[0005] However, in Patent Document 1, for example, it is not the case that "selection is made for each item and diagnosis is made based on the selected result." Also, for projects, the likelihood of success differs for each client and project, and there was no system that could provide a probability forecast of whether or not the desired likelihood of success would be achieved in relation to that "desired likelihood of success" or advice on how to achieve that likelihood. [Means for solving the problem]

[0006] In light of such problems, the present invention aims to develop a system in which the system asks questions for each item that is empirically known to affect project execution, has the user select the degree (value) of influence on project execution by answering the questions, and obtains and outputs the probability distribution of project success based on the selected results. In addition, for projects, the desired possibility of success differs for each client and project, and the system aims to develop a system that provides the probability of whether or not the "desired possibility of success" will be reached based on the degree of influence on project execution obtained from the answers, as well as advice on how to achieve that probability.

[0007] Specifically, the present invention includes a performance influence pre-element holding unit that holds performance influence pre-elements, which are pre-elements that affect project performance and are empirically known to affect the success of a project; a question unit that outputs questions to prompt input for associating performance influence pre-elements with performance influence pre-elements, which are one or more of numerical values, presence / absence, YES or NO, level, and option values, which are values ​​in a specific project, assuming a specific project; a probability distribution derivation function holding unit that holds a probability distribution derivation function, which is a function for calculating the probability distribution of success of a specific project using the performance influence pre-elements, and a performance influence pre-element holding unit that holds the performance influence pre-element obtained by input to the question unit. Simply A project success probability calculation system is provided, which has a success probability distribution calculation unit that calculates the success probability distribution of a specific project whose input has been received by a query unit using an associated pre-execution state value and a probability distribution derivation function stored in a probability distribution derivation function storage unit, and a success probability distribution storage unit that stores the calculated success probability distribution.

[0008] In addition to the above features, the present invention provides a project success probability calculation system that further includes a desired success probability information acquisition unit that acquires desired success probability information, which is information regarding the desired probability of success, and a realization probability calculation unit that calculates a realization probability, which is the probability that the desired success probability will be realized, based on the retained success probability distribution and the acquired desired success probability information.

[0009] In addition to the above features, the present invention provides a project success probability calculation system that further includes an impact acquisition unit that acquires the impact based on the pre-execution state value associated with the performance influence pre-element on the realization probability, an advice acquisition rule holding unit that holds advice acquisition rules, which are rules for acquiring advice regarding the performance influence pre-element based on the performance influence pre-state value in order to improve the realization probability, an advice acquisition unit that acquires advice regarding the performance influence pre-element based on the acquired impact and the advice acquisition rules, and an advice output unit that outputs the acquired advice.

[0010] In addition to the above features, the present invention provides a project success probability calculation system, characterized in that the probability distribution derivation function is a function modified by a combination of machine learning and Bayesian statistics.

[0011] The present invention also provides a project success probability calculation method and a project success probability calculation program corresponding to these project success probability calculation systems.

[0012] Further, the present invention includes a performance influence pre-element identification information holding unit that holds performance influence pre-element identification information for identifying performance influence pre-elements that are pre-elements that affect project performance and are known empirically to have an effect on the success of the project, a questioning unit that outputs a question for associating a performance influence pre-element identification information with a specific project that is specifically specified, the performance influence pre-element identification information with the performance influence pre-element identification information, the performance influence pre-state value being one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the specific project, to prompt input, a probability distribution derivation function holding unit that holds a probability distribution derivation function that is a function for calculating a probability distribution of success of the specific project using the performance influence pre-state value associated with the performance influence pre-element identification information, and a performance influence pre-element identification information obtained by input to the questioning unit. To A project success probability calculation system is provided, which has a success probability distribution calculation unit that calculates the success probability distribution of a specific project whose input has been received by a query unit using an associated pre-execution state value and a probability distribution derivation function stored in a probability distribution derivation function storage unit, and a success probability distribution storage unit that stores the calculated success probability distribution. Effect of the Invention

[0013] As a result, instead of calculating a specific value as the project success probability or calculating a specific probability range, it is possible to calculate the project success probability distribution, and by using this success probability distribution, it is possible to calculate the probability of achieving a success probability equal to or greater than a specified success probability, that is, the probability of probability. Furthermore, it is possible to know the advice required for the project to improve the probability of probability. This makes it possible to obtain more detailed information about the project success probability, which was previously answered linearly according to the satisfaction of the project success factors, and therefore clarifies in advance the probability of project success and the issues that need to be resolved to improve the project success probability. [Brief description of the drawings]

[0014] [Figure 1]FIG. 1 is a diagram showing a functional configuration of a project success probability calculation system according to a first embodiment. [Diagram 2] FIG. 1 is a diagram showing a hardware configuration of a project success probability calculation system according to a first embodiment. [Diagram 3] FIG. 1 is a diagram showing a process flow when the project success probability calculation system according to the first embodiment is used. [Figure 4] FIG. 1 is a diagram showing a functional configuration of a project success probability calculation system according to a second embodiment. [Diagram 5] FIG. 13 is a diagram showing a hardware configuration of a project success probability calculation system according to a second embodiment. [Figure 6] FIG. 13 is a diagram showing a process flow when the project success probability calculation system according to the second embodiment is used. [Figure 7] FIG. 13 is a diagram showing a functional configuration of a project success probability calculation system according to a third embodiment. [Figure 8] FIG. 13 is a diagram showing a hardware configuration of a project success probability calculation system according to a third embodiment. [Figure 9] FIG. 13 is a diagram showing a process flow when the project success probability calculation system according to the third embodiment is used. [Figure 10] Success probability distribution diagram DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, the embodiments of the present invention will be described with reference to the attached drawings. The relationship between the embodiments and the claims is as follows. 、4、5、8、9、12、13 The second embodiment relates to claim 2. 、6、10 The third embodiment relates to claim 3. 、7、11 is related to. The third embodiment includes features of the first and second embodiments, and the second embodiment includes features of the first embodiment.

[0016] The present invention should not be limited to these embodiments in any way, and can be embodied in various forms without departing from the spirit of the invention.

[0017] <Hardware that can constitute the present invention> The present invention is, in principle, an invention that utilizes an electronic computer, but it can also be realized by software, hardware, or the collaboration of software and hardware. The hardware that realizes all or part of each of the constituent elements of the present invention is composed of the basic components of a computer, such as a CPU (Central Processing Unit), memory, a bus, input / output devices, various peripheral devices, and a user interface.

[0018] Various peripheral devices include storage devices, internet interfaces, internet devices, displays, keyboards, mice, speakers, cameras, videos, televisions, various sensors for grasping production conditions in laboratories or factories, etc. (flow sensors, temperature sensors, weight sensors, liquid volume sensors, infrared sensors, shipment counting machines, package counting machines, foreign body inspection devices, defective product counting machines, radiation inspection devices, surface condition inspection devices, circuit inspection devices, human presence sensors, worker work status grasping devices (video, ID, PC (Personal Computer) workload, etc.), CD (Compact Disc) devices, DVD (Digital Versatile Disc) devices, Blu-ray devices, USB (Universal Serial Bus) memory, USB memory interfaces, removable hard disks, general hard disks, projector devices, SSDs (Solid State Drives), telephones, faxes, copy machines, printing devices, movie editing devices, various sensor devices, etc.

[0019] In addition, the present system does not necessarily have to be configured by one housing, and may be configured by connecting multiple housings through communication. In addition, the communication may be LAN (Local Area Network), WAN (Wide Area Network), Wi-Fi (registered trademark), Bluetooth (registered trademark), infrared communication, or ultrasonic communication, and further, some of the housings may be installed across borders. Furthermore, each of the multiple housings may be operated by a different entity, or may be operated by a single entity. The operating entity of the system of the present invention may be either single or multiple. Furthermore, the invention may be configured as a system including a terminal used by a third party in addition to the present system, and a terminal used by another third party. Furthermore, these terminals may be installed across borders. Furthermore, in addition to the present system and the terminals, a device used to register related information of third parties, related persons, a device used for a database for recording the contents of registration, etc. may be provided. These may be provided in the present system, or may be provided outside the present system to configure the present system to make such information available.

[0020] <Satisfying the use of the laws of nature in the present invention> The present invention functions through the collaboration of computers, communication equipment, and software. It not only enables the processing that project participants have traditionally performed in interviews to be processed using ICT (Information and Communication Technology), but also includes processing unique to ICT, such as determining the effects of many complex information exchanges, procedures, authentication, and approvals related to projects using ICT, and supporting the accumulation, retention, and exchange of effective information that fulfills all the necessary items that would normally require skill through ICT, making it a so-called business model patent. In addition, various identification information, risk information, issue information, and task information are retained and processed in each section, and from this perspective, if the present invention is judged based on the resources such as computers in the claims and specifications and the technical common sense related to those items, it is considered that the present invention utilizes the law of nature.

