製造業向け予知保全の世界市場規模：コンポーネント別、展開別、組織規模別、技術別、手法別、業界別、地域範囲別および予測

製造業向け予知保全の市場規模と予測

製造業向け予知保全の市場規模は、2024年に82億6,000万米ドルと評価され、2026～2032年のCAGRは24.49%で成長し、2032年には476億4,000万米ドルに達すると予測されます。

• 製造産業の予知保全は、データ分析ツールや手法を採用して、運用プロセスや機械の異常を検出します。メンテナンスの実施時期を予測し、予定外のダウンタイムを減らし、メンテナンス計画を最適化することを目指します。この戦略は、状態モニタリング技術と、機械に設置されたセンサからの履歴データとリアルタイムデータの分析に基づいています。
• この技術は、機械や設備の性能をモニタリングするために生産現場で使用されています。予測アルゴリズムは、温度、振動、騒音、その他の運転特性に関するデータを収集することで、起こりうる故障を予測することができます。これにより、メンテナンス担当者は懸念事項を事前に処理することができ、機械が円滑かつ効果的に稼動することが保証されます。一般的な用途としては、CNCマシン、コンベアシステム、ロボットアームのモニタリングなどがあります。この方法は、計画外の停止を防ぎ、機器の寿命を延ばし、全体的な生産性と安全性を向上させるのに役立ちます。
• 製造業における予知保全は、IoTセンサ、データ分析プラットフォーム、機械学習アルゴリズムの統合を伴います。主要機能には、リアルタイムのデータ収集、異常検知、予測分析、自動警告などがあります。先進的予知保全システムにはさらに、機器の状態を可視化するダッシュボード、企業資源計画（ERP）システムとの相互作用、意思決定支援ツールが含まれる場合があります。さらに、これらの技術により、遠隔モニタリング、履歴データの傾向分析、自動メンテナンス・スケジューリングが可能になり、これらすべてがより効率的で信頼性の高い生産プロセスに貢献します。

製造業向け予知保全の世界市場力学

世界の製造業向け予知保全市場を形成している主要市場力学は以下の通りです。

主要市場促進要因

• IoTとセンサ技術の進歩：IoTとセンサ技術の進歩：IoTとセンサ技術は、製造業におけるデータ収集と分析に変革をもたらしました。これらの技術は、温度、振動、圧力などの重要な要素を含む機器の健全性をリアルタイムでモニタリングします。継続的に高解像度のデータを収集できるため、より正確な予知保全モデルが可能になり、計画外のダウンタイムが短縮され、保全スケジュールが最適化されます。
• ビッグデータと分析の採用拡大：ビッグデータ分析の導入が進んでいるため、メーカーは機械が生成する大量のデータを評価できるようになりました。先進的分析ツールと機械学習アルゴリズムは、パターンを検出し、機器の故障を高い精度で予測することができます。このようなデータ主導の戦略により、製造業者はメンテナンス・スケジュール、リソースの割り当て、プロセスの強化について十分な情報に基づいた意思決定を行うことができ、その結果、業務効率の向上とダウンタイムの短縮が実現します。
• 企業システムとの統合：予知保全ソリューションをERPやCMMSなどの企業システムと統合することで、産業オペレーションを包括的に把握することができます。この容易なインターフェースにより、製造業者はメンテナンス活動を生産スケジュールと整合させ、ワークフローを合理化し、部門間の協力体制を強化することができます。その結果、全体的な企業目標を満たす、より効率的で迅速なメンテナンスアプローチが実現します。
• 技術革新とAIの統合：AIと機械学習の進歩は、予知保全システムを大幅に改善しました。AIを活用した予測モデルは、大規模なデータセットを調査し、微妙なパターンを検出し、より正確に故障を予測することができます。AIと機械学習アルゴリズムの継続的な改善により、予知保全の精度と信頼性が向上し、製造業での導入が加速すると予測されます。

