供应链预测分析和预防性维护市场－全球产业规模、份额、趋势、机会及预测（按组件、应用、组织规模、最终用户产业、地区和竞争格局划分），2021-2031年

全球预测分析和供应链维护市场预计将迎来显着成长，从2025年的117.9亿美元成长到2031年的483.4亿美元，复合年增长率（CAGR）高达26.51%。该行业利用历史数据、机器学习演算法和统计建模来预测设备故障，并在营运中断发生之前优化维护计划。推动市场成长的关键因素包括：减少非计划性停机时间的重要性日益凸显（这会严重影响利润率），以及延长高价值资产运作的需求。因此，各组织正积极投资于提高效率。正如《2025年三菱重工年度产业报告》所强调的，55%的价值链领导者表示，到2025年，他们将增加对技术和创新的投资，以增强营运的韧性。

然而，阻碍市场扩张的一大挑战在于如何将现代分析工具与老旧的传统基础设施整合。许多供应链网路依赖分散的资料孤岛，无法无缝聚合精确建模所需的资讯。这项技术障碍不仅使实施过程复杂化，延缓了投资收益的实现，也使得一些公司儘管看到了明显的优势，却仍然不愿采用全面的预测性维护解决方案。因此，如何克服这些基础设施差异仍然是业界广泛采用该方案的主要障碍。

市场驱动因素

工业IoT(IIoT) 和连网设备的快速普及正成为全球预测分析和供应链维护市场的关键技术驱动力。透过在物流基础设施和生产设施中部署连网感测器，企业能够产生持续、详细的资料流，从而及早发现设备故障的征兆。这种无所不在的互联互通正在将静态的供应链转变为响应迅速的数位化生态系统，使操作人员能够即时监控资产健康状况，而无需依赖週期性的人工检查。根据罗克韦尔自动化于 2024 年 3 月发布的第九份年度智慧製造报告，95% 的製造商目前正在实施或评估智慧製造技术，从而建立强大的预测性维护策略所需的数位化基础。

同时，人工智慧 (AI) 和机器学习正日益融合，成为处理大量数据并优化维护计画的智慧引擎。这些演算法分析历史性能数据和即时遥测数据，在故障中断营运之前进行预测，从而显着降低机器停机造成的经济损失。斑马技术公司 (Zebra Technologies) 于 2024 年 6 月发布的《2024 年製造业愿景研究》也印证了这一趋势，该研究发现，61% 的全球製造业领导者预计，到 2029 年，人工智慧将推动成长。资源限制进一步推动了人工智慧的普及。笛卡尔系统集团 (Descartes Systems Group) 在 2024 年的报告中指出，76% 的供应链和物流领导者面临严重的劳动力短缺，迫使企业依靠自动化预测工具，在人手不足的情况下维持业务连续性。

市场挑战

全球供应链预测分析和维护市场的关键阻碍因素是难以将现代分析工具与过时的传统基础设施整合。先进的预测模型需要高品质的集中式资料才能准确预测设备故障并优化维护计划。然而，目前许多企业仍使用分散的手动系统，造成严重的资料孤岛，使得资讯无缝流动几乎不可能。这种脱节迫使企业将大量资源用于资料收集和清洗，而非分析，从而抵消了预测性维护所承诺的效率提升。

根据供应管理协会 (ISM) 发布的《2024 年资料分析调查》，到 2024 年，92% 的供应管理机构将「始终或经常」使用 Excel 作为其主要资料工具。 32% 的受访者表示，他们至少花费 21% 的时间在搜寻资料。这种对非整合式手动工具的根深蒂固的依赖，使得自动化预测解决方案的实施变得复杂。因此，由于支援进阶分析的底层资料架构现代化改造的复杂性，许多公司被迫推迟采用这些解决方案。

