WO2022264344A1 - Business strategy evaluation device and business strategy evaluation program - Google Patents

Business strategy evaluation device and business strategy evaluation program Download PDF

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Publication number
WO2022264344A1
WO2022264344A1 PCT/JP2021/022939 JP2021022939W WO2022264344A1 WO 2022264344 A1 WO2022264344 A1 WO 2022264344A1 JP 2021022939 W JP2021022939 W JP 2021022939W WO 2022264344 A1 WO2022264344 A1 WO 2022264344A1
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evaluation
business
strategy
business strategy
execution unit
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French (fr)
Japanese (ja)
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久知 山下
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久知 山下
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Priority to JP2021560910A priority Critical patent/JP7073029B1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

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  • the present invention relates to a business strategy evaluation device and a business strategy evaluation program that support the formulation of business strategies.
  • BSC balanced score card
  • the present invention has been devised to solve the above-mentioned problems. It enables a strategy planner to quickly formulate a high-quality strategy, and enables a strategy proposal decision-maker to improve the accuracy of judgment on the execution adoption or rejection of a strategy proposal. It is an object of the present invention to provide a business strategy evaluation device and a business strategy evaluation program that can be used.
  • a business strategy evaluation device teaches a data set including input parameters, which are explanatory variables relating to the success or failure of a plurality of business strategies, and objective variables indicating the probability of success of the business strategy.
  • an evaluation execution unit that inputs input parameters related to a business strategy to be evaluated to a learned model obtained by machine learning as data, and obtains evaluation results including an evaluation value that indicates the probability of success of the business strategy; and an evaluation result presentation unit for presenting the user with the evaluation result obtained by the evaluation execution unit.
  • the evaluation execution unit may include input parameters to be improved in order to improve the evaluation value in the evaluation results, and the evaluation result presentation unit may present the input parameters to be improved together with the evaluation value to the user.
  • the evaluation execution unit inputs the input parameters to the trained model to obtain a plurality of evaluation values corresponding to each of the plurality of frameworks used for analyzing the business strategy
  • the evaluation result presentation unit is the evaluation value of multiple frameworks, multiple intermediate evaluation values corresponding to each of the multiple frameworks, and arranging them in the standard order of consideration when proceeding with consideration when conducting business strategy evaluation. It is good to present it as an evaluation value.
  • the business strategy evaluation device causes the evaluation execution unit to re-evaluate the already evaluated business strategy at a predetermined timing using the input parameters updated from the time of the previous evaluation, and causes the evaluation result presentation unit to present the re-evaluation results. It is preferable to further include a follow-up evaluation management section.
  • a business strategy evaluation program is characterized by causing a computer to function as any of the above business strategy evaluation devices.
  • FIG. 1 is a schematic diagram showing a business strategy evaluation device 1 together with devices connected to the business strategy evaluation device 1;
  • FIG. 1 is a schematic diagram showing a hardware configuration of a business strategy evaluation device 1;
  • FIG. 1 is a functional block diagram of a business strategy evaluation device 1;
  • FIG. 10 is a diagram showing an example of presentation of evaluation results (coordinates in positioning strategy);
  • 4 is a flow chart showing an operation process of the business strategy evaluation device 1;
  • a business strategy evaluation device 1 according to an embodiment of the present invention will be described below with reference to the drawings.
  • FIG. 1 is a schematic diagram showing a business strategy evaluation device 1 according to an embodiment of the present invention together with an external information server 5 and a user terminal 6 connected to the business strategy evaluation device 1 via a network NW.
  • the business strategy evaluation device 1 is a device that evaluates a business proposed by a user based on information input by the user and information such as market and business environment, and outputs evaluation results.
  • FIG. 2 is a schematic diagram showing the hardware configuration of the business strategy evaluation device 1.
  • the business strategy evaluation device 1 is implemented as a computer, for example.
  • the business strategy evaluation device 1 includes a processor 101 , a RAM 102 , an HDD 103 , a graphics processing section 104 , an input interface 105 and a network interface 106 .
  • FIG. 2 shows an example in which the business strategy evaluation device 1 is implemented by one computer as a so-called stand-alone type.
  • the business strategy evaluation device 1 can also be realized by a mode in which one server computer and a plurality of client computers connected by LAN cooperate with each other.
  • the business strategy evaluation device 1 is entirely controlled by the processor 101 .
  • Processor 101 may be a multiprocessor.
  • the processor 101 is, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), GPU (Graphics Processing Unit), or PLD (Programmable Logic Device).
  • the processor 101 may be a combination of two or more of CPU, MPU, DSP, ASIC, and PLD.
  • the RAM 102 (Random Access Memory) is used as the main storage device of the business strategy evaluation device 1.
  • the RAM 102 temporarily stores at least part of an OS (Operating System) program and application programs to be executed by the processor 101 .
  • OS Operating System
  • Various data necessary for processing by the processor 101 are stored in the RAM 102 .
  • the HDD 103 (Hard Disk Drive) is used as an auxiliary storage device for the business strategy evaluation device 1.
  • the HDD 103 stores an OS program, application programs, and various data.
  • Other types of non-volatile storage devices such as SSDs (Solid State Drives) can also be used as auxiliary storage devices.
  • a display device 104 a is connected to the graphics processing unit 104 .
  • the graphics processing unit 104 displays an image on the screen of the display device 104a according to an instruction from the processor 101.
  • FIG. A liquid crystal display, an organic EL (Electro Luminescence) display, or the like is used as the display device 104a.
  • An input device 105 a is connected to the input interface 105 .
  • the input interface 105 transmits signals output from the input device 105 a to the processor 101 .
  • the input device 105a includes a keyboard, pointing device, and the like.
  • Pointing devices include mice, touch panels, tablets, touch pads, trackballs, and the like.
  • the network interface 106 realizes communication with external devices via the network NW. Communication via the network NW by the network interface 106 may be wired communication or wireless communication. As shown in FIG. 1, an external information server 5 is connected to the network, and the business strategy evaluation device 1 can communicate with the external information server 5 via the network NW. The business strategy evaluation device 1 may be configured to communicate with the user terminal 6 in addition to the external information server 5 . Then, the business strategy evaluation apparatus 1 may receive input of operations and information from a user terminal instead of input using the input interface 105 .
  • the external information server 5 is an external device that provides information indicating the external environment, etc. used by the business strategy evaluation device 1 .
  • the business strategy evaluation device 1 is configured to be able to acquire, for example, news, market information, company financial results information, law revision information, etc. from the external information server 5 .
  • the user terminal 6 is a terminal device used by the user of the business strategy evaluation device 1.
  • the user terminal 6 may be, for example, a computer, a mobile information terminal, or the like that can communicate with the business strategy evaluation apparatus 1 via the network NW.
  • the user uses the user terminal 6 to input operations and information for causing the business strategy evaluation device 1 to perform evaluation, and receives evaluation results, notifications, and the like from the business strategy evaluation device 1 .
  • the business strategy evaluation device 1 can be realized.
  • FIG. 3 shows a functional block diagram of the business strategy evaluation device 1.
