智慧工廠之人機協作規劃與導入 -以IPC系統測試製程為例

NCUIR > School of Management at National Central University > Executive Master of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/88094

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/88094

Title:	智慧工廠之人機協作規劃與導入 -以IPC系統測試製程為例
Authors:	蕭奇邦;Hsiao, Chi-Pang
Contributors:	工業管理研究所在職專班
Keywords:	工業4.0;智慧工廠;工業電腦;限制理論;人機協作;痛點人才;少量多樣;Industry 4.0;smart factory;IPC;TOC;HRC;pain-point talent;SVLV
Date:	2022-04-25
Issue Date:	2022-07-13 18:05:20 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著2011年德國提出工業4.0議題後，智慧工廠的發展也被提到了快車道上，傳統依靠人力為主的工廠要如何朝向智能化、自動化及人機協作的智慧工廠 (Smart Factory) 發展呢? 本研究將以工業電腦 (IPC) 產業的系統組裝工廠為例，以限制理論 (TOC) 進行流程痛點及瓶頸分析，進一步研究開發創新技術的人機協作 (HRC) 解決方案：包含研究開發混流測試平台 (MTP) 、測試程式模組化系統，再透過串聯廠內製造執行系統 (MES) 以建構多機混流測試流程的人機協作模式，預期達成提升產量、減少人力、降低成本及節省廠房使用面積等效益。更進一步以此研究為基礎，建構發展智慧工廠的痛點人才培育機制及智能橫向組織、持續精煉各類數據並發掘流程痛點、擴大技術應用與創新解決方案及方案導入檢討等四步驟並形成管理循環方法論，並以此做為少量多樣 (Small-Volume Large-Variety) 生產模式邁向智慧工廠道路上的里程碑。;With the proposal of Industry 4.0 by Germany in 2011, the development of the smart factory has been growing faster and faster nowadays. How can traditional factories that used to rely mainly on manpower could develop towards a smart factory that is equipped with intelligence, automation and human-robot collaboration? This study will take the system assembly factory of the IPC industry as an example, to analyze the pain points in the process ,and also bottlenecks within the Theory of Constrains (TOC); furthermore, it also provides human-robot collaboration (HRC) solutions for innovative technologies, including the R&D of mixed testing platform (MTP), and the system of testing program module. Then building a human-robot collaboration mode of multi-machine mixed testing process by connecting the manufacturing execution system ( MES). It is expected to harvest benefits such as growth in output, reductions in both manpower and cost and efficiency in plant space utilization. Furthermore, this study could serve as a foundation for developing, first, a pain-point talent cultivation mechanism and intelligent horizontal organization within a smart factory; second, continuous refinement of various data in order to discover pain points hidden in process; third, extending technology applications, and the last, reviews of programs. With these four steps, a management cycling methodology will be formed. This methodology is a milestone of the Smart Factory for the Small-Volume Large-Variety production model.
Appears in Collections:	[Executive Master of Industrial Management] Electronic Thesis & Dissertation

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