Smart manufacturing (SM)—the use of advanced, highly integrated technologies in manufacturing processes—is revolutionizing how companies operate. Evolving technologies and an increasingly globalized and digitalized marketplace have driven manufacturers to adopt smart manufacturing technologies to maintain competitiveness and profitability.
智能製造 （SM） — 在製造流程中使用先進、高度集成的技術 — 正在徹底改變公司的運營方式。不斷發展的技術以及日益全球化和數位化的市場促使製造商採用智慧製造技術來保持競爭力和盈利能力。

An innovative application of the Industrial Internet of Things (IIoT), SM systems rely on the use of high-tech sensors to collect vital performance and health data from an organization’s critical assets.
SM 系統是工業物聯網 （IIoT） 的創新應用，它依賴於使用高科技感測器從組織的關鍵資產中收集重要的性能和健康數據。

Smart manufacturing, as part of the digital transformation of Industry 4.0, deploys a combination of emerging technologies and diagnostic tools (e.g., artificial intelligence (AI) applications, the Internet of Things (IoT), robotics and augmented reality, among others) to optimize enterprise resource planning (ERP), making companies more agile and adaptable. 
作為工業 4.0 數字化轉型的一部分，智慧製造部署了新興技術和診斷工具（例如人工智慧 （AI） 應用程式、物聯網 （IoT）、機器人和增強現實等）的組合，以優化企業資源規劃 （ERP），使公司更加敏捷和適應性強。

This article will explore the key technologies associated with smart manufacturing systems, the benefits of adopting SM processes, and the ways in which SM is transforming the manufacturing industry.
本文將探討與智慧製造系統相關的關鍵技術、採用SM流程的好處以及SM改變製造業的方式。

Smart manufacturing (SM) is a sophisticated process, dependent on a network of new technologies working collaboratively to streamline the entire production ecosystem.
智慧製造 （SM） 是一個複雜的過程，依賴於新技術網路的協同工作，以簡化整個生產生態系統。

Key SM tools include the following:
主要的 SM 工具包括：

Industrial Internet of Things (IIoT)
工業物聯網 （IIoT）

The IIoT is a network of interconnected machinery, tools and sensors that communicate with each other and the cloud to collect and share data. IIoT-connected assets help industrial manufacturing facilities manage and maintain equipment by utilizing cloud computing and facilitating communication between enabled machinery. These features use data from multiple machines simultaneously, automate processes and provide manufacturers more sophisticated analyses.
IIoT 是一個由相互連接的機器、工具和感測器組成的網路，它們相互通信並與雲通訊以收集和共享數據。IIoT 連接資產通過利用雲計算和促進支援的機器之間的通信，幫助工業製造設施管理和維護設備。這些功能同時使用來自多台機器的數據，實現流程自動化，併為製造商提供更複雜的分析。

In smart factories, IIoT devices are used to enhance machine vision, track inventory levels and analyze data to optimize the mass production process. 
在智慧工廠中，IIoT 設備用於增強機器視覺、跟蹤庫存水準和分析數據以優化大規模生產流程。

The IIoT not only allows internet-connected smart assets to communicate and share diagnostic data, enabling instantaneous system and asset comparisons, but it also helps manufacturers make more informed decisions about the entire mass production operation.
IIoT 不僅允許連接互聯網的智慧資產進行通信和共用診斷數據，實現即時系統和資產比較，還可以幫助製造商對整個大規模生產運營做出更明智的決策。

Artificial intelligence (AI)
人工智慧 （AI）

One of the most significant benefits of AI technology in smart manufacturing is its ability to conduct real-time data analysis efficiently. With IoT devices and sensors collecting data from machines, equipment and assembly lines, AI-powered algorithms can quickly process and analyze inputs to identify patterns and trends, helping manufacturers understand how production processes are performing.
AI 技術在智慧製造中最重要的優勢之一是它能夠有效地進行實時數據分析。借助物聯網設備和感測器從機器、設備和裝配線收集數據，人工智慧驅動的演算法可以快速處理和分析輸入以識別模式和趨勢，幫助製造商瞭解生產流程的執行情況。

Companies can also use AI systems to identify anomalies and equipment defects. Machine learning algorithms and neural networks, for instance, can help identify data patterns and make decisions based on those patterns, allowing manufacturers to catch quality control issues early in the production process.
公司還可以使用 AI 系統來識別異常和設備缺陷。例如，機器學習演算法和神經網路可以幫助識別數據模式並根據這些模式做出決策，使製造商能夠在生產過程的早期發現品質控制問題。

