On Nov 28, the latest report “Global Predictive Maintenance In Manufacturing Market 2026 by Manufacturers, Regions, Types and Applications, Forecast to 2032” from Global Info Research provides a detailed and comprehensive analysis of the global Predictive Maintenance In Manufacturing market