Establishment And Emprical Study Of The Mechanism Of Intelligent Manufacturing Internal Control

碩士 === 國立中正大學 === 會計與資訊科技研究所 === 100 === Affected by the global environment and Economic Cooperation Framework Agreement (ECFA), industries in Taiwan have been under a lot of pressure. In order to upgrade intelligent automation and adjust the industrial structure of Taiwan’s manufacturing industry,...

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Bibliographic Details
Main Authors: He, Tsung-Han, 何宗翰
Other Authors: Chang, She-I
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/45016650520648664710
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Summary:碩士 === 國立中正大學 === 會計與資訊科技研究所 === 100 === Affected by the global environment and Economic Cooperation Framework Agreement (ECFA), industries in Taiwan have been under a lot of pressure. In order to upgrade intelligent automation and adjust the industrial structure of Taiwan’s manufacturing industry, the government in Taiwan particularly proposed the "Intelligent Automation for Manufacturing and Service Industries Promotion" project in 2010. Accordingly, this study aims to investigate the internal control and audit mechanism of intelligent manufacturing from the perspective of the production cycle. It is hoped that the in-depth analysis of the effects of intelligent manufacturing application and internal control mechanism will assist the manufacturing industry in improving operating performance and enhancing internal control. Gowin’s Vee (Gowin, 1981) is adopted as the main research strategy in this study. First, on the theoretical side, this study uses Grounded Theory to collect and code relevant literature; afterwards, the prototype of internal control is formed through literature review. Then the expert questionnaire is adopted to edit the prototype of internal control. The finalized Establishmentand Emprical Study of the Mechanism of Intelligent Manufacturing Internal Control consists of 9 dimensions and 43 control items. Finally, this study uses Multi-case to assess the feasibility of the research in the practice. It is hoped that the results of this study will be helpful for the manufacturing industry in terms of implementing intelligent manufacturing and that the operating strategies and objectives will be achieved effectively.