主要課題

• 高額な初期投資とROIの懸念：予知保全計画を実施するには、IoTセンサやデータ分析プラットフォームの購入・設置、既存インフラのアップグレードなど、大規模な先行投資が必要となります。多くの製造業者、特に中小企業（SME）にとって、これらの初期費用は大きな障害となるかもしれません。明確な投資収益率（ROI）を示すことは、ダウンタイムの削減や機器寿命の延長といった予知保全の利点が必ずしも明らかでないため、難しい場合があります。製造業者は、費用対効果を慎重に評価し、長期的な節約と短期的な出費を天秤にかける必要があります。
• サイバーセキュリティリスク：予知保全システムの接続とデータ交換の拡大は、製造業務にサイバーセキュリティの問題をもたらします。IoT機器とデータ伝送ネットワークはサイバー攻撃の対象となり、データ漏洩、操業中断、機器破壊工作を引き起こす可能性があります。機密データを保護し、予知保全（PdM）システムの完全性を確保するためには、強力なサイバーセキュリティ対策が必要です。
• 拡大性の問題：予知保全の規模を、パイロットプロジェクトから、すべての機器や設備への本格的な展開へと拡大することは、課題となる可能性があります。機械が異なれば、独自のセンサやデータ分析手法が必要になることもあり、ある事業領域でうまくいったことが、による事業領域でそのまま適用できるとは限らないです。規模を拡大するには、新たなセンサ、データストレージ、処理能力への大規模な投資が頻繁に必要になります。メーカーは、システム全体の一貫性と信頼性を確保しながら、さまざまな機器や運転条件に適用できるスケーラブルなソリューションを構築しなければならないです。
• 規制とコンプライアンスの問題：製造企業は、産業特有の規則や要件を遵守しなければならないです。運転の安全性、品質、信頼性を保証するために、予知保全システムはこれらの規則に従わなければなりません。しかし、特に新技術を導入する場合、複雑な規制の世界を交渉することは困難です。メーカーは、関連法規の最新情報を常に入手し、自社のPdMシステムが必要な基準をすべて満たしていることを検証しなければならないです。このため、追加の文書化、報告、検証手順が必要になる場合があり、導入の複雑さとコストが増大します。

主要動向

• クラウドベースの予知保全ソリューション：クラウドコンピューティングは、予知保全データの保存、処理、評価の方法を変えつつあります。クラウドベースのPdMソリューションには、拡大性、適応性、費用対効果など、さまざまな利点があります。これらの技術により、メーカーはITインフラに多額の財政支出をすることなく、強力なコンピューティング・リソースを利用することができます。クラウドプラットフォームにより、さまざまなソースからの膨大なデータセットの集約と分析が容易になり、機器の性能や故障パターンに関するより詳細な洞察が得られます。
• 人間と機械のコラボレーションの強化：予知保全技術の採用は、人間と機械の共同作業の方法を変えつつあります。先進的PdMシステムは、詳細な洞察と推奨事項を提供し、メンテナンスチームがより良い意思決定を行えるようにします。人間と機械のコラボレーションは、直感的なユーザーインターフェース、AR（拡張現実）、VR（仮想現実）システムによって改善され、技術者がメンテナンス作業を達成できるようになります。ARとVRは、段階的な指示を提供し、複雑なデータを表示し、修理方法を模倣することができるため、メンテナンス活動の効率と精度を高めることができます。
• デジタルツインの利用：デジタルツインとは、物理的な物体、システム、プロセスを仮想的に表現したものです。予知保全では、デジタルツインを活用して、さまざまなシナリオのもとでの機器の挙動を模倣し、評価します。メーカーは機械のデジタルツインを作成することで、リアルタイムで機械の性能をモニタリングし、起こりうる故障を検出し、メンテナンススケジュールを最適化することができます。デジタルツインは、実際の運転に影響を与えることなく、多くの状況を広範囲に調査し、テストすることを可能にします。この技術は、より正確で効果的な予知保全戦略を可能にするため、受け入れられつつあります。
• カスタマイズ型予知保全ソリューション：生産設定や要件が大きく異なるため、特定の需要に適したカスタマイズ型予知保全ソリューションへの需要が高まっています。一般的なPdMソリューションでは、各メーカー固有の困難や運用設定を解決できない可能性があります。カスタマイズ型ソリューションには、個々の機器タイプ、運転条件、ビジネス目標が含まれ、その結果、より適切で実用的なデータが得られます。