[0021] <The significance of using the laws of nature required in patent law> The use of the laws of nature required by the Patent Act is required to ensure that the invention can be used industrially in a useful manner, based on the purpose of the law, from the viewpoint that the invention must have industrial applicability and contribute to the development of industry. In other words, it is required that the invention be industrially useful, that is, that the effect of the invention declared at the time of application can be reproduced with a certain degree of certainty by implementing the invention. From this perspective, the use of the laws of nature is interpreted as the function of each of the invention-specific matters (invention constituent elements), which are the components of the invention for achieving the effect of the invention, being achieved by utilizing the laws of nature. Furthermore, the effect of an invention only needs to be able to provide a certain usefulness to the user who uses the invention, and should not be viewed from the perspective of how the user feels or thinks about that usefulness. Therefore, even if the effect that the user gets from this system is a psychological effect, the effect itself is not subject to the required use of the laws of nature. Embodiment 1

[0022] This embodiment includes an execution influence pre-element holding unit that holds execution influence pre-elements, which are pre-elements that affect project execution and are known empirically to affect the success of a project; a questioning unit that outputs questions to prompt input for relating execution influence pre-elements, which are one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in a specific project, to the execution influence pre-elements, assuming a specific project that is specifically specified; a probability distribution derivation function holding unit that holds a probability distribution derivation function, which is a function for calculating the probability distribution of success of a specific project using the execution pre-state values ​​associated with the execution influence pre-elements; and Finally Line impact advance requirements Simply A project success probability calculation system is provided, which has a success probability distribution calculation unit that calculates the success probability distribution of a specific project whose input has been received by a query unit using an associated pre-execution state value and a probability distribution derivation function stored in a probability distribution derivation function storage unit, and a success probability distribution storage unit that stores the calculated success probability distribution.

[0023] The functional configuration, hardware configuration, and processing flow of the project success probability calculation system in this embodiment will be described below in order.

[0024] <Embodiment 1: Functional configuration> 1 is a diagram showing the functional configuration of a project success probability calculation system according to embodiment 1. The project success probability calculation system 0100 according to this embodiment includes an execution influence prior element storage unit 0101, a question unit 0102, a probability distribution derivation function storage unit 0103, a success probability distribution calculation unit 0104, and a success probability distribution storage unit 0105.

[0025] The performance-influencing pre-element holding unit 0101 has a function of holding performance-influencing pre-elements, which are pre-elements that affect project performance and are known empirically to affect the success of the project. Note that performance-influencing pre-elements can also be read as performance-influencing pre-element identification information, which is information for identifying performance-influencing pre-elements, throughout this specification. Also, in configurations related to advice acquisition and advice output, they can be referred to as performance-influencing pre-elements, and for other configurations, they can be referred to as performance-influencing pre-element identification information.

[0026] "Performance-influencing ex-ante factors" are ex-ante factors that affect project performance that are empirically known to affect the success of a project. However, this does not necessarily have to be limited to information before the start of the project, and can include information that affects the future of the project during the project's execution. By not necessarily limiting it to information before the start of the project in this way, the accuracy of the success probability distribution can be known with even greater certainty during project execution. Therefore, by repeatedly inputting values ​​associated with performance-influencing ex-ante factors that are assumed to be closer to reality into the probability distribution derivation function from before the start of the project through the execution of the project, it is possible to execute the project while increasing the possibility of leading the project to success.

[0027] Specifically, the pre-performance impact factors mainly refer to human resources such as people who are invested in or involved in the project and who can influence people involved in the project, capital that can be invested in the project or factors that influence the capital that can be invested in the project, materials required for the project that must be prepared in advance, or factors that influence the procurement of said materials. However, in addition to these, factors that affect the pre-performance impact factors may also be included. The pre-performance impact factors may include transportation infrastructure (for example, the impact of the COVID-19 pandemic in recent years, which has caused the economy to stagnate), communication infrastructure (for example, large-scale network failures), and economic infrastructure (for example, market functions such as the foreign exchange market, stock market, bond market, government bond market, crude oil market, precious metals market, grain market, and interest rate market). In addition, social conditions may be pre-performance impact factors. Social conditions are mainly international conditions. For example, the US-China trade friction can be cited in recent years. This caused China's Huawei to suddenly drop in sales, and electronic parts manufacturers in Japan also stopped shipping to Huawei, causing a major economic blow. In this way, various elements of the international situation may be understood as pre-execution influence factors and taken into consideration within the system. Major factors that influence the international situation include the political systems of each country, statements made by the leaders of each country, military actions of some countries, and scandals involving the leaders of some countries. Biased reporting by the media may also be added as a factor. Below, specific examples of representative pre-execution influence factors are given. Furthermore, the value obtained for the pre-execution influence factor is the pre-execution state value. In the following, "value" will be interpreted as "pre-execution state value" as appropriate. Pre-execution influence factors may be composed of one or more of the following. Note that the pre-execution influence factors are not limited to those described below, and the necessary ones will be selected depending on the nature of the project and the probability distribution derivation function used.

[0028] <<Human resources>>

[0029] <Human Resources 1: Information on the number of people available for the project> As an example of human resources or related factors, the number of people that can be allocated to a project can be mentioned first. This does not necessarily have to be the number of people themselves. For example, when planning the number of people with a certain project execution skill required for each skill, the information on which skills people can be allocated in the planned number becomes a factor of a representative performance influence a priori factor related to human resources. For example, when there is a plan for a project that requires 10 people with skill A to be allocated, the value is the degree of possibility of arranging the same number of people with that skill. For example, if 15 people can be allocated, there is a margin, so the value of the performance influence a priori factor can be assigned a value of 100 (%: a number that represents satisfaction). However, if only 10 people can be allocated, there is no margin, and there is a possibility that the factor cannot be satisfied due to some accidental reason, so a value of 80 (%: a number that represents satisfaction) is assigned. In this way, a predetermined value according to the state is assigned to each item of the performance influence a priori factor according to the actual situation. The actual situation is input by the project manager (by selecting from options, etc.), and the system is configured to assign a value to that factor.

[0030] <Human Resources 2: Capabilities of project participants> An example of the "ability of project participants" is the degree of familiarity with the database language used in the project. This is an important factor when the purpose of the project is to build a database for certain types of data. Database language elements are classified into Data Manipulation Language (DML): a language or language element for searching, registering, updating, and deleting target data, Data Definition Language (DDL): a language or language element for creating, updating, and deleting data structures, and Data Control Language (DCL): a language or language element for access control. For example, Structured Query Language (SQL), a widely used database language, contains all of the above language elements as a language system that combines various command statements.), and participants can be asked to indicate their familiarity with each language according to the section they are assigned to, as well as their past achievements, knowledge of related technologies, and familiarity with related Application Programming Interfaces (APIs). For each of these items, such as familiarity with the language, past performance, knowledge of the surrounding technologies, and familiarity with related APIs, options are provided to indicate the state, and the system is configured so that when the project manager selects an option, the value that has been assigned to that state in advance is assigned to the item of the pre-element that influences the execution of the project.For example, in the hierarchical database IMS (Information Management System) made by IBM, there are languages ​​specific to the data definition language and data manipulation language (DL / I (Data manipulation Language of IMS) and assembler macros).

[0031] <Human resources 3: ability distribution> "Capability distribution" is also one of the prior factors that affect project performance. In order to ensure the completion of a project, the capabilities of the participants in the project must exceed expectations. The capability distribution of project participants is a two-dimensional and three-dimensional value of this capability. Capability distribution can be rated in multiple dimensions, such as the capability values ​​of each individual participant for multiple skills, the capability distribution of a group made up of multiple participants, and the capabilities of a higher-level group made up of multiple groups. For example, if there is a project task that is directed from upstream to downstream, even if the capabilities of the upstream group are high, if the capabilities of the stationed group are low in comparison, the balance of the project as a whole will be poor. In this way, the capability distribution that is predicted in advance has a large impact on project performance, and is therefore important as a prior factor that affects project performance.

[0032] <Human Resources 4: Expertise> "Expertise" is the field of the ability required of project participants to perform their duties as professionals. Expertise as a pre-element influencing performance differs depending on the purpose of the project. For example, if the purpose of the project is to build a database on new patents at the Patent Office, background knowledge becomes very important when building the database. For example, knowledge on the legal deadlines of patents, knowledge on patent procedures, knowledge on patent law, patent classification, and knowledge on technical fields are required. The expertise of these knowledge is required as a pre-element influencing performance. For example, regarding the expertise of a certain project participant, if the participant has specialized knowledge on the legal deadlines of patents and specialized knowledge on patent classification, but does not have knowledge on procedures, knowledge on patent law, or knowledge on technical fields, the participant has two expertises, so the system may be configured so that 1×2 is associated with the participant. These are configured so that the project manager selects an option from the options corresponding to each participant, and the system associates a score with the participant according to the selection. Whether or not the expertise required for the execution of the project is sufficiently met is scored and retained according to the item of expertise.

[0033] <Human resource 5: Resistance> "Torrespondence" is the ability to persevere through a project task when it is difficult to complete due to obstacles, or in other words, injury tolerance. In other words, injury tolerance is a quantification of the results of a psychological test given to project participants. R The optimal value is a value obtained by accumulating past successful and unsuccessful project cases. As a pre-factor that influences execution, the probability of project success is affected by whether the optimal value is exceeded or fallen short. Specifically, the system or the system's peripheral devices ask questions about "personality traits," "behavioral traits," "negative traits," "stress tolerance," etc. to each project participant (including those who are scheduled to participate; the same applies throughout the specification). Out The answer is then entered and a score is calculated. value are compared and used to calculate the probability of project success.

[0034] <Human Resources 6: Reliability and Validity> "Reliability and validity" refers to the reliability of a project participant in carrying out a project, and the validity of assigning project tasks. Reliability is calculated according to the participant's past performance history. It is calculated using rules that use past performance as input to calculate reliability. This may be configured to be calculated using artificial intelligence (AI). Validity is also calculated by quantifying people's attributes for multiple items and using the past task processing performance of project participants. For example, if a project participant has the attributes "age," "system conceptual design ability," and "number of participations in SQL server-related projects" with values ​​of 40 years old, 8 / 10, and 4, ,P Depending on how many points people with similar attributes have received in past projects similar to the project as a validity score ,The probability of success is increased by using the execution-influencing prior factors. Calculated do.For example, if a participant with similar attributes in a similar project in the past was given a score of 9 / 10 for relevance, then the system will similarly give that participant a score of 9 / 10 for relevance as a pre-performance influence factor.

