洞悉智慧製造服務化之關鍵因素評估

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Title:	洞悉智慧製造服務化之關鍵因素評估
Other Titles:	Insight into the Evaluation of Key Factors of Service-Oriented in Smart Manufacturing
Authors:	孫樹森 Sun, Benson S. S.
Contributors:	陳坤成 Dr. James K. C. Chen 經營管理學系
Keywords:	製造執行系統;網宇實體系統;工業物聯網;智慧製造 Cyber-Physical System;Manufacturing Execution System;Industrial Internet of Things;Smart Manufacturing
Date:	2023
Issue Date:	2023-11-22 01:36:05 (UTC+0)
Abstract:	自工業強權德國提出工業4.0成為生產策略顯學，結合演算法韌體及電機技術加上製程雲端大數據，提供產業升級轉型一套智慧化生產的解決方案。然而，導入高端技術的同時，一定會面臨通訊相容性、系統整合能力、生產參數調整、軟硬體整合等設備調整問題；以及員工技術專業訓練、企業學習能力、人力與物料追蹤等內部營運管理問題。引此，本研究以資訊系統整合數據架構的方式為基礎，探討資訊系統介入資訊系統傳統製造業後所演化出的智慧製造可能促成之服務創新，分析產業轉型升級出關鍵因素發展優先順序。使用層級分析研究方法(AHP)針對多目標評估因子，匯集專家意見分析本研究假說。有效問卷共207份，回收率94.1%。研究結果顯示，構面前三位排序分別為：企業學習能力、設備智慧程度、虛實整合能力；替代方案排序為：新技術整合與價值驗證、敏捷製與集體治理、即時現況分析系統。研究結果排序有利評估智慧製造關鍵因素對於台灣中小企業導入模式之影響。 Since the industrial power Germany proposed that Industry 4.0 has become a prominent theory of production strategy, it combines algorithm firmware and motor technology with process cloud big data to provide a set of intelligent production solutions for industrial upgrading and transformation. However, while introducing high-end technology, there will definitely be problems with equipment adjustments such as communication compatibility, system integration capabilities, production parameter adjustments, and software and hardware integration; as well as internal operations such as employee technical training, corporate learning capabilities, and human and material tracking. management issues. Citing this, this study is based on the way information systems integrate data structures, explores service innovations that may be facilitated by smart manufacturing evolved after information systems intervene in traditional manufacturing, and analyzes the development priorities of key factors in industrial transformation and upgrading. Using the Analytical Hierarchy Research Method (AHP) for multi-objective evaluation factors, expert opinions were gathered to analyze the research hypothesis. A total of 207 valid questionnaires, the recovery rate was 94.1%. The research results show that the top three rankings of the facets are: enterprise learning ability, equipment intelligence, and virtual-real integration ability; the rankings of alternatives are: New technology integration and value verification, agile system and collective governance, real-time status analysis system. The ranking of the research results is beneficial to assess the impact of the key factors of smart manufacturing on the introduction model of small and medium-sized enterprises in Taiwan.
Appears in Collections:	[經營管理學系 ] 博碩士論文

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