  • the business strategy evaluation device 1 includes a machine learning execution unit 11, an evaluation execution unit 12, an evaluation result presentation unit 13, and a follow-up evaluation management unit . These functional blocks are realized by the processor 101 in the hardware configuration of the business strategy evaluation apparatus 1 described above executing programs stored in the RAM 102 and the HDD 103 .
  • the machine learning execution unit 11 generates a learned model (prediction model) M for evaluating business strategy.
  • the learned model M uses as input parameters various types of information that can be explanatory variables regarding the success or failure (win or lose) of the business, and outputs the success or failure (win or lose) of the business.
  • the input parameters of the trained model M are parameters related to the external environment from each perspective of PESTLE (Politics, Economy, Society, Technology, Legal, Ecology) , Parameters related to the 3Cs (Customer, Competitor, Company) centering on the company, 4Ps (Product (product / service), Price (price), Place (location / distribution) in sales ⁇ It would be good to include those corresponding to the analysis items in various frameworks used for business strategy analysis, such as parameters related to sales channels), promotion (sales promotion / advertisement)).
  • the input parameters may also include one or more strategy models that apply to the business (eg, type of initial strategy, middle stage strategy, scale strategy).
  • the output (that is, the evaluation result) of the trained model M should be a numerical value (score) representing the accuracy of the business strategy (a scale indicating the probability of the business succeeding (winning)) and coordinates in the positioning strategy.
  • the score is a numerical value from 0 to 1 for the output of the trained model M, and the closer to 1, the synergy with the existing assets occurs and exponential change (growth) occurs, leading to highly superior capability positioning. It is preferable that the probability of success (winning) is high as discontinuous growth, and the closer to 0, the probability of failure (losing) is high.
  • the trained model M has a plurality of (for example, 2 ) axes and outputs the coordinates of the current position of the positioning strategy that can be taken at the present time on the output axis, the position of the competition, the destination with a high winning rate, etc.
  • the current position of the coordinates of the positioning strategy that can be taken at the present time can be calculated from the current feature amount by PESTLE/3C/4P.
  • any one of the input parameters of the learned model M may be output as a significant axis in the positioning strategy.
  • the machine learning execution unit 11 generates data in which input parameters for various business models in the past and present are associated with the success or failure of the business (value 1 for successful business (win), value 0 for unsuccessful business (loss)). Machine learning and relearning of the learned model M are performed using the set as teacher data.
  • the learned model M preferably includes a decision tree for the relationship between the input parameter (i.e., explanatory variable) and the output value (i.e., objective variable). Carry out learning.
  • the trained model M may include a plurality of decision trees with different characteristics, and the final output may be obtained by majority decision or averaging the outputs of the plurality of decision trees.
  • the learned model M contains multiple decision trees, (some of them) may correspond to various frameworks used for business strategy analysis.
  • the trained model M may include a decision tree input with parameters corresponding to 3C analysis, a decision tree input with parameters corresponding to 4P analysis, and a decision tree input with parameters corresponding to SWOT analysis.
  • the final output of the learned model M should be a value obtained by combining the outputs of these decision trees.
  • the learned model M may also output the output values of individual decision trees in association with the information of the decision trees.
  • the trained model M be a model (so-called XAI (Explainable AI)) that allows humans to explain and understand the process leading to the prediction results and estimation results.
  • XAI Explainable AI
  • the configuration including the above-described decision tree is an example of a model in which the process leading to prediction results and estimation results can be explained and understood by humans.
  • the evaluation execution unit 12 inputs various input parameters related to the business strategy (business plan) to be evaluated (that is, input parameters of the same type as those used for learning) to the trained model generated by the machine learning execution unit 11. By doing so, we obtain the evaluation results for the business strategy.
  • the evaluation result presentation unit 13 presents to the user the evaluation results obtained by the evaluation execution unit 12 inputting the input parameters to the learned model M. For example, as shown in FIG. 4, the evaluation result presentation unit 13 presents a comprehensive evaluation value and a plurality of intermediate evaluation values according to the framework of the business strategy as evaluation results for the business strategy to be evaluated. It should be presented to the user.
  • the evaluation result presenting unit 13 provides the evaluation values of the various frameworks to the consulting company for the business strategy. It is good to present them as an intermediate evaluation value by arranging them in the standard order of consideration when conducting the evaluation. In this way, the user can recognize at a glance how far the study of the business strategy has progressed.
  • the evaluation result presentation unit 13 also displays input parameters (explanatory variables) that have a strong influence on the evaluation results, and input parameters that should be improved/changed to improve the evaluation value (main factors for low evaluation results). explanatory variables) and details of changes (for example, increase/decrease in numerical values, proposals for changes to the strategy model, etc.) should be presented along with the evaluation values.
  • the evaluation execution unit 12 performs evaluation when changing individual input parameters, and based on changes in evaluation results (comprehensive evaluation value and intermediate evaluation value), input parameters with high influence and It is good to know which input parameters should be improved. By doing so, it is possible to easily grasp the weak points, improvement points, etc. of the strategy.
  • the evaluation result presentation unit 13 plots the coordinates of the current position of the positioning strategy that can be taken at the present time on the axis included in the output of the trained model M and the coordinates of the destination with a high winning rate. It would be good to present a graph with In addition, it is preferable to plot and present the position of competition on the graph as well.
  • the evaluation result presentation unit 13 may present the evaluation result by a method other than displaying it on the display device 104a.
  • the evaluation result presenting unit 13 may print the evaluation result as a report, or store the electronic data of the report in a storage means such as the HDD 103 or the like.
  • the evaluation result presenting unit 13 may send the report to the user terminal 6 or other delivery destinations by e-mail or the like.
  • the follow-up evaluation management unit 14 causes the evaluation execution unit 12 to re-evaluate the already evaluated business strategy at a predetermined timing, and causes the evaluation result presentation unit 13 to present the re-evaluation results. At this time, the follow-up evaluation management unit 14 updates the input parameters from the time of the previous evaluation to perform the evaluation.
  • the input parameters that can be obtained from the external information server 5, such as information indicating the external environment, etc. the latest parameters are obtained and re-evaluated.
  • the input parameters that the user needs to input may be the same as the previous one, or may be configured to prompt the user to input again. Further, if there is no input within a predetermined period of time after prompting for input, the same parameters as the previous time may be used for re-evaluation.
  • the follow-up evaluation management unit 14 may, for example, store the timing of re-evaluation for each evaluated business strategy in a storage means such as the HDD 103, and cause the business strategy to be re-evaluated when the timing is reached. Alternatively, the follow-up evaluation management unit 14 may re-evaluate the already evaluated business strategy at a predetermined cycle/timing (for example, at the end of every month).
  • the follow-up evaluation management unit 14 may be configured to notify the user and/or a predetermined manager of an alert when the evaluation value of the business strategy after re-evaluation exceeds a predetermined value. In this way, at the timing when the evaluation of the business strategy proposed in the past has increased due to changes in the external environment, etc., the business strategy can be materialized and executed without missing an opportunity.
  • FIG. 6 is a flowchart showing the operation process of the business strategy evaluation device 1.
  • the business strategy evaluation device 1 At the start of operation, the business strategy evaluation device 1 generates a learned model M by performing machine learning with a large number of training data sets input by the machine learning execution unit 11 (step S01).
