表紙
市場調查報告書
商品編碼
931995

通信產業中的AI致能維護和測試

AI-Enabled Maintenance and Testing in Telecoms

出版日期: | 出版商: ABI Research | 英文 | 商品交期: 最快1-2個工作天內

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  • 全貌
  • 簡介
  • 目錄
簡介

本報告討論供應商在為了雲端型實裝更佳維護解決方案的市場化時所需考慮的重點,CSP為了確立自立型可導入的故障排除之主要AI/ML功能分析,次世代維護解決方案所需因應的營運要求等議題。

■實用優勢

  • 確定推動智能維護解決方案需求的關鍵趨勢。
  • 教育決策者和產品開發者,AI、ML、雲端對通信業者維護解決方案的影響。
  • 識別供應商開發次世代維護及測試工具時可使用的核心技術

■本報告回答的關鍵疑問

  • 從雲端工具、軟體、DevOps方法觀點,維護解決方案是如何進化?
  • 無關技術及位置、穿越多個雲端及通信網域的工具之主要推動因素是什麼?
  • 部分供應商該如何將AI及ML納入維護與測試解決方案之中?

■目錄

第1章 摘要整理

第2章 建議

第3章 次世代維護和測試工具趨勢和推動因素

  • RAN與核心網路趨勢
  • 手動流程舊有設備
  • 重新設計、重新設置工具的需求,和以服務為中心營運的準備

第4章 對AI、ML、雲端維護與測試的影響

  • AI三大功能
  • 無線網路中AI最佳生態系統Dimension
  • 舊有設備維護與測試的演進
  • Closed-Loop維護
  • 硬體與物理導向測試和維護

第5章 AI致能維護與測試的核心支柱

  • 常見AI致能自動化平台
  • 無關位置的維護與測試
  • 以服務為中心營運的維護與測試
  • 通信業者維護與測試和其他關鍵支柱

第6章 AI致能維護工具市場預測

第7章 供應商生態系統

  • Amdocs
  • Cisco
  • Ericsson
  • Netcracker
  • Nokia
  • TEOCO
  • ZTE
目錄
Product Code: AN-5306

Actionable Benefits:

  • Determine key trends that are driving the need for ‘intelligent' maintenance solutions.
  • Educate decision makers and product developers on the impact that AI, ML and the cloud has on telco maintenance solutions.
  • Identify core technologies that vendors can use to design and develop next-generation maintenance and testing tools.

Critical Questions Answered:

  • How are maintenance solutions evolving in light of cloud tools, software and DevOps methodologies?
  • What are the key drivers that call for tools that are technology- and location-agnostic and that traverse multiple clouds and telco domains?
  • How are some telco vendors implementing AI and ML in their maintenance and testing solutions?

Research Highlights:

  • Summaries of key strands that vendors must consider as they seek to bring to market maintenance solutions that are more suitable to cloud-native implementation.
  • Analysis of main AI/ML functions and their applicability in aiding CSPs to establish autonomous and adaptable troubleshooting.
  • An overview of operational requirements that next-generation maintenance solutions should address.

Who Should Read This?

  • Chief Technologists and other key decision makers for product design and development.
  • Innovation Leaders, Architects and Strategy Principals from CSPs who need to understand how cloud and software will impact maintenance in their operations.
  • CSPs, network equipment vendors and system integrators who need to understand the dynamics of AI and ML integration in their operations and processes.

Companies Mentioned:

  • Amdocs Limited
  • Cisco
  • Ericsson
  • Netcracker
  • Nokia
  • TEOCO
  • ZTE Corporation

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

2. RECOMMENDATIONS

3. TRENDS AND DRIVERS FOR NEXT-GENERATION MAINTENANCE AND TESTING TOOLS

  • 3.1. Trends in RAN and Core Networks
  • 3.2. Manual Processes and Legacy Equipment
  • 3.3. A Need to Redesign, Retool, and Prepare for Service-Centric Operations

4. IMPACT OF AI, ML, AND THE CLOUD ON MAINTENANCE AND TESTING

  • 4.1. Three Major Types of AI Functions
  • 4.2. Optimum Ecosystem Dimensions for AI in Radio Networks
  • 4.3. Evolution of Legacy Maintenance and Testing
  • 4.4. Closed-Loop Maintenance
  • 4.5. Hardware and Physical-Oriented Testing and Maintenance

5. CORE PILLARS FOR AI-ENABLED MAINTENANCE AND TESTING

  • 5.1. A Common AI-Enabled Automation Platform
  • 5.2. Location-Agnostic Maintenance and Testing
  • 5.3. Maintenance and Testing for Service-Centric Operations
  • 5.4. Other Key Pillars of Telco Maintenance and Testing

6. FORECASTS FOR AI-ENABLED MAINTENANCE TOOLS

7. VENDOR ECOSYSTEM

  • 7.1. Amdocs
  • 7.2. Cisco
  • 7.3. Ericsson
  • 7.4. Netcracker
  • 7.5. Nokia
  • 7.6. TEOCO
  • 7.7. ZTE