JP7073029B1 - Business strategy evaluation device and business strategy evaluation program - Google Patents

Abstract

It provides a business strategy evaluation device and a business strategy evaluation program that enable a strategy planner to quickly formulate a high-quality strategy and improve the accuracy of a strategy plan adoption / rejection decision.
The business strategy evaluation device has been learned by performing machine learning using a dataset containing input parameters, which are explanatory variables for success or failure of multiple business strategies, and objective variables, which indicate the success potential of the business strategy, as teacher data. The evaluation execution unit that inputs the input parameters related to the business strategy to be evaluated to the model and obtains the evaluation result including the evaluation value indicating the success possibility of the business strategy, and the evaluation execution unit obtains the evaluation result to the user. It is provided with an evaluation result presentation unit to be presented.

Description

The present invention relates to a business strategy evaluation device and a business strategy evaluation program that support the formulation of a business strategy.

Currently, many companies are planning their business strategies using the strategy planning methods advocated by universities, business schools, consulting companies, and so on. In a company, building an accurate business strategy determines the fate of the company, and it is a universally important issue to formulate an accurate business strategy based on current situation analysis or future forecasts.

In many cases, strategy planning methods require advanced knowledge and skills, so business strategy planning is provided as a service by consultants of consulting companies, etc. while collecting compensation, and the person in charge within the company, etc. It is not easy to formulate a business strategy. A Balanced Scorecard (BSC) and the like have been devised as a mechanism for facilitating the planning of business strategies, and a system using such a Balanced Scorecard has also been proposed (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2004-118668

However, the conventional technique formulates a business strategy based on input parameters, and does not evaluate and judge a business strategy that has been formulated or a business strategy that is in the process of being formulated. Therefore, the evaluation and judgment of a strategy depends on the intuition and experience of the manager and the strategy planner, but it is easy for the manager and the strategy planner to evaluate and judge the strategy planned and the strategy in the middle of planning. Instead, there were problems such as mistakes in the decision to adopt or reject the strategy plan, lengthening of the strategy planning period, and an increase in the number of steps for strategy planning.

The present invention has been made to solve the above problems, and it is possible for a strategy planner to quickly formulate a high-quality strategy, and for a strategy plan adopter / rejection judge to improve the accuracy of a decision on whether or not to execute a strategy plan. The purpose is to provide a business strategy evaluation device and a business strategy evaluation program that can be performed.

In order to solve the above problems, the business strategy evaluation device according to the present invention teaches a data set including an input parameter which is an explanatory variable for success or failure of a plurality of business strategies and an objective variable which indicates the success possibility of the business strategy. With the evaluation execution department that inputs input parameters related to the business strategy to be evaluated for the trained model obtained by performing machine learning as data, and obtains evaluation results including evaluation values indicating the success potential of the business strategy. It also includes an evaluation result presentation unit that presents the evaluation results obtained by the evaluation execution unit to the user.

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 result, and the evaluation result presentation unit may present the input parameters to be improved together with the evaluation value to the user.

In the present invention, the evaluation execution unit acquires a plurality of evaluation values corresponding to each of the plurality of frameworks used for the analysis of the business strategy by inputting the input parameters into the trained model, and the evaluation result presentation unit. Arranges the evaluation values of multiple frameworks and the multiple intermediate evaluation values corresponding to each of the multiple frameworks in the order of standard examination when conducting the evaluation of the business strategy. It should be presented as an evaluation value.

The business strategy evaluation device causes the evaluation execution unit to re-evaluate the already evaluated business strategy using the input parameters updated from the previous evaluation at a predetermined timing, and causes the evaluation result presentation unit to present the re-evaluation result. It would be better to have a follow-up evaluation management department.

The business strategy evaluation program according to another example of the present invention is characterized in that the computer functions as any of the above-mentioned business strategy evaluation devices.

It is a schematic diagram which shows the business strategy evaluation apparatus 1 together with the apparatus connected to the business strategy evaluation apparatus 1. It is a schematic diagram which shows the hardware composition of the business strategy evaluation apparatus 1. It is a functional block diagram of the business strategy evaluation device 1. It is a figure which shows the presentation example (score) of the evaluation result. It is a figure which shows the presentation example (coordinates in a positioning strategy) of an evaluation result. It is a flowchart which shows the operation process of the business strategy evaluation apparatus 1.

Hereinafter, the business strategy evaluation device 1 according to the embodiment of the present invention will be described with reference to the drawings.

[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 planned by a user based on information input by the user and information such as a market and a business environment, and outputs an evaluation result.