  • the business strategy evaluation device 1 may add a new data set of teacher data and perform re-learning as appropriate.
  • the evaluation execution unit 12 is caused to execute evaluation based on the input parameters, and output the evaluation result of the business strategy (step S02).
  • some or all of the input parameters may be obtained from the external information server 5 via the network NW.
  • the evaluation result presentation unit 13 presents the evaluation results output by the evaluation execution unit 12 to the user (step S03).
  • the follow-up evaluation management unit 14 is set to perform re-evaluation (step S04; Yes)
  • the process returns to step S02 to perform re-evaluation.
  • step S04 when it is the setting which does not implement re-evaluation (step S04; No), a series of processes are complete
  • the business strategy evaluation device and business strategy evaluation program of this embodiment can automatically generate a learning network with a structure suitable for the target.

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Abstract

Provided are a business strategy evaluation device and a business strategy evaluation program through which a strategy planner can rapidly plan a high-quality strategy and which can increase the determination accuracy regarding whether a strategy plan adoption determiner adopts the execution of a strategic plan.　The business strategy evaluation device comprises: an evaluation execution unit which inputs input parameters pertaining to a business strategy to be evaluated to a trained model, which is obtained by machine-learning by taking, as teaching data, a data set including the input parameters that are explanatory variables pertaining to the success or failure of a plurality of business strategies, and objective variables that indicate success possibilities of the business strategies, and obtains the evaluation result including an evaluation value that indicates a success possibility of the business strategy; and an evaluation result presentation unit which presents the evaluation result obtained by the evaluation execution unit to a user.

Description

事業戦略評価装置、および事業戦略評価プログラムBusiness strategy evaluation device and business strategy evaluation program
　本発明は、事業戦略の策定を支援する事業戦略評価装置および事業戦略評価プログラムに関する。 The present invention relates to a business strategy evaluation device and a business strategy evaluation program that support the formulation of business strategies.
　現在、多くの企業では、大学、ビジネススクール、コンサルテーション会社などの提唱する戦略立案手法を用いて、事業戦略を立案している。企業においては、的確な事業戦略を構築することが、企業の命運を左右するものであり、現状分析あるいは将来予測に基づいていかに的確な事業戦略を策定するかが普遍的な重要課題である。 Currently, many companies formulate business strategies using strategy planning methods advocated by universities, business schools, consulting companies, etc. In a company, establishing an accurate business strategy determines the fate of the company, and how to formulate an accurate business strategy based on analysis of the current situation or future prediction is a universally important issue.
　多くの場合、戦略立案手法は高度な知識や技術を要するものであるため、事業戦略の立案はコンサルテーション会社のコンサルタント等が対価を徴収しつつサービスとして提供されており、企業内の担当者等が手軽に事業戦略を立案することは容易ではない。事業戦略の立案を実施しやすくする仕組みとしてバランススコアカード（ＢＳＣ：Ｂａｌａｎｃｅｄ  Ｓｃｏｒｅ  Ｃａｒｄ）等が考案され、このようなバランススコアカードを利用したシステムも提案されている（例えば特許文献１を参照）。 In many cases, strategy planning methods require advanced knowledge and skills, so business strategy planning is provided as a service by consultants of consultation companies, etc. It is not easy to formulate a business strategy easily. A balanced score card (BSC) or the like has been devised as a mechanism to facilitate planning of business strategies, and a system using such a balanced score card has also been proposed (see, for example, Patent Document 1).
特開２００４－１１８６６８号公報JP-A-2004-118668
　しかし、従来の技術は、入力パラメータに基づいて、事業戦略を立案するものであり、立案された事業戦略や立案途中の事業戦略を評価・判断するものではかった。そのため、戦略の評価・判断は、経営者や戦略立案者の勘や経験に頼るところとなるが、経営者や戦略立案者にとって、立案した戦略や立案途中の戦略を評価・判断することは容易なことではなく、戦略案の採否判断の間違いや、戦略立案期間の長期化、戦略立案工数の増大を引き起こすという問題があった。 However, conventional technologies are designed to formulate business strategies based on input parameters, and are not designed to evaluate and judge business strategies that have been drafted or are in the process of being drafted. Therefore, the evaluation and judgment of strategies depend on the intuition and experience of managers and strategic planners, but it is easy for managers and strategic planners to evaluate and judge the strategies that have been formulated or are in the process of being formulated. Instead, there were problems such as making mistakes in determining the adoption of strategy proposals, prolonging the strategy planning period, and increasing the number of man-hours required to formulate strategies.
　本発明は上記問題点を解決するためになされたもので、戦略立案者が迅速に質の高い戦略を立案でき、戦略案採否判定者が、戦略案の実行採否の判断の精度を高めることができる事業戦略評価装置および事業戦略評価プログラムを提供することを目的とする。 The present invention has been devised to solve the above-mentioned problems. It enables a strategy planner to quickly formulate a high-quality strategy, and enables a strategy proposal decision-maker to improve the accuracy of judgment on the execution adoption or rejection of a strategy proposal. It is an object of the present invention to provide a business strategy evaluation device and a business strategy evaluation program that can be used.
　上記の課題を解決すべく、本発明に係る事業戦略評価装置は、複数の事業戦略についての成否に関する説明変数である入力パラメータと事業戦略の成功可能性を示す目的変数とを含むデータセットを教師データとして機械学習をすることによって得た学習済みモデルに対し、評価対象の事業戦略に関する入力パラメータを入力し、当該事業戦略の成功可能性を示す評価値を含んだ評価結果を得る評価実行部と、評価実行部が得た評価結果をユーザに提示する評価結果提示部と、を備える。 In order to solve the above problems, a business strategy evaluation device according to the present invention teaches a data set including input parameters, which are explanatory variables relating to the success or failure of a plurality of business strategies, and objective variables indicating the probability of success of the business strategy. an evaluation execution unit that inputs input parameters related to a business strategy to be evaluated to a learned model obtained by machine learning as data, and obtains evaluation results including an evaluation value that indicates the probability of success of the business strategy; and an evaluation result presentation unit for presenting the user with the evaluation result obtained by the evaluation execution unit.
　本発明では、評価実行部は、評価値を向上するために改善すべき入力パラメータを評価結果に含めるとよく、評価結果提示部は、改善すべき入力パラメータを評価値とともにユーザに提示するとよい。 In the present invention, the evaluation execution unit may include input parameters to be improved in order to improve the evaluation value in the evaluation results, and the evaluation result presentation unit may present the input parameters to be improved together with the evaluation value to the user.
　本発明では、評価実行部は、入力パラメータを学習済モデルに入力することで、事業戦略の分析に利用される複数のフレームワークのそれぞれに対応した複数の評価値を取得し、評価結果提示部は、複数のフレームワークの評価値を、複数のフレームワークのそれぞれに対応した複数の中間評価値を、事業戦略の評価を実施する際に検討を進める際の標準的な検討順に並べて中間的な評価値として提示するとよい。 In the present invention, the evaluation execution unit inputs the input parameters to the trained model to obtain a plurality of evaluation values corresponding to each of the plurality of frameworks used for analyzing the business strategy, and the evaluation result presentation unit is the evaluation value of multiple frameworks, multiple intermediate evaluation values corresponding to each of the multiple frameworks, and arranging them in the standard order of consideration when proceeding with consideration when conducting business strategy evaluation. It is good to present it as an evaluation value.
