Global Predictive Maintenance In Manufacturing Market 2026 by Company, Regions, Type and Application, Forecast to 2032
Description
According to our (Global Info Research) latest study, the global Predictive Maintenance In Manufacturing market size was valued at US$ 9814 million in 2025 and is forecast to a readjusted size of US$ 31260 million by 2032 with a CAGR of 18.2% during review period.
Predictive Maintenance in Manufacturing is an intelligent maintenance strategy that uses technologies such as sensors, IoT, and artificial intelligence to monitor the real-time condition of equipment and predict potential failures before they occur. Analyzing operational data it enables maintenance to be performed at the optimal time, reducing unplanned downtime, minimizing repair costs, improving production efficiency, and extending equipment lifespan. It is a key component of smart manufacturing and Industry 4.0 initiatives.
Global key predictive maintenance in manufacturing players include SAP, Schneider and Siemens etc. The top 3 companies hold a share about 19%. North America is the largest market with a share about 35%, followed by Europe and Asia-Pacific. In terms of product, cloud based product is the largest segment with a share about 77%. And in terms of applications, the largest application is industrial and manufacturing with a share about 47%.
Market Drivers
Widespread Adoption of IoT, AI, and ML: Manufacturers are increasingly deploying IoT sensors and AI/ML analytics to continuously monitor equipment parameters like vibration, temperature, and pressure. This enables accurate predictions of failures and facilitates timely maintenance interventions, shifting the maintenance model from reactive to proactive.
Cost Reduction & Operational Efficiency: Predictive Maintenance significantly reduces unplanned downtime and unnecessary maintenance, resulting in cost savings of 10–40%. It also extends asset lifespan, boosts overall equipment effectiveness (OEE), and enhances production efficiency.
Industry 4.0 Integration: The evolution toward smart manufacturing fosters demand for predictive solutions. Predictive Maintenance is becoming integral to digital factories, integrated with ERP, CMMS, and other enterprise systems to streamline workflows.
Cloud & Edge Computing Enable Scalability: Cloud-based platforms facilitate scalable, centralized analytics without heavy IT infrastructure. Edge computing further supports real-time decision-making at the equipment level, reducing latency and bandwidth needs.
Regulatory Compliance & Asset Reliability: In regulated industries like automotive, energy, and aerospace, predictive maintenance supports safety and compliance requirements by proactively managing equipment health and reducing failure risk.
Market Challenges
High Upfront Investment & ROI Uncertainty: Implementing PdM requires investment in sensors, analytic platforms, data integration, and training. Especially for SMEs, justifying these investments can be difficult due to delayed or indirect ROI.
Data Integration & Quality Issues: Manufacturers often struggle with disparate, noisy data from legacy systems and heterogeneous devices. Ensuring accurate, consistent data for reliable predictions is a significant hurdle.
Cybersecurity Vulnerabilities: As predictive systems increasingly rely on networked sensors and cloud infrastructure, they expose operations to cyber risks. Protecting data integrity and privacy is essential—and costly.
Skilled Workforce Shortage: Effective PdM deployment demands expertise in data science, ML, and industrial systems—skills that are often lacking, and training or hiring new specialists adds complexity and cost.
Scalability & Interoperability Barriers: Scaling pilot systems across diverse machines and sites often encounters issues like vendor-specific formats, lack of standard protocols, and maintenance of consistency across equipment types.
Cultural Resistance to Change: Some manufacturers remain cautious about adopting ML-based maintenance tools due to trust issues, fear of job displacement, or preference for traditional methods.
This report is a detailed and comprehensive analysis for global Predictive Maintenance In Manufacturing market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Predictive Maintenance In Manufacturing market size and forecasts, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market size and forecasts by region and country, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market size and forecasts, by Type and by Application, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market shares of main players, in revenue ($ Million), 2021-2026
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Predictive Maintenance In Manufacturing
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Predictive Maintenance In Manufacturing market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include IBM, Microsoft, SAP, GE Digital, Schneider, Hitachi, Siemens, Intel, RapidMiner, Rockwell Automation, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Predictive Maintenance In Manufacturing market is split by Type and by Application. For the period 2021-2032, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Cloud Based
On-premises
Market segment by Application
Automotive
Electronics and Semiconductor
Consumer Goods
Chemical
Pharmaceutical
Others
Market segment by players, this report covers
IBM
Microsoft
SAP
GE Digital
Schneider
Hitachi
Siemens
Intel
RapidMiner
Rockwell Automation
Software AG
Cisco
Oracle
Fujitsu
Dassault Systemes
Augury Systems
TIBCO Software
Uptake
Honeywell
PTC
Huawei
ABB
AVEVA
SAS
SKF
Emerson
Mpulse
Maintenance Connection
Dingo
Particle
Bosch
C3.ai
Dell
Sigma Industrial Precision
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Predictive Maintenance In Manufacturing product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Predictive Maintenance In Manufacturing, with revenue, gross margin, and global market share of Predictive Maintenance In Manufacturing from 2021 to 2026.
Chapter 3, the Predictive Maintenance In Manufacturing competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2021 to 2032.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2021 to 2026.and Predictive Maintenance In Manufacturing market forecast, by regions, by Type and by Application, with consumption value, from 2027 to 2032.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Predictive Maintenance In Manufacturing.
Chapter 13, to describe Predictive Maintenance In Manufacturing research findings and conclusion.