FIG. 2 is a schematic diagram showing the hardware configuration of the business strategy evaluation device 1. The business strategy evaluation device 1 is realized as, for example, a computer. That is, the business strategy evaluation device 1 includes a processor 101, a RAM 102, an HDD 103, a graphic processing unit 104, an input interface 105, and a network interface 106. Note that FIG. 2 shows an example in which the business strategy evaluation device 1 is realized by one computer as a so-called stand-alone type, but a plurality of computers connected to each other via a network line such as a LAN ( For example, the business strategy evaluation device 1 can be realized by a mode in which one server computer connected to a LAN and a plurality of client computers cooperate with each other.

The entire device of the business strategy evaluation device 1 is controlled by the processor 101. The processor 101 may be a multiprocessor. The processor 101 is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a GPU (Graphics Processing Unit), or a PLD (Programmable Logic Device). .. Further, the processor 101 may be a combination of two or more elements of the 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 a part of an OS (Operating System) program or an application program to be executed by the processor 101. Further, the RAM 102 stores various data necessary for processing by the processor 101.

The HDD 103 (Hard Disk Drive) is used as an auxiliary storage device of the business strategy evaluation device 1. The OS program, application program, and various data are stored in the HDD 103. As the auxiliary storage device, another type of non-volatile storage device such as SSD (Solid State Drive) can also be used.

A display device 104a is connected to the graphic processing unit 104. The graphic processing unit 104 displays an image on the screen of the display device 104a according to an instruction from the processor 101. As the display device 104a, a liquid crystal display, an organic EL (Electro Luminescence) display, or the like is used.

An input device 105a is connected to the input interface 105. The input interface 105 transmits a signal output from the input device 105a to the processor 101. The input device 105a includes a keyboard, a pointing device, and the like. Pointing devices include mice, touch panels, tablets, touchpads, trackballs, and the like.

The network interface 106 realizes communication with an external device via the network NW. The 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 differentially 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 device 1 may receive an operation or an input of information from the user terminal instead of the input using the input interface 105.

The external information server 5 is an external device that provides information indicating an external environment or the like 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, corporate settlement 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 device 1 via the network NW. The user inputs operations and information for causing the business strategy evaluation device 1 to execute the evaluation using the user terminal 6, and receives evaluation results and notifications from the business strategy evaluation device 1.

With the hardware configuration as described above, the business strategy evaluation device 1 can be realized.

[Functional block of business strategy evaluation device 1]
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 tracking evaluation management unit 14. Each of these functional blocks is realized by the processor 101 in the hardware configuration of the above-mentioned business strategy evaluation device 1 executing a program stored in the RAM 102 or the HDD 103.

The machine learning execution unit 11 generates a trained model (prediction model) M for evaluating the business strategy. The trained model M uses various information that can be an explanatory variable for the success / failure (win / loss) of the business as an input parameter, and outputs the success / failure (win / loss) of the business. The input parameters of the trained model M are parameters related to the external environment from the viewpoints of PESTLE (Politics, Economy, Society, Technology, Legal, Ecology). , 3C (Customer (market / customer), Competitor (competitor), Company (company)) centered on the company, 4P (Product (product / service), Price (price), Place (location / distribution) for sales) -It is desirable to include items corresponding to analysis items in various frameworks used for analysis of business strategies, such as parameters related to (sales channels) and Promotion (promotion / advertising)). Also, one or more strategic models applicable to the business (eg, initial strategy, midfield strategy, scale strategy type) may be included in the input parameters. Further, in order to incorporate information representing the external environment as an input parameter, information such as news may be included in the input parameter. Further, these parameters may be dimensionally compressed and featured, and used as input parameters. Specifically, the machine learning execution unit 11 maximizes the business value by investment V = (existing business assets at the present time + source of competitiveness (advantage that cannot be imitated)) A × (business decision-making speed + human resources skills) + A synergy strategy accuracy) It is advisable to perform reinforcement learning based on the square of S (ValueNet / PolicyNet).

Further, the output of the trained model M (that is, the evaluation result) may be a numerical value (score) indicating the accuracy of the business strategy (a measure indicating the possibility that the business will succeed (win)) and coordinates in the positioning strategy. For example, for the score, the output of the trained model M is set to a numerical value from 0 to 1, and the closer it is to 1, synergies with existing assets occur and exponential changes (growth) occur, resulting in highly superior capability positioning. It is good to indicate that there is a high possibility of success (winning) as discontinuous growth, and that the closer it is to 0, the higher the possibility of failure (losing).