　事業戦略評価装置は、既に評価された事業戦略について、所定のタイミングで評価実行部に前回の評価時から更新された入力パラメータを用いて再評価させ、評価結果提示部に再評価結果を提示させる追跡評価管理部をさらに備えるとよい。 The business strategy evaluation device causes the evaluation execution unit to re-evaluate the already evaluated business strategy at a predetermined timing using the input parameters updated from the time of the previous evaluation, and causes the evaluation result presentation unit to present the re-evaluation results. It is preferable to further include a follow-up evaluation management section.
　本発明の他の例に係る事業戦略評価プログラムは、コンピュータを上記何れかの事業戦略評価装置として機能させることを特徴とする。 A business strategy evaluation program according to another example of the present invention is characterized by causing a computer to function as any of the above business strategy evaluation devices.
事業戦略評価装置１を、事業戦略評価装置１に接続される機器とともに示す模式図である。1 is a schematic diagram showing a business strategy evaluation device 1 together with devices connected to the business strategy evaluation device 1; FIG. 事業戦略評価装置１のハードウェア構成を示す模式図である。1 is a schematic diagram showing a hardware configuration of a business strategy evaluation device 1; FIG. 事業戦略評価装置１の機能ブロック図である。1 is a functional block diagram of a business strategy evaluation device 1; FIG. 評価結果の提示例（スコア）を示す図である。It is a figure which shows the example of presentation (score) of an evaluation result. 評価結果の提示例（ポジショニング戦略における座標）を示す図である。FIG. 10 is a diagram showing an example of presentation of evaluation results (coordinates in positioning strategy); 事業戦略評価装置１の運用プロセスを示すフローチャートである。4 is a flow chart showing an operation process of the business strategy evaluation device 1;
　以下、図面を参照して本発明の実施形態に係る事業戦略評価装置１について説明する。 A business strategy evaluation device 1 according to an embodiment of the present invention will be described below with reference to the drawings.
〔事業戦略評価装置のハードウェア構成〕
　図１は、本発明の実施形態に係る事業戦略評価装置１を、ネットワークＮＷを介して事業戦略評価装置１に接続される外部情報サーバ５およびユーザ端末６とともに示す模式図である。事業戦略評価装置１は、ユーザの入力する情報や市場、経営環境等の情報に基づいて、ユーザが立案する事業を評価し、評価結果を出力する装置である。 [Hardware configuration of business strategy evaluation device]
FIG. 1 is a schematic diagram showing a business strategy evaluation device 1 according to an embodiment of the present invention together with an external information server 5 and a user terminal 6 connected to the business strategy evaluation device 1 via a network NW. The business strategy evaluation device 1 is a device that evaluates a business proposed by a user based on information input by the user and information such as market and business environment, and outputs evaluation results.
　図２は、事業戦略評価装置１のハードウェア構成を示す模式図である。事業戦略評価装置１は、例えばコンピュータとして実現される。すなわち、事業戦略評価装置１は、プロセッサ１０１、ＲＡＭ１０２、ＨＤＤ１０３、グラフィック処理部１０４、入力インタフェース１０５、およびネットワークインターフェース１０６を備える。なお、図２は事業戦略評価装置１がいわゆるスタンドアローンタイプとして１台のコンピュータで実現される例を示したものであるが、ＬＡＮ等のネットワーク回線を介して相互に接続された複数のコンピュータ（例えばＬＡＮ接続された１台のサーバコンピュータと複数のクライアントコンピュータ）が協働する態様によって事業戦略評価装置１を実現することもできる。 FIG. 2 is a schematic diagram showing the hardware configuration of the business strategy evaluation device 1. FIG. The business strategy evaluation device 1 is implemented as a computer, for example. Specifically, the business strategy evaluation device 1 includes a processor 101 , a RAM 102 , an HDD 103 , a graphics processing section 104 , an input interface 105 and a network interface 106 . FIG. 2 shows an example in which the business strategy evaluation device 1 is implemented by one computer as a so-called stand-alone type. For example, the business strategy evaluation device 1 can also be realized by a mode in which one server computer and a plurality of client computers connected by LAN cooperate with each other.
　事業戦略評価装置１は、プロセッサ１０１によって装置全体が制御されている。プロセッサ１０１は、マルチプロセッサであってもよい。プロセッサ１０１は、例えばＣＰＵ（Central Processing Unit）、ＭＰＵ（Micro Processing Unit）、ＤＳＰ（Digital Signal Processor）、ＡＳＩＣ（Application Specific Integrated Circuit）、ＧＰＵ（Graphics Processing Unit）、またはＰＬＤ（Programmable Logic Device）である。また、プロセッサ１０１は、ＣＰＵ、ＭＰＵ、ＤＳＰ、ＡＳＩＣ、ＰＬＤのうちの２以上の要素の組み合わせであってもよい。 The business strategy evaluation device 1 is entirely controlled by the processor 101 . Processor 101 may be a multiprocessor. The processor 101 is, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), GPU (Graphics Processing Unit), or PLD (Programmable Logic Device). . Also, the processor 101 may be a combination of two or more of CPU, MPU, DSP, ASIC, and PLD.
　ＲＡＭ１０２（Random Access Memory）は、事業戦略評価装置１の主記憶装置として使用される。ＲＡＭ１０２には、プロセッサ１０１に実行させるＯＳ（Operating System）プログラムやアプリケーションプログラムの少なくとも一部が一時的に格納される。また、ＲＡＭ１０２には、プロセッサ１０１による処理に必要な各種データが格納される。 The RAM 102 (Random Access Memory) is used as the main storage device of the business strategy evaluation device 1. The RAM 102 temporarily stores at least part of an OS (Operating System) program and application programs to be executed by the processor 101 . Various data necessary for processing by the processor 101 are stored in the RAM 102 .
　ＨＤＤ１０３（Hard Disk Drive）は、事業戦略評価装置１の補助記憶装置として使用される。ＨＤＤ１０３には、ＯＳプログラム、アプリケーションプログラム、および各種データが格納される。なお、補助記憶装置としては、ＳＳＤ（Solid State Drive）などの他の種類の不揮発性記憶装置を使用することもできる。 The HDD 103 (Hard Disk Drive) is used as an auxiliary storage device for the business strategy evaluation device 1. The HDD 103 stores an OS program, application programs, and various data. Other types of non-volatile storage devices such as SSDs (Solid State Drives) can also be used as auxiliary storage devices.
　グラフィック処理部１０４には、表示装置１０４ａが接続されている。グラフィック処理部１０４は、プロセッサ１０１からの命令に従って、画像を表示装置１０４ａの画面に表示させる。表示装置１０４ａとしては、液晶ディスプレイや有機ＥＬ（Electro Luminescence）ディスプレイなどが用いられる。 A display device 104 a is connected to the graphics processing unit 104 . The graphics processing unit 104 displays an image on the screen of the display device 104a according to an instruction from the processor 101. FIG. A liquid crystal display, an organic EL (Electro Luminescence) display, or the like is used as the display device 104a.
