作者：文思特咨詢師杜建生
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上一篇我們分享了ASQ推出的《質量工具箱第3版（Quality Toolbox 3rd Edition）》中關于汽車供應鏈常用的“控制計劃”的片段，本文繼續為您分享：
片段2：數字時代的 工業4.0和質量4.0
In the fast-paced world of technology, industries are constantly evolving to meet the demands of the market. One such evolution is the emergence of Industry 4.0, a term that encompasses the integration of digital technologies into manufacturing processes. Quality 4.0, an offshoot of Industry 4.0, focuses on leveraging these technologies to enhance quality control and assurance. This section highlights the concept of Industry 4.0, its impact on quality management and the role of quality professionals, and quality tools that can help professionals in this digital era.
在快節奏的科技世界中，各行各業都在不斷發展以滿足市場需求。其中之一就是工業4.0的出現，它是一個將數字技術融入制造流程的術語。質量4.0是工業4.0的一個分支，其重點是利用這些技術加強質量控制和保證。本節重點介紹工業4.0的概念、其對質量管理的影響和質量專業人員的作用，以及可在這個數字化時代幫助專業人員的質量工具。
Industry 4.0
工業4.0
Industry 4.0, also known as the “Fourth Industrial Revolution,” represents a paradigm shift in manufacturing and production. It involves the integration of various advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud computing, and robotics. These technologies enable automation, real-time data analysis, and connectivity between machines, products, and humans. The primary goal of Industry 4.0 is to create smart factories that are highly efficient, flexible, and capable of autonomous decision-making.
工業4.0，又稱“第四次工業革命”，代表著制造和生產模式的轉變。它涉及各種先進技術的整合，如物聯網（IoT）、人工智能（AI）、大數據分析、云計算和機器人技術。這些技術實現了自動化、實時數據分析以及機器、產品和人類之間的連接。工業 4.0 的主要目標是創建高效、靈活、能夠自主決策的智能工廠。
Quality 4.0
質量4.0
Quality 4.0 builds upon the foundation of Industry 4.0 by incorporating quality management principles and practices into the digital transformation of manufacturing. It aims to leverage digital technologies to improve quality control and assurance processes, enhance product traceability, and enable predictive maintenance. Quality 4.0 emphasizes the use of real-time data analytics, machine learning, and advanced analytics to detect and prevent defects, reduce variability, and optimize production processes. By embracing Quality 4.0, organizations can achieve higher levels of product quality, customer satisfaction, and operational efficiency.
質量4.0建立在工業4.0的基礎之上，將質量管理原則和實踐融入到制造業的數字化轉型中。它旨在利用數字技術改進質量控制和保證流程，提高產品可追溯性，并實現預測性維護。質量4.0強調使用實時數據分析、機器學習和高級分析來檢測和預防缺陷、降低變異和優化生產流程。通過采用質量4.0，企業可以實現更高水平的產品質量、客戶滿意度和運營效率。
Empowering Quality Professionals
增強質量專業技能
Quality professionals play a crucial role in ensuring that products and services meet the desired standards and quality excellence. In the era of Industry 4.0 and Quality 4.0, their roles become even more critical and transformative as they help organizations thrive in disruption. Here are some key ways in which Industry 4.0 empowers quality professionals.
質量專業人員在確保產品和服務達到預期標準和卓越質量方面發揮著至關重要的作用。在工業4.0和質量4.0時代，他們的作用變得更加關鍵和具有變革性，因為他們幫助組織在混亂中茁壯成長。以下是工業4.0增強質量專業能力的一些主要方式。
1. Real-time Monitoring and Control
1. 實時監視和控制
Industry 4.0 technologies enable quality professionals to monitor and control production processes in real time. Through connected sensors and IoT devices, they can gather data on various parameters such as temperature, pressure, humidity, and machine performance. These data provide valuable insights into process variations, potential defects, and deviations from quality standards. Quality professionals can take immediate corrective actions, minimizing the risk of quality issues and ensuring consistent product quality.
工業4.0技術使質量專業人員能夠實時監控生產流程。通過連接的傳感器和物聯網設備，他們可以收集溫度、壓力、濕度和機器性能等各種參數的數據。這些數據為了解流程變差、潛在缺陷和質量標準偏差提供了寶貴的信息。質量專業人員可以立即采取糾正措施，最大限度地降低質量問題的風險，確保產品質量的一致性。
2. Predictive Analytics
2. 預測分析
With the advent of big data analytics and machine learning algorithms, quality professionals can now predict quality issues before they occur. By analyzing historical data and identifying patterns, they can forecast potential defects , equipment failures , and deviations in process parameters. This proactive approach allows for preventive maintenance, early defect detection, and better resource allocation, leading to improved quality outcomes and reduced downtime.
