研究生: |
陳治瑾 Chen, Chih-Chin |
---|---|
論文名稱: |
以基於多準則決策之情境分析推衍中小型機械公司數位轉型之策略 Multiple Criteria Decision Making Methods Based Scenario Analysis for Defining Digital Transformation Strategies of Small and Medium Sized Machinery Manufacturers |
指導教授: |
黃啟祐
Huang, Chi-Yo |
口試委員: |
羅乃維
Lo, Nai-Wei 何秀青 Ho, Hsiu-Ching 黃啟祐 Huang, Chi-Yo |
口試日期: | 2022/07/16 |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系科技應用管理碩士在職專班 Department of Industrial Education_Continuing Education Master's Program of Technological Management |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 英文 |
論文頁數: | 96 |
中文關鍵詞: | 數位轉型 、情境分析 、多準決策分析 、中小企業 、機械產業 |
英文關鍵詞: | Digital Transformation, Scenario Analysis, Multi-Criteria Decision Making, Small and Medium Enterprise, Machinery Industry |
研究方法: | 德爾菲法 |
DOI URL: | http://doi.org/10.6345/NTNU202201828 |
論文種類: | 學術論文 |
相關次數: | 點閱:194 下載:0 |
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隨著第五代(5th Generation,5G)行動通信、巨量資料分析(Big Data Analytics)、人工智慧(Artificial Intelligence)、物聯網(Internet of Things)等新興科技之成熟,數位轉型已成為多數企業追求永續經營之目標及持續成長之必要戰略,中小型機械業者亦然,唯相關研究甚少。中小型機械業為部份已開發國家或開發中國家經濟推展的重要推力,如何導入數位轉型,為當前最重要之課題之一。
因此,本研究以情境分析和混合多準則決策分析法(Multiple Criteria Decision Making, MCDM),定義中小型機械產業數位轉型之情境與對應策略。第一階段分析,以宏觀環境(Political, Economic, Social, Technological, Environmental and Legal, PESTEL)模型為基礎,導入混合多準則決策模型,擬訂最適情境。第二階段分析導入混合多準則決策模型,訂定策略。兩階段皆利用專家問卷,採德爾菲法篩選合適的準則,再以決策試驗實驗室評估法(Decision Making Trial and Evaluation Laboratory, DEMATEL)計算出構面及準則間的影響關係與重要性,並結合基於決策實驗室評估法之網路流程(DEMATEL-based Analytic Network Process, DANP),權衡與評估準則的權重。最後運用多準則折衷評估方法(VlseKriterijumska Optimizacija I Kompromisno Resenje, VIKOR),從折衷排名中獲得數位轉型之四個主要發展情境。
為訂定中小型機械業者之數位轉型策略,本研究邀集台灣中小型機械業者與學研界專家填答問卷。依據實證研究結果,得到主要影響中小型機械產業數位轉型之情境因素為經濟、社會以及政治,另各情境之下最適發展之策略為建立無形資產、數位時代的人才管理、提供互補服務、打造差異化等。本研究結果,可作為中小型機械業者數位轉型之參考,分析架構也可為其他產業數位轉型之用。
With the maturation of emerging technologies such as 5th Generation (5G), Big Data, Artificial Intelligence (AI), and Internet of thing (IoT), digital transformation has become a necessary strategy for enterprises to pursue sustainable management and sustainable growth and so has the small and medium-size machinery industry. However, there hasn't been much study on this issue, though. One of the most critical concerns facing the small and medium-size machinery industry, which serves as a major engine for economic growth in both developed and emerging nations, is how to implement digital transformation.
This study defines the strategies for digital transformation of small and medium-size machinery industry by introducing scenario analysis and Multiple Criteria Decision Making (MCDM) methods. The first step adopts the Political, Economic, Social, Technological, Environmental and Legal (PESTEL) model. And a hybrid MCDM model is introduced to determine the most suitable scenario. A hybrid MCDM model was used in the study' second phase to establish the approach. The Decision Making and Trial Evaluation Laboratory (DEMATEL) based Analytic Network Process (ANP), and DEMATEL-Based ANP (DANP) was used to integrate the ANP and determine the influence relationships and importance of the dimensions and criteria. The weight of the criteria was calculated and assessed using the DANP. Lastly, using the compromise ranking method, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), four primary development scenarios of digital transformation were derived.
This study invited managers of small and medium-size machinery manufacturers in Taiwan, as well as experts from academic and research institutes, to provide opinions for determining digital transformation strategies. According to the findings of the empirical study, the main factors influencing the digital transformation of the small and medium-size machinery industry are economic, social, and political. The best development strategies in each scenario are developing intellectual capitals, talent management, provisions of compromise assets, differentiation, and so on. The study's findings can be used as a basis for the digital transformation of the small and medium-size machinery industries, and the analytic framework can be applied to the digital transformation of other industries.
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