Furthermore, utilizing AI solutions as a part of smart maintenance programs can help manufacturers:
此外，將 AI 解決方案用作智慧維護計畫的一部分可以幫助製造商：

• Implement predictive maintenance
  實施預測性維護
• Streamline supply chain management
  簡化供應鏈管理
• Identify workplace safety hazards
  識別工作場所安全隱患

Robotics 機器人

Robotic process automation (RPA) has been a key driver of smart manufacturing, with robots taking on repetitive and/or dangerous tasks like assembly, welding and material handling. Robotics technology can perform repetitive tasks faster and with a much higher degree of accuracy and precision than human workers, improving product quality and reducing defects. 
機器人流程自動化 （RPA） 一直是智慧製造的關鍵驅動力，機器人承擔了重複性和/或危險的任務，如組裝、焊接和材料處理。機器人技術可以比人類工人更快、更準確、更精密地執行重複性任務，從而提高產品品質並減少缺陷。

Robotics are also extremely versatile and can be programmed to perform a wide range of tasks, making them ideal for manufacturing processes that require high flexibility and adaptability. At a Phillips plant in the Netherlands, for example, robots are making the brand’s electric razors. And a Japanese Fanuc plant uses industrial robots to manufacture industrial robots, reducing personnel requirements to only four supervisors per shift.
機器人技術也用途廣泛，可以程式設計以執行各種任務，使其成為需要高度靈活性和適應性的製造過程的理想選擇。例如，在荷蘭的一家 Phillips 工廠，機器人正在製造該品牌的電動剃鬚刀。日本 Fanuc 工廠使用工業機器人製造工業機器人，將人員需求減少到每班僅需 4 名主管。

Perhaps most significantly, manufacturers interested in an SM approach can integrate robotics with IIoT sensors and data analytics to create a more flexible and responsive production environment.
也許最重要的是，對SM方法感興趣的製造商可以將機器人技術與IIoT感測器和數據分析集成，以創建更靈活、回應更迅速的生產環境。

Cloud and edge computing  雲和邊緣計算

Cloud computing and edge computing play a significant role in how smart manufacturing plants operate. Cloud computing helps organizations manage data collection and storage remotely, eliminating the need for on-premises software and hardware and increasing data visibility in the supply chain. With cloud-based solutions, manufacturers can leverage IIoT applications and other forward-thinking technologies (like edge computing) to monitor real-time equipment data and scale their operations more easily.
雲計算和邊緣計算在智慧製造工廠的運營方式中發揮著重要作用。雲計算可幫助組織遠端管理數據收集和存儲，無需本地軟體和硬體，並提高供應鏈中的數據可見性。借助基於雲端的解決方案，製造商可以利用 IIoT 應用程式和其他前瞻性技術（如邊緣計算）來監控即時設備數據並更輕鬆地擴展其運營。

Edge computing, on the other hand, is a distributed computing paradigm that brings computation and data storage closer to manufacturing operations, rather than storing it in a central cloud-based data center. In the context of smart manufacturing, edge computing deploys computing resources and data storage at the edge of the network—closer to the devices and machines generating the data—enabling faster processing with higher volumes of equipment data.
另一方面，邊緣計算是一種分散式計算範式，它使計算和數據存儲更接近製造運營，而不是將其存儲在基於雲的中央數據中心。在智慧製造的背景下，邊緣計算將計算資源和數據存儲部署在網路邊緣，更靠近生成數據的設備和機器，從而能夠更快地處理更多的設備數據。

Edge computing in smart manufacturing also helps manufacturers do the following:
智慧製造中的邊緣計算還可以幫助製造商執行以下操作：

• Reduce the network bandwidth requirements, latency issues and costs associated with long-distance big data transmission.
  降低與遠距離大數據傳輸相關的網路頻寬要求、延遲問題和成本。
• Ensure that sensitive data remains within their own network, improving security and compliance.
  確保敏感數據保留在自己的網路中，從而提高安全性和合規性。
• Improve operational reliability and resilience by keeping critical systems working during central data center downtime and/or network disruptions.
  通過在中央數據中心停機和/或網路中斷期間保持關鍵系統正常工作，提高運營可靠性和彈性。
• Optimize workflows by analyzing data from multiple sources (e.g., inventory levels, machine performance and customer demand) to find areas for improvement and increase asset interoperability.
  通過分析來自多個來源（例如庫存水準、機器性能和客戶需求）的數據來優化工作流程，以找到需要改進的領域並提高資產互操作性。

