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  • 學位論文

企業推動工業4.0導入智慧工廠之研究-以A公司為例

A Research of Industry 4.0 Technology Implementation in Enterprise Smart Factory-A Case Study of Company A

指導教授 : 蘇友珊
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摘要


隨著進入工業4.0時代,世界各地都在積極運用物聯網、大數據分析等技術來進行企業數位化轉型。對於製造業,因應現今產品週期的縮短與大量客製化趨勢,智慧化即成為企業提升競爭力的關鍵,而近年麥肯錫與世界經濟論壇所提倡的全球燈塔工廠,更是激勵企業進行智慧化升級的指標。本研究探討企業如何運用工業4.0技術進行智慧工廠的導入,對於工業4.0技術、智慧工廠之建置架構與企業在工廠各層面的智慧應用進行彙整後,將導入智慧工廠分為智慧製造、智慧生產設備、智慧決策分析、智慧資訊網路、智慧倉儲及智慧廠務六大構面及其下準則共22項,透過HDM分層決策模型法對A公司導入智慧工廠專案之26位專家進行背景分群調查與重要程度權重分析,建構企業在推動智慧工廠專案的架構與發展專案時的依據。依本研究調查結果顯示,A公司導入智慧工廠的目標在發展智慧製造,次之為智慧生產設備、智慧決策分析與智慧資訊網路。研究發現於不同背景下在部分面向的重要性排名及程度有所差異,得知專家經驗及背景必然影響重要性之評估,且專家團隊之組合同為發展智慧工廠決策時須注意的要點。

並列摘要


After the era Industry 4.0, the trend of digital transformation and intelligent manufacture is sweeping over the world. Enterprises are focus on I4.0 technology development such as IOT and Big data analysis. For the manufacturing industry, in response to the current shortening of product lifecycle and a large number of customizations, the introduction of smart manufacturing has become the key to improve the company competitiveness. Therefore, this research will discuss how enterprises use Industry 4.0 technology to introduce intelligent transformation in factory. After discussing the related I4.0 technologies of smart factory, sorting out the structure and application at all levels of enterprises, conclude the construction to aspect: smart manufacturing, smart equipment, smart decision and analysis, smart information networking, smart warehouse and smart facility and total 22 criteria under aspects, implementing investigation through hierarchical decision-making method aims to 26 experts from relevant units in company A and divide to 3 groups of different perspective to carry out an analysis of the importance from aspects and criteria in order to build the enterprises smart factory project structure and strategy-making. According to the survey, the focus of smart factories introduction is the development of smart manufacturing, followed by smart equipment, smart decision analysis and smart information networking, also found that expert experience and background will inevitably affect the evaluation by grouping result.

並列關鍵字

Smart Factory HDM model Industry 4.0

參考文獻


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