　入力インタフェース１０５には、入力装置１０５ａが接続されている。入力インタフェース１０５は、入力装置１０５ａから出力される信号をプロセッサ１０１に送信する。入力装置１０５ａとしては、キーボードやポインティングデバイスなどがある。ポインティングデバイスとしては、マウス、タッチパネル、タブレット、タッチパッド、トラックボールなどがある。 An input device 105 a is connected to the input interface 105 . The input interface 105 transmits signals output from the input device 105 a to the processor 101 . The input device 105a includes a keyboard, pointing device, and the like. Pointing devices include mice, touch panels, tablets, touch pads, trackballs, and the like.
　ネットワークインターフェース１０６は、ネットワークＮＷを介した外部機器との通信を実現する。ネットワークインターフェース１０６によるネットワークＮＷを介した通信は有線通信であってもよいし無線通信であってもよい。なお、図１に示すようにネットワークには外部情報サーバ５が差接続されており、事業戦略評価装置１はネットワークＮＷを介して外部情報サーバ５と通信を行うことができる。事業戦略評価装置１は、外部情報サーバ５の他、ユーザ端末６と通信を行うように構成してもよい。そして、事業戦略評価装置１は、入力インタフェース１０５を用いた入力に代えてユーザ端末から操作や情報の入力を受けるようにしてもよい。 The network interface 106 realizes communication with external devices via the network NW. Communication via the network NW by the network interface 106 may be wired communication or wireless communication. As shown in FIG. 1, an external information server 5 is connected to the network, and the business strategy evaluation device 1 can communicate with the external information server 5 via the network NW. The business strategy evaluation device 1 may be configured to communicate with the user terminal 6 in addition to the external information server 5 . Then, the business strategy evaluation apparatus 1 may receive input of operations and information from a user terminal instead of input using the input interface 105 .
　外部情報サーバ５は、事業戦略評価装置１が利用する外部環境等を示す情報を提供する外部装置である。事業戦略評価装置１は、外部情報サーバ５から、例えば、ニュース、マーケット情報、企業の決算情報、法改正情報等を取得できるように構成される。 The external information server 5 is an external device that provides information indicating the external environment, etc. used by the business strategy evaluation device 1 . The business strategy evaluation device 1 is configured to be able to acquire, for example, news, market information, company financial results information, law revision information, etc. from the external information server 5 .
　ユーザ端末６は、事業戦略評価装置１の利用者が使用する端末装置である。ユーザ端末６は、例えば、ネットワークＮＷを介して事業戦略評価装置１と通信することができるコンピュータ、携帯情報端末等とするとよい。利用者はユーザ端末６を用いて事業戦略評価装置１に評価を実行させるための操作や情報を入力したり、事業戦略評価装置１から評価結果や通知等を受け取る。 The user terminal 6 is a terminal device used by the user of the business strategy evaluation device 1. The user terminal 6 may be, for example, a computer, a mobile information terminal, or the like that can communicate with the business strategy evaluation apparatus 1 via the network NW. The user uses the user terminal 6 to input operations and information for causing the business strategy evaluation device 1 to perform evaluation, and receives evaluation results, notifications, and the like from the business strategy evaluation device 1 .
　以上のようなハードウェア構成によって、事業戦略評価装置１を実現することができる。 With the above hardware configuration, the business strategy evaluation device 1 can be realized.
〔事業戦略評価装置１の機能ブロック〕
　図３は事業戦略評価装置１の機能ブロック図を示している。
　事業戦略評価装置１は、機械学習実行部１１、評価実行部１２、評価結果提示部１３、追跡評価管理部１４を備える。これらの各機能ブロックは、上述の事業戦略評価装置１のハードウェア構成におけるプロセッサ１０１が、ＲＡＭ１０２やＨＤＤ１０３に格納されたプログラムを実行することにより実現される。 [Functional Blocks of Business Strategy Evaluation Device 1]
FIG. 3 shows a functional block diagram of the business strategy evaluation device 1. As shown in FIG.
The business strategy evaluation device 1 includes a machine learning execution unit 11, an evaluation execution unit 12, an evaluation result presentation unit 13, and a follow-up evaluation management unit . These functional blocks are realized by the processor 101 in the hardware configuration of the business strategy evaluation apparatus 1 described above executing programs stored in the RAM 102 and the HDD 103 .
　機械学習実行部１１は、事業戦略を評価する学習済モデル（予測モデル）Ｍを生成する。学習済モデルＭは、事業の成否（勝敗）について説明変数になり得る各種の情報を入力パラメータとし、事業の成否（勝敗）を出力とする。学習済モデルＭの入力パラメータは、ＰＥＳＴＬＥ（Politics（政治）、Economy（経済）、Society（社会）、Technology（技術）、Legal（法律）、Ecology（環境））の各観点での外部環境に関するパラメータ、自社を中心とした３Ｃ（Customer(市場・顧客）、Competitor（競合）、Company（自社）)に関するパラメータ、販売にあたっての４Ｐ（Product（製品・サービス）、Price（価格）、Place（立地・流通・販路）、Promotion（販促・広告））に関するパラメータ等、事業戦略の分析に利用される各種フレームワークにおける分析項目に対応したものを含むとよい。また、事業に適用される１つまたは複数の戦略モデル（例えば初期戦略、中盤戦略、スケール戦略の種類）を入力パラメータに含めるとよい。また、外部環境を表す情報を入力パラメータとして取り入れるべく、ニュース等の情報を入力パラメータに含めてもよい。また、これらのパラメータを次元圧縮して特徴量化したものを入力パラメータとしてもよい。具体的には、機械学習実行部１１は、投資による事業価値最大化Ｖ＝（現在時点での既存事業アセット＋競争力の源泉（模倣できない優位性））Ａ×（事業意思決定速度＋人材スキル＋Ａとのシナジー戦略精度）Ｓの２乗をValueNet・PolicyNetのベースにして強化学習を行うとよい。 The machine learning execution unit 11 generates a learned model (prediction model) M for evaluating business strategy. The learned model M uses as input parameters various types of information that can be explanatory variables regarding the success or failure (win or lose) of the business, and outputs the success or failure (win or lose) of the business. The input parameters of the trained model M are parameters related to the external environment from each perspective of PESTLE (Politics, Economy, Society, Technology, Legal, Ecology) , Parameters related to the 3Cs (Customer, Competitor, Company) centering on the company, 4Ps (Product (product / service), Price (price), Place (location / distribution) in sales・It would be good to include those corresponding to the analysis items in various frameworks used for business strategy analysis, such as parameters related to sales channels), promotion (sales promotion / advertisement)). The input parameters may also include one or more strategy models that apply to the business (eg, type of initial strategy, middle stage strategy, scale strategy). Also, information such as news may be included in the input parameters in order to incorporate information representing the external environment as the input parameters. Also, the input parameters may be obtained by compressing the dimensions of these parameters to convert them into feature quantities. Specifically, the machine learning execution unit 11 maximizes business value through investment V = (existing business assets at the present time + source of competitiveness (advantages that cannot be imitated)) A × (business decision-making speed + personnel skills + Synergy strategy accuracy with A) It is preferable to perform reinforcement learning based on ValueNet/PolicyNet.