目次

第1章 世界の製造業向け予知保全市場の導入

• 市場の導入
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 製造業向け予知保全の世界市場展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析

第5章 製造業向け予知保全の世界市場：コンポーネント別

• 概要
• ソリューション
  • 統合型
  • スタンドアロン
• サービス
  • プロフェッショナル
  • マネージド
• ハードウェア

第6章 製造業向け予知保全の世界市場：展開別

• 概要
• クラウドベース
• オンプレミス

第7章 製造業向け予知保全の世界市場：産業別

• 概要
• 政府・防衛
• 製造業
• エネルギー公益事業
• 運輸・物流
• ヘルスケアとライフサイエンス

第8章 製造業向け予知保全の世界市場：技術別

• 概要
• 人工知能（AI）
• モノのインターネット（IoT）プラットフォーム
• センサ
• その他

第9章 製造業向け予知保全の世界市場：手法別

• 概要
• オイル分析
• 振動分析
• 音響モニタリング
• モーター回路分析
• その他

第10章 製造業向け予知保全の世界市場：組織規模別

• 概要
• 中小企業
• 大企業

第11章 製造業向け予知保全の世界市場：地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他の欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他のアジア太平洋
• その他
  • ラテンアメリカ
  • 中東・アフリカ

第12章 世界の製造業向け予知保全市場の競合情勢

• 概要
• 各社の市場ランキング
• 主要開発戦略

第13章 企業プロファイル

• IBM
• SAS Institute
• Robert Bosch GmbH
• Software AG
• Rockwell Automation
• eMaint Enterprises
• Schneider Electric
• General Electric
• Siemens
• PTC

第14章 付録

• 関連調査

Predictive Maintenance For Manufacturing Industry Market Size And Forecast

Predictive Maintenance For Manufacturing Industry Market size was valued at USD 8.26 Billion in 2024 and is projected to reach USD 47.64 Billion by 2032, growing at a CAGR of 24.49% from 2026 to 2032.

• Predictive Maintenance For Manufacturing Industry employs data analysis tools and methodologies to detect anomalies in operational processes and machinery. It seeks to anticipate when maintenance should be conducted, reducing unplanned downtime and optimizing maintenance plans. This strategy is based on condition-monitoring technology and the analysis of historical and real-time data from sensors installed in machinery.
• This technology is used in production to monitor the performance of machines and equipment. Predictive algorithms can anticipate probable failures by gathering data on temperature, vibration, noise, and other operational characteristics. This enables maintenance personnel to handle concerns proactively, ensuring that machines operate smoothly and effectively. Common uses include monitoring CNC machines, conveyor systems, and robotic arms. This method helps to prevent unplanned outages, increase equipment lifespan, and improve overall productivity and safety.
• Predictive maintenance in the manufacturing industry entails the integration of IoT sensors, data analytics platforms and machine learning algorithms. Key features include real-time data collection, anomaly detection, predictive analytics, and automatic warnings. Advanced predictive maintenance systems may additionally include dashboards for visualizing equipment status, interaction with enterprise resource planning (ERP) systems, and decision-support tools. Furthermore, these technologies allow for remote monitoring, historical data trend analysis, and automatic maintenance scheduling, all of which contribute to a more efficient and dependable production process.

Global Predictive Maintenance For Manufacturing Industry Market Dynamics

The key market dynamics that are shaping the global Predictive Maintenance For Manufacturing Industry Market include:

Key Market Drivers:

