Today, the world is getting to be very chaotic. On one hand, globalization or the borderless society is improving. On the other hand, nationalism in resources, technology and industry is improving. Our society faces trilemma problems of environment, energy and economic growth. These problems are not only economic problems, but also political and social problems. I believe academic intelligence concerning research policy, technology and science is useful to resolve these complicated problems.

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The OECD has produced science and technology indicators since early 1960's. Recognizing that science and technology is a driving force of economical development, the OECD takes an active role in science and technology indicators. The author introduces the aims and activities of the EASD (Economic Analysis and Statistics Division) that is in charge of science and technology indicators in the OECD: (1) Construction of a statistical database. -Development, collection and analysis of internationally comparable statistics. -Publish of "Main Science and Technology Indicators", "Basic Science and Technology Statistics" and "OECD Science and Technology Indicators". (2) Methodological works. -Development and improvement of methodologies in data collection and interpretation. -Publish of "The Proposed Standard Practice for Survey of Research and Experimental Development - Frascati Manual" and other related manuals. (3) Estimation and interpretation of specific indicators. -Development of tools and databases focusing on special issues. (4) Quantitative analysis -For example, evaluation of international competitiveness, technology diffusion, evaluation of output and impact of science and technology activities, and techno-globalism. Finally, the author expalins manuals related to statistics and indicators in detail. The author concludes that the OECD is shifting the aims of indicators from the simple status-quo reports to policy-oriented reports with more detailed analysis.

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The most important statistical indicator of the level of a nation's technology is perhaps the one related to technology trade balance. By comparing the payment for technology imports with the receipts from technology exports, it is possible calculate the level to which a nation's technology has developed. In Japan, two organizations conduct surveys on technology trade balance and compile and publish indicators every year based on these surveys. However, the figures released by the two differ widely. What is more, the difference is widening each year. The two organizations are the Bank of Japan, which has been doing the survey for many years, and the Statistics Bureau of the Management and Coordination Agency, which started its survey in 1971. Measurement of Japan's technology level is not the main purpose of the surveys conducted by these organizations. The data produced is only incidental to the main purpose of the survey; it is, so to speak, merely a "by-product". Even so, these are very important indicators because they are the only ones available ad also because they are cited in the Science and Technology White Paper published by the government every year. They are so important that a statistical research project aimed mainly at obtaining their values, not one which produces them as by-products, should be conducted. There is another aspect to this problem. For the reasons listed below, we are now at a time when it is necessary to get a clear grasp of the situation. According to the results of the survey by the Statistics Bureau of the Management and Coordination Agency, the index (which is represented as receipts from technology exports/payment for technology imports × 100) reached 100 (%) in 1989. Supposing that this value is a correct indication of the level of Japan's technology, it means that Japan has reached a critical turning point in its 120-odd-year history of technology and industry since the Meiji ear started (in 1868). This is not bad news but good news. However, the Bank of Japan's figure for this index in 1989 is about a third of that of the Management and Coordination Agency. According to the Bank of Japan statistics, the absolute value of technology imports has increased very sharply since 1979. This value (result of a statistical survey) leaves some doubt as to its validity when it is compared with other data. At the present stage, neither the Statistics Bureau's nor the Bank of Japan's survey produces convincing data indicating the level of Japanese technology. In this paper, the author analyzes the characteristics of the statistics of both organizations and discusses their respective merits and demerits. The author then presents arguments to show why a new statistical survey aimed primarily at measuring the level of a nation's technology is necessary.

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The Ministry of Science and Technology of Japanese Government has been implementing technology forecasts every five years, since 1971. This article considers the characteristics of the data published in 1992. The study focuses on degrees of speciality, degrees of importance and forecasting realization time. In the field of life science, 84% of the researchers forecasted theoretical performances would be accomplished, while 16% of the researchers forecasted practical performances would be accomplished. On the other hand, in the energy, industry and constructive technology fields, over 80% of the researchers forecasted practical performance would be accomplished. Degrees of speciality and degrees of importance are estimated by the three levels of high, middle and low. On average, the degree of speciality of most researchers who answered the questionnaire was middle level while the average dgree of importance of every field was upper meddle level. However, life science, materials, information technology and electronics were estimated to have high degrees of importance. The realization time was forecasted to be from 2001 to 2010, in 79% of themes. Therefore we conclude that almost every researcher is looking ahead about 30 years.