[0035] <Human resources 7: Possible input labor costs> "Available labor costs" are, The labor costs for this project, Based on past performance of projects of similar scale, appropriate personnel were allocated appropriately Expenses and Compare Systems By acquisition Will be For example, for similar projects in the past, appropriate labor costs for similar human resources are stored, and by inputting the labor costs for this project, Labor costs for this project and past projects Comparisons are made to obtain values ​​as performance-influencing prior factors. For example, past similar projects of the same type and scale of In the example of human resource investment, if the optimal labor cost input is 2 billion yen, and the labor cost input this time is 2.1 billion yen, the comparison result will be 10 / 10.

[0036] <Human Resources 8: Supply and Demand in the Manpower Market for a Given Skill> "Supply and demand conditions in the labor market for a given capability" is an item related to the probability of being able to recruit the necessary labor for a project, and is a value given to the probability of being able to gather labor from the labor market at the start, middle, or end of a project. The supply and demand conditions in the labor market are a concept that includes the current state as well as the future state. For example, the electronics industry is showing signs of decline, but the possibility that in the near future, restructuring will cause a large outflow of labor from this industry field and that they will be circulated in the market can be quantified. The correlation between the past labor supply and demand conditions and the past performance of a project is held as a function, and the value of the ex-ante factors that affect project execution can be obtained by inputting current and future predicted labor supply and demand data into that function.

[0037] <Human Resources 9: The quality and quantity of connections that influence projects> "Quality and quantity of personal connections that influence the project" is a function that holds the correlation between the success and failure of past projects and the quality and quantity of personal connections. By inputting the quantified quality and quantity of personal connections for the current project, the execution impact that affects the probability of project success can be calculated. advance The values ​​of the elements can be obtained. Specifically, the executive officer in charge of the project, his superior officer, and those with decision-making authority such as the CEO and vice president can be understood as representative connections that influence the project. Others include the technical supervisor, star engineers who possess key technology, the accounting executive officer, the head of the human resources department, the section chief of the human resources department who can influence the personnel affairs of employees involved in the project, key people at the consulting firm that supports the project from the outside, and key people at the think tank that supports the company's management. Quality and quantity can be seen from the perspective of whether each person in these connections is favorable, hostile, or neutral toward the project, and their attitude, whether they are positive or negative about their position, as well as the person's past achievements, the attitude they took toward past projects, and the friendly or hostile relationships and the degree of these relationships between the people who make up the network. These of The value of the performance-influencing prior factors is calculated by discretely quantifying and computing the results. of It is possible to obtain and quantify the impact on a project.

[0038] <Capital available for input> Investable capital refers to the amount of capital that can be invested in a project. The most typical example is money. Investable capital may also be configured to include equipment, devices, systems, programs, etc. Alternatively, it may be configured to convert equipment, devices, systems, programs, etc. into monetary units and treat them as investable capital.

[0039] <Inputable capital 1: Inputable capital amount> The amount of investable capital is, in principle, money. Money is consumed by a project, and the probability of a project succeeding varies depending on whether there is a sufficient amount of money to complete the project, or only the exact amount of money, or whether there is a high probability that there will be a shortage of money. The amount of investable capital is a representative ex-ante factor that affects execution. This investment Possible To evaluate the amount of capital, the amount of capital required for the project is calculated and compared with the estimated amount of capital required. This evaluation value is then processed as a pre-performance factor.

[0040] <Available capital 2: Amount of additional capital that can be invested (to allow for room for changes in project strategy)> In contrast to the amount of investable capital that is known in advance to be consumed in a project, the amount of capital that is not required in the prior project plan but is prepared in preparation for changes in the project is called additional investable capital. In principle, the amount of additional investable capital required in monetary units in response to changes in project goals, issues, tasks, etc. in comparable past projects is obtained, and the amount of additional investable capital that exists as reserve before the start of the project is evaluated against the amount of additional capital that has been invested. For example, if A is the average amount of additional invested capital for past projects of the same scale and quality, and B is the amount of additional investable capital for the current project, the system can be configured to obtain the value of B / A as the ex-ante execution impact factor.

[0041] <Deployable capital 3: Future exchange rate forecasts when deployable capital is denominated in a foreign currency> When deployable capital or additional deployable capital is prepared in a foreign currency, the amount of deployable capital in the home country of the project is affected based on future forecasts of exchange rates, and it is necessary to obtain this impact as an advance performance impact factor. In many cases, future exchange rates are estimated based on the center line of exchange rate fluctuations and the forecast volatility (range of variation) from that center line. In the present invention, the forecast volatility can be replaced with a random variable and obtained as one element of the advance performance impact factor. In addition, since exchange rates change over time, it is possible to assume the timing at which deployable capital will actually be deployed from the project plan, obtain the advance performance impact factor at each timing, and average the values ​​for each timing to obtain the value of the advance performance impact factor.

[0042] <Available capital 4: Status of securing sources of available capital (credit line, investment intentions, possibility of issuing corporate bonds in some cases)> If the deployable capital is already secured in the form of cash, the value of 1 can be given as the performance impact ex ante factor. However, if it is not secured as cash, the risk of converting it into cash must be obtained as the performance impact ex ante factor. This is a probability value. For example, if it is planned to finance the investment with a loan from a financial institution, and that financial institution has zero risk, the interest rate at the time of the loan and the timing of the cash loan can be set as the performance impact ex ante factor. element However, if the credit line and interest rate are committed in advance, the probability as an ex ante factor influencing performance will be 1. Furthermore, if the deployable capital is raised through investment from investors, the risks of those investors and investment institutions must be acquired as ex ante factors influencing performance. Furthermore, if it is planned to finance this by issuing corporate bonds, the total of the issue amount of the corporate bonds, the status of securing underwriters, the economic risk of the underwriters, etc. is configured to be acquired as an ex ante factor influencing performance.

[0043] <Available capital 5: Room for stock increase to raise investment capital, status of securing new shareholder> When available capital is raised through an initial public offering or underwriting of new shares, the expected size of these, and in the case of an IPO, the expected selling price, are compared with past performance. The same is true for underwriting of new shares. Comparing past performance with the current plan, if the past performance was risk A and the current plan is risk B, the probability of success this time can be obtained as B / A. This is the value that needs to be obtained as the ex ante factor influencing performance.

[0044] <Procurement of materials, etc.> The certainty of procuring necessary materials and materials is a factor that greatly affects projects. Recently, due to the US embargo on China and the political influence that made it mandatory for other countries around the world, Chinese smartphone manufacturers found it difficult to procure smartphone parts, and smartphone operating systems (OS) were no longer provided by the US, forcing them to use their own OS. There are cases where this has had a major impact on the execution of projects.

[0045] <Procurement of materials, etc. 1: Status of availability of necessary materials> The status of the possibility of securing necessary materials can be obtained as a probability value, which is the possibility of the occurrence of the above-mentioned events. For example, if political friction intensifies more than usual and the possibility of a trade war increases, the probability of securing necessary materials will fall below 1. It is preferable to configure these so that they can be calculated as probability values ​​by inputting values ​​for the political situation, war situation, possibility of disasters, the degree of epidemic and the possibility of epidemics, etc. into a specified function based on information from economists, consultants, think tanks, etc.

[0046] <Procurement of materials, etc. 2: Future forecast of procurement costs of necessary materials> Future predictions of the procurement costs of necessary materials are also important. For example, there have been cases where a trade war has broken out and tariffs have been imposed that are more than twice as high as usual. In such cases, the procurement costs of necessary materials exceed normal expectations, which has a negative impact on the impact on project success as an ex-ante factor influencing performance. If the maximum value of the success probability is set to 1, a value that makes the success probability smaller than 1 depending on the predicted rate of increase in procurement costs is obtained as an ex-ante factor influencing performance based on the future predictions of the procurement costs of necessary materials.

[0047] <Procurement of materials, etc. 3: Future forecast for ensuring the quality of necessary materials> If the future quality of necessary materials cannot be assured, it will affect the success of the project. For example, if the product yield is 99% and necessary materials are to be procured, but the forecast falls to 90%, the amount of materials procured will increase and the number of quality checkers will increase, which will increase the burden on the project. Also, if the calculation error rate of the server to be used in the system is lower than expected, an extra redundant system will have to be built to compensate for the errors, which will also cause a burden on the execution of the project. As these factors affect the probability of project success, they must be quantified and obtained as ex-ante execution influence factors.

[0048] <Procurement of Materials, etc. 4: Other> Other factors that affect the probability of project success include "future forecasts of storage costs for necessary materials," "future forecasts of transportation costs for necessary materials," and "future forecasts of processing costs for necessary materials." These factors may need to be quantified and treated as ex-ante factors that influence project execution.

[0049] <Information resources> Information resources, broadly speaking, are the level of information processing capabilities. They can be understood as discovery, collection, development (research capabilities), etc. As in the previous example, these can also be quantified using a specified function to obtain factors that affect the probability of project success (specifically, ex-ante factors that affect execution).

[0050] <Information resource 1: Ability to find information that contributes to solving problems when they occur> This is an expression when viewed from the perspective of the project as a whole, but from a micro perspective, it is composed of the capabilities of the participants taking part in the project. It is preferable that project participants are configured to have a project participant database that stores the history of past projects, and this database is configured to evaluate the various capabilities of project participants, for example, on a scale of 10 points. A score is then assigned in terms of whether the participant was able to successfully complete the tasks and manage the management they are responsible for. Based on this database, the ability to find information that will contribute to solving problems when they occur can be obtained as a value, based on the capabilities of each participant in this project and the similarities between the tasks they are responsible for, etc.

