供應鏈預測分析和預防性維護市場－全球產業規模、佔有率、趨勢、機會及預測（按組件、應用、組織規模、最終用戶產業、地區和競爭格局分類），2021-2031年

全球預測分析和供應鏈維護市場預計將迎來顯著成長，從2025年的117.9億美元成長到2031年的483.4億美元，複合年成長率（CAGR）高達26.51%。該行業利用歷史數據、機器學習演算法和統計建模來預測設備故障，並在營運中斷發生之前最佳化維護計劃。推動市場成長的關鍵因素包括：減少非計劃性停機時間的重要性日益凸顯（這會嚴重影響利潤率），以及延長高價值資產運作的需求。因此，各組織正積極投資於提高效率。正如《2025年三菱重工年度產業報告》所強調的，55%的價值鏈領導者表示，到2025年，他們將增加對技術和創新的投資，以增強營運的韌性。

然而，阻礙市場擴張的一大挑戰在於如何將現代分析工具與老舊的傳統基礎設施整合。許多供應鏈網路依賴分散的資料孤島，無法無縫聚合精確建模所需的資訊。這項技術障礙不僅使實施過程複雜化，延緩了投資收益的實現，也使得一些公司儘管看到了明顯的優勢，卻仍然不願採用全面的預測性維護解決方案。因此，如何克服這些基礎設施差異仍然是業界廣泛採用該方案的主要障礙。

市場促進因素

工業IoT(IIoT) 和連網設備的快速普及正成為全球預測分析和供應鏈維護市場的關鍵技術驅動力。透過在物流基礎設施和生產設施中部署連網感測器，企業能夠產生持續、詳細的資料流，從而及早發現設備故障的徵兆。這種無所不在的互聯互通正在將靜態的供應鏈轉變為響應迅速的數位化生態系統，使操作人員能夠即時監控資產健康狀況，而無需依賴週期性的人工檢查。根據羅克韋爾自動化於 2024 年 3 月發布的第九份年度智慧製造報告，95% 的製造商目前正在實施或評估智慧製造技術，從而建立強大的預測性維護策略所需的數位化基礎。

同時，人工智慧 (AI) 和機器學習正日益融合，成為處理大量數據並最佳化維護計畫的智慧引擎。這些演算法分析歷史性能數據和即時遙測數據，在故障中斷營運之前進行預測，從而顯著降低機器停機造成的經濟損失。斑馬技術公司 (Zebra Technologies) 於 2024 年 6 月發布的《2024 年製造業願景研究》也印證了這一趨勢，該研究發現，61% 的全球製造業領導者預計，到 2029 年，人工智慧將推動成長。資源限制進一步推動了人工智慧的普及。笛卡爾系統集團 (Descartes Systems Group) 在 2024 年的報告中指出，76% 的供應鏈和物流領導者面臨嚴重的勞動力短缺，迫使企業依靠自動化預測工具，在人手不足的情況下維持業務連續性。

市場挑戰

全球供應鏈預測分析和維護市場的關鍵阻礙因素是難以將現代分析工具與過時的傳統基礎設施整合。先進的預測模型需要高品質的集中式資料才能準確預測設備故障並最佳化維護計劃。然而，目前許多企業仍使用分散的手動系統，造成嚴重的資料孤島，使得資訊無縫流動幾乎不可能。這種脫節迫使企業將大量資源用於資料收集和清洗，而非分析，從而抵消了預測性維護所承諾的效率提升。

根據供應管理協會 (ISM) 發布的《2024 年資料分析調查》，到 2024 年，92% 的供應管理機構將「始終或經常」使用 Excel 作為其主要資料工具。 32% 的受訪者表示，他們至少花費 21% 的時間在搜尋資料。這種對非整合式手動工具的根深蒂固的依賴，使得自動化預測解決方案的實施變得複雜。因此，由於支援進階分析的底層資料架構現代化改造的複雜性，許多公司被迫推遲採用這些解決方案。

