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市場調查報告書
商品編碼
1953408
智慧型手機製造機器人流程自動化市場-全球產業規模、佔有率、趨勢、機會及預測(按機器人類型、組件、公司規模、地區和競爭格局分類,2021-2031年)Robotic Process Automation for Smartphone Manufacturing Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Robot Type, By Component, By Organization Size, By Region & Competition, 2021-2031F |
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全球智慧型手機製造機器人流程自動化 (RPA) 市場預計將從 2025 年的 58.3 億美元成長到 2031 年的 195.7 億美元,複合年成長率為 22.36%。
該行業正在廣泛採用自動化系統和智慧軟體機器人來執行重複性的、基於規則的任務,例如管理供應鏈數據和協調組裝。市場成長的主要驅動力是降低營運成本的迫切需求以及為支援日益小型化的設備外形而對高精度的需求。此外,製造商正在加速採用這些技術,以提高生產柔軟性並應對外部經濟挑戰。例如,IPC報告稱,到2025年,31%的電子產品製造商將投資於最佳化或自動化策略,以抵消不斷上漲的貿易關稅的影響。
| 市場概覽 | |
|---|---|
| 預測期 | 2027-2031 |
| 市場規模:2025年 | 58.3億美元 |
| 市場規模:2031年 | 195.7億美元 |
| 複合年成長率:2026-2031年 | 22.36% |
| 成長最快的細分市場 | 自動化設備 |
| 最大的市場 | 亞太地區 |
阻礙該市場進一步擴張的主要障礙在於,將現代自動化工具與老舊的製造基礎設施整合需要大量的資本投入。許多製造商難以將先進的機器人軟體與業務線計畫 (ERP) 系統同步,這增加了市場進入門檻,延緩了全面普及,並延長了投資回報週期。
工業4.0和智慧工廠技術的融合,需要部署智慧化的網路系統,這正在重塑全球智慧型手機製造機器人流程自動化(RPA)市場。隨著智慧型手機設計日益複雜,製造商正從孤立的自動化系統轉向完全整合的智慧平台,在這些平台上,RPA機器人負責管理工程、供應鏈和生產部門之間的資料交換。這種向高科技基礎設施的策略轉變也體現在主要產業參與者的投資趨勢中。例如,2025年8月發布的《亞洲科技》報告《富士康將在美國投資10億美元用於人工智慧和機器人技術》指出,該公司已核准一項10億美元的投資,用於推動智慧製造和機器人技術的發展。如此巨額的資本投資表明,該行業決心創建一個自我最佳化的環境,利用RPA來減少缺陷並實現即時決策。
同時,加快產品上市速度和追求供應鏈柔軟性使得RPA成為應對貿易波動和外部經濟壓力的關鍵工具。製造商正利用自動化技術快速重新配置組裝並調整物流,以應對不斷變化的全球政策,確保生產計劃的順利進行。美國全國製造商協會(NAM)於2025年3月發布的《2025年第一季製造業展望調查》也印證了這種對適應性的需求。調查發現,76.2%的製造商認為貿易不確定性是關鍵挑戰,因此需要靈活的生產系統。為了滿足這些需求並提高應對力,企業正在在地化建造先進的生產設施。根據2025年12月發布的《區域發展報告》,富士康科技已承諾投資超過1.73億美元在肯塔基州建造一座工廠,該工廠將把人工智慧和機器人技術整合到所有生產環節。
將現代機器人流程自動化系統整合到現有基礎設施中所需的大量資本投入,嚴重阻礙了市場成長。智慧型手機製造商通常依賴現有的業務線計劃 (ERP) 框架,而這些框架與新的自動化軟體存在根本性的不相容,需要耗資巨資進行複雜的整合工作。這種財務負擔不僅包括機器人單元的初始購買成本,還包括系統改造、專業技術人員以及實施階段長時間運作所帶來的巨額費用。因此,這種高准入門檻對中小型製造商的影響尤其顯著,迫使它們推遲現代化計畫以維持流動性,儘管這些計畫具有長期效率提升的潛力。
由於不願投資大規模自動化,整個電子產業正出現明顯的萎縮。來自自動化促進協會(Association for Advancing Automation)的數據顯示,2025年初,電子和半導體產業的機器人訂單較去年同期下降了37%。這一急劇下降表明技術和財務壁壘的直接影響。面對老舊生產線維修的複雜性,生產商很可能會延後或縮減自動化計劃。除非解決與互通性相關的成本問題,否則這種資本密集的整合過程可能會繼續造成市場波動,並減緩智慧型手機製造市場採用機器人流程的步伐。
人工智慧 (AI) 和機器學習的應用已超越了基礎自動化,涵蓋了能夠增強智慧型手機生產線營運彈性和品管的生成模型。製造商正擴大利用這些技術來檢測細微缺陷並最佳化工作流程,而靜態的、基於規則的機器人則無法做到這一點,從而有效地彌合了自適應智慧和標準機器人流程自動化 (RPA) 之間的差距。這種向自糾錯系統的轉變是可以量化的。根據羅克韋爾自動化公司於 2025 年 6 月發布的《2025 年智慧製造現況報告》,95% 的製造商已經投資或計劃在未來五年內投資人工智慧和機器學習技術,並將品管列為關鍵應用案例。這一激增反映出生產環境正在發生重大轉變,即在複雜移動部件的生產中實現自主最大化產量比率並最小化廢棄物。
同時,隨著人們對永續和環保製造方式的日益關注,供應商正在積極推廣自動化技術的應用,以確保嚴格的能源管理和符合環境法規。機器人流程自動化(RPA)對於即時監測碳足跡和建構循環經濟至關重要,例如,它可以對廢棄設備進行精密拆解和回收,從而提取高價值材料。這種對環保營運的承諾也清楚地體現在各大原始設備製造商(OEM)的策略中。根據三星電子於2025年6月發布的《2025年永續發展報告》,其統籌智慧型手機生產的設備體驗部門計畫到2024年底,在全球各製造地實現93.4%的可再生能源轉型率。這些努力表明,自動化不僅用於提高生產速度,更被用於在電子產品大規模生產領域實現雄心勃勃的碳中和目標。