[0051] <Information Resources 2: Other> In addition, values ​​for "ability to collect information necessary for project execution," "ability to collect information required when making changes to project tasks, etc.", and "research ability to solve issues necessary for project execution" can also be obtained by using similar systems and databases as above.

[0052] The question unit 0102 has a function of outputting questions to prompt input for relating pre-execution state values, which are one or more of numerical values, presence / absence, YES or NO, level, and option, which are values ​​in the project, to pre-execution influence elements assuming a specific project. These values ​​are ultimately substituted into the variable portion of the probability distribution derivation function and used to calculate the success probability distribution, so they are treated as numerical values. Questions that allow free text entry are also acceptable. Free-form answers can be scored by artificial intelligence or the like, and ultimately treated as numerical values ​​as well.

[0053] The value for the answer to the question 0102, i.e., the pre-execution state value, must be set appropriately according to the form of the probability distribution derivation function. The value is determined by appropriately estimating the impact on the success probability distribution derived by the probability distribution derivation function. This can be set based on the empirical rules of multiple past projects. For example, in response to the question, "How much is the available capital?", even if the amount is the same 500 million yen in two different projects, if the probability distribution derivation function used is different, the value may be 100 (%: a numerical value representing the degree of satisfaction) in project A and 80 (%: a numerical value representing the degree of satisfaction) in project B.

[0054] It is preferable that the person for whom the question section 0102 prompts input is different depending on the role of the project. For example, a question about human resources is directed to a person who approves personnel matters for the project in advance, and a question about capital resources is directed to a person who makes a fundraising plan for the project in advance.

[0055] Question Section 010 2 is , Question The system can be configured to output questions from the terminal of each project participant, and to output questions prepared in advance according to the identification information of the project participant input to the terminal. Furthermore, the questioning unit 0102 can be configured to omit questions of a duplicated nature among questions to the same person in past projects. For example, gender, age, family structure, place of residence, etc. It is preferable that the questioning unit 0102 is configured to accumulate values ​​associated with the pre-performance influencing element or the pre-performance influencing element identification information. This accumulated information is preferably associated with project attributes acquired from the answer of the questioning unit 0102. In addition, this answer is preferably associated with the person who gave the answer and the role of that person in the project.

[0056] Moreover, it is preferable that these values ​​associated with each past project and accumulated are associated with the progress information and final conclusion information of the project. It is also preferable to configure the system to compare the predicted success probability with the actual degree of success and to correct the weighting of each value of the probability distribution derivation function and the handling of values ​​in the probability distribution derivation function. This can be realized by deep learning using artificial intelligence.

[0057] Below are example questions for each performance-influencing pre-factor.

[0058] <Questions about human resources> (1) How many people can be invested per unit scale of the project (scale is obtained based on input capital, project duration, etc.)? (2) What is the average number of tasks each project participant is responsible for? (3) What is the ability score (ability scored in some way) demonstrated by each project participant in a comparable project? (4) What is the available labor cost? (5) What is the distribution of the project participants’ capabilities across tasks? (6) In terms of expertise, how many people are qualified? (7) What is the supply and demand situation in the labor market for a given skill? ,supply If there is sufficient salary margin, what is the deviation value of 50?

[0059] <Question regarding deployable capital> (1) What is the amount of available capital in yen? (2) How much additional capital is available in yen (to allow for room for strategic changes in the project)? (3) When the deployable capital is denominated in a foreign currency, what percentage range above or below the past five-year average would the future forecast for exchange rates be? How would this be expressed using Bollinger bands? ("Bollinger bands" are an index based on the concepts of standard deviation and normal distribution in statistics, and show how much the price over a certain period of time varies or is dispersed from the average value over that period. For example, they show the range of variation in the closing prices of the dollar / yen exchange rate over a five-day period.) (4) The status of securing sources of funds that can be invested (credit lines, investment intentions, and possible issuance of corporate bonds) (5) What rating should be used to express the scope for raising capital through equity capital increases and the status of securing new share underwriters? For example, input the rating from Standard & Poor's as the value.

[0060] <Questions regarding procurement of materials, etc.> (1) Please enter the availability of necessary materials as a value between 0% and 100%. (2) Enter the future forecast of procurement costs for necessary materials as a deviation value against the average of the past five years. Alternatively, enter the value assuming that the average of the past five years is 50. (3) Enter the future forecast for ensuring the quality of necessary materials as a deviation value against the average of the past five years. Alternatively, enter a value assuming that the average of the past five years is 50. (4) Enter the future forecast of storage costs for necessary materials as a deviation from the average for the past five years. Alternatively, enter the value assuming that the average for the past five years is 50. (5) Enter the future forecast of the transportation cost of necessary materials as a deviation value against the average of the past five years. Alternatively, enter the value assuming that the value against the average of the past five years is 50. (6) Enter the future forecast of the processing cost of the necessary materials as a deviation value against the average of the past five years. Alternatively, enter the value assuming that the value against the average of the past five years is 50.

[0061] <Questions about information resources> (1) Please enter the ability to find information that will contribute to solving problems when they occur as a standard deviation against the average of the past five years of project discovery ability. Alternatively, please enter a value assuming that the value for the average of the past five years is 50. (2) Ability to gather information necessary for project execution Ability to gather information necessary when making changes to project tasks, etc. Research capabilities to solve problems necessary for project execution

[0062] <Embodiment 1: Probability distribution derivation function storage unit> The probability distribution derivation function holding unit 0103 has a function of holding a probability distribution derivation function, which is a function for deriving the probability distribution of the success of a project by using the pre-execution state value associated with the performance-influencing pre-element.

[0063] A probability distribution derivation function is a function for deriving a success probability distribution function, for example, where the horizontal axis is the probability of project success and the vertical axis is the frequency at which a project will succeed with that probability, and is a function in which the derived success probability distribution function changes depending on the pre-execution state value, which is the value of the pre-execution element. In other words, it is a function in which the success probability distribution function is determined when the pre-execution state value is determined. In other words, it is a function of functions. The derived success probability distribution function is most typically a normal distribution function in which the frequency is distributed equally to the left and right of the average success probability, or a function that is distributed in a shape close to a normal distribution function. If it is a normal distribution function or if it is close to a normal distribution function, the variance σ 2 (σ squared) and the mean μ is determined by the execution pre-state value. That is, the variance σ 2 The probability distribution derivation function is a function where (σ squared) and the mean μ are functions of the execution prior state value.

[0064] However, the degree of variance of the normal distribution and the degree of distortion from the normal distribution that the distribution is close to the normal distribution are determined by the variance σ 2It depends on the form of the function of (σ squared) and the form of the function of the mean μ, which is also a probability distribution derivative function. In other words, the probability distribution derivative function is a function that is determined according to the nature of the project and the attributes of the project executor. The form of the probability distribution derivative function, that is, the variance σ 2 Once the function for (σ squared) or mean μ is specified, the variance σ can be calculated by substituting the execution pre-state value into the function. 2 (σ squared) and mean μ are determined. Variance σ 2 Once (σ squared) and the mean μ are determined, the normal distribution function is determined and the success probability distribution is specified.

[0065] For example, if there are 100 pre-performance influence factors, each of which is associated with one pre-performance state value, the success probability distribution of the project can be obtained by substituting the pre-performance state value corresponding to the pre-performance influence factor into each of the 100 variables of the probability distribution derivation function.

[0066] Although the probability distribution is typically a normal distribution, it is not necessarily limited to this and may take other forms. For example, the variance σ 2A distribution can be considered in which a normal distribution function (probability distribution derivative function) determined by (σ squared) and the mean μ is modified by a function F(x). This F(x) is called a modification function. The modification function is also a probability distribution derivative function. F(x) is also a function determined by the execution pre-state value given to one or more execution-influencing pre-elements. In this case, the distribution is expressed as the product of the normal distribution fa(x) and the modification function F(x), in the form fb(x) = F(x) × fa(x). This expression is the final desired probability distribution function and success probability distribution. Note that the modification function is not necessarily involved in the normal distribution function in the form of a product, but may be used as a constant term added or subtracted, or added to or subtracted from x, or of course, the modification function may be used in the form of a mix of two or more of these. Furthermore, the modification function is not limited to one type, and a configuration may be used in which multiple modification functions are used. These modifications may be configured to be appropriately performed using artificial intelligence machine learning (e.g., deep learning, etc.), or may be configured to be appropriately performed using a combination of machine learning and known statistical methods (e.g., Bayesian statistics, etc.).

[0067] For example, in Bayesian statistics, P(A|B) can be defined by the following formula: P(A|B) represents the posterior probability, which is the probability that event A occurs under the condition that event B occurs. <Calculation formula> P(A|B)=P(B|A)P(A) / P(B) Here, P(A) represents the prior probability. The prior probability is the probability that event A occurs before event B occurs. The prior probability can be set subjectively by the user of this system. P(B|A) represents the likelihood. The likelihood is the probability that event B occurs under the condition that event A occurs (or when event A is assumed to be true). P(B) represents the marginal likelihood. The marginal likelihood is the probability that event B occurs before event A occurs. In other words, the marginal likelihood is the probability that event B is true among all events A and B. For example, information such as the project not reaching its goal can be adopted as event A, and information such as the project's large scale or the project manager's low level of experience can be adopted as event B. According to Bayesian statistics, the probability of event A can be changed based on event B.