　また、学習済モデルＭの出力（つまり評価結果）は、事業戦略の精度（事業が成功する（勝つ）可能性を示す尺度）を表す数値（スコア）、およびポジショニング戦略における座標とするとよい。例えば、スコアは、学習済モデルＭの出力を０から１までの数値とし、１に近いほど既存アセットとシナジーが起きて指数関数的な変化（成長）が発生し優位性の高いケイパビリティ・ポジショニングをとり非連続成長として成功する（勝つ）可能性が高く、０に近いほど失敗する（負ける）可能性が高いことを表すようにするとよい。 In addition, the output (that is, the evaluation result) of the trained model M should be a numerical value (score) representing the accuracy of the business strategy (a scale indicating the probability of the business succeeding (winning)) and coordinates in the positioning strategy. For example, the score is a numerical value from 0 to 1 for the output of the trained model M, and the closer to 1, the synergy with the existing assets occurs and exponential change (growth) occurs, leading to highly superior capability positioning. It is preferable that the probability of success (winning) is high as discontinuous growth, and the closer to 0, the probability of failure (losing) is high.
　また、ポジショニング戦略における座標を提示すべく、学習済モデルＭは、ポジショニング戦略において有意な（つまり現在地から目的地への遷移や競合との差別化のために重要度の高い）複数の（例えば２つの）軸を探索して出力するとともに、出力された軸における現時点でとりうるポジショニング戦略の現在地、競合の位置、勝率の高い目的地等の座標を出力する。つまり、時流と自社アセットとのシナジーを生み、経済活動しての投資による事業化によりイノベーション（指数関数的な変化（成長）が発生する状態＝シナジー）を実現する条件を提示する。現時点でとりうるポジショニング戦略の座標の現在地については、現状のＰＥＳＴＬＥ／３Ｃ／４Ｐによる特徴量から算出するとよい。軸の具体例としては、ポジショニング戦略において有意な軸として、学習済モデルＭの入力パラメータの何れかを出力するとよい。 In addition, in order to present the coordinates in the positioning strategy, the trained model M has a plurality of (for example, 2 ) axes and outputs the coordinates of the current position of the positioning strategy that can be taken at the present time on the output axis, the position of the competition, the destination with a high winning rate, etc. In other words, it presents the conditions for creating synergies between current trends and company assets, and for realizing innovation (state where exponential change (growth) occurs = synergy) through commercialization through investment in economic activities. The current position of the coordinates of the positioning strategy that can be taken at the present time can be calculated from the current feature amount by PESTLE/3C/4P. As a specific example of the axis, any one of the input parameters of the learned model M may be output as a significant axis in the positioning strategy.
　機械学習実行部１１は、過去及び現状の様々な事業モデルについての入力パラメータと事業の成否（成功した事業（勝ち）について値１、失敗した事業（負け）について値０）とを対応付けたデータセットを教師データとして、学習済モデルＭの機械学習・再学習を実施する。 The machine learning execution unit 11 generates data in which input parameters for various business models in the past and present are associated with the success or failure of the business (value 1 for successful business (win), value 0 for unsuccessful business (loss)). Machine learning and relearning of the learned model M are performed using the set as teacher data.
　学習済モデルＭは、入力パラメータ（すなわち説明変数）と出力値（すなわち目的変数）との関係についての決定木を含んで構成されるとよく、学習済モデルＭは、この決定木についての決定木学習を実施する。学習済モデルＭは、特性の異なる複数の決定木を含んで構成されてもよく、複数の決定木の出力を多数決や平均を取ることで最終的な出力としてもよい。 The learned model M preferably includes a decision tree for the relationship between the input parameter (i.e., explanatory variable) and the output value (i.e., objective variable). Carry out learning. The trained model M may include a plurality of decision trees with different characteristics, and the final output may be obtained by majority decision or averaging the outputs of the plurality of decision trees.
　学習済モデルＭが複数の決定木を含む場合には、個々の決定木（のいくつか）が、事業戦略の分析に利用される各種フレームワークに対応するようにするとよい。例えば、学習済モデルＭが、３Ｃ分析に対応するパラメータを入力とする決定木、４Ｐ分析に対応するパラメータを入力とする決定木、ＳＷＯＴ分析に対応するパラメータを入力とする決定木等を備えるように構成し、これらの決定木の出力を総合した値を学習済モデルＭの最終出力とするとよい。学習済モデルＭは、個々の決定木の出力値についても、その決定木の情報と紐づけて出力するとよい。 If the learned model M contains multiple decision trees, (some of them) may correspond to various frameworks used for business strategy analysis. For example, the trained model M may include a decision tree input with parameters corresponding to 3C analysis, a decision tree input with parameters corresponding to 4P analysis, and a decision tree input with parameters corresponding to SWOT analysis. , and the final output of the learned model M should be a value obtained by combining the outputs of these decision trees. The learned model M may also output the output values of individual decision trees in association with the information of the decision trees.
　学習済モデルＭは、予測結果や推定結果に至るプロセスが人間によって説明・理解可能なモデル（いわゆるＸＡＩ（Explainable AI））とすることが好ましい。上述の決定木を含む構成は人間によって予測結果や推定結果に至るプロセスが人間によって説明・理解可能なモデルの一例である。 It is preferable that the trained model M be a model (so-called XAI (Explainable AI)) that allows humans to explain and understand the process leading to the prediction results and estimation results. The configuration including the above-described decision tree is an example of a model in which the process leading to prediction results and estimation results can be explained and understood by humans.
　評価実行部１２は、機械学習実行部１１が生成した学習済みモデルに対し、評価対象の事業戦略（事業計画）に関する各種入力パラメータ（すなわち、学習に用いたのと同種の入力パラメータ）を入力することで、当該事業戦略に対する評価結果を得る。 The evaluation execution unit 12 inputs various input parameters related to the business strategy (business plan) to be evaluated (that is, input parameters of the same type as those used for learning) to the trained model generated by the machine learning execution unit 11. By doing so, we obtain the evaluation results for the business strategy.
　評価結果提示部１３は、評価実行部１２が入力パラメータを学習済モデルＭに入力することによって得た評価結果をユーザに提示する。評価結果提示部１３は、例えば図４に示すように、総合的な評価値と、事業戦略のフレームワークに応じた複数の中間的な評価値とを、評価対象の事業戦略についての評価結果としてユーザに提示するとよい。学習済モデルＭが、事業戦略の分析に利用される各種フレームワークに対応した複数の評価値を出力する場合、評価結果提示部１３は、各種フレームワークの評価値を、コンサルテーション会社が事業戦略の評価を実施する際に検討を進める標準的な順に並べて中間的な評価値として提示するとよい。このようにすれば、ユーザは事業戦略の検討がどの程度まで進んでいるかを一見して認識することができる。また、評価結果提示部１３は、評価結果に影響力の高い入力パラメータ（説明変数）、評価値を向上するために改善・変更すべき入力パラメータ（評価結果が低く算出される主要因となっている説明変数）とその変更の内容（例えば、数値の増減、戦略モデルの変更案等）を評価値とともに提示するとよい。例えば、評価実行部１２が、個々の入力パラメータを変化させた場合の評価を行い、評価結果（総合的な評価値や中間的な評価値）の変化に基づいて、影響力の高い入力パラメータや改善すべき入力パラメータを把握するとよい。このようにすれば、戦略の弱点・改善点等を容易に把握することができる。 The evaluation result presentation unit 13 presents to the user the evaluation results obtained by the evaluation execution unit 12 inputting the input parameters to the learned model M. For example, as shown in FIG. 4, the evaluation result presentation unit 13 presents a comprehensive evaluation value and a plurality of intermediate evaluation values according to the framework of the business strategy as evaluation results for the business strategy to be evaluated. It should be presented to the user. When the trained model M outputs a plurality of evaluation values corresponding to various frameworks used for business strategy analysis, the evaluation result presenting unit 13 provides the evaluation values of the various frameworks to the consulting company for the business strategy. It is good to present them as an intermediate evaluation value by arranging them in the standard order of consideration when conducting the evaluation. In this way, the user can recognize at a glance how far the study of the business strategy has progressed. The evaluation result presentation unit 13 also displays input parameters (explanatory variables) that have a strong influence on the evaluation results, and input parameters that should be improved/changed to improve the evaluation value (main factors for low evaluation results). explanatory variables) and details of changes (for example, increase/decrease in numerical values, proposals for changes to the strategy model, etc.) should be presented along with the evaluation values. For example, the evaluation execution unit 12 performs evaluation when changing individual input parameters, and based on changes in evaluation results (comprehensive evaluation value and intermediate evaluation value), input parameters with high influence and It is good to know which input parameters should be improved. By doing so, it is possible to easily grasp the weak points, improvement points, etc. of the strategy.
　また、図５に示すように、評価結果提示部１３は、学習済モデルＭの出力に含まれる軸について、軸における現時点でとりうるポジショニング戦略の現在地の座標および勝率の高い目的地の座標をプロットしたグラフを提示するとよい。また、当該グラフには競合の位置も併せてプロットして提示するとよい。 In addition, as shown in FIG. 5, the evaluation result presentation unit 13 plots the coordinates of the current position of the positioning strategy that can be taken at the present time on the axis included in the output of the trained model M and the coordinates of the destination with a high winning rate. It would be good to present a graph with In addition, it is preferable to plot and present the position of competition on the graph as well.
　評価結果提示部１３がユーザに評価結果を提示する方法としては、例えば、評価結果を表示装置１０４ａに表示するとよい。また、評価結果提示部１３は、評価結果を表示装置１０４ａに表示する以外の方法で提示してもよい。例えば、評価結果提示部１３は、評価結果を報告書として印刷したり、報告書の電子データをＨＤＤ１０３等の記憶手段に格納したりしてもよい。また、評価結果提示部１３は、報告書を電子メール等によりユーザ端末６やその他の配信先に送付してもよい。 As a method for the evaluation result presentation unit 13 to present the evaluation results to the user, for example, the evaluation results may be displayed on the display device 104a. Moreover, the evaluation result presenting unit 13 may present the evaluation result by a method other than displaying it on the display device 104a. For example, the evaluation result presenting unit 13 may print the evaluation result as a report, or store the electronic data of the report in a storage means such as the HDD 103 or the like. Also, the evaluation result presenting unit 13 may send the report to the user terminal 6 or other delivery destinations by e-mail or the like.
　追跡評価管理部１４は、既に評価された事業戦略について、所定のタイミングで評価実行部１２に再評価させ、評価結果提示部１３に再評価結果を提示させる。この際、追跡評価管理部１４は、前回の評価時から入力パラメータを更新して評価を実施させる。外部環境等を示す情報等の外部情報サーバ５から取得可能な入力パラメータについては最新のものを取得して再評価をさせる。また、ユーザが入力する必要のある入力パラメータについては、前回と同じものを使ってもよいし、再度入力を促すように構成してもよい。また、入力を促してから所定期間経過しても入力が無い場合に前回と同じパラメータを用いて再評価をするようにしてもよい。追跡評価管理部１４は、例えば、個々の評価済みの事業戦略についての再評価のタイミングをＨＤＤ１０３等の記憶手段に格納しておき、当該タイミングに達した事業戦略について再評価を実施させるとよい。あるいは、追跡評価管理部１４は予め定められた周期・タイミング（例えば毎月末等）で既に評価された事業戦略について再評価を実施させてもよい。 The follow-up evaluation management unit 14 causes the evaluation execution unit 12 to re-evaluate the already evaluated business strategy at a predetermined timing, and causes the evaluation result presentation unit 13 to present the re-evaluation results. At this time, the follow-up evaluation management unit 14 updates the input parameters from the time of the previous evaluation to perform the evaluation. As for the input parameters that can be obtained from the external information server 5, such as information indicating the external environment, etc., the latest parameters are obtained and re-evaluated. Also, the input parameters that the user needs to input may be the same as the previous one, or may be configured to prompt the user to input again. Further, if there is no input within a predetermined period of time after prompting for input, the same parameters as the previous time may be used for re-evaluation. The follow-up evaluation management unit 14 may, for example, store the timing of re-evaluation for each evaluated business strategy in a storage means such as the HDD 103, and cause the business strategy to be re-evaluated when the timing is reached. Alternatively, the follow-up evaluation management unit 14 may re-evaluate the already evaluated business strategy at a predetermined cycle/timing (for example, at the end of every month).
　また、追跡評価管理部１４は、再評価による事業戦略の評価値が所定値を超えた場合に、ユーザおよび／または所定の管理者等にアラートを通知するように構成するとよい。このようにすれば、外部環境の変化等により過去に立案した事業戦略の評価が高まったタイミングで、機を逃さずに事業戦略を具体化・実行することができる。 In addition, the follow-up evaluation management unit 14 may be configured to notify the user and/or a predetermined manager of an alert when the evaluation value of the business strategy after re-evaluation exceeds a predetermined value. In this way, at the timing when the evaluation of the business strategy proposed in the past has increased due to changes in the external environment, etc., the business strategy can be materialized and executed without missing an opportunity.
　続いて、事業戦略評価装置１の動作を説明する。 Next, the operation of the business strategy evaluation device 1 will be explained.
　図６は、事業戦略評価装置１の運用プロセスを示すフローチャートである。 FIG. 6 is a flowchart showing the operation process of the business strategy evaluation device 1.
　運用開始時において、事業戦略評価装置１は、機械学習実行部１１により教師データのデータセットを多数入力した機械学習を行うことにより学習済モデルＭを生成する（ステップＳ０１）。なお、事業戦略評価装置１は、新たな教師データのデータセットを追加して適宜再学習を実行してもよい。 At the start of operation, the business strategy evaluation device 1 generates a learned model M by performing machine learning with a large number of training data sets input by the machine learning execution unit 11 (step S01). The business strategy evaluation device 1 may add a new data set of teacher data and perform re-learning as appropriate.