The Global Robotic Process Automation for Smartphone Manufacturing Market is projected to expand from USD 5.83 Billion in 2025 to USD 19.57 Billion by 2031, registering a CAGR of 22.36%. This sector involves the implementation of automated systems and intelligent software bots to perform repetitive, rule-governed tasks, ranging from managing supply chain data to coordinating assembly lines. The market's growth is primarily fueled by the urgent need to reduce operational expenses and the demand for high precision to support increasingly compact device forms. Additionally, manufacturers are expediting the adoption of these technologies to improve production flexibility and navigate external economic challenges; for instance, the IPC reported that in 2025, 31% of electronics manufacturers funded optimization or automation strategies to offset the effects of increasing trade tariffs.
| Market Overview | |
|---|---|
| Forecast Period | 2027-2031 |
| Market Size 2025 | USD 5.83 Billion |
| Market Size 2031 | USD 19.57 Billion |
| CAGR 2026-2031 | 22.36% |
| Fastest Growing Segment | Automation Equipment |
| Largest Market | Asia Pacific |
A major obstacle restricting the wider expansion of this market is the significant capital expenditure necessary to merge modern automation tools with aging manufacturing infrastructure. Numerous producers struggle to synchronize sophisticated robotic software with legacy enterprise resource planning systems, creating a high barrier to entry that delays full implementation and extends the time required to realize a return on investment.
Market Driver
The convergence of Industry 4.0 and Smart Factory technologies is reshaping the Global Robotic Process Automation for Smartphone Manufacturing Market by requiring the deployment of intelligent, networked systems. As smartphone designs grow more complex, manufacturers are shifting from standalone automation islands to fully integrated smart platforms where RPA bots oversee data exchanges across engineering, supply chain, and production units. This strategic transition toward high-tech infrastructure is highlighted by the investment habits of major industry players; for example, a Tech in Asia report from August 2025 titled 'Foxconn to invest $1b in US for AI, robotics' noted that the company authorized a $1 billion investment specifically to advance smart manufacturing and robotics. Such substantial capital commitments demonstrate the industry's dedication to building self-optimizing environments that utilize RPA for defect reduction and real-time decision-making.