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This article focuses on indicators of company level R&D investment in view of business management. As usual, R&D investment decision making is based on previous amounts spent ratio of R&D investment to sales, and political intention. In addition, few companies implement quantitative pre-evaluation of R&D investment because measurement of performance itself and economic contribution is very difficult. Moreover, it is made more and more difficult by the change in circumstances in R&D activities, such as emergence of technology fields based on science, product lifecycle shortage, concurrent engineering, and so on. Particularly, treatment of expanding multi- (or pan-) purpose research activities, like basic and exploratory research or software development, affects the evaluation of R&D investment. Therefore contribution of R&D output to profit should be considered. The author recommends two indicators based on this idea for R&D investment management. They are; ratio of R&D investment that indirectly contributes to profit to R&D investment that directly contributes to profit; and OVI (Overall Value Index).

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Research, manufacturing, and sales as stages of innovation are inter-connected through 'knowledge". The source of corporate growth is indeed 'knowledge', that is created and maintained during innovation processes. Based on this idea, the author suggests indicators of R&D efficiency. The first index (called "Research Efficiency") is the ratio of technological progress to R&D investment. This index measures of the efficiency of an R&D division. The second index is the ratio of profit to technological progress. This index measures the efficiency of manufacturing and sales divisions. Total efficiency (ratio of profit to R&D investment) is equivalent to the first index by the second index. That is 'knowledge' as interface among stages of innovation, is explicitly treated in this index system. Finally, the author measures technological progress as the number of awarded research themes. This index of technological progress is expressed as a function of the period needed to complete it. That is, technological progress = αt exp (-βt^2). This equation was acquired by approximation in the case of Toyota Central Research Laboratory. From this equation, the period for producing maximum efficiency is (t_0+1√2β)[t_0: lead time]. So, we can say, we need to reconsider research projects that continue more than (t_0+1√2β) years.

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The R&D Department of Sumitomo Denko corporation has developed and introduced quantitative estimation methods for research projects. To estimate research projects which have reached the developed state, the R&D department tried to develop quantitative estimation methods. In 1973 they developed the Profitability-method. In 1983 they developed the New Score-method. In 1991 they introduced the Decision Management method from the American corporation SDG. The Profitability-method was developed to judge which research projects have to be invested in. of course corporate resources are limited so all projects can not be invested in. Therefore, they must be judged whether to go or not. The Profitability-method calculates the project's profit and loss by variables such as the number of researchers and staff, investment, cost of materials, revenue, and so on. The New score-method was developed to judge the priority of research projects. The Main variables are the project's influence, growth probability, realizability, and efficiency of R&D. And each variable has 5 points, so 20 points is the highest possible mark. The Decision Management-method was introduced by the American company SDG in 1991. This DM method has more of an analytical approach than the profitable-method and New score-method. The DM method can clarify the uncertain points of each project y identifying the promotive opinions and objections for a project. They estimate that the most optimistic case and most pessimistic case about the uncertain points. They also describe the decision tree using these alternatives. It is very important to simplify the variables of the estimation model in developing the quantitative estimation method because the variables are so complicated the estimation method is not feasible.

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これまでの企業経営において,OEM (Original Equipment Manufacturing : 相手先ブランドによる生産)は,主に市場の成熟期に多用される戦略とされてきた。この時期におけるOEMの狙いは,受託・委託企業共に,コストダウンにあった。しかしエレクトロニクスを中心とする規格・標準化がからむ分野においては,市場の導入着にOEMを戦略的に活用することが,競争上重要になってきた。これは単にコストダウンのためではなく。さまざまな狙いをもっている。ケース分析をもとにこれらのOEMの狙いを製品ライフサイクル別に見ると,OEM委託側企業(販売側企業)においては,市場導入期は「機会模索」,市場成長期は「製品ライン拡張」,市場成熟期は「製品ライン維持」がある。他方受託側企業(生産側企業)においては,導入期は「良い競争業者づくり」,成長期「得意分野の強化」,成熟期「競争業者減らし」などがあげられる。

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