博碩士論文 106450008 詳細資訊




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姓名 徐文俊(Wen-Chun Hsu)  查詢紙本館藏   畢業系所 高階主管企管碩士班
論文名稱 AIoT智慧工業4.0製造之研究─以通用電氣(GE)為例
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摘要(中) 網路的發達讓世界變小,通信基礎設施的建立讓資訊傳遞變得方便,感應器/傳導器的普及和微型化讓數據的收集變得方便,而大數據的應用讓我們擴展了不同的視野。我們站在技術革命的邊緣,這將徹底改變我們生活習慣,工作和相互聯繫的方式。在其規模,範圍和複雜性方面,轉型將不同於人類之前所經歷的任何事情。我們還不知道它將如何展開,但有一件事是清楚的,它的反應必須是綜合和全面的,涉及全球所有利益相關方,從公共和民營部門到學術界和民間社會。
第一次工業革命使用水和蒸汽動力來實現生產機械化。第二次使用電力來創造大規模生產。第三次使用電子和信息技術實現生產自動化。現在,第四次工業革命正在建立自上世紀中葉以來一直在發生的數字革命。它的特點是融合了物理,數字和生物領域之間界限的技術。第四次工業革命正在以指數而非線性的速度發展。而且,它幾乎擾亂了每個國家的每個行業。這些變化的廣度和深度預示著整個生產,管理和治理系統的轉變。
工業4.0智慧製造時代來臨,工業4.0的生產方式以物聯網、大數據、雲端系統、AI、5G等新型科技為基礎,以數據匯流串接產業價值鏈每一個環節,強調跨領域虛實整合,打破生產與服務和公司的疆界,正在重新解構價值鏈並形塑全球製造分工。企業如何運用數據進行決策,目前大部分企業經營模式,是利用有限的數據和資訊來預測產品的需求,並建置產能,而在接到訂單前後,先生產一批量出來,然後想方法銷售,以滿足訂單的需求和提升產能利用率。由於生產數據與終端市場數據並沒有全面連結,最後一定會因為供需落差,而出現存貨和缺貨同時存在的現象。工業4.0核心概念是基於大數據下的彈性決策和聰明生產。也就是說,將各個顧客的需求與具備彈性決策能力的智慧製造系統連結,透過物聯網、互聯網等社群媒體來虛實整合每一個環節,使數據貫穿整個價值鏈而匯流,製造商在掌握產業鏈大數據和各個需求的前提下,「有需求才生產」,一方面滿足市場端少量多樣,甚至是極端個人化的需求,使產品附加價值更高。也就是說,靠資訊科技和網路讓企業發展出可預測人類行為習慣的系統,將偵測變化和即時決策的能力,從個別企業部門、公司整合到整個製造網絡,並延伸到產業鏈的最末端。
最後,第四次工業革命不僅會改變我們的行為,也會改變我們的本質。它將影響我們的身份以及與之相關的所有問題,我們的隱私權,我們的所有權概念,我們的消費模式,我們投入工作和休閒的時間,以及我們如何發展自己的事業等等。它已經在改變我們的一切。技術和隨之而來的破壞都不是人類無法控制的外在力量。我們所有人都有責任引導其發展。因此,我們應該抓住機遇和力量來塑造第四次工業革命,並將其引向一個反映我們共同目標和價值觀的未來。
摘要(英) The development of the Internet has made the world smaller, the establishment of communication infrastructure has made information transmission convenient, the popularity and miniaturization of sensors/conductors have made data collection convenient, and the application of big data has allowed us to expand differently. Vision. We stand on the edge of the technological revolution, which will revolutionize the way we live, work and connect with each other. In terms of its size, scope and complexity, the transformation will be different from anything that humans have experienced before. We don′t know how it will unfold, but one thing is clear. Its response must be comprehensive and comprehensive, involving all stakeholders around the world, from the public and private sectors to academia and civil society.
The first industrial revolution used water and steam power to mechanize production. The second use of electricity to create mass production. The third time using electronic and information technology to achieve production automation. Now, the fourth industrial revolution is building a digital revolution that has been taking place since the middle of the last century. It is characterized by a technique that combines the boundaries between the physical, digital and biological domains. The fourth industrial revolution is developing at an exponentially and non-linear rate. Moreover, it almost disrupts every industry in every country. The breadth and depth of these changes heralds a shift in the entire production, management and governance system.
