Summary

Global Predictive Maintenance Market is valued approximately at USD 5.45 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 30.90% over the forecast period 2023-2030. Predictive Maintenance is a proactive maintenance strategy employed by organizations to anticipate and mitigate equipment failures before they occur, thereby optimizing operational efficiency and reducing downtime. This approach relies on advanced data analytics, machine learning algorithms, and sensor technologies to monitor the condition of machinery and predict potential faults or failures based on historical performance data and real-time operational parameters. The application of Predictive Maintenance spans various industries, including manufacturing, transportation, energy, and healthcare, among others. By continuously monitoring equipment health and performance metrics, organizations can identify patterns and anomalies indicative of impending failures or degradation in asset condition. This enables timely intervention through scheduled maintenance activities, part replacement, or corrective actions, thereby preventing costly breakdowns, minimizing production disruptions, and extending the lifespan of critical assets. Moreover, the growing adoption industrial internet of things (IIoT), increasing focus on asset performance management, and rising shift from reactive to proactive maintenance are anticipated to create the lucrative demand for the market during forecast period 2023-2030.

Additionally, the proliferation of IIoT devices and connectivity solutions in industrial settings has facilitated the collection of vast amounts of real-time data from equipment and machinery. This data can be leveraged for predictive analytics and condition monitoring, enabling proactive maintenance actions, driving the growth of the Predictive Maintenance market. In 2021, the global industrial internet of things (IIoT) market was valued USD 263.52 billion and it is anticipated to reach USD 2,188.73 billion by 2028. Aa a result, the growing adoption of IIoT is anticipated to support the market growth. Moreover, the growing advancement in sensor technology, IoT devices, cloud computing, and machine learning algorithms, and growing industrialization are anticipated to create lucrative opportunity for the market growth. However, the high initial implementing costs, and inadequate availability of skilled workforce stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Predictive Maintenance Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 with largest market share owing to the increasing adoption of IOT and sensor technologies, growing awareness of predictive maintenance, rise of industry 4.0 and digital transformation initiatives, and advancement in data analytics and machine learning. Whereas, the Asia Pacific region is expected to grow with the fastest growth rate during the forecast period, owing to factors such as the rapid adoption of industrial IoT and sensor technologies across various industries such as manufacturing, energy, transportation, and healthcare, government initiatives promoting industry 4.0, growing advancements in data analytics and ai technologies, and expansion of manufacturing and infrastructure sectors in the region.

Major market players included in this report are:
IBM Corporation
Microsoft Corporation
SAP SE
Schneider Electric SE
Hitachi, Ltd.
SAS Institute, Inc.
Oracle Corporation
Siemens AG
Axiomtek Co., Ltd.
Banner Engineering Corp.

Recent Developments in the Market:
 In April 2023, TrendMiner has unveiled an upgraded version of its predictive maintenance software, named the Digital Twin Manager. This latest version boasts improved compatibility with cloud data sources from leading providers like AWS and Microsoft. Additionally, it introduces interactive search capabilities, empowering users to glean insights from data swiftly and make informed decisions with heightened efficiency.
 In May 2023, Cisco Systems, Inc. and NTT, a leading provider of telecom infrastructure services, have joined forces to co-create a suite of solutions aimed at delivering real-time data insights, bolstering decision-making processes, and fortifying security measures. Leveraging predictive maintenance, supply chain management, and asset tracking capabilities, this collaboration aims to empower organizations with advanced tools for optimizing operations and safeguarding critical assets.
Global Predictive Maintenance Market Report Scope:
 Historical Data – 2020 - 2021
 Base Year for Estimation – 2022
 Forecast period - 2023-2030
 Report Coverage - Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
 Segments Covered - Component, Deployment Model, Organization Size, Industry Vertical, Region
 Regional Scope - North America; Europe; Asia Pacific; Latin America; Middle East & Africa
 Customization Scope - Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:
Solutions
Services

By Deployment Model:
Cloud
On-premise

By Organization Size:
Large Enterprises
Small and Medium-sized Enterprises

By Industry Vertical:
Government & Defense
Manufacturing
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences

By Region:

