高科技廠導入智慧製造對員工教育訓練產能績效影響之研究;The Impact of Introducing Intelligence Manufacturing in High-tech Factories on Employee Education and Training Capacity Performance

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81873

Title:	高科技廠導入智慧製造對員工教育訓練產能績效影響之研究;The Impact of Introducing Intelligence Manufacturing in High-tech Factories on Employee Education and Training Capacity Performance
Authors:	朱凱莙;Chu, Kai-Chun
Contributors:	企業管理學系
Keywords:	智慧製造;工業4.0;無線感測網路;螞蟻族群最佳化演算法;教育訓練;Intelligent Manufacturing;Industry 4.0;Wireless Sensor Networks;Ant Colony Optimization Algorithm;Education and Training
Date:	2019-10-07
Issue Date:	2020-01-07 14:26:54 (UTC+8)
Publisher:	國立中央大學
Abstract:	目的：探討科技業僱主導入智慧製造自動化如何運用有效企業策略管理，經由無線感測網路與螞蟻族群最佳化演算法並實施智慧製造自動化並執行教育訓練對勞工的企業管理智慧製造認知、態度、自我效能之影響。最後予以比對智慧化工廠成熟度評量結果資料，來評估勞工導入智慧製造自動化的差異，如此可協助僱主在智慧化工廠底層產線將以機器設備取代執行落實成效。方法：智慧製造自動化使用無線感測網路與螞蟻族群最佳化演算法，採橫斷性相關研究設計，自編修改「勞工智慧製造認知、態度、自我效能量表」，並檢視工具之信效度。採立意取樣方式，於2019年4月份針對某科技業工廠的導入智慧製造自動化落實程度的兩個(A廠與B廠)場區，共計徵得330位勞工於不同作業環境管理自動化、設計自動化及工廠智慧化之研究調查結果。分析以描述性統計、獨立樣本T檢定、單因子變異數分析、皮爾森積差相關及線性迴歸分析，探討各研究自變項以及有接受實施智慧製造自動化教育訓練對於勞工智慧製造認知、態度、自我效能及自動化管理之影響差異，分析工具使用SPSS 21。結果：導入智慧製造自動化的蟻群最佳化演算法，可節約網絡中節點的能量消耗，延長網絡的生命週期。針對兩個廠區中落實相對較高程度的B廠勞工，其於智慧製造認知、態度與自我效能表現上較佳，有接受智慧製造自動化教育訓練課程，為該廠勞工之智慧製造認知、態度與自我效能的主要關聯因子。相反的，在A廠勞工(對照單位廠區)部分，其智慧製造認知、態度及自我效能則表現較差，人口學影響因子主要為性別、總年資、作業單位、智慧自動化、智慧生產線課程學習次數、學習效果、智慧自動化經驗、自動化支持、工作環境，經控制人口學影響因素後，不同於較高程度有接受智慧製造自動化教育訓練課程的B廠勞工, 該對照廠區勞工之智慧製造自動化教育訓練課程認知差異仍然會對於其智慧製造認知、態度與自我效能具有顯著關聯性。結論：勞工有接受智慧製造自動化教育訓練課程落實程度愈高，勞工之智慧製造認知、態度、自我效能及課程學習效果越好且影響因子較為單純，本研究結果可做為各行業職場勞工導入智慧化工廠之調整及智慧自動化課程設計之參考。在實務面貢獻可以提供僱主透過此研究，能對科技業投入自動化智慧化的執行有進一步的認知，進而對企業經營者提供智慧自動化企業建置工業4.0與QCDS管理程序，可提升競爭力之參考。;Purposes: This paper explores how laborers’ perception, attitude, and self-efficacy of intelligent manufacturing in enterprise management are influenced when employers in technology industries introduce Intelligent Manufacturing Automation (IMA) and use an effective enterprise strategy management, implement intelligent manufacturing through automation wireless sensor networks and ant colony optimization algorithms, and carry out related education and training. It also assesses the differences among the laborers after IMS introduction by comparing the evaluation results of the maturity of intelligent factories, which can help employers to effectively replace the machinery and equipment in the bottom-level production line of intelligent factories. Method: As intelligent Manufacturing Automation (IMA) employs wireless sensor networks and ant colony optimization algorithm, this study adopts a cross-sectional research design and the self-compiled and modified “Scale of Laborer’s Perception, Attitude and Self-efficacy in Intelligence Manufacturing”, with a review of the reliability and effectiveness of the research tool. In total, 330 laborers were selected in April 2019 by means of intentional sampling from two fields (Field A and Field B) of a technology factory with different degrees of IMA implementation, including different levels of working environment management automation, design automation, and factory intelligence. The research results were then analyzed by means of descriptive statistics, independent sample T test, one-way ANOVA, Pearson product difference correlation, and linear regression analysis. The purpose is to investigate the different impacts of various independent variables and IMA related education and training on laborers’ perception, attitude, self-efficacy in intelligent manufacturing, and automation management. SPSS 21 was used as the analysis tool. Results: Introducing ant colony optimization algorithm in IMA can save the energy consumption of nodes in the network and prolong the life cycle of the network. Among the two fields, laborers in Field B, which has a relatively higher level of IMA, exhibit better performance in the perception, attitude, and self-efficacy of intelligent manufacturing. The fact that they have received education and training courses on IMA is the major correlation factor for their perception, attitude, and self-efficacy. On the contrary, laborers in Field A (control unit field) have poorer performance in their perception, attitude, and self-efficacy in intelligent manufacturing. The demographic impact factors are mainly gender, seniority, work unit, intelligent automation, the times and effects of learning in intelligent production line courses, intelligent automation experience, automation support, and working environment. By controlling the demographic impact factors, the differences in the perception of the laborers in the control unit field of IMA related education and training courses still have a significant correlation with their perception, attitude, and self-efficacy in intelligent manufacturing, which runs contrary to the case with the laborers in Field B who have received a higher degree of IMA related education and trainings. Conclusion: The higher the implementation degree is for the IMA education and training courses among the laborers, the better are the laborers’ perception, attitude, and self-efficacy in intelligent manufacturing and learning effect of the courses, and the impact factors are simpler. The research results of this study can be used as a reference for labor adjustment in various industries after introducing intelligent factories and for the curriculum design of intelligent automation courses. In terms of practical contributions, it enables employers to have better cognition of the implementation of introducing intelligent automation in technology industries and further provides business operators with procedures for establishing intelligent automation enterprise Industry 4.0 and QCDS management procedures, which can enhance their competitiveness.
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