市场趋势

生成式人工智慧与先进机器学习的融合正在从根本上改变维护团队与资料互动以及执行维修的方式。传统的预测模型只能识别异常情况，而生成式人工智慧则扮演着智慧副驾驶的角色，它能够综合海量技术文檔，即时生成分步维修指南，并透过自然语言提示排除复杂故障。这种变革使技术专长更加普及，让经验不足的技术人员也能执行高阶维护任务，并大幅缩短设备故障的解决时间。根据罗克韦尔自动化2025年6月发布的第十份年度智慧製造报告，投资生成式和因果式人工智慧的组织数量年增12%，这标誌着人工智慧正从实验性试点转向可扩展的部署。

同时，对永续性和绿色供应链分析的关注正在重塑市场优先事项，透过利用预测性洞察来满足严格的环境、社会和管治(ESG) 标准。企业越来越多地部署分析技术，不仅用于预防停机，还用于优化能源消耗和延长老旧资产的使用寿命，从而减少与生产新备件和机械相关的运作足迹。这种「绿色维护」方法将资产管理转变为公司脱碳策略的关键要素。根据三菱重工 (MHI) 于 2025 年 3 月发布的《2025 年年度产业报告》，44% 的供应链专业人士认为环境问题和永续性倡议是影响其公司营运策略的最重要趋势。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球供应链市场展望中的预测分析和预防性维护－全球产业规模、份额、趋势、机会与预测：按组件、应用、组织规模、最终用户产业、地区和竞争格局划分，2021-2031年

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 按组件（解决方案、服务（託管服务、专业服务））
  • 依部署类型（本机部署、云端部署）
  • 依应用领域（库存管理、预测性维护、预测性路线规划、需求预测等）
  • 依组织规模（大型公司、中小企业）
  • 依最终用途产业（零售、製造、航空、医疗、能源/电力、其他）划分
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章 北美供应链市场展望－预测分析与预防性维护：全球产业规模、份额、趋势、机会及预测：按组件、应用、组织规模、最终用户产业、地区及竞争格局划分，2021-2031年

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 美国
  • 加拿大
  • 墨西哥

第七章 欧洲供应链市场展望中的预测分析和预防性维护－全球产业规模、份额、趋势、机会及按组件、应用、组织规模、最终用户产业、地区和竞争格局分類的预测（2021-2031）

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章 亚太地区供应链预测分析与预防性维护市场展望－全球产业规模、份额、趋势、机会及预测（按组件、应用、组织规模、最终用户产业、地区及竞争格局划分），2021-2031年

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章 中东和非洲供应链市场展望－预测分析与预防性维护：全球产业规模、份额、趋势、机会及预测：按组件、应用、组织规模、最终用户产业、地区和竞争格局划分，2021-2031年

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章 南美供应链预测分析与预防性维护市场展望－全球产业规模、份额、趋势、机会及预测：按组件、应用、组织规模、最终用户产业、地区及竞争格局划分，2021-2031年

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章 全球供应链市场：SWOT分析中的预测分析和预防性维护－全球产业规模、份额、趋势、机会和预测：按组件、应用、组织规模、最终用户产业、地区和竞争格局划分，2021-2031年

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• international Business Machines Corporation
• Microsoft Corporation
• SAP SE
• General Electric Company
• Schneider Electric SE
• Google LLC
• Oracle Corporation
• Hewlett Packard Enterprise Co.
• SAS Institute Inc.
• TIBCO Software Inc.
• Siemens AG
• Robert Bosch GmbH
• Cisco Systems, Inc.
• Dell, Inc.
• Intel Corporation

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Predictive Analytics And Maintenance In Supply Chain Market is projected to experience substantial growth, expanding from USD 11.79 Billion in 2025 to USD 48.34 Billion by 2031, representing a Compound Annual Growth Rate (CAGR) of 26.51%. This sector leverages historical data, machine learning algorithms, and statistical modeling to forecast equipment malfunctions and refine maintenance timelines before operational interruptions occur. The market is primarily driven by the critical need to reduce unplanned downtime, which severely impacts profit margins, and the necessity of extending the operational life of high-value assets. Consequently, organizations are actively directing capital toward these efficiencies; as highlighted in the '2025 MHI Annual Industry Report', 55% of supply chain leaders indicated in 2025 that they are increasing investments in technology and innovation to enhance operational resilience.