• Advancements in IoT and Sensor Technology: IoT and sensor technology have transformed data collection and analysis in manufacturing. These technologies provide real-time monitoring of equipment health, including vital factors like temperature, vibration, and pressure. The capacity to collect continuous, high-resolution data enables more accurate predictive maintenance models, which reduces unplanned downtime and optimizes the maintenance schedule.
• Increasing Adoption of Big Data and Analytics: Manufacturers may now evaluate large amounts of data generated by their machines thanks to the growing adoption of big data analytics. Advanced analytics tools and machine learning algorithms can detect patterns and predict equipment failures with great accuracy. This data-driven strategy enables manufacturers to make informed decisions about maintenance schedules, resource allocation, and process enhancements, resulting in increased operational efficiency and reduced downtime.
• Integration with Enterprise Systems: Integrating predictive maintenance solutions with enterprise systems, including ERP and CMMS, offers a comprehensive perspective of industrial operations. This effortless interface allows manufacturers to align maintenance activities with production schedules, streamline workflows, and increase departmental cooperation. The result is a more efficient and responsive maintenance approach that meets overall corporate objectives.
• Technological Innovations and AI Integration: Advancements in AI and machine learning have greatly improved predictive maintenance systems. AI-powered prediction models can examine large datasets, detect subtle patterns, and anticipate failures more accurately. Continuous improvements in AI and machine learning algorithms are projected to improve the precision and dependability of predictive maintenance, accelerating its adoption in the manufacturing industry.

Key Challenges:

• High Initial Investment and ROI Concerns: Implementing a predictive maintenance plan requires major upfront investments, such as purchasing and installing IoT sensors, data analytics platforms, and maybe upgrading existing infrastructure. For many manufacturers, particularly small and medium-sized firms (SMEs), these initial expenses might be a significant obstacle. Showing a clear return on investment (ROI) can be difficult because the benefits of predictive maintenance, such as reduced downtime and increased equipment lifespan, are not always obvious. Manufacturers must carefully assess the cost-benefit ratio and weigh long-term savings against short-term expenses.
• Cybersecurity Risks: Predictive maintenance systems' growing connection and data interchange offer cybersecurity issues for manufacturing operations. IoT devices and data transmission networks are subject to cyberattacks, which can result in data breaches, operational disruptions, and equipment sabotage. Strong cybersecurity measures are required to secure sensitive data and ensure the integrity of predictive maintenance (PdM) systems.
• Scalability Issues: Scaling predictive maintenance from pilot projects to full-scale deployment across all equipment and facilities might pose challenges. Different machines may necessitate unique sensors and data analytics methodologies, and what works in one area of the operation may not be directly applicable in another. Scaling up frequently necessitates large investments in new sensors, data storage, and processing power. Manufacturers must create scalable solutions that can be applied to a variety of equipment and operational conditions while ensuring consistency and reliability throughout the system.
• Regulatory and Compliance Issues: Manufacturing companies must adhere to industry-specific rules and requirements. These rules must be followed by predictive maintenance systems to assure operational safety, quality and dependability. However, negotiating the complicated world of regulatory regulations can be difficult, particularly when introducing new technologies. Manufacturers must stay current on relevant legislation and verify that their PdM systems meet all necessary criteria. This may necessitate additional documentation, reporting, and validation procedures, increasing the complexity and cost of implementation.

Key Trends:

• Cloud-based Predictive Maintenance Solutions: Cloud computing is changing the way predictive maintenance data is stored, processed, and evaluated. Cloud-based PdM solutions have various benefits, including scalability, adaptability, and cost-effectiveness. These technologies enable manufacturers to use strong computing resources without requiring large financial expenditure in IT infrastructure. Cloud platforms make it easier to aggregate and analyze huge datasets from various sources, resulting in more detailed insights about equipment performance and failure patterns.
• Enhanced Human-Machine Collaboration: The adoption of predictive maintenance technologies is changing the way humans and machines collaborate. Advanced PdM systems provide detailed insights and recommendations, allowing maintenance teams to make better decisions. Human-machine collaboration is improved by intuitive user interfaces, augmented reality (AR), and virtual reality (VR) systems that help technicians accomplish maintenance jobs. AR and VR can provide step-by-step instructions, display complex data, and mimic repair methods, hence increasing the efficiency and accuracy of maintenance activities.
• Use of Digital Twins: A digital twin is a virtual representation of a physical object, system, or process. In predictive maintenance, digital twins are utilized to mimic and assess equipment behavior under various scenarios. Manufacturers can create a digital twin of a machine to monitor its performance in real time, detect possible faults, and optimize maintenance schedules. Digital twins allow for extensive investigation and testing of many situations without affecting actual operations. This technology is gaining acceptance because it enables more precise and effective predictive maintenance strategies.
• Customized Predictive Maintenance Solutions: As production settings and requirements vary greatly, there is an increasing demand for customized predictive maintenance solutions that are suited to specific demands. Generic PdM solutions may fail to solve each manufacturer's specific difficulties and operational settings. Customized solutions include the individual types of equipment, operating conditions, and business objectives, resulting in more relevant and actionable data.