Predictive Maintenance in Manufacturing is an intelligent maintenance strategy that uses technologies such as sensors, IoT, and artificial intelligence to monitor the real-time condition of equipment and predict potential failures before they occur. Analyzing operational data it enables maintenance to be performed at the optimal time, reducing unplanned downtime, minimizing repair costs, improving production efficiency, and extending equipment lifespan. It is a key component of smart manufacturing and Industry 4.0 initiatives.
Global key predictive maintenance in manufacturing players include SAP, Schneider and Siemens etc. The top 3 companies hold a share about 19%. North America is the largest market with a share about 35%, followed by Europe and Asia-Pacific. In terms of product, cloud based product is the largest segment with a share about 77%. And in terms of applications, the largest application is industrial and manufacturing with a share about 47%.
Market Drivers
Widespread Adoption of IoT, AI, and ML: Manufacturers are increasingly deploying IoT sensors and AI/ML analytics to continuously monitor equipment parameters like vibration, temperature, and pressure. This enables accurate predictions of failures and facilitates timely maintenance interventions, shifting the maintenance model from reactive to proactive.
Cost Reduction & Operational Efficiency: Predictive Maintenance significantly reduces unplanned downtime and unnecessary maintenance, resulting in cost savings of 10–40%. It also extends asset lifespan, boosts overall equipment effectiveness (OEE), and enhances production efficiency.
Industry 4.0 Integration: The evolution toward smart manufacturing fosters demand for predictive solutions. Predictive Maintenance is becoming integral to digital factories, integrated with ERP, CMMS, and other enterprise systems to streamline workflows.
Cloud & Edge Computing Enable Scalability: Cloud-based platforms facilitate scalable, centralized analytics without heavy IT infrastructure. Edge computing further supports real-time decision-making at the equipment level, reducing latency and bandwidth needs.
Regulatory Compliance & Asset Reliability: In regulated industries like automotive, energy, and aerospace, predictive maintenance supports safety and compliance requirements by proactively managing equipment health and reducing failure risk.
Market Challenges
High Upfront Investment & ROI Uncertainty: Implementing PdM requires investment in sensors, analytic platforms, data integration, and training. Especially for SMEs, justifying these investments can be difficult due to delayed or indirect ROI.
Data Integration & Quality Issues: Manufacturers often struggle with disparate, noisy data from legacy systems and heterogeneous devices. Ensuring accurate, consistent data for reliable predictions is a significant hurdle.
Cybersecurity Vulnerabilities: As predictive systems increasingly rely on networked sensors and cloud infrastructure, they expose operations to cyber risks. Protecting data integrity and privacy is essential—and costly.
Skilled Workforce Shortage: Effective PdM deployment demands expertise in data science, ML, and industrial systems—skills that are often lacking, and training or hiring new specialists adds complexity and cost.
Scalability & Interoperability Barriers: Scaling pilot systems across diverse machines and sites often encounters issues like vendor-specific formats, lack of standard protocols, and maintenance of consistency across equipment types.
Cultural Resistance to Change: Some manufacturers remain cautious about adopting ML-based maintenance tools due to trust issues, fear of job displacement, or preference for traditional methods.
This report is a detailed and comprehensive analysis for global Predictive Maintenance In Manufacturing market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global Predictive Maintenance In Manufacturing market size and forecasts, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market size and forecasts by region and country, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market size and forecasts, by Type and by Application, in consumption value ($ Million), 2021-2032
Global Predictive Maintenance In Manufacturing market shares of main players, in revenue ($ Million), 2021-2026
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for Predictive Maintenance In Manufacturing
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global Predictive Maintenance In Manufacturing market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include IBM, Microsoft, SAP, GE Digital, Schneider, Hitachi, Siemens, Intel, RapidMiner, Rockwell Automation, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
Predictive Maintenance In Manufacturing market is split by Type and by Application. For the period 2021-2032, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Cloud Based
On-premises
Market segment by Application
Automotive
Electronics and Semiconductor
Consumer Goods
Chemical
Pharmaceutical
Others
Market segment by players, this report covers
IBM
Microsoft
SAP
GE Digital
Schneider
Hitachi
Siemens
Intel
RapidMiner
Rockwell Automation
Software AG
Cisco
Oracle
Fujitsu
Dassault Systemes
Augury Systems
TIBCO Software
Uptake
Honeywell
PTC
Huawei
ABB
AVEVA
SAS
SKF
Emerson
Mpulse
Maintenance Connection
Dingo
Particle
Bosch
C3.ai
Dell
Sigma Industrial Precision
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Predictive Maintenance In Manufacturing product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Predictive Maintenance In Manufacturing, with revenue, gross margin, and global market share of Predictive Maintenance In Manufacturing from 2021 to 2026.
Chapter 3, the Predictive Maintenance In Manufacturing competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2021 to 2032.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2021 to 2026.and Predictive Maintenance In Manufacturing market forecast, by regions, by Type and by Application, with consumption value, from 2027 to 2032.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Predictive Maintenance In Manufacturing.
Chapter 13, to describe Predictive Maintenance In Manufacturing research findings and conclusion.
Table of Contents
190 Pages
- 1 Market Overview
- 2 Company Profiles
- 3 Market Competition, by Players
- 4 Market Size Segment by Type
- 5 Market Size Segment by Application
- 6 North America
- 7 Europe
- 8 Asia-Pacific
- 9 South America
- 10 Middle East & Africa
- 11 Market Dynamics
- 12 Industry Chain Analysis
- 13 Research Findings and Conclusion
- 14 Appendix
Pricing
Currency Rates
Questions or Comments?
Our team has the ability to search within reports to verify it suits your needs. We can also help maximize your budget by finding sections of reports you can purchase.