Also, in order to present the coordinates in the positioning strategy, the trained model M is a plurality of significant (that is, important for the transition from the current location to the destination and differentiation from the competition) in the positioning strategy (for example, 2). (Two) axes are searched and output, and 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. are output. In other words, it presents the conditions for creating synergies between current trends and in-house assets and realizing innovation (state in which exponential change (growth) occurs = synergies) through commercialization through investment through economic activities. The current location of the coordinates of the positioning strategy that can be taken at the present time may be calculated from the current feature amount by PESTLE / 3C / 4P. As a specific example of the axis, it is preferable to output any of the input parameters of the trained model M as a significant axis in the positioning strategy.

The machine learning execution unit 11 is data in which input parameters for various past and present business models are associated with success or failure of the business (value 1 for successful business (win), value 0 for unsuccessful business (loss)). Machine learning / re-learning of the trained model M is performed using the set as teacher data.

The trained model M may be configured to include a decision tree about the relationship between the input parameter (ie, the explanatory variable) and the output value (ie, the objective variable), and the trained model M is a decision tree about this decision tree. Conduct learning. The trained model M may be configured to include a plurality of decision trees having different characteristics, and may be the final output by taking a majority vote or averaging the outputs of the plurality of decision trees.

If the trained model M contains multiple decision trees, then (some of) the individual decision trees may correspond to the various frameworks used to analyze the business strategy. For example, the trained model M may include a decision tree for inputting parameters corresponding to 3C analysis, a decision tree for inputting parameters corresponding to 4P analysis, a decision tree for inputting parameters corresponding to SWOT analysis, and the like. The final output of the trained model M should be the sum of the outputs of these decision trees. The trained model M may output the output value of each decision tree in association with the information of the decision tree.

The trained model M is preferably a model (so-called XAI (Explainable AI)) in which the process leading to the prediction result and the estimation result can be explained and understood by humans. The configuration including the above decision tree is an example of a model in which the process leading to prediction results and estimation results by humans can be explained and understood by humans.

The evaluation execution unit 12 inputs various input parameters (that is, input parameters of the same type as those used for learning) related to the business strategy (business plan) to be evaluated to the trained model generated by the machine learning execution unit 11. By doing so, the evaluation result for the business strategy is obtained.

The evaluation result presentation unit 13 presents the evaluation result obtained by the evaluation execution unit 12 inputting the input parameter into the trained model M to the user. As shown in FIG. 4, for example, the evaluation result presentation unit 13 uses 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 learned model M outputs a plurality of evaluation values corresponding to various frameworks used for analysis of business strategy, the evaluation result presentation unit 13 outputs the evaluation values of various frameworks to the consultation company for the business strategy. It is advisable to arrange them in the order of standard to be examined when conducting the evaluation and present them as intermediate evaluation values. In this way, the user can recognize at a glance how far the examination of the business strategy has progressed. In addition, the evaluation result presentation unit 13 is an input parameter (explanatory variable) that has a high influence on the evaluation result, and an input parameter that should be improved or changed in order to improve the evaluation value (main factor for calculating the evaluation result low). Explanatory variables) and the content of the change (for example, increase / decrease of numerical value, proposed change of strategic model, etc.) should be presented together with the evaluation value. For example, the evaluation execution unit 12 evaluates when each input parameter is changed, and based on the change in the evaluation result (comprehensive evaluation value or intermediate evaluation value), the input parameter having a high influence or It is good to know the input parameters to be improved. By doing so, it is possible to easily grasp the weak points / improvements of the strategy.

Further, 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 and the coordinates of the destination with a high winning percentage for the axis included in the output of the trained model M. It is advisable to present the graph. In addition, it is advisable to plot and present the position of the competition in the graph.

As a method for the evaluation result presentation unit 13 to present the evaluation result to the user, for example, the evaluation result may be displayed on the display device 104a. Further, the evaluation result presentation unit 13 may present the evaluation result by a method other than displaying the evaluation result on the display device 104a. For example, the evaluation result presentation unit 13 may print the evaluation result as a report, or store the electronic data of the report in a storage means such as HDD 103. Further, the evaluation result presentation unit 13 may send the report to the user terminal 6 or another delivery destination 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 result. At this time, the tracking evaluation management unit 14 updates the input parameters from the time of the previous evaluation and causes the evaluation to be performed. The latest input parameters that can be acquired from the external information server 5, such as information indicating the external environment, are acquired and re-evaluated. Further, the input parameters that the user needs to input may be the same as the previous ones, or may be configured to prompt the input again. Further, if there is no input even after a lapse of a predetermined period after prompting the input, the same parameters as the previous time may be used for re-evaluation. For example, the tracking evaluation management unit 14 may store the timing of re-evaluation of each evaluated business strategy in a storage means such as HDD 103, and have the business strategy re-evaluated when the timing is reached. Alternatively, the follow-up evaluation management unit 14 may re-evaluate the business strategy that has already been evaluated at a predetermined cycle / timing (for example, at the end of each month).