隨著大數據分析和機器學習算法的出現，質量專業人員現在可以在質量問題發生之前進行預測。通過分析歷史數據和識別模式，他們可以預測潛在的缺陷、設備故障和工藝參數偏差。這種積極主動的方法可以實現預防性維護、早期缺陷檢測和更好的資源分配，從而提高質量成果并減少停機時間。
3. Enhanced Product Traceability
3. 增強產品可追溯性
Quality 4.0 enables end-to-end product traceability throughout the supply chain. Through technologies like radio frequency identification (RFID) tags and barcode scanning, quality professionals can track the movement of raw materials, components, and finished products. This traceability ensures accountability, facilitates faster recalls in case of quality issues, and strengthens compliance with regulatory standards. Quality professionals can leverage these data to investigate quality incidents, identify root causes, and implement corrective actions.
質量4.0實現了整個供應鏈中端到端的產品可追溯性。通過射頻識別（RFID）標簽和條形碼掃描等技術，質量專業人員可以跟蹤原材料、部件和成品的移動。這種可追溯性確保了產品責任，有助于在出現質量問題時更快地召回產品，并加強對監管標準的合規性。質量專業人員可以利用這些數據調查質量事故，找出根本原因，并實施糾正措施。
4. Integration of Quality Systems
4. 質量體系整合
Industry 4.0 encourages the integration of quality management systems with other enterprise systems such as enterprise resource planning (ERP) and manufacturing execution systems (MES). This integration allows quality professionals to access real-time data from multiple sources, enabling a holistic view of quality performance across the organization. It facilitates seamless communication, collaboration, and decision-making, leading to streamlined quality processes and faster response times.
工業 4.0 鼓勵將質量管理體系與企業資源規劃 (ERP) 和制造執行系統 (MES) 等其他企業系統進行整合集成。這種集成使質量專業人員能夠訪問來自多個來源的實時數據，從而全面了解整個組織的質量績效。它促進了無縫溝通、協作和決策，從而簡化了質量流程，加快了響應速度。
Quality 4.0 Tools
質量4.0工具
While definitive Quality 4.0 tools have yet to be recognized, most industry leaders agree that there is a shortage of appropriate tools to support Quality 4.0 initiatives.
雖然明確的質量4.0工具尚未得到認可，但大多數行業領導者都認為，目前缺乏支持質量 4.0 計劃的適當工具。
Nicole Radziwill, a pioneer in Quality 4.0 and author of Connected, Intelligent, Automated, identifies an ecosystem of Quality 4.0 tools that can help map business drivers to potential solutions in evaluating business needs (Figure 2.6):
質量4.0 的先驅、《互聯、智能、自動化》一書的作者 Nicole Radziwill 指出，質量 4.0 工具的生態系統有助于在評估業務需求時將業務驅動因素與潛在解決方案聯系起來（圖 2.6）：
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Artificial intelligence 人工智能 |
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Deep learning 深層學習算法 |
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Big data 大數據 |
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Enabling technologies 輔助技術 |
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Blockchain 區塊鏈 |
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Data science 數據科學 |
Figure 2.6 Relationships among AI, ML, and infrastructure elements.
圖2.6 人工智能、機器學習和基礎設施要素之間的關系
(Source: N. M. Radziwill; Connected, Intelligent, Automated (Quality Press, Milwaukee, WI; 2020), p. 53)./《引自質量進展》雜志
Although machine learning (ML) and continuous improvement are analogous, ML adds intelligence and automation to continuous improvement but cannot replace it.
雖然機器學習（ML）和持續改進有相似之處，但ML增加了持續改進的智能化和自動化，而不能取而代之。
The 8D reporting tool covered in Chapter 3 is one such tool. While not exclusively a 4.0 tool, the 8D methodology is a potent problem-solving and communication instrument, particularly in customer complaint scenarios.
第3章介紹的8D報告工具就是這樣一種工具。雖然8D方法不完全是4.0工具，但它是一種有效的問題解決和溝通工具，尤其是在客戶投訴的情況下。
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這本書摘抄內容就到這里吧，如果您想對質量工具箱中148種工具有更多的了解，請向ASQ官網購買該書（英文版）。
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