Global Predictive Maintenance For Manufacturing Industry Market Regional Analysis

Here is a more detailed regional analysis of the global Predictive Maintenance For Manufacturing Industry Market:

North America:

• North America's dominance in the manufacturing predictive maintenance market. The region benefits from a well-developed industrial environment, with a high concentration of production facilities in industries such as automotive, aerospace, electronics, and pharmaceuticals.
• These industries were early adopters of predictive maintenance systems, motivated by the need to reduce downtime, increase productivity, and maintain a competitive edge in the global market. The vibrant industrial ecosystem in North America promotes innovation and collaboration among industry participants, technology providers, and research institutes, resulting in rapid advancement and acceptance of predictive maintenance solutions.
• North America is at the forefront of technological innovation, particularly in the areas of artificial intelligence, machine learning, and the Internet of Things. The region is home to some of the world's best technology businesses and research organizations that specialize in advanced predictive analytics algorithms and IoT platforms designed for industrial applications.
• Furthermore, the availability of a trained workforce with experience in data science, engineering, and industrial automation has accelerated the region's adoption of predictive maintenance solutions. As manufacturers grasp the strategic relevance of predictive maintenance in improving operating efficiency, lowering costs, and increasing competitiveness, the demand for novel PdM technology grows, fueling North America's dominance in the industry.

Asia Pacific:

• The Asia Pacific region is expected to see significant expansion in the predictive maintenance industry in the near future. This spike is mostly driven by the region's growing industrialization, with countries such as China, India, and South Korea emerging as significant manufacturing centers. As these countries invest extensively in infrastructure development and industrial expansion, there is a stronger emphasis on implementing new technology to improve operational efficiency and productivity in manufacturing processes.
• Furthermore, the region's increased emphasis on upgrading its industrial sector coincides with an increase in demand for predictive maintenance solutions to prevent equipment breakdowns and save downtime.
• The Asia Pacific area has a large pool of technical expertise, which contributes to the quick adoption of cutting-edge technology like cloud-based predictive maintenance solutions. The growth of cloud computing platforms enables firms in the region to use scalable and cost-effective predictive maintenance solutions, allowing for real-time monitoring and analysis of equipment performance.
• As more businesses in the Asia Pacific recognize the transformative power of predictive maintenance in optimizing maintenance schedules, lowering costs, and improving overall operational performance, the market for PdM solutions is expected to grow exponentially, cementing the region's position as a key player in the global predictive maintenance market.

Global Predictive Maintenance For Manufacturing Industry Market: Segmentation Analysis

The Global Predictive Maintenance For Manufacturing Industry Market is Segmented on the basis of Component, Deployment, Verticals, Technology, Technique, Organization Size, And Geography.

Predictive Maintenance For Manufacturing Industry Market, By Component

• Solutions
• Integrated
• Standalone
• Services
• Professional
• Managed
• Hardware

Based on Component, The market is segmented into Solutions, Services, and Hardware. The solutions segment is projected to hold majority of the share in the market. This dominance is primarily due to there is constant requirement of using predictive analytics and data-driven information to speed up as well as improve maintenance process. The use of solutions in businesses is projected to help in cost saving and streamline maintenance in the manufacturing industry.

Predictive Maintenance For Manufacturing Industry Market, By Deployment

• Cloud-Based
• On Premise

Based on Deployment, The market is segmented into Cloud-based and On Premise. The predictive maintenance market for manufacturing is dominated by cloud-based solutions. Their scalability, low cost, and remote access make them suitable for enterprises of all sizes. While on-premise solutions continue to be deployed, their growth rate is slowing. The high upfront expenditures and maintenance strain of on-premise equipment are pushing the migration to cloud-based solutions.

Predictive Maintenance For Manufacturing Industry Market, By Verticals

• Government And Defense
• Manufacturing
• Energy And Utilities
• Transportation And Logistics
• Healthcare And Life Sciences

Based on Verticals, the market is segmented into Government And Defense, Manufacturing, Energy And Utilities, Transportation And Logistics, and Healthcare And Life Sciences. The manufacturing sector has the largest proportion of the predictive maintenance market. Manufacturers stand to benefit significantly from proactive maintenance, which reduces downtime, optimizes production processes, and saves money. The energy and utilities sector is expected to see the most rapid adoption of predictive maintenance solutions. This is motivated by the desire for dependable and efficient electricity generation and distribution. Predictive maintenance can assist prevent equipment failures that cause power outages and interruptions.

Predictive Maintenance For Manufacturing Industry Market, By Technology

• Artificial Intelligence (AI)
• Internet of Things (IoT) Platform
• Sensors
• Others

Based on Technology, The market is segmented into Sensors, Internet of Things (IoT) Platforms, Artificial Intelligence (AI), and Others. The artificial intelligence segment is projected to dominate the market over the forecast period. The ease in training predictive maintenance models using historical data is surging the use of AI technology. Thus, the failure analysis helps understand the service demand and lower machine damage, repair costing, and optimize necessary components.

Predictive Maintenance For Manufacturing Industry Market, By Technique

• Oil Analysis
• Vibration Analysis
• Acoustic Monitoring
• Motor Circuit Analysis
• Others

Based on Technique, The market is segmented into Oil Analysis, Vibration Analysis, Acoustic Monitoring, Motor Circuit Analysis, and Others. Vibration analysis segment is projected to dominate the market over the forecast period. This technology helps detect the connectivity of sensors with the centralized system and offer real-time data. In addition to this, the oil analysis segment is projected to exhibit rapid growth as there is constant need for analysis of lubrication in the machinery in the manufacturing industry.

Predictive Maintenance For Manufacturing Industry Market, By Organization Size

• Small And Medium Enterprises
• Large Enterprises

Based on Organization Size, The market is segmented into Small And Medium Enterprises and Large Enterprises. The demand for large enterprise for handling the manufacturing, distribution, and selling products across wider range of supply chain is surging use of real-time tracking and maintenance technologies. Thus, the integration of predictive maintenance for manufacturing in the larger enterprises is projected to rise over the years.

Predictive Maintenance For Manufacturing Industry Market, By Geography

• North America
• Europe
• Asia Pacific
• Rest of the World

Based on Geography, The Global Predictive Maintenance For Manufacturing Industry Market is segmented into North America, Europe, Asia Pacific, and the Rest of the World. North America leads the market. This dominance can be attributed to a number of causes, including the strong presence of large manufacturing businesses, early adoption of advanced technologies such as AI and IoT, and government measures to promote industrial automation. The Asia-Pacific region is expected to experience the most rapid growth in the future years. This rapid expansion is being driven by causes such as rapid industrialization, increased government investment in infrastructure development, and a growing emphasis on enhancing operational efficiency in manufacturing.

Key Players

The "Global Predictive Maintenance For Manufacturing Industry Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are IBM, SAS Institute, ABB Ltd, Microsoft Corporation, Robert Bosch GmbH, Software AG, Rockwell Automation, eMaint Enterprises, Schneider Electric, Siemens, PTC, and General Electric. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis.