市場趨勢

生成式人工智慧與先進機器學習的融合正在從根本上改變維護團隊與資料互動以及執行維修的方式。傳統的預測模型只能識別異常情況，而生成式人工智慧則扮演著智慧副駕駛的角色，它能夠綜合海量技術文檔，即時生成分步維修指南，並透過自然語言提示排除複雜故障。這種變革使技術專長更加普及，讓經驗不足的技術人員也能執行高階維護任務，並大幅縮短設備故障的解決時間。根據羅克韋爾自動化2025年6月發布的第十份年度智慧製造報告，投資生成式和因果式人工智慧的組織數量年增12%，這標誌著人工智慧正從實驗性試點轉向可擴展的部署。

同時，對永續性和綠色供應鏈分析的關注正在重塑市場優先事項，透過利用預測性洞察來滿足嚴格的環境、社會和管治(ESG) 標準。企業擴大部署分析技術，不僅用於預防停機，還用於最佳化能源消耗和延長老舊資產的使用壽命，從而減少與生產新備件和機械相關的運作足跡。這種「綠色維護」方法將資產管理轉變為公司脫碳策略的關鍵要素。根據三菱重工 (MHI) 於 2025 年 3 月發布的《2025 年年度產業報告》，44% 的供應鏈專業人士認為環境問題和永續性舉措是影響其公司營運策略的最重要趨勢。

目錄

第1章概述

第2章調查方法

第3章執行摘要

第4章：客戶評價

第5章 全球供應鏈市場展望中的預測分析和預防性維護－全球產業規模、佔有率、趨勢、機會與預測：按組件、應用、組織規模、最終用戶產業、地區和競爭格局分類，2021-2031年

• 市場規模及預測
  • 按金額
• 市佔率及預測
  • 按組件（解決方案、服務（託管服務、專業服務））
  • 依部署類型（本機部署、雲端部署）
  • 依應用領域（庫存管理、預測性維護、預測性路線規劃、需求預測等）
  • 依組織規模（大型公司、中小企業）
  • 依最終用途產業（零售、製造、航空、醫療、能源/電力、其他）分類
  • 按地區
  • 按公司（2025 年）
• 市場地圖

第6章 北美供應鏈市場展望－預測分析與預防性維護：全球產業規模、佔有率、趨勢、機會及預測：按組件、應用、組織規模、最終用戶產業、地區及競爭格局分類，2021-2031年

• 市場規模及預測
• 市佔率及預測
• 北美洲：國家分析
  • 美國
  • 加拿大
  • 墨西哥

第7章 歐洲供應鏈市場展望中的預測分析和預防性維護－全球產業規模、佔有率、趨勢、機會及按組件、應用、組織規模、最終用戶產業、地區和競爭格局分類的預測（2021-2031）

• 市場規模及預測
• 市佔率及預測
• 歐洲：國家分析
  • 德國
  • 法國
  • 英國
  • 義大利
  • 西班牙

第8章 亞太地區供應鏈預測分析與預防性維護市場展望－全球產業規模、佔有率、趨勢、機會及預測（按組件、應用、組織規模、最終用戶產業、地區及競爭格局分類），2021-2031年

• 市場規模及預測
• 市佔率及預測
• 亞太地區：國家分析
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳洲

第9章 中東和非洲供應鏈市場展望－預測分析與預防性維護：全球產業規模、佔有率、趨勢、機會及預測：按組件、應用、組織規模、最終用戶產業、地區和競爭格局分類，2021-2031年

• 市場規模及預測
• 市佔率及預測
• 中東和非洲：國家分析
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 南非

第10章 南美供應鏈預測分析與預防性維護市場展望－全球產業規模、佔有率、趨勢、機會及預測：按組件、應用、組織規模、最終用戶產業、地區及競爭格局分類，2021-2031年

• 市場規模及預測
• 市佔率及預測
• 南美洲：國家分析
  • 巴西
  • 哥倫比亞
  • 阿根廷

第11章 市場動態

• 促進要素
• 任務

第12章 市場趨勢與發展

• 併購
• 產品發布
• 最新進展

第13章 全球供應鏈市場：SWOT分析中的預測分析和預防性維護－全球產業規模、佔有率、趨勢、機會和預測：按組件、應用、組織規模、最終用戶產業、地區和競爭格局分類，2021-2031年

第14章：波特五力分析

• 產業競爭
• 新進入者的可能性
• 供應商電力
• 顧客權力
• 替代品的威脅

第15章 競爭格局

• 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16章 策略建議

第17章：關於研究公司及免責聲明

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