“Think Tank” with technology and management, theory and practice, MOT and Innovation Capital
The purpose of this project is for MOT—which conducts practical education fusing theory with practice in technology management—and TUS Innovation Capital—which leads financial engineering—to interact with each other including the networks surrounding their organizations and personal connections, and to try various theoretical tools. The aim is to develop products utilizing advanced technologies and knowledge information, to conduct empirical research on new services, and to implement them in society. This can be called a think tank for the new Reiwa era, “TUS Research Institute.”
In Management of Technology (MOT), many theoretical studies and case studies relating to innovation have been accumulated from large enterprises to venture companies. Various kinds of know-how, such as successful and failed cases of such technology strategies and investments in venture companies, are hidden as tacit knowledge in the field, including MOT, TUSIM, and VB investment track records of TUS. Some of them have been identified as case studies in papers and reports, although they are superficial, not transformed into explicit knowledge or practical knowledge, and nor are they related. They will become useful only when experts’ knowledge is available. However, such know-how has not been passed on to the next generation, nor has it been shared within TUS and society. On the macro side, on the other hand, not only statistical data but also a lot of big data will be accumulated in the future. However, such macro data is explicit knowledge and is not linked to tacit knowledge and practical knowledge on a micro basis that is the background to making decisions in management strategy and conducting evaluations in VB investment. This may be due to the vertical division of expertise in each field, as well as insufficient exchange between macro and micro experts, technical and financial experts, and academic and business experts. In the future, many documents and kinds of know-how will be digitized and patterned by the development of AI. In addition, Center for Data Science of TUS has been established to enable knowledge sharing. Utilizing such AI, fintech, and ICT technologies, it will become possible to share and digitize kinds of know-how, accumulated at the micro level such as those within MOT and IM, link with macro statistical data, and fuse them.
In-depth learning of tacit knowledge accumulated in hop, KPI extraction in step, and digitization in jump
The first step is to extract keywords and KPIs that have been selected to the extent possible with respect to past MOT papers, and success/failure of published technical strategies and VB investments. Analysis is then performed on the relationship between these keywords/KPIs and data published externally, such as macro statistical data and annual securities reports. For example, we will analyze the correlation among the age and background of the manager, the mobility of employees, technology strategy, earnings, and VB investment.
As the second step, we will conduct interviews with corporate managers and VB investors who have many successful cases. In this way, we will analyze what are the key points for evaluation and investment decisions, and what decisions are made based on explanatory materials and questions and answers; and if necessary, we will conduct a questionnaire survey. We will then examine the relationship between this and the sample analysis described above.
For the third step, we will extract KPIs and keywords using AI, analyze the correlation with the macro-statistical data and the published data of annual securities reports, expand N, and make a database.
The figure shows the correlation analysis and mutual feedback between the real field know-how and practical knowledge of enterprises and VB investments at the micro level and macro statistical data. The horizontal axis shows the sharing of the cases and corporate strategies in MOT and the know-how in IM as a VC. On the Management of Technology side, it will become science and technology policy at the macro level while corporate strategy at the micro level; and on the IM and financial side, it will become portfolio construction in GPIF and policy of public and private funds at the macro level, while criteria for individual VCs to decide at the micro level.
It has been pointed out for a long time that innovation and the start-up of new businesses are stagnant processes in Japan, particularly lagging behind in the areas of big data analysis, deep learning and AI.
This may be due to overserious business practices, a fixed industrial structure, a highly mature working-age population structure, and other micro and macro conditions peculiar to Japan. Although from another perspective, it can be said that resources remain for high-quality growth. There is a possibility of creating a new industry by exploiting the opportunities for efficient use of abundant, highly useful data that have been kept idle in enterprises, although this can be achieved only through academic knowledge backed by practical experience. Collaborations with enterprises, external research institutes, and venture capital firms will be accumulated as research findings, and passed on not only to joint research partners but also widely to society.
Future Development Goals
Creation of a database on know-how in evaluation will make it possible to increase the success rate of research theme selection, new business creation, M&As, and investment in venture companies.
There will be a coming together of top economists, top analysts, and venture capitalists who have conducted field surveys for many years in the fields of macro, semi-macro, and micro for the purpose of fusing technology with finance, and social science approaches with science and engineering approaches. They will transform the tacit knowledge of their analysis know-how into explicit knowledge using the latest technologies such as AI. In other words, the approach of professional specialists will be implemented in society using AI.