Simultaneously, the drive for accelerated time-to-market and supply chain flexibility is positioning RPA as a crucial tool for buffering against trade volatility and external economic pressures. Manufacturers are using automation to swiftly reorganize assembly lines and adjust logistics in response to changing global policies, ensuring production schedules remain unbroken. The necessity of this adaptability is underscored by the National Association of Manufacturers' '2025 First Quarter Manufacturers' Outlook Survey' from March 2025, where 76.2% of manufacturers identified trade uncertainties as a primary challenge requiring flexible production systems. To address these needs and improve responsiveness, companies are localizing advanced facilities; as reported by Area Development in December 2025, Foxconn Technology Co. confirmed an investment exceeding $173 million to build a Kentucky facility that integrates AI and robotics into all production phases.
Market Challenge
The heavy capital expenditure required to align modern robotic process automation with legacy infrastructure presents a severe barrier to market growth. Smartphone manufacturers frequently rely on established enterprise resource planning frameworks that are fundamentally incompatible with newer automated software, requiring expensive and complex integration efforts. This financial burden goes beyond the initial purchase of robotic units to include significant costs for system retrofitting, specialized technical personnel, and extended downtime during the deployment phase. Consequently, this high barrier to entry disproportionately affects small and mid-sized manufacturing entities, compelling them to postpone modernization initiatives to preserve liquidity despite the potential for long-term efficiency gains.
This reluctance to commit to large-scale automation investments has led to measurable contractions within the wider electronics sector. Data from the Association for Advancing Automation in early 2025 indicated that robot orders from the electronics and semiconductor industries dropped by 37% year-over-year. This sharp decline illustrates the direct impact of these technical and financial hurdles, as producers delay or scale back automation projects when faced with the complexities of retrofitting aging production lines. Until the costs associated with interoperability are addressed, this capital-intensive integration process will likely continue to cause volatility and delay the widespread adoption of robotic processes across the smartphone manufacturing market.
Market Trends
The incorporation of Artificial Intelligence and Machine Learning is progressing beyond basic automation to encompass generative models that bolster operational resilience and quality control within smartphone production lines. Manufacturers are increasingly utilizing these technologies to detect subtle defects and optimize workflows in ways that static, rule-based bots cannot, effectively bridging the gap between adaptive intelligence and standard robotic process automation. This shift toward self-correcting systems is quantifiable; according to the '2025 State of Smart Manufacturing Report' by Rockwell Automation in June 2025, 95% of manufacturers have invested in or intend to invest in AI and machine learning technologies over the next five years, with quality control identified as the leading use case. This surge reflects a critical move toward production environments that autonomously maximize yields and minimize waste for complex mobile components.
At the same time, the focus on Sustainable and Green Manufacturing Practices is driving vendors to employ automation for rigorous energy management and environmental compliance. Robotic process automation is becoming essential for real-time monitoring of carbon footprints and orchestrating the circular economy, such as the precise disassembly and recycling of valuable materials from discarded devices. This commitment to eco-friendly operations is evident in the strategies of major OEMs; according to the '2025 Sustainability Report' by Samsung Electronics in June 2025, the company's Device eXperience Division, which oversees smartphone production, attained a 93.4% renewable energy transition rate across its global manufacturing sites by the end of 2024. Such initiatives highlight how automation is being leveraged not just for speed, but to meet aggressive carbon neutrality targets in high-volume electronics production.
Report Scope
In this report, the Global Robotic Process Automation for Smartphone Manufacturing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies present in the Global Robotic Process Automation for Smartphone Manufacturing Market.
Global Robotic Process Automation for Smartphone Manufacturing Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: