影響傳統製造業導入智慧製造之關鍵因素-以S化纖公司為例

傳統製造業屬於高度勞力密集的產業，面對日益嚴重的缺工、缺人才，生產管理如仍靠人工統計與分析數據，產品組態少量多樣化趨勢下，傳統產業將面臨淘汰。自民國98年起台灣製造業營運中的工廠家數，每年平均增加約1千多家工廠，因此產業的人才需求增加，但是少子化與高齡化社會影響，工作人口結構正快速流失。傳統製造業必須導入智慧製造，改善過去高度仰賴人力的作業模式。本研究之個案為國內知名之 S化纖公司，於2016年前已察覺科技大廠紛紛導入工業4.0應用，但工業4.0並不適合傳統製造業，其原因在於缺乏科技業在設備投資的先天優勢，且傳統製造業亦無科技業的高額毛利，因此難以支撐工業4.0驚人投資。為此， S化纖公司開始探索較適合傳統製造業的工業3.5，並積極啟動產官學界專家輔導，定期進行各廠處的找尋痛點與改善，但如何藉由智慧製造的導入以提升營運績效與產業競爭力，一直是個案公司所關注的焦點。相關文獻對於影響傳統製造業導入智慧製造之關鍵因素也甚少著墨。本研究透過德爾菲訪談業界專家，建立適用於傳統製造業導入智慧製造之研究架構，並運用以決策實驗室法針對傳統製造業導入智慧製造之問卷資料進行分析。透過德爾菲法依專家們的共識，淬取出3大構面與12項關鍵準則。研究結果顯示專業資深經理人對於「設備自動化」、「人機協同」、「設備通訊介面整合」、「生產資訊監測」、「大數據分析」與「最佳化生產」等6項為影響導入智慧製造之關鍵因素。本研究認為傳統製造業應以「設備自動化」作為智慧製造的改善源頭，改善策略包含「把握設備閒置時期」與「耗能設備汰舊換新」，研究結果可供欲導入智慧製造傳統製造業者做為參考，以謀求提升企業競爭優勢。關鍵詞：傳統製造業、智慧製造、德爾菲法、決策實驗室法、設備自動化

關鍵字

傳統製造業；智慧製造；德爾菲法；決策實驗室法；設備自動化

並列摘要

The traditional manufacturing is a highly labor-intensive industry. Facing the increasingly serious shortage of labor and talented person, if production management still were relied on manual statistics and data analysis and the small amount of diversification of product composition trend, the traditional industries will face elimination issue afterwards. The number of factories operating in Taiwan's manufacturing industry has increased by an average of more than 1,000 factories per year since 2009, thus the industries were increasing the demand for talented person, but with the impact of a lower birth rate and aging society, the workforce is rapidly losing its structure. The traditional manufacturing industry must be adapted to intelligent manufacturing to improve the previous mode of operation which is highly dependent on Human Resources. The case of this study is a well-known domestic S chemical fiber company. it was noticed that large technology companies have introduced Industry 4.0 applications before 2016, but Industry 4.0 is not suitable for traditional manufacturing. The reason is that the technology industry lacks the inherent advantages on equipment investment, and the traditional manufacturing industry didn’t have the high gross profit than the technology industry as well, thus enhancing the difficulty on supporting the amazing investment of Industry 4.0. As discussed above, S Chemical Fiber Company began to explore Industry 3.5, which is more suitable for traditional manufacturing, and positively initiated the guidance from industry-academia experts and regularly conducted pain points and improvement in each factory. However, how to improve operational performance through the introduction of intelligent manufacturing competitiveness with the industry has always been the focus from the case of corporations. The key factors influencing the adoption of intelligent manufacturing in traditional manufacturing industries have also received little attention from the related literature. In this study, Delphi interviewed industry experts to develop a applicable research framework to introduce traditional manufacturing into intelligent manufacturing, and in turn to apply DEMATEL to analyze the questionnaire data of traditional manufacturing into intelligent manufacturing. The Delphi method was used to extract three major components and 12 key criteria based on the consensus of the experts. The results of the study show that senior managers have identified six key factors that influence the introduction of intelligent manufacturing including “equipment automation", "human-machine collaboration", "equipment communication interface integration", "production information monitoring", "big data analysis" and "production optimization". The study considered that the traditional manufacturing industry should take "equipment automation" as a source of improvement for intelligent manufacturing. The improvement strategies include "seizing the period of equipment idleness" and "replacing energy-consuming equipment with new ones". In order to enhance the competitive advantages among the industries, the results of the study can be used as a reference for traditional manufacturers who would like to introduce intelligent manufacturing. Keywords: Traditional manufacturing, Intelligent manufacturing, Delphi method, DEMATEL, Equipment automation