However, a major obstacle hindering broader market expansion is the challenge of merging modern analytical tools with aging legacy infrastructure. Many supply chain networks depend on fragmented data silos that obstruct the seamless aggregation of information needed for precise modeling. This technical barrier complicates the implementation process and delays the realization of return on investment, causing some enterprises to hesitate in adopting comprehensive predictive maintenance solutions despite their clear benefits. As a result, the difficulty of overcoming these infrastructural disparities remains a significant friction point for widespread adoption within the industry.

Market Driver

The rapid proliferation of Industrial IoT and connected devices acts as the primary technical catalyst for the Global Predictive Analytics And Maintenance In Supply Chain Market. By embedding networked sensors throughout logistics infrastructure and production assets, organizations generate the continuous, granular data streams necessary to identify early warning signs of equipment failure. This extensive connectivity converts static supply chains into responsive digital ecosystems, enabling operators to monitor asset health in real-time rather than depending on scheduled manual inspections. According to Rockwell Automation's '9th Annual State of Smart Manufacturing Report' from March 2024, 95% of manufacturers are now using or evaluating smart manufacturing technology, establishing the essential digital foundation for robust predictive maintenance strategies.

In parallel, the increasing integration of Artificial Intelligence and Machine Learning serves as the intelligence engine that processes this influx of data to optimize maintenance schedules. These algorithms analyze historical performance and real-time telemetry to predict breakdowns before they disrupt operations, significantly mitigating the financial impact of idle machinery. Highlighting this trend, Zebra Technologies' '2024 Manufacturing Vision Study' from June 2024 reveals that 61% of manufacturing leaders globally expect AI to drive growth by 2029. This adoption is further accelerated by resource constraints; the Descartes Systems Group reported in 2024 that 76% of supply chain and logistics leaders faced notable workforce shortages, compelling enterprises to rely on automated predictive tools to maintain operational continuity with fewer personnel.

Market Challenge

The difficulty of integrating modern analytical tools with outdated legacy infrastructure serves as a primary restraint on the Global Predictive Analytics And Maintenance In Supply Chain Market. Advanced predictive models require high-quality, centralized data to accurately forecast equipment failures and optimize schedules. However, a significant portion of the industry continues to operate on fragmented, manual systems that create deep data silos, making seamless information flow nearly impossible. This disconnection forces organizations to expend excessive resources on data retrieval and cleaning rather than analysis, thereby neutralizing the efficiency gains that predictive maintenance promises to deliver.

According to the Institute for Supply Management's (ISM) '2024 Data and Analytics Survey', 92% of supply management organizations in 2024 reported utilizing Excel "always or very often" as their primary data tool, while 32% of respondents indicated they spend at least 21% of their operational time simply locating data. Such entrenched reliance on non-integrated, manual tools complicates the deployment of automated predictive solutions. Consequently, many enterprises are forced to delay adoption due to the sheer complexity involved in modernizing their foundational data architecture to support advanced analytics.

Market Trends

The integration of Generative AI and Advanced Machine Learning is fundamentally transforming how maintenance teams interact with data and execute repairs. While traditional predictive models merely flag anomalies, generative AI functions as an intelligent co-pilot, capable of synthesizing vast amounts of technical documentation to generate instant, step-by-step repair guides and troubleshoot complex issues via natural language prompts. This shift democratizes technical expertise, allowing less experienced technicians to perform high-level maintenance tasks and significantly accelerating the time-to-resolution for equipment faults. According to Rockwell Automation's '10th Annual State of Smart Manufacturing Report' from June 2025, the number of organizations investing in generative and causal AI increased by 12% year-over-year, marking a decisive shift from experimental pilots to scalable deployments.