North America
U.S.
Canada

Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC

Latin America
Brazil
Mexico

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

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Table of Contents

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Predictive Maintenance Market, by region, 2020-2030 (USD Billion)
1.2.2. Predictive Maintenance Market, by Component, 2020-2030 (USD Billion)
1.2.3. Predictive Maintenance Market, by Deployment Model, 2020-2030 (USD Billion)
1.2.4. Predictive Maintenance Market, by Organization Size, 2020-2030 (USD Billion)
1.2.5. Predictive Maintenance Market, by Industry Vertical, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Predictive Maintenance Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Predictive Maintenance Market Dynamics
3.1. Predictive Maintenance Market Impact Analysis (2020-2030)
3.1.1. Market Drivers
3.1.1.1. Growing adoption industrial internet of things (IIoT)
3.1.1.2. Increasing focus on asset performance management
3.1.1.3. Rising shift from reactive to proactive maintenance
3.1.2. Market Challenges
3.1.2.1. High initial implementation costs
3.1.2.2. Inadequate availability of skilled workforce
3.1.3. Market Opportunities
3.1.3.1. Advancement in sensor technology, IoT devices, cloud computing, and machine learning algorithms
3.1.3.2. Growing industrialization
Chapter 4. Global Predictive Maintenance Market: Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economic
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Predictive Maintenance Market, by Component
5.1. Market Snapshot
5.2. Global Predictive Maintenance Market by Component, Performance - Potential Analysis
5.3. Global Predictive Maintenance Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. Predictive Maintenance Market, Sub Segment Analysis
5.4.1. Solutions
5.4.2. Services
Chapter 6. Global Predictive Maintenance Market, by Deployment Model
6.1. Market Snapshot
6.2. Global Predictive Maintenance Market by Deployment Model, Performance - Potential Analysis
6.3. Global Predictive Maintenance Market Estimates & Forecasts by Deployment Model 2020-2030 (USD Billion)
6.4. Predictive Maintenance Market, Sub Segment Analysis
6.4.1. Cloud
6.4.2. On-premise
Chapter 7. Global Predictive Maintenance Market, by Organization Size
7.1. Market Snapshot
7.2. Global Predictive Maintenance Market by Organization Size, Performance - Potential Analysis
7.3. Global Predictive Maintenance Market Estimates & Forecasts by Organization Size 2020-2030 (USD Billion)
7.4. Predictive Maintenance Market, Sub Segment Analysis
7.4.1. Large Enterprises
7.4.2. Small and Medium-sized Enterprise
Chapter 8. Predictive Maintenance Market, by Industry Vertical
8.1. Market Snapshot
8.2. Global Predictive Maintenance Market by Industry Vertical, Performance - Potential Analysis
8.3. Global Predictive Maintenance Market Estimates & Forecasts by Industry Vertical 2020-2030 (USD Billion)
8.4. Predictive Maintenance Market, Sub Segment Analysis
8.4.1. Government & Defense
8.4.2. Manufacturing
8.4.3. Energy & Utilities
8.4.4. Transportation & Logistics
8.4.5. Healthcare & Life Sciences
Chapter 9. Global Predictive Maintenance Market, Regional Analysis
9.1. Top Leading Countries
9.2. Top Emerging Countries
9.3. Predictive Maintenance Market, Regional Market Snapshot
9.4. North America Predictive Maintenance Market
9.4.1. U.S. Predictive Maintenance Market
9.4.1.1. Component breakdown estimates & forecasts, 2020-2030
9.4.1.2. Deployment Model breakdown estimates & forecasts, 2020-2030
9.4.1.3. Organization Size breakdown estimates & forecasts, 2020-2030
9.4.1.4. Industry Vertical breakdown estimates & forecasts, 2020-2030
9.4.2. Canada Predictive Maintenance Market
9.5. Europe Predictive Maintenance Market Snapshot
9.5.1. U.K. Predictive Maintenance Market
9.5.2. Germany Predictive Maintenance Market
9.5.3. France Predictive Maintenance Market
9.5.4. Spain Predictive Maintenance Market
9.5.5. Italy Predictive Maintenance Market
9.5.6. Rest of Europe Predictive Maintenance Market
9.6. Asia-Pacific Predictive Maintenance Market Snapshot
9.6.1. China Predictive Maintenance Market
9.6.2. India Predictive Maintenance Market
9.6.3. Japan Predictive Maintenance Market
9.6.4. Australia Predictive Maintenance Market
9.6.5. South Korea Predictive Maintenance Market
9.6.6. Rest of Asia Pacific Predictive Maintenance Market
9.7. Latin America Predictive Maintenance Market Snapshot
9.7.1. Brazil Predictive Maintenance Market
9.7.2. Mexico Predictive Maintenance Market
9.8. Middle East & Africa Predictive Maintenance Market
9.8.1. Saudi Arabia Predictive Maintenance Market
9.8.2. South Africa Predictive Maintenance Market
9.8.3. Rest of Middle East & Africa Predictive Maintenance Market

Chapter 10. Competitive Intelligence
10.1. Key Company SWOT Analysis
10.1.1. Company 1
10.1.2. Company 2
10.1.3. Company 3
10.2. Top Market Strategies
10.3. Company Profiles
10.3.1. IBM Corporation
10.3.1.1. Key Information
10.3.1.2. Overview
10.3.1.3. Financial (Subject to Data Availability)
10.3.1.4. Product Summary
10.3.1.5. Recent Developments
10.3.2. Microsoft Corporation
10.3.3. SAP SE
10.3.4. Schneider Electric SE
10.3.5. Hitachi, Ltd.
10.3.6. SAS Institute, Inc.
10.3.7. Oracle Corporation
10.3.8. Siemens AG
10.3.9. Axiomtek Co., Ltd.
10.3.10. Banner Engineering Corp.
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption