WO2022264344A1 - 事業戦略評価装置、および事業戦略評価プログラム - Google Patents

事業戦略評価装置、および事業戦略評価プログラム Download PDF

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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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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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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PCT/JP2021/022939 2021-06-16 2021-06-16 事業戦略評価装置、および事業戦略評価プログラム Ceased WO2022264344A1 (ja)

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PCT/JP2021/022939 WO2022264344A1 (ja) 2021-06-16 2021-06-16 事業戦略評価装置、および事業戦略評価プログラム

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WO2025177381A1 (ja) * 2024-02-19 2025-08-28 株式会社LegalOn Technologies 処理装置、処理プログラム及び処理方法

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JP2025141725A (ja) * 2024-03-14 2025-09-29 株式会社アイアイビー 企業の事業化計画や学術論文に対して、生成ａｉと事業化評価スコアを用いて事業化の判定評価を行う方法及びこの方法を用いたクラウド対応型の事業化判定支援システム

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JP2004185539A (ja) * 2002-12-06 2004-07-02 Yunitekku:Kk 商圏分析システム、方法、プログラム、及び記録媒体
JP6848230B2 (ja) * 2016-07-01 2021-03-24 日本電気株式会社 処理装置、処理方法及びプログラム

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Publication number Priority date Publication date Assignee Title
JP2004185539A (ja) * 2002-12-06 2004-07-02 Yunitekku:Kk 商圏分析システム、方法、プログラム、及び記録媒体
JP6848230B2 (ja) * 2016-07-01 2021-03-24 日本電気株式会社 処理装置、処理方法及びプログラム

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025177381A1 (ja) * 2024-02-19 2025-08-28 株式会社LegalOn Technologies 処理装置、処理プログラム及び処理方法

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