Further, the tracking evaluation management unit 14 may be configured to notify a user and / or a predetermined manager or the like of an alert when the evaluation value of the business strategy by re-evaluation exceeds a predetermined value. By doing so, it is possible to embody and execute the business strategy without missing an opportunity at the timing when the evaluation of the business strategy planned in the past is increased due to changes in the external environment or the like.

Next, the operation of the business strategy evaluation device 1 will be described.

FIG. 6 is a flowchart showing the operation process of the business strategy evaluation device 1.

At the start of operation, the business strategy evaluation device 1 generates a trained model M by performing machine learning in which a large number of data sets of teacher data are 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.

Subsequently, input parameters for the business strategy to be evaluated are input, the evaluation execution unit 12 is made to execute the evaluation based on the input parameters, and the evaluation result of the business strategy is output (step S02). At this time, some or all of the input parameters may be acquired from the external information server 5 via the network NW.

Subsequently, the evaluation result presentation unit 13 presents the evaluation result output by the evaluation execution unit 12 to the user (step S03). After that, when the follow-up evaluation management unit 14 is set to execute the re-evaluation (step S04; Yes), after waiting until the timing of instructing the execution of the re-evaluation (step S05), the process is returned to the step S02 and the re-evaluation is performed. Conduct and present the evaluation results. If the setting is such that re-evaluation is not performed (step S04; No), a series of processes is terminated.

With the configuration and procedure described above, the business strategy evaluation device and the business strategy evaluation program of the present embodiment can automatically generate a learning network having a structure suitable for the target.

Although the present embodiment has been described above, the present invention is not limited to these examples. Further, those skilled in the art appropriately adding, deleting, or changing the design of the above-described embodiments, or those appropriately combining the features of each embodiment also have the gist of the present invention. As long as it is included in the scope of the present invention.

1 Business strategy evaluation device 11 Machine learning execution unit 12 Evaluation execution unit 13 Evaluation result presentation unit 14 Tracking evaluation management department

Claims (4)

An evaluation target for a trained model obtained by machine learning using a data set containing input parameters, which are explanatory variables for success or failure of multiple business strategies, and objective variables, which indicate the success potential of business strategies, as teacher data. An evaluation execution unit that inputs the input parameters related to the business strategy of the company and obtains an evaluation result including an evaluation value indicating the success possibility of the business strategy.
An evaluation result presentation unit that presents the evaluation results obtained by the evaluation execution unit to the user,
Equipped with
The input parameters are parameters related to the external environment from the viewpoints of PESTLE (Politics, Economy, Society, Technology, Legal, Ecology), centered on the company. 3C (Customer (market / customer), Competitor (competitor), Company (company)) parameters, 4P (Product (product / service), Price (price), Place (location / distribution / sales channel)) , Promotions, including analysis items in the framework used to analyze business strategies, including parameters
The evaluation execution unit outputs the evaluation when each of the input parameters is changed to the trained model, grasps the input parameters to be improved in order to improve the evaluation value based on the change of the evaluation result, and obtains the input parameters to be improved. Include the input parameters to be improved in the evaluation results
The evaluation result presenting unit is a business strategy evaluation device that presents the input parameters to be improved together with the evaluation values to the user. By inputting the input parameters into the trained model, the evaluation execution unit acquires a plurality of evaluation values corresponding to each of the plurality of frameworks used for analysis of the business strategy.
The evaluation result presentation unit is a standard for examining the evaluation values of the plurality of frameworks and the plurality of intermediate evaluation values corresponding to each of the plurality of frameworks when carrying out the evaluation of the business strategy. The business strategy evaluation device according to claim 1, wherein the evaluation values are arranged in the order of examination and presented as intermediate evaluation values. The tracking evaluation management unit that causes the evaluation execution unit to re-evaluate the already evaluated business strategy using the input parameters updated from the previous evaluation at a predetermined timing, and causes the evaluation result presentation unit to present the re-evaluation result. The business strategy evaluation device according to claim 1 or 2 , further comprising. 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 3 .

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