[0068] In this way, while it is difficult to predict the success probability correctly, Bayesian statistics, which can make predictions by varying the arguments (parameters) that represent the correct answer from the execution pre-state values ​​given to the execution influence pre-elements, works well with machine learning. Therefore, adopting a combination of machine learning and Bayesian statistics is more useful for predicting the success probability than adopting machine learning alone.

[0069] In addition, the correction is made by feedback of the probability distribution derivation function or by deep learning of artificial intelligence, but rather than correcting the probability distribution derivation function initially used when the corresponding project is completely completed, it can also be configured to correct the probability distribution derivation function by feeding back the progress of the project as the project progresses.

[0070] For example, since the values ​​associated with performance impact prior factors may change over time, and there may be performance impact prior factors that have already been determined, it is conceivable that, at least for the performance impact prior factors that have already been determined, the values ​​selected and obtained in the query section may be compared with the actual values, and the probability distribution derivation function may be transformed.

[0071] <Embodiment 1: Success Probability Distribution Calculation Unit> The success probability distribution calculation unit 0104 has a function of calculating the success probability distribution of the project that has been received by the query unit 0102, using the execution pre-state value associated with each execution influence pre-element (execution influence pre-element identification information) obtained by input to the query unit 0102 and the probability distribution derivation function held in the probability distribution derivation function holding unit 0103. The success probability distribution is typically a normal distribution, but is not limited to this as described above. As described above, the calculation of the success probability distribution using the execution influence pre-element is not limited to before the start of the project, and it is preferable to calculate the success probability distribution while increasing the accuracy of the execution influence pre-element value at any time during the execution of the project. For this reason, the query unit 0102 is preferably configured to have a schedule for outputting re-questions according to the progress of the project, and to appropriately select appropriate project participants in the intermediate process of the project, output questions from their terminals, correct the values ​​of the execution influence pre-elements, and calculate and present a more accurate success probability distribution. It is also preferable to calculate the probability that the success probability can be made higher than a certain level at an intermediate stage of the project, and to continuously output advice for increasing the success probability even during the execution of the project.

[0072] <Embodiment 1: Success Probability Distribution Storage Unit> The success probability distribution holding unit 0105 has a function of holding the calculated success probability distribution. The success probability distribution is calculated before the start of the project, but since a more accurate success probability distribution can be obtained by using the values ​​of the performance-influencing prior elements that have already been determined as described above, or by using the values ​​of the performance-influencing prior elements whose accuracy has improved over time, it is preferable to configure the unit to repeatedly calculate the success probability distribution during the execution of the project according to the schedule of the interrogator, and to hold the success probability distribution of the same project in chronological order. In other words, the unit is configured to have a success probability distribution history holding means.

[0073] <Embodiment 1: Hardware Configuration> 2 is a diagram showing an example of a hardware configuration in this embodiment. The hardware configuration of the project success probability calculation system 0100 in this embodiment will be described with reference to the diagram.

[0074] As shown in this diagram, a computer is composed of a chipset, CPU, non-volatile memory, main memory, various buses, BIOS (Basic Input Output System), various interfaces, a real-time clock, etc., which are configured on a motherboard. These operate in cooperation with an operating system, device drivers, various programs, etc. The various programs and data that constitute the present invention are configured to efficiently utilize these hardware resources to execute various processes.

[0075] Chipset A "chipset" is a set of large-scale integrated circuits (LSI (Large Scale Integration)) that are mounted on a computer motherboard and integrate a communication function between the CPU's external bus and the standard bus that connects memory and peripheral devices, in other words a bridge function. There are cases where a two-chipset configuration is used and cases where a single chipset configuration is used. The north bridge is located on the side closest to the CPU and main memory, and the south bridge is located on the far side and acts as an interface with the relatively slow external I / O (Input / Output).

[0076] (North Bridge) The north bridge includes a CPU interface, memory controller, and graphics interface. Most of the functions of a conventional north bridge can be handled by the CPU. The north bridge is connected to the memory slot of the main memory via a memory bus, and to the graphics card slot of the graphics card via a high-speed graphics bus (AGP (Accelerated Graphics Port), PCI (Peripheral Component Interconnect) Express).

[0077] (South Bridge) The south bridge is connected to the PCI interface (PCI slot) via a PCI bus, and is responsible for I / O functions with the ATA (Advanced Technology Attachment) (SATA (Serial ATA)) interface, USB interface, Ethernet (registered trademark) interface, and sound functions. Since incorporating circuits that support PS / 2 ports, floppy disk drives, serial ports, parallel ports, and ISA (Industry Standard Architecture) buses, which do not require or are impossible to operate at high speed, will hinder the speed of the chipset itself, they may be separated from the south bridge chip and handled by another LSI called a super I / O chip. A bus is used to connect the CPU (MPU (Micro Processor Unit)) to peripheral devices and various control units. The bus is linked by the chipset. The memory bus used to connect to the main memory may instead adopt a channel structure to increase speed. A serial bus or a parallel bus can be used as the bus. Whereas a serial bus transfers data one bit at a time, a parallel bus transmits the original data itself or multiple bits extracted from the original data as a single block, simultaneously over multiple communication paths. A dedicated line for the clock signal runs parallel to the data line to synchronize data demodulation on the receiving side. It is also used as a bus to connect a CPU (chipset) to an external device, and examples include GPIB (General Purpose Interface Bus), IDE (Integrated Drive Electronics) / (parallel) ATA, SCSI (Small Computer System Interface), and PCI. Because there is a limit to how fast it can be made, in PCI Express, an improved version of PCI, and Serial ATA, an improved version of Parallel ATA, the data line can be a serial bus.

[0078] CPU The CPU sequentially reads, interprets, and executes a sequence of instructions called a program in the main memory, and outputs information consisting of signals to the main memory. The CPU functions as the center of calculations within the computer. The CPU is composed of the CPU core, which is the center of calculations, and its peripheral parts, and includes registers, cache memory, an internal bus that connects the cache memory and the CPU core, a DMA (Direct Memory Access) controller, a timer, and an interface with the connection bus to the north bridge. A single CPU (chip) may have multiple CPU cores. In addition to the CPU, processing may be performed by a graphics processing unit (GPU (Graphics Processing Unit)) or an FPU (Floating Point Unit).

[0079] <Non-volatile memory> (HDD (Hard Disk Drive)) The basic structure of a hard disk drive consists of a magnetic disk, a magnetic head, and an arm on which the magnetic head is mounted. The external interface can be SATA (previously ATA). A high-performance controller, such as SCSI, is used to support communication between hard disk drives. For example, when copying a file to another hard disk drive, the controller can read the sectors and transfer them to the other hard disk drive for writing. During this time, the host CPU's memory is not accessed. Therefore, there is no increase in the CPU load.

[0080] <Main memory> The CPU directly accesses and executes various programs in main memory. Main memory is a volatile memory and uses DRAM. Programs in main memory are expanded from non-volatile memory to main memory upon receiving a program start command. The CPU then executes the program according to various execution commands and execution procedures within the program.

[0081] Operating System (OS) An operating system is used to manage the resources on a computer so that applications can use them, to manage various device drivers, and to manage the computer itself (the hardware). Small computers sometimes use firmware as the operating system.

[0082] ≪BIOS≫ The BIOS starts up the computer hardware and has the CPU execute procedures to run the operating system, and is most typically the hardware that the CPU reads first when it receives a computer startup command. The address of the operating system stored on the disk (non-volatile memory) is recorded here, and the BIOS deployed in the CPU sequentially deploys the operating system into main memory and makes it operational. The BIOS also has a check function that checks the presence or absence of various devices connected to the bus. The results of the check are stored in main memory and made available to the operating system as appropriate. The BIOS may also be configured to check for external devices, etc.

[0083] The above also applies to other embodiments.

[0084] As shown in Fig. 2, the present invention can basically be configured with a general-purpose computer program and various devices. The computer basically operates in a form in which a program recorded in non-volatile memory is loaded into main memory, and processing is executed by the main memory, CPU, and various devices. Communication with devices is performed via an interface connected to a bus line. Possible interfaces include a display interface, a keyboard, and a communication buffer.

[0085] figure As stated in 2, The non-volatile memory stores various programs.The "Performance Influence Pre-Factor Retention Program" is a program that retains pre-factors that affect project execution, which are pre-factors that are empirically known to affect project success. ,P Project A specific project that has been specifically identified Expected impact factors on execution identification a "question program" which outputs a question to prompt input for associating a pre-execution state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project; a "probability distribution derivation function holding program" which holds a probability distribution derivation function, which is a function for calculating the probability distribution of the success of the project, using the pre-execution state values ​​associated with the performance-influencing pre-elements; and a probability distribution derivation function which is held in a probability distribution derivation function holding unit 0103, which calculates the probability of success of the project inputted to the question unit 0102 by using the pre-execution state values ​​associated with each performance-influencing pre-element obtained by inputting to the question unit 0102 and the probability distribution derivation function held in the probability distribution derivation function holding unit 0103. distribution A "success probability distribution calculation program" that calculates the success probability distribution The computer has a "success probability distribution holding program" that holds the probability of success distribution, and these programs are loaded into the main memory based on a series of program execution commands, and are executed based on an operation start command. This computer is configured such that the non-volatile memory, main memory, CPU, and interfaces (for example, display, keyboard, communication, etc.) are connected to a bus line and can communicate with each other.

[0086] <Embodiment 1: Processing flow> 3 is a diagram showing the process flow when using the project success probability calculation system 0100 in this embodiment. The process flow of this embodiment is an operation method of the project success probability calculation system 0100, which is a computer having execution influence prior element holding step S0301, questioning step S0302, probability distribution derivation function holding step S0303, success probability distribution calculation step S0304, and success probability distribution holding step S0305. Each step will be explained below.