Simultaneously, the focus on sustainability and green supply chain analytics is reshaping market priorities by leveraging predictive insights to meet rigorous environmental, social, and governance (ESG) standards. Organizations are increasingly deploying analytics not just to prevent downtime, but to optimize the energy consumption of aging assets and extend their operational life, thereby reducing the carbon footprint associated with manufacturing new spare parts and machinery. This "green maintenance" approach transforms asset management into a critical component of corporate decarbonization strategies. According to the '2025 MHI Annual Industry Report' released in March 2025, 44% of supply chain professionals identified environmental concerns and sustainability initiatives as the most significant trend impacting their operational strategies.

Key Market Players

• international Business Machines Corporation
• Microsoft Corporation
• SAP SE
• General Electric Company
• Schneider Electric SE
• Google LLC
• Oracle Corporation
• Hewlett Packard Enterprise Co.
• SAS Institute Inc.
• TIBCO Software Inc.
• Siemens AG
• Robert Bosch GmbH
• Cisco Systems, Inc.
• Dell, Inc.
• Intel Corporation

Report Scope

In this report, the Global Predictive Analytics And Maintenance In Supply Chain Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Predictive Analytics And Maintenance In Supply Chain Market, By Component

• Solutions
• Services (Managed Services, Professional Services)

Predictive Analytics And Maintenance In Supply Chain Market, By Deployment

• On-Premises
• Cloud

Predictive Analytics And Maintenance In Supply Chain Market, By Application

• Inventory Management
• Predictive Maintenance
• Predictive Route Planning
• Demand Forecasting
• Others

Predictive Analytics And Maintenance In Supply Chain Market, By Organization Size

• Large Enterprises
• SMEs

Predictive Analytics And Maintenance In Supply Chain Market, By End-Use Industry

• Retail
• Manufacturing
• Aviation
• Healthcare
• Energy and Power
• Others

Predictive Analytics And Maintenance In Supply Chain Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Predictive Analytics And Maintenance In Supply Chain Market.

Available Customizations:

Global Predictive Analytics And Maintenance In Supply Chain 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:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Solutions, Services (Managed Services, Professional Services))
  • 5.2.2. By Deployment (On-Premises, Cloud)
  • 5.2.3. By Application (Inventory Management, Predictive Maintenance, Predictive Route Planning, Demand Forecasting, Others)
  • 5.2.4. By Organization Size (Large Enterprises, SMEs)
  • 5.2.5. By End-Use Industry (Retail, Manufacturing, Aviation, Healthcare, Energy and Power, Others)
  • 5.2.6. By Region
  • 5.2.7. By Company (2025)
• 5.3. Market Map

6. North America Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Deployment
  • 6.2.3. By Application
  • 6.2.4. By Organization Size
  • 6.2.5. By End-Use Industry
  • 6.2.6. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Deployment
      • 6.3.1.2.3. By Application
      • 6.3.1.2.4. By Organization Size
      • 6.3.1.2.5. By End-Use Industry
  • 6.3.2. Canada Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Deployment
      • 6.3.2.2.3. By Application
      • 6.3.2.2.4. By Organization Size
      • 6.3.2.2.5. By End-Use Industry
  • 6.3.3. Mexico Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Deployment
      • 6.3.3.2.3. By Application
      • 6.3.3.2.4. By Organization Size
      • 6.3.3.2.5. By End-Use Industry