Together, edge computing and cloud computing allow organizations to utilize software as a service (SaaS), expanding technology accessibility to a wider range of manufacturing operations.
邊緣計算和雲計算相結合，使組織能夠利用軟體即服務 （SaaS），將技術可訪問性擴展到更廣泛的製造業務。

In manufacturing environments, where delays in decision-making can have significant impacts on production outcomes, cloud computing and edge computing help manufacturing companies quickly identify and respond to equipment failures, quality defects, production line bottlenecks, etc.
在製造環境中，決策延遲可能會對生產結果產生重大影響，雲計算和邊緣計算可幫助製造公司快速識別和回應設備故障、品質缺陷、生產線瓶頸等。

Blockchain 區塊鏈

Blockchain is a shared ledger that helps companies record transactions, track assets and improve cybersecurity within a business network. In a smart manufacturing execution system (MES), blockchain creates an immutable record of every step in the supply chain, from raw materials to the finished product. By using blockchain to track the movement of goods and materials, manufacturers can ensure that every step in the production process is transparent and secure, reducing the risk of fraud and improving accountability.
區塊鏈是一種共享帳本，可説明公司記錄交易、跟蹤資產並改善業務網路內的網路安全。在智慧製造執行系統 （MES） 中，區塊鏈為供應鏈中從原材料到成品的每一步創建不可變的記錄。通過使用區塊鏈來跟蹤貨物和材料的移動，製造商可以確保生產過程中的每一步都是透明和安全的，從而降低欺詐風險並提高問責制。

Blockchain can also be used to improve supply chain efficiency by automating many of the processes involved in tracking and verifying transactions. For instance, an organization can utilize smart contracts—self-executing contracts with the terms of the agreement written directly into lines of code—to verify the authenticity of products, track shipments and make payments. This can help reduce the time and cost associated with manual processes, while also improving accuracy and reducing the risk of errors.
區塊鏈還可用於通過自動化跟蹤和驗證交易所涉及的許多流程來提高供應鏈效率。例如，組織可以利用智慧合約（自動執行的合同，協定條款直接寫入代碼行）來驗證產品的真偽、跟蹤發貨和付款。這有助於減少與手動流程相關的時間和成本，同時還可以提高準確性並降低錯誤風險。

Manufacturers can also utilize blockchain technologies to protect intellectual property by creating a record of ownership and improve sustainability practices by tracking the environmental impact of production processes.
製造商還可以利用區塊鏈技術通過創建擁有權記錄來保護智慧財產權，並通過跟蹤生產過程對環境的影響來改進可持續性實踐。

Digital twins 數位孿生

Digital twins have become an increasingly popular concept in the world of smart manufacturing. A digital twin is a virtual replica of a physical object or system that is equipped with sensors and connected to the internet, allowing it to collect data and provide real-time performance insights. Digital twins are used to monitor and optimize the performance of manufacturing processes, machines and equipment. 
數位孿生已成為智慧製造領域越來越流行的概念。數位孿生是物理物件或系統的虛擬副本，它配備了感測器並連接到互聯網，使其能夠收集數據並提供即時性能洞察。數位孿生用於監控和優化製造流程、機器和設備的性能。

By collecting sensor data from equipment, digital twins can detect anomalies, identify potential problems, and provide insights on how to optimize production processes. Manufacturers can also use digital twins to simulate scenarios and test configurations before implementing them and to facilitate remote maintenance and support.
通過從設備收集感測器數據，數位孿生可以檢測異常、識別潛在問題，並提供有關如何優化生產流程的見解。製造商還可以使用數位孿生來類比場景和測試配置，然後再實施它們，並促進遠端維護和支援。

3D printing 3D 列印

3D printing, also known as additive manufacturing, is a rapidly growing technology that has changed the way companies design, prototype and produce products. Smart factories primarily use 3D printing to manufacture complex parts and components quickly and precisely.
3D 列印，也稱為增材製造，是一項快速發展的技術，它改變了公司設計、原型製作和生產產品的方式。智慧工廠主要使用 3D 列印來快速、精確地製造複雜的零部件。