[0087] The performance-influencing prior factor holding step S0301 is a stage of holding performance-influencing prior factors, which are prior factors that affect project performance and are known empirically to have an effect on the success of a project.

[0088] What is question step S0302? ,P Project A specific project that has been specifically identified Expected impact factors on execution identification This is the step of prompting input by outputting a question for associating a pre-execution state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project.

[0089] The probability distribution derivation function holding step S0303 is a step of holding a probability distribution derivation function, which is a function for calculating the probability distribution of the success of the project by using the performance pre-state value associated with the performance influencing pre-element.

[0090] The success probability distribution calculation step S0304 is a step in which the success probability distribution of the project inputted in the query step S0302 is calculated using the pre-performance state values ​​associated with each performance-influencing pre-element obtained by input in the query step S0302 and the probability distribution derivation function stored in the probability distribution derivation function storage unit 0103.

[0091] The success probability distribution holding step S0305 is a step of holding the calculated success probability distribution.

[0092] <Embodiment 2: Overview> In addition to the embodiments described above, this embodiment provides a project success probability calculation system which has the characteristic of having the function of acquiring desired success probability information, which is information regarding the desired probability of success, and calculating the realization probability, which is the probability that the desired probability of success will be realized, based on the retained success probability distribution and the acquired desired success probability information.

[0093] <Embodiment 2: Functional Configuration> 4 is a diagram showing the functional configuration of a project success probability calculation system 0400 of the embodiment 2. The project success probability calculation system 0400 of the embodiment has an execution influence prior element holding unit 0401, a questioning unit 0402, a probability distribution derivation function holding unit 0403, a success probability distribution calculation unit 0404, and a success probability distribution holding unit 0405. Furthermore, the embodiment 2 has a desired success possibility information acquisition unit 0406 and a realization probability calculation unit 0407.

[0094] <Embodiment 2: Desired success probability information acquisition unit> The desired success probability information acquisition unit 0406 is configured to acquire desired success probability information, which is information related to the desired success probability. The success probability distribution held in the first embodiment has the horizontal axis representing the success probability (e.g., success probability) and the vertical axis representing the execution time of the project. influence case Preface It represents the frequency with which each success probability is realized for each pre-execution state value given to the element. The value that obtains that the project's success probability is equal to or greater than a certain value, assuming the success probability distribution, is the desired success probability information. For example, if you want the success probability to be 75% or greater, then "75%" or "75% or greater" would be the desired success probability information. For example, as shown in Figure 10, if the value S2 is the collection of all frequencies of 75% or greater, and the value S1 is the collection of all frequencies of values ​​less than 75%, then the total frequency is S1+S2, so the realization probability, which is the probability that the project will succeed with a probability of 75% or greater, is S2 / (S1+S2).

[0095] <Embodiment 2: Realization Probability Calculation Unit> The realization probability calculation unit 0407 calculates the realization probability, which is the probability that the desired success probability will be realized, based on the stored success probability distribution and the acquired desired success probability information. As described above, the realization probability is a value obtained by dividing the frequency of probabilities equal to or greater than the desired success probability by the total probability frequency using the desired success probability and success probability distribution indicated in the desired success probability information, and the realization probability calculation unit 0407 performs this calculation to calculate the realization probability.

[0096] The calculation result in the realization probability calculation unit 0407 is calculated according to the success probability distribution. Therefore, when the value of the execution-influencing prior element changes according to the progress of the project as described above, the success probability distribution calculated by the probability distribution derivation function changes according to the value with improved accuracy, so it is preferable to configure so that the realization probability is recalculated each time the change occurs.

[0097] Furthermore, it is preferable to configure the system so that the calculation results of the realization probability are stored in the system in association with the progress information of the project. It is also preferable to configure the system so that a chart or graph showing the change in the realization probability in the order of the project's milestones and progress information is output. Even if the value of the performance-influencing pre-element does not actually change according to the progress of the project, it is preferable to input one or more values ​​of the performance-influencing pre-element to virtually simulate the change in the realization probability according to the change in the value, and output the chart or graph. This is because the project performance policy can be determined via the chart or graph.

[0098] <Embodiment 2: Hardware Configuration> Fig. 5 is a diagram showing the hardware configuration of the project success probability calculation system in this embodiment. As shown in this diagram, the computer is composed of a chipset, a CPU, a non-volatile memory, a main memory, various buses, a BIOS, various interfaces, a real-time clock, and the like, which are configured on a motherboard. These operate in cooperation with an operating system, device drivers, various programs, and the like. The various programs and various data that constitute the present invention are configured to efficiently utilize these hardware resources to execute various processes.

[0099] Here, the "main memory" reads out programs that perform various processes so that the "CPU" can execute them, and at the same time provides a work area for those programs. In addition, this "main memory" and "HDD" are each assigned multiple addresses, and the programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses.

[0100] As shown in this figure, The non-volatile memory is The "Performance Influence Pre-Factor Retention Program" is a program that retains pre-factors that affect project execution, which are pre-factors that are empirically known to affect project success. , P Project A specific project that has been specifically identified Expected impact factors on execution identification a "question program" for outputting a question for associating a pre-execution state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project, to prompt input; a "probability distribution function holding program" for holding a probability distribution function, which is a function for calculating a probability distribution of the success of the project, using the pre-execution state value associated with the execution influence pre-element; a "success probability distribution calculation program" for calculating the success probability distribution of the project, the input of which has been accepted by the question unit 0402, using the pre-execution state value associated with each execution influence pre-element obtained by input to the question unit 0402 and the probability distribution derivation function held in the probability distribution derivation function holding unit 0403; a "success probability distribution holding program" for holding the calculated success probability distribution; a "desired success probability information acquisition program" for acquiring desired success probability information, which is information regarding the desired success probability; and a "realization probability calculation program" for calculating the realization probability, which is the probability that the desired success probability will be realized, based on the held success probability distribution and the acquired desired success probability information. Based on a series of program execution instructions, these programs are loaded into main memory, and based on an operation start instruction, these programs are executed. In addition, this computer is configured such that the non-volatile memory, main memory, CPU, and interfaces (for example, display, keyboard, communication, etc.) are connected to a bus line and can communicate with each other. Non-volatile memory isThe project diagnostician identification information acquisition program is provided for acquiring diagnostician identification information for identifying a diagnostician who inputs a diagnosis into the diagnostic sheet in order to associate the diagnostician identification information with the project diagnostic sheet.

[0101] <Embodiment 2: Processing flow> FIG. 6 is a diagram showing a process flow when the project success probability calculation system 0400 in this embodiment is used. The process flow of this embodiment is as follows: A performance-influencing pre-factor retention step S0601 for retaining performance-influencing pre-factors that are pre-factors that affect project performance and are empirically known to affect the success of the project; , P Project A specific project that has been specifically identified Expected impact factors on execution identification A questioning step S0602 for outputting a question for associating a performance pre-state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project, to prompt input; a probability distribution derivation function holding step S0603 for holding a probability distribution derivation function, which is a function for calculating a success probability distribution of the project using the performance pre-state value associated with the performance influence pre-element; and a probability distribution derivation function holding step S0603 for holding the performance pre-state value associated with each performance influence pre-element obtained by input in the questioning step and the probability distribution derivation function holding step S0604 for holding the performance pre-state value associated with each performance influence pre-element obtained by input in the questioning step and the probability distribution derivation function holding step S0605 for holding the performance pre-state value associated with each performance influence pre-element obtained by input in the questioning step and the probability distribution derivation function holding step S0606 for holding the performance pre-state value associated with each performance influence pre-element obtained by input in the questioning step and the execution pre-state value associated with each performance influence pre-element obtained by input in the questioning step; Step S0603 The probability distribution derivative function held in and the question by Step S0602 The method includes a success probability distribution calculation step S0604 for calculating the success probability distribution of the project inputted by the input terminal, a success probability distribution holding step S0605 for holding the calculated success probability distribution, a desired success probability information acquisition step S0606 for acquiring desired success probability information which is information related to the desired success probability, and a realization probability calculation step S0607 for calculating the realization probability which is the probability that the desired success probability will be realized based on the held success probability distribution and the acquired desired success probability information. Of these, steps S0601 to S0605 except for the desired success probability information acquisition step S0606 and the realization probability calculation step S0607 are the same as those in the first embodiment.

[0102] The desired success probability information acquisition step S0606 is a stage for acquiring desired success probability information, which is information relating to the desired success probability.

[0103] The realization probability calculation step S0607 is a step of calculating the realization probability, which is the probability that the desired success probability is realized, based on the stored success probability distribution and the acquired desired success probability information.

[0104] <Embodiment 3: Overview> This embodiment is based on embodiment 2 and is configured to be able to obtain and output advice for improving the realization probability shown in embodiment 2.

[0105] <Embodiment 3: Functional Configuration> 7 is a diagram showing the functional configuration of a project success probability calculation system 0700 of the third embodiment. The project success probability calculation system 0700 of this embodiment has an execution influence prior element holding unit 0701, a questioning unit 0702, a probability distribution derivation function holding unit 0703, a success probability distribution calculation unit 0704, a success probability distribution holding unit 0705, a desired success possibility information acquisition unit 0706, and a realization probability calculation unit 0707. The project success probability calculation system 0700 of this embodiment is based on the configuration of the second embodiment and further has an influence degree acquisition unit 0708, an advice acquisition rule holding unit 0709, an advice acquisition unit 0710, and an advice output unit 0711.