7. Europe Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Deployment
  • 7.2.3. By Application
  • 7.2.4. By Organization Size
  • 7.2.5. By End-Use Industry
  • 7.2.6. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Deployment
      • 7.3.1.2.3. By Application
      • 7.3.1.2.4. By Organization Size
      • 7.3.1.2.5. By End-Use Industry
  • 7.3.2. France Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Deployment
      • 7.3.2.2.3. By Application
      • 7.3.2.2.4. By Organization Size
      • 7.3.2.2.5. By End-Use Industry
  • 7.3.3. United Kingdom Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Deployment
      • 7.3.3.2.3. By Application
      • 7.3.3.2.4. By Organization Size
      • 7.3.3.2.5. By End-Use Industry
  • 7.3.4. Italy Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Deployment
      • 7.3.4.2.3. By Application
      • 7.3.4.2.4. By Organization Size
      • 7.3.4.2.5. By End-Use Industry
  • 7.3.5. Spain Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Deployment
      • 7.3.5.2.3. By Application
      • 7.3.5.2.4. By Organization Size
      • 7.3.5.2.5. By End-Use Industry

8. Asia Pacific Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Deployment
  • 8.2.3. By Application
  • 8.2.4. By Organization Size
  • 8.2.5. By End-Use Industry
  • 8.2.6. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Deployment
      • 8.3.1.2.3. By Application
      • 8.3.1.2.4. By Organization Size
      • 8.3.1.2.5. By End-Use Industry
  • 8.3.2. India Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Deployment
      • 8.3.2.2.3. By Application
      • 8.3.2.2.4. By Organization Size
      • 8.3.2.2.5. By End-Use Industry
  • 8.3.3. Japan Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Deployment
      • 8.3.3.2.3. By Application
      • 8.3.3.2.4. By Organization Size
      • 8.3.3.2.5. By End-Use Industry
  • 8.3.4. South Korea Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Deployment
      • 8.3.4.2.3. By Application
      • 8.3.4.2.4. By Organization Size
      • 8.3.4.2.5. By End-Use Industry
  • 8.3.5. Australia Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Deployment
      • 8.3.5.2.3. By Application
      • 8.3.5.2.4. By Organization Size
      • 8.3.5.2.5. By End-Use Industry

9. Middle East & Africa Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Deployment
  • 9.2.3. By Application
  • 9.2.4. By Organization Size
  • 9.2.5. By End-Use Industry
  • 9.2.6. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Deployment
      • 9.3.1.2.3. By Application
      • 9.3.1.2.4. By Organization Size
      • 9.3.1.2.5. By End-Use Industry
  • 9.3.2. UAE Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Deployment
      • 9.3.2.2.3. By Application
      • 9.3.2.2.4. By Organization Size
      • 9.3.2.2.5. By End-Use Industry
  • 9.3.3. South Africa Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Deployment
      • 9.3.3.2.3. By Application
      • 9.3.3.2.4. By Organization Size
      • 9.3.3.2.5. By End-Use Industry

10. South America Predictive Analytics And Maintenance In Supply Chain Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Deployment
  • 10.2.3. By Application
  • 10.2.4. By Organization Size
  • 10.2.5. By End-Use Industry
  • 10.2.6. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Deployment
      • 10.3.1.2.3. By Application
      • 10.3.1.2.4. By Organization Size
      • 10.3.1.2.5. By End-Use Industry
  • 10.3.2. Colombia Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Deployment
      • 10.3.2.2.3. By Application
      • 10.3.2.2.4. By Organization Size
      • 10.3.2.2.5. By End-Use Industry
  • 10.3.3. Argentina Predictive Analytics And Maintenance In Supply Chain Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By Application
      • 10.3.3.2.4. By Organization Size
      • 10.3.3.2.5. By End-Use Industry

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Predictive Analytics And Maintenance In Supply Chain Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. international Business Machines Corporation
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Microsoft Corporation
• 15.3. SAP SE
• 15.4. General Electric Company
• 15.5. Schneider Electric SE
• 15.6. Google LLC
• 15.7. Oracle Corporation
• 15.8. Hewlett Packard Enterprise Co.
• 15.9. SAS Institute Inc.
• 15.10. TIBCO Software Inc.
• 15.11. Siemens AG
• 15.12. Robert Bosch GmbH
• 15.13. Cisco Systems, Inc.
• 15.14. Dell, Inc.
• 15.15. Intel Corporation

16. Strategic Recommendations

17. About Us & Disclaimer