Industry 4.0 The era of smart manufacturing is coming. The production mode of Industry 4.0 is based on new technologies such as Internet of Things, Big Data, Cloud System, AI, 5G, etc., and data convergence is connected to every link of the industrial value chain, emphasizing cross-domain virtual and real integration, breaking Production and services and the boundaries of the company are re-deconstructing the value chain and shaping the global manufacturing division. How do companies use data to make decisions? At present, most business models use limited data and information to predict product demand and build capacity. Before and after receiving orders, they first produce a batch and then sell it. To meet the needs of orders and increase capacity utilization. Since the production data and the terminal market data are not fully linked, in the end, there will be a phenomenon of both inventory and shortage due to the gap between supply and demand. The core concept of Industry 4.0 is based on flexible decision making and smart production under big data. In other words, we connect the needs of each customer with the smart manufacturing system with flexible decision-making capabilities, and integrate every link through social media such as the Internet of Things and the Internet, so that data flows through the entire value chain, and manufacturers are in the industry. Under the premise of chain big data and various needs, “there is demand to produce”, on the one hand, it meets the needs of a small variety of market and even extreme personalization, which makes the added value of products higher. That is to say, relying on information technology and the Internet to enable enterprises to develop a system that can predict human behavior habits, the ability to detect changes and make timely decisions, from individual enterprise departments and companies to the entire manufacturing network, and to the industrial chain. The end.
Finally, the fourth industrial revolution will not only change our behavior, but also change our essence. It will affect our identity and all the issues related to it, our privacy, our ownership concept, our consumption patterns, the time we put into work and leisure, and how we develop our careers. It is already changing everything about us. Technology and the consequent destruction are not external forces beyond human control. All of us have a responsibility to guide their development. Therefore, we should seize the opportunity and strength to shape the fourth industrial revolution and lead it to a future that reflects our common goals and values.
關鍵字(中) ★ 工業4.0
★ 物聯網
★ 智慧工廠
★ ML機器學習
★ AI人工智慧
★ 智慧物聯網
★ 5G
關鍵字(英) ★ Industry 4.0
★ Internet of Things
★ Smart Factory
★ ML Machine Learning
★ AI Artificial Intelligence
★ Smart Internet of Things
★ 5G
論文目次 第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 論文架溝 3
第二章 文獻探討 5
2.1 何謂工業4.0 智慧製造 5
2.2 麥可波特所描繪的企業新形貌 7
2.2.1 新的產品能力 7
2.2.2 重塑製造業者 7
2.2.3 新的資料資源 7
2.3 ML機器學習和AI人工智慧改善企業工作流程 9
第三章 研究方法 12
3.1 研究架構 12
3.2 研究資料來源 12
3.2.1 五力分析 12
3.2.2 SWOP分析 14
3.3 研究對象 17
第四章 智慧製造的發展 18
4.1 工業4.0的導入 18
4.1.1 工業4.0基礎的九大技術 19
4.1.2 製造系統供應商創造價值的能力 21
4.1.3 基於價值的工業4.0發展方法 24
4.2 智慧物聯網(AIoT)的運用 27
4.2.1 產品開發 27
4.2.2 支援新的商業模式 28
4.2.3 新的加工製造模式 28
4.2.4 新物流模式 28
4.2.5 行銷與銷售 29
4.2.6 售後服務與遠距支持 30
4.2.7 安全性 31
4.2.8 新人力組織 33
4.3 ML機器學習與AI人工智慧的協作 35
4.3.1 人幫機器的三種方式 37
4.3.2 機器幫人的三種方式 38
4.3.3 人工智慧的治理和信息管理 39
4.3.4 為機器學習(ML)和人工智慧(AI)制定戰略和計劃 40
4.3.5 機器學習系統的風險 41
4.3.6 增強數據科學和機器學習 43
第五章 個案研究 44
5.1 通用電氣公司(GE) 44
5.1.1 公司簡介 44
5.1.2 創始人 44
5.1.3 財務狀況 44
5.1.4 願景聲明 45
5.1.5 使命宣言 45
5.1.6 運營管理領域 46
5.1.7 多元化組織結構 47
5.1.8 再造奇異(GE),企業轉型 48
5.1.9 建立工業物聯網路平台與增材製造 51
5.1.10 接納新類型人才 52
5.2 通用電氣(GE)的智慧製造 53
5.2.1 增材製造(3D列印) 53
5.2.2 工業物聯網(IIoT) 54
5.2.3 機器學習與AI人工智慧 55
5.3 通用電氣(GE)五力分析 57
1.供應商(賣方)的議價能力 57
2.買方討價還價的能力 57
3.替代產品的威脅 57
4.新進入者的威脅 58
5.來自現有競爭者的威脅 58
5.4 通用電氣(GE)SWOT分析 58
1.通用電氣(GE)的優勢 (內部戰略因素) 58
2.通用電氣(GE)的弱點 (內部戰略因素) 59
3.通用電氣(GE)的機會 (外部戰略因素) 59
4.通用電氣(GE)的威脅 (外部戰略因素) 59
5.總結與建議 60
第六章 結論與建議 61
6.1 研究結論 61
6.2 工業4.0的影響 61
6.2.1 工業4.0的建議 62
6.3 智慧物聯網(AIoT)的影響 65
6.3.1 智慧物聯網(AIoT)的建議 66
6.4 ML機器學習與AI人工智慧的影響 70
1.解決複雜問題 72
2.批判性思維 72
3.創造力 72
6.4.1 ML機器學習與AI人工智慧的建議 75
參考文獻 77
附錄 81
附錄一: 通用電氣時間表 81
附錄二: 展望2025: 深度變革 85
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指導教授 王弓 鄭有為 審核日期 2019-5-24
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