[0106] <Third embodiment: Impact degree acquisition unit> The influence degree acquisition unit 0708 acquires the influence degree of the pre-execution state value associated with the execution influence pre-element on the realization probability. "Influence degree" is synonymous with "contribution rate." It is the influence degree (increase or decrease) of the value of the execution influence pre-element when the growth rate (change rate) of the overall realization probability is set to 100, and is expressed as a component ratio (%). A specific formula can be expressed as "contribution rate = increase or decrease in the value of the execution influence pre-element (pre-execution state value) / increase or decrease in the overall realization probability (×100)." Therefore, when acquiring that the success probability of the project is a certain value or more on the premise of the acquired success probability distribution, as described above, if the value S2 is the value of all frequencies of 75% or more in the success probability distribution diagram (FIG. 10) and the value S1 is the value of all frequencies of values ​​smaller than 75%, the total frequency is S1+S2, so the realization probability, which is the probability that the project will be successful with a probability of 75% or more, is S2 / (S1+S2)=J%. This value can be defined as ΔA / ΔJ (×100) when the value of the performance impact pre-element (pre-execution state value) is A, the change in the value of the performance impact pre-element is ΔA, and the value is A+ΔA, and the realization probability is J+ΔJ%. Therefore, the performance impact pre-element value, which is the value of the performance impact pre-element for each project, is determined, and the realization probability obtained from the success probability distribution obtained by substituting it into the probability distribution derivation function is further calculated, and the realization probability obtained by changing the value of the performance impact pre-state value by ΔA in the same probability distribution derivation function is divided by ΔJ, and the impact of each performance impact pre-element can be obtained by calculating the value for the number of performance impact pre-state values. When the impact of one performance impact pre-state value is obtained, the other performance impact pre-state values ​​are fixed at the value when they were input into the initial probability distribution derivation function. The impact is obtained for each performance impact pre-state value.

[0107] In addition, the degree of influence of the execution influence pre-element on the realization probability may be stored as information in advance for each execution influence pre-element, or may be obtained by calculating each time as described above. Note that the degree of influence generally changes depending on the value of the execution influence pre-element, that is, the execution pre-state value. influenceThe slope changes depending not only on the type of pre-factor but also on whether its value still has enough room to grow toward the project's success or is nearing saturation toward the project's success.

[0108] <Third embodiment: Advice acquisition rule storage unit> The advice acquisition rule holding unit 0709 holds advice acquisition rules, which are rules for acquiring advice on pre-performance influencing factors that are premised on a pre-performance state value in order to improve the realization probability. Here, "pre-performance state value is premised" means that the advice is for further improving the pre-performance state value, and the content of the advice may differ depending on the pre-performance state value. For example, there are case 1 in which the amount of investable capital is 500 million yen, and case 2 in which the amount of investable capital is 1 billion yen. If the probability of successful realization of the project increases by 10% around 500 million yen and 5% around 1 billion yen with the amount of investable capital per unit, in the former, an increase in investable capital of 100 million yen increases the realization probability by 10%, whereas in the latter, it is 5%. Therefore, if it is desired to improve the realization probability by 15%, the advice for securing additional investable capital of 150 million yen is required in the former, but the advice for securing additional investable capital of 300 million yen is required in the latter, and the quality of the advice may differ between the two. For example, in case 1, the advice may be for additional investment from the parent company, and in the latter, the advice may be for a capital increase.

[0109] In this way, appropriate advice may differ depending on the assumed pre-execution state value and the target realization probability value, and the advice acquisition rule is a rule for selecting advice stored according to the pre-execution state value and the target realization probability. The advice acquisition rule is configured to acquire advice identification information that identifies advice to be acquired by a function of the pre-execution influence element, its pre-execution state value, and the target realization probability. Therefore, it is preferable that the project success probability calculation system 0700 is configured to have an advice acquisition rule storage unit 0709 in which a large number of advices are stored in association with advice identification information.

[0110] The advice acquisition unit 0710 acquires advice regarding the execution influence pre-element based on the acquired impact and advice acquisition rule. As already explained, this "impact" changes according to the execution pre-state value associated with the execution influence pre-element. When advice is acquired without setting a realization probability target, the variable substituted into the advice acquisition rule is only the impact for each execution influence pre-element, but when a realization probability target is set, it is preferable that the advice acquisition rule is configured so that the difference between the realization probability target and the current realization probability, or the current realization probability itself, are also variables substituted into the advice acquisition rule.

[0111] Furthermore, since the advice acquisition unit 0710 is considered to select performance-influencing pre-elements in order of decreasing impact and acquire advice using the advice acquisition rules, it is preferable to configure it so that, when acquiring advice, it acquires identification information of the performance-influencing pre-elements in order of impact and acquires advice by selecting, for example, the top N (N is a natural number) performance-influencing pre-elements (identification information).

[0112] In addition, when a project manager or the like identifies a pre-factor affecting performance and requests advice, a performance-affecting pre-factor affecting performance input reception unit (not shown) can be provided to acquire performance-affecting pre-factor identifying information, and advice can be acquired using advice acquisition rules related to the acquired performance-affecting pre-factors.

[0113] <Embodiment 3: Advice Output Unit> The advice output unit 0711 outputs the acquired advice. The advice output may be configured to be selected alternatively from the advice stored, or it may be configured to output advice by combining multiple pieces of acquired advice. There are cases where performance-influencing prior elements are interrelated, and in such cases, the advice can be configured to be aggregated in an advice aggregation unit (not shown) and output in a concise manner. The advice aggregation unit stores attribute identification information and syntax identification information of the advice according to the advice identification information, and it is conceivable to aggregate advice using advice aggregation rules that aggregate advice according to this information associated with the multiple pieces of acquired advice.

[0114] It is preferable that the advice aggregation rules are stored so that they are determined according to the advice identification information and the attribute identification information and syntax identification information associated therewith. The advice can be outputted not only via a display or printer, but also via e-mail or voice. It is also possible to configure the advice output unit 0711 to output advice according to the contents of the agenda in response to voice information from a meeting or the like. The voice of the meeting can be analyzed using artificial intelligence or the like to determine which execution method is being used. influence It is preferable to configure the system to determine what is on the agenda regarding the advance elements and automatically output relevant advice.

[0115] <Embodiment 3: Hardware Configuration> Fig. 8 is a diagram showing the hardware configuration of the project success probability calculation system in this embodiment. As shown in this diagram, the computer is composed of a chipset, a CPU, a non-volatile memory, a main memory, various buses, a BIOS, various interfaces, a real-time clock, and the like, which are configured on a motherboard. These operate in cooperation with an operating system, device drivers, various programs, and the like. The various programs and various data that constitute the present invention are configured to efficiently utilize these hardware resources to execute various processes.

[0116] Here, the "main memory" reads out programs that perform various processes so that the "CPU" can execute them, and at the same time provides a work area for those programs. In addition, this "main memory" and "HDD" are each assigned multiple addresses, and the programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses.

[0117] As shown in this figure, The non-volatile memory is The "Performance Influence Pre-Factor Retention Program" is a program that retains pre-factors that affect project execution, which are pre-factors that are empirically known to affect project success. , P Project A specific project that has been specifically identified Expected impact factors on execution identificationa "question program" for outputting a question for associating a pre-execution state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project, to prompt input; a "probability distribution derivation function holding program" for holding a probability distribution derivation function, which is a function for calculating a probability distribution of the success of the project, using the pre-execution state value associated with the execution influence pre-element; a "success probability distribution calculation program" for calculating a success probability distribution of the project, the input of which has been accepted by the question unit 0702, using the pre-execution state value associated with each execution influence pre-element obtained by input to the question unit 0702 and the probability distribution derivation function held in the probability distribution derivation function holding unit 0703; a "success probability distribution holding program" for holding the calculated success probability distribution; a "desired success probability information acquisition program" for acquiring desired success probability information, which is information regarding the desired success probability; and a "realization probability calculation program" for calculating a realization probability, which is the probability that the desired success probability will be realized, based on the held success probability distribution and the acquired desired success probability information. Based on a series of program execution instructions, these programs are loaded into main memory, and based on an operation start instruction, these programs are executed.

[0118] In addition, this computer is configured such that the non-volatile memory, main memory, CPU, and interfaces (e.g., display, keyboard, communication, etc.) are connected to a bus line and can communicate with each other. Non-volatile memory is The system is equipped with an "impact acquisition program" that acquires the impact based on the pre-execution state value associated with the performance impact pre-element on the realization probability, an "advice acquisition rule holding program" that holds advice acquisition rules which are rules for acquiring advice regarding the performance impact pre-element based on the performance impact pre-state value in order to improve the realization probability, an "advice acquisition program" that acquires advice regarding the performance impact pre-element based on the acquired impact and advice acquisition rules, and an "advice output program" that outputs the acquired advice.