Traditional manufacturing processes like injection molding can be limited by the complexity of a prototype’s part geometry, and they may require multiple steps and operations to produce. With 3D printing, manufacturers can produce complex geometries in a single step, reducing manufacturing time and costs.
注塑成型等傳統製造工藝可能會受到原型零件幾何形狀複雜性的限制，並且可能需要多個步驟和操作才能生產。借助 3D 列印，製造商只需一步即可生產出複雜的幾何形狀，從而減少製造時間和成本。

3D printing can also help companies:
3D 列印還可以説明公司：

• Develop customized products and components by using digital design files.
  使用數位設計檔開發定製產品和元件。
• Build and test prototypes right on the shop floor.
  直接在車間構建和測試原型。
• Enable on-demand manufacturing to streamline inventory management processes.
  實現按需製造以簡化庫存管理流程。

Predictive analytics  預測分析

Smart manufacturing relies heavily on data analytics to collect, process and analyze data from various sources, including IIoT sensors, production systems and supply chain management systems. Using advanced data analytics techniques, predictive analytics can help identify inefficiencies, bottlenecks and quality issues proactively.
智慧製造在很大程度上依賴於數據分析來收集、處理和分析來自各種來源的數據，包括 IIoT 感測器、生產系統和供應鏈管理系統。使用先進的數據分析技術，預測分析可以幫助主動識別效率低下、瓶頸和質量問題。

The primary benefit of predictive analytics in the manufacturing sector is their ability to enhance defect detection, allowing manufacturers to take preemptive measures to prevent downtime and equipment failures. Predictive analysis also enables organizations to optimize maintenance schedules to determine the best time for maintenance and repairs.
預測分析在製造業的主要好處是它們能夠增強缺陷檢測，使製造商能夠採取先發制人的措施來防止停機和設備故障。預測分析還使組織能夠優化維護計劃，以確定維護和維修的最佳時間。

Smart manufacturing solutions, like IBM Maximo Application Suite, offer a number of benefits compared to more traditional manufacturing approaches, including the following:
與更傳統的製造方法相比，智慧製造解決方案（如IBM Maximo Application Suite）具有許多優勢，包括：

• Increased efficiency: Smart manufacturing can improve organizational efficiency by optimizing production processes and facilitating data convergence initiatives. By leveraging new information technologies, manufacturers can minimize production errors, reduce waste, lower costs and improve overall equipment effectiveness.
  提高效率：智慧製造可以通過優化生產流程和促進數據融合計劃來提高組織效率。通過利用新的資訊技術，製造商可以最大限度地減少生產錯誤、減少浪費、降低成本並提高整體設備效率。
• Improved product quality: Smart manufacturing helps companies produce higher-quality products by improving process control and product testing. Using IIoT sensors and data analytics, manufacturers can monitor and control production throughputs in real time, identifying and correcting issues before they impact product quality.
  提高產品品質：智慧製造通過改進過程控制和產品測試來説明公司生產更高品質的產品。使用 IIoT 感測器和數據分析，製造商可以即時監控和控制生產輸送量，在問題影響產品品質之前識別和糾正問題。
• Increased flexibility: Smart manufacturing improves production flexibility by enabling manufacturers to adapt quickly to changing market demands and maximizing the benefits of demand forecasting. By deploying robotics and AI tools, manufacturers can quickly reconfigure production lines throughout the lifecycle to accommodate changes in product design or production volume, effectively optimizing the value chain.
  提高靈活性：智慧製造使製造商能夠快速適應不斷變化的市場需求並最大限度地發揮需求預測的優勢，從而提高生產靈活性。通過部署機器人和 AI 工具，製造商可以在整個生命週期中快速重新配置生產線，以適應產品設計或產量的變化，從而有效優化價值鏈。

IBM Maximo Application Suite is a comprehensive enterprise asset management system that helps organizations optimize asset performance, extend asset lifespan and reduce unplanned downtime. IBM Maximo provides users an integrated AI-powered, cloud-based platform with comprehensive CMMS capabilities that produce advanced data analytics and help maintenance managers make smarter, more data-driven decisions.
IBM Maximo Application Suite 是一個全面的企業資產管理系統，可説明組織優化資產性能、延長資產使用壽命並減少計劃外停機時間。IBM Maximo 為使用者提供一個基於 AI 的集成、基於雲的平臺，該平臺具有全面的 CMMS 功能，可生成高級數據分析，並幫助維護經理做出更明智、更數據驅動的決策。