從產業組織學習狀況探討知識管理之應用

Abstract

全球化以及知識經濟時代，企業所面臨的環境變化遠勝於過往。面對資訊與知識快速傳遞，知識的獲取、傳遞、開創是獲取競爭力的最大利器。面對當前動態且複雜的經營環境，組織學習的導入是為維持企業長期的競爭優勢。導入學習型組織並非一蹴可幾，需搭配導入階段的考量與衡量工具的選擇，然而組織在發展學習型組織的過程中，除了五項修練之外，知識管理亦是一項重要的發展策略。本研究即從二部分來探究，首先從產業角度，探討組織學習於不同產業中，發展的狀況與影響。進而深入組織，以半導體產業為例，實際瞭解知識管理之發展與面對之困境，並尋求一最佳模式，嚐試解決當前之問題。 就產業之分析面來看，高科以及金融產業在推動組織學習成效上，較傳產、服務以及其它產業來的顯著。成熟產業所造成人才的群聚效果，強化知識的獲取與交流，促使組織學習導入成效佳，由此可推論知識管理是帶動組織學習的策略方向。就組織面而言，隱性知識的萃取與外部化，是知識管理中面臨的最大課題。以半導體產業為例，本研究以統計多變量為工具，開發針對半導體製程之動態系統監測、故障檢測與分類模式，該模式可有效的判讀與分析，將隱性知識外部化。就製程方面可有效改善製程以提升良率；對設備而言，維護的週期與零件的更換，在維持製程品與成本降低上均有明顯的影響。除此之外，藉助此模式之引導研發工程師對於製程與設備之改善與研發，將更具功效。

Previous studies of learning organizations are mostly based on Peter M. Senge’s “The Fifth Discipline: The Art and Practice of the Learning Organization”, but there are more than five disciplines for developing learning organizations. Actually, knowl-edge management will be the sixth discipline to improve the formation of learning organizations and to advance organizational changes. In the knowledge economy generation, knowledge and keeping learning are the most important determinants of competitiveness. This research attempts to understand the general viewpoint of or-ganizational learning from industry, and delves into learning organizations to under-stand the actual applied process of knowledge management. In Part I, it is consistently shown from this part of the research that the success determinant of organization learning in different industries is talented individuals (human capital). On the one hand, the ability of knowledge acquisition for organiza-tions is important. On the other, organizations can gain a competitive advantage by increasing the organization's intelligence through knowledge management. The re-search can infer that knowledge management is the strategy to push organizational learning forward. In Part II, the data of the trait knowledge of information can be ap-plied as a predictor or an analyzer for semiconductor equipment. Knowledge man-agement of fault detection and classification (FDC) is a typical application for finding faults and addresses their attribution. This model, which was developed using multi-variable statistical monitoring, can successfully provide clear and exact information to engineers.