[0119] <Embodiment 3: Processing flow> 9 is a diagram showing a process flow when the project success probability calculation system 0700 in this embodiment is used. The process includes a performance influencing prior element holding step S0901 for holding performance influencing prior elements, which are prior elements that affect project performance and are empirically known to affect the success of a project, and , P Project A specific project that has been specifically identified Expected impact factors on execution identification a questioning step S0902 for outputting a question for associating a performance pre-state value, which is one or more of a numerical value, presence / absence, YES or NO, level, and option, which is a value in the project, to prompt input; a probability distribution derivation function holding step S0903 for holding a probability distribution derivation function, which is a function for calculating a probability distribution of the success of the project, using the performance pre-state values ​​associated with the performance influence pre-elements; and a probability distribution derivation function holding step S0903 for holding the performance pre-state values ​​associated with each performance influence pre-element obtained by input in the questioning step S0902 and the probability distribution derivation function holding step S0903. Step S0903 The probability distribution derivative function held in and the question by Step S0902the success probability distribution calculation step S0904 for calculating the success probability distribution of the project for which input has been received, a success probability distribution holding step S0905 for holding the calculated success probability distribution, a desired success probability information acquisition step S0906 for acquiring desired success probability information which is information relating to the desired success probability, a realization probability calculation step S0907 for calculating the realization probability which is the probability that the desired success probability will be realized based on the held success probability distribution and the acquired desired success probability information, an impact acquisition step S0908 for acquiring the impact of a pre-performance state value associated with a performance influence pre-element on the realization probability, an advice acquisition rule holding step S0909 which is a rule for acquiring advice regarding a performance influence pre-element that assumes a performance pre-state value in order to improve the realization probability, an advice acquisition step S0910 for acquiring advice regarding a performance influence pre-element based on the acquired impact and the advice acquisition rule, and an advice output step S0911 for outputting the acquired advice. Of these, steps S0901 to S0907, excluding the impact acquisition step S0908, the advice acquisition rule storage step S0909, the advice acquisition step S0910, and the advice output step S0911, ,fruit Same as embodiment 2.

[0120] The influence obtaining step S0908 is a step of obtaining the influence due to the pre-execution state value associated with the pre-execution influence element on the realization probability.

[0121] The advice acquisition rule holding step S0909 is a step of holding advice acquisition rules, which are rules for obtaining advice regarding execution-influencing pre-factors that assume execution-pre-state values ​​in order to improve the realization probability.

[0122] The advice acquisition step S0910 is a stage for acquiring advice regarding the performance-influencing prior elements based on the acquired influence degree and the advice acquisition rule.

[0123] Advice output step S0911 is a step for outputting the obtained advice.

[0124] The various programs described above in the first to third embodiments can be recorded on a computer-readable storage medium (excluding carrier waves). When distributing the programs, the programs are sold in the form of portable storage media such as DVDs (Digital Versatile Discs) and CD-ROMs (Compact Disc Read Only Memory) on which the programs are recorded. The programs can also be stored in a storage device of a computer and transferred from one computer to another computer via a network. [Explanation of symbols]

[0125] 0100,0400,0700 Project success probability calculation system 0101,0401,0701 Performance Influence Advance Element Storage Unit 0102,0402,0702 Questions Department 0103,0403,0703 Probability distribution derivation function storage unit 0104,0404,0704 Success probability distribution calculation section 0105,0405,0705 Success probability distribution storage unit 0406,0706 Desired Success Possibility Information Acquisition Department 0407,0707 Realization Probability Calculation Unit 0708 Influence degree acquisition part 0709 Advice Acquisition Rule Storage Unit 0710 Advice Acquisition Department 0711 Advice output section

Claims

1. a performance-influencing prior element holding unit for holding a performance-influencing prior element, which is a prior element that influences project performance and is known empirically to have an effect on the success of the project; a question section for outputting a question for associating a pre-execution state value, which is one or more of a numerical value, a presence / absence, a YES or NO, a level, and a choice, which is a value in the specific project, with the pre-execution state value, which is one or more of a numerical value, a presence / absence, a YES or NO, a level, and a choice, with the pre-execution state value, which is a value in the specific project, assuming a specific project that is specifically specified as the project; a probability distribution derivation function storage unit that stores a probability distribution derivation function that is a function for calculating a probability distribution of the success of the specific project by using the execution pre-state value associated with the execution influence pre-element; a success probability distribution calculation unit that calculates a success probability distribution of the specific project for which the input is received by the query unit, using the execution pre-state value associated with the execution influencing pre-element obtained by input to the query unit and the probability distribution derivation function stored in the probability distribution derivation function storage unit; a success probability distribution storage unit that stores the calculated success probability distribution; A project success probability calculation system having the above structure.

2. a desired success probability information acquisition unit that acquires desired success probability information, which is information regarding a desired success probability; an actualization probability calculation unit that calculates an actualization probability, which is the probability that the success probability will be realized, based on the stored success probability distribution and the acquired desired success probability information; The project success probability calculation system according to claim 1 , further comprising:

3. an impact degree acquisition unit that acquires an impact degree due to the execution pre-state value associated with the execution impact pre-element on the realization probability; an advice acquisition rule storage unit that stores advice acquisition rules, which are rules for acquiring advice regarding the performance-influencing pre-element on the premise of the performance pre-state value in order to improve the realization probability; an advice acquisition unit that acquires advice regarding the performance-influencing prior element based on the acquired influence degree and the advice acquisition rule; an advice output unit that outputs the obtained advice; 3. The project success probability calculation system according to claim 2, further comprising:

4. The probability distribution derivation function is a function modified by a combination of machine learning and Bayesian statistics.

4. The project success probability calculation system according to claim 1, wherein the project success probability calculation system is a system for calculating a project success probability based on a probability of project success.

5. A performance-influencing pre-factor holding step for holding a performance-influencing pre-factor that is a pre-factor that influences project performance and is empirically known to affect the success of the project; A questioning step of outputting a question for associating a pre-performance state value, which is one or more of a numerical value, a presence / absence, a YES or NO, a level, and a choice, which is a value in the specific project, with the pre-performance influence element, by assuming a specific project that is specifically specified as the project, to prompt input; a probability distribution derivation function storage step for storing a probability distribution derivation function which is a function for calculating a probability distribution of the success of the specific project by using the performance pre-state value associated with the performance influence pre-element; a success probability distribution calculation step of calculating a success probability distribution of the specific project for which the input has been received, using the execution pre-state value associated with the execution influencing pre-element obtained by the input and the stored probability distribution derivation function; a success probability distribution holding step of holding the calculated success probability distribution; A computer-implemented method for calculating the probability of project success.

6. A desired success probability information acquisition step of acquiring desired success probability information which is information regarding a desired success probability; an actualization probability calculation step of calculating an actualization probability, which is the probability that the success probability will be realized, based on the stored success probability distribution and the acquired desired success probability information; The method for calculating a project success probability according to claim 5, further comprising the steps of:

7. an impact degree acquisition step of acquiring an impact degree due to the execution pre-state value associated with the execution impact pre-element on the realization probability; an advice acquisition rule holding step for holding advice acquisition rules, which are rules for obtaining advice regarding the performance-influencing pre-element on the premise of the performance pre-state value in order to improve the realization probability; an advice acquisition step of acquiring advice regarding the performance-influencing prior element based on the acquired influence degree and the advice acquisition rule; an advice output step of outputting the advice obtained; The method for calculating a project success probability according to claim 6, further comprising the steps of:

8. The probability distribution derivation function is a function modified by a combination of machine learning and Bayesian statistics.

8. The method for calculating a project success probability according to claim 5, wherein the project success probability is calculated based on the project success probability.

9. A performance-influencing pre-factor holding step for holding a performance-influencing pre-factor that is a pre-factor that influences project performance and is empirically known to affect the success of the project; A questioning step of outputting a question for associating a pre-performance state value, which is one or more of a numerical value, a presence / absence, a YES or NO, a level, and a choice, which is a value in the specific project, with the pre-performance influence element, by assuming a specific project that is specifically specified as the project, to prompt input; a probability distribution derivation function storage step for storing a probability distribution derivation function which is a function for calculating a probability distribution of the success of the specific project by using the performance pre-state value associated with the performance influence pre-element; a success probability distribution calculation step of calculating a success probability distribution of the specific project for which the input has been received, using the performance pre-state value associated with each of the performance-influencing pre-elements obtained by the input and the stored probability distribution derivation function; a success probability distribution holding step of holding the calculated success probability distribution; A program for calculating the probability of project success on a computer.

10. A desired success probability information acquisition step of acquiring desired success probability information which is information regarding a desired success probability; an actualization probability calculation step of calculating an actualization probability, which is the probability that the success probability will be realized, based on the stored success probability distribution and the acquired desired success probability information; 10. The project success probability calculation program according to claim 9, further comprising:

11. an impact degree acquisition step of acquiring an impact degree due to the execution pre-state value associated with the execution impact pre-element on the realization probability; an advice acquisition rule holding step for holding advice acquisition rules, which are rules for obtaining advice regarding the performance-influencing pre-element on the premise of the performance pre-state value in order to improve the realization probability; an advice acquisition step of acquiring advice regarding the performance-influencing prior element based on the acquired influence degree and the advice acquisition rule; an advice output step of outputting the advice obtained; 11. The project success probability calculation program according to claim 10, further comprising:

12. The probability distribution derivation function is a function modified by a combination of machine learning and Bayesian statistics.

12. The project success probability calculation program according to claim 9, wherein the project success probability calculation program is a program for calculating a project success probability based on the project success probability.

13. a performance-influencing pre-element identification information holding unit for holding performance-influencing pre-element identification information that identifies performance-influencing pre-elements that are pre-elements that affect project performance and are known empirically to have an effect on the success of the project; a question section for outputting a question for associating a pre-execution state value, which is one or more of a numerical value, a presence / absence, a YES or NO, a level, and a choice, which is a value in the specific project, with the execution-influencing pre-element identification information, and prompting input; a probability distribution derivation function storage unit that stores a probability distribution derivation function that is a function for calculating a probability distribution of success of the specific project by using the execution pre-state value associated with the execution influence pre-element identification information; a success probability distribution calculation unit that calculates a success probability distribution of the specific project for which the input is received by the query unit, using the execution pre-state value associated with the execution influencing pre-element identification information obtained by input to the query unit and the probability distribution derivation function stored in the probability distribution derivation function storage unit; a success probability distribution storage unit that stores the calculated success probability distribution; A project success probability calculation system having the above structure.

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