• Predictive Maintenance For Manufacturing Industry Market Recent Developments
• In June 2023, Predictive maintenance is at the forefront of digitalization initiatives in packaging and processing, and use is growing rapidly. This is according to PMMI Business Intelligence's 2023 research, "Sustainability and Technology - The Future of Packaging and Processing." In a poll of industry stakeholders performed for the report, 71% stated they used predictive maintenance technology, compared to 37% for collaborative robots, the next most popular digitalization endeavor.
• In April 2024, Predictive maintenance: Al's role in reducing production downtime Al uses powerful machine learning models to predict equipment faults.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET

• 1.1 Introduction of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY COMPONENT

• 5.1 Overview
• 5.2 Solutions
  • 5.2.1 Integrated
  • 5.2.2 Standalone
• 5.3 Services
  • 5.3.1 Professional
  • 5.3.2 Managed
• 5.4 Hardware

6 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY DEPLOYMENT

• 6.1 Overview
• 6.2 Cloud-based
• 6.3 On Premise

7 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY VERTICALS

• 7.1 Overview
• 7.2 Government And Defense
• 7.3 Manufacturing
• 7.4 Energy And Utilities
• 7.5 Transportation And Logistics
• 7.6 Healthcare And Life Sciences

8 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY TECHNOLOGY

• 8.1 Overview
• 8.2 Artificial Intelligence (AI)
• 8.3 Internet of Things (IoT) Platform
• 8.4 Sensors
• 8.5 Others

9 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY TECHNIQUE

• 9.1 Overview
• 9.2 Oil Analysis
• 9.3 Vibration Analysis
• 9.4 Acoustic Monitoring
• 9.5 Motor Circuit Analysis
• 9.6 Others

10 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY ORGANIZATION SIZE

• 10.1 Overview
• 10.1 Small & Medium Enterprises
• 10.1 Large Enterprises

11 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET, BY GEOGRAPHY

• 11.1 Overview
• 11.2 North America
  • 11.2.1 U.S.
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 U.K.
  • 11.3.3 France
  • 11.3.4 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 China
  • 11.4.2 Japan
  • 11.4.3 India
  • 11.4.4 Rest of Asia Pacific
• 11.5 Rest of the World
  • 11.5.1 Latin America
  • 11.5.2 Middle East and Africa

12 GLOBAL PREDICTIVE MAINTENANCE FOR MANUFACTURING INDUSTRY MARKET COMPETITIVE LANDSCAPE

• 12.1 Overview
• 12.2 Company Market Ranking
• 12.3 Key Development Strategies

13 COMPANY PROFILES

• 13.1 IBM
  • 13.1.1 Overview
  • 13.1.2 Financial Performance
  • 13.1.3 Product Outlook
  • 13.1.4 Key Developments
• 13.2 SAS Institute
  • 13.2.1 Overview
  • 13.2.2 Financial Performance
  • 13.2.3 Product Outlook
  • 13.2.4 Key Developments
• 13.3 Robert Bosch GmbH
  • 13.3.1 Overview
  • 13.3.2 Financial Performance
  • 13.3.3 Product Outlook
  • 13.3.4 Key Developments
• 13.4 Software AG
  • 13.4.1 Overview
  • 13.4.2 Financial Performance
  • 13.4.3 Product Outlook
  • 13.4.4 Key Developments
• 13.5 Rockwell Automation
  • 13.5.1 Overview
  • 13.5.2 Financial Performance
  • 13.5.3 Product Outlook
  • 13.5.4 Key Developments
• 13.6 eMaint Enterprises
  • 13.6.1 Overview
  • 13.6.2 Financial Performance
  • 13.6.3 Product Outlook
  • 13.6.4 Key Developments
• 13.7 Schneider Electric
  • 13.7.1 Overview
  • 13.7.2 Financial Performance
  • 13.7.3 Product Outlook
  • 13.7.4 Key Development
• 13.8 General Electric
  • 13.8.1 Overview
  • 13.8.2 Financial Performance
  • 13.8.3 Product Outlook
  • 13.8.4 Key Developments
• 13.9 Siemens
  • 13.9.1 Overview
  • 13.9.2 Financial Performance
  • 13.9.3 Product Outlook
  • 13.9.4 Key Developments
• 13.10 PTC
  • 13.10.1 Overview
  • 13.10.2 Financial Performance
  • 13.10.3 Product Outlook
  • 13.10.4 Key Development

14 Appendix

• 14.1 Related Research