　続いて、評価対象の事業戦略についての入力パラメータを入力し、評価実行部１２に入力パラメータに基づく評価を実行させ、事業戦略の評価結果を出力させる（ステップＳ０２）。このとき一部または全部の入力パラメータをネットワークＮＷを介して外部情報サーバ５から取得してもよい。 Subsequently, the input parameters for the business strategy to be evaluated are input, the evaluation execution unit 12 is caused to execute evaluation based on the input parameters, and output the evaluation result of the business strategy (step S02). At this time, some or all of the input parameters may be obtained from the external information server 5 via the network NW.
　続いて、評価実行部１２が出力した評価結果を、評価結果提示部１３がユーザに提示する（ステップＳ０３）。その後、追跡評価管理部１４が再評価を実施する設定の場合（ステップＳ０４；Ｙｅｓ）、再評価の実施を指示するタイミングまで待機した後（ステップＳ０５）、処理をステップＳ０２に戻して再評価を実施し、評価結果を提示する。また、再評価を実施しない設定である場合(ステップＳ０４；Ｎｏ)には、一連の処理を終了する。 Subsequently, the evaluation result presentation unit 13 presents the evaluation results output by the evaluation execution unit 12 to the user (step S03). After that, if the follow-up evaluation management unit 14 is set to perform re-evaluation (step S04; Yes), after waiting until the timing of instructing the implementation of re-evaluation (step S05), the process returns to step S02 to perform re-evaluation. Implement and present the evaluation results. Moreover, when it is the setting which does not implement re-evaluation (step S04; No), a series of processes are complete|finished.
　以上で説明した構成及び手順により、本実施形態の事業戦略評価装置および事業戦略評価プログラムは、対象に適した構造の学習ネットワークを自動的に生成することができる。 With the configuration and procedures described above, the business strategy evaluation device and business strategy evaluation program of this embodiment can automatically generate a learning network with a structure suitable for the target.
　なお、上記に本実施形態を説明したが、本発明はこれらの例に限定されるものではない。また、前述の実施形態に対して、当業者が適宜、構成要素の追加、削除、設計変更を行ったものや、各実施形態の特徴を適宜組み合わせたものも、本発明の要旨を備えている限り、本発明の範囲に包含される。 Although the present embodiment has been described above, the present invention is not limited to these examples. In addition, additions, deletions, and design changes made by those skilled in the art to the above-described embodiments, and combinations of the features of each embodiment as appropriate also include the gist of the present invention. as long as it is within the scope of the present invention.
１　事業戦略評価装置
１１　機械学習実行部
１２　評価実行部
１３　評価結果提示部
１４　追跡評価管理部 1 Business strategy evaluation device 11 Machine learning execution unit 12 Evaluation execution unit 13 Evaluation result presentation unit 14 Tracking evaluation management unit

Claims (7)

  1. 　複数の事業戦略についての成否に関する説明変数である入力パラメータと事業戦略の成功可能性を示す目的変数とを含むデータセットを教師データとして機械学習をすることによって得た学習済みモデルに対し、評価対象の事業戦略に関する前記入力パラメータを入力し、当該事業戦略の成功可能性を示す評価値を含んだ評価結果を得る評価実行部と、
    　前記評価実行部が得た評価結果をユーザに提示する評価結果提示部と、
    　を備える事業戦略評価装置。     Evaluation target for a trained model obtained by machine learning using a data set containing input parameters, which are explanatory variables related to the success or failure of multiple business strategies, and objective variables, which indicate the probability of success of business strategies, as training data. an evaluation execution unit that inputs the input parameters related to the business strategy of and obtains an evaluation result including an evaluation value that indicates the probability of success of the business strategy;
    an evaluation result presentation unit that presents the user with the evaluation result obtained by the evaluation execution unit;
    A business strategy evaluation device with
  2. 　前記評価実行部は、評価値を向上するために改善すべき入力パラメータを評価結果に含め、
    　前記評価結果提示部は、前記改善すべき入力パラメータを前記評価値とともに前記ユーザに提示することを特徴とする請求項１に記載の事業戦略評価装置。     The evaluation execution unit includes an input parameter to be improved in order to improve the evaluation value in the evaluation result,
    2. The business strategy evaluation apparatus according to claim 1, wherein said evaluation result presenting unit presents said input parameter to be improved together with said evaluation value to said user.
  3. 　前記評価実行部は、前記入力パラメータを前記学習済モデルに入力することで、事業戦略の分析に利用される複数のフレームワークのそれぞれに対応した複数の評価値を取得し、
    　前記評価結果提示部は、前記複数のフレームワークの評価値を、複数のフレームワークのそれぞれに対応した複数の中間評価値を、事業戦略の評価を実施する際に検討を進める際の標準的な検討順に並べて中間的な評価値として提示することを特徴とする請求項１または２に記載の事業戦略評価装置。     The evaluation execution unit acquires a plurality of evaluation values corresponding to each of a plurality of frameworks used for business strategy analysis by inputting the input parameters into the trained model,
    The evaluation result presentation unit generates evaluation values of the plurality of frameworks, a plurality of intermediate evaluation values corresponding to each of the plurality of frameworks, and a standard standard when proceeding with consideration when evaluating business strategies. 3. The business strategy evaluation device according to claim 1, wherein the evaluation values are arranged in order of examination and presented as an intermediate evaluation value.
  4. 　前記評価実行部は、ポジショニング戦略において有意な複数の軸と、当該軸における現時点でとりうるポジショニング戦略の現在地の座標および勝率の高い目的地の座標を評価結果に含め、
    　前記評価結果提示部は、前記軸における現時点でとりうるポジショニング戦略の現在地の座標および勝率の高い目的地の座標をプロットしたグラフを提示することを特徴とする請求項１に記載の事業戦略評価装置。     The evaluation execution unit includes a plurality of significant axes in the positioning strategy, the coordinates of the current position of the positioning strategy that can be taken at the present time on the axes, and the coordinates of the destination with a high winning rate in the evaluation result,
    2. The business strategy evaluation device according to claim 1, wherein the evaluation result presentation unit presents a graph plotting the coordinates of the current position of the positioning strategy that can be taken at the present time on the axis and the coordinates of the destination with a high winning rate. .
  5. 　前記評価結果提示部は、前記グラフに競合の位置を併せてプロットして提示することを特徴とする請求項４に記載の事業戦略評価装置。 The business strategy evaluation device according to claim 4, wherein the evaluation result presentation unit plots and presents the position of competition on the graph as well.
  6. 　既に評価された事業戦略について、所定のタイミングで前記評価実行部に前回の評価時から更新された入力パラメータを用いて再評価させ、前記評価結果提示部に再評価結果を提示させる追跡評価管理部をさらに備えることを特徴とする請求項１から５の何れか１項に記載の事業戦略評価装置。 A follow-up evaluation management unit that causes the evaluation execution unit to re-evaluate an already evaluated business strategy at a predetermined timing using input parameters updated from the time of the previous evaluation, and causes the evaluation result presentation unit to present the re-evaluation results. The business strategy evaluation device according to any one of claims 1 to 5, further comprising:
  7. 　コンピュータを請求項１から６の何れか１項に記載の事業戦略評価装置として機能させる事業戦略評価プログラム。

          A business strategy evaluation program that causes a computer to function as the business strategy evaluation device according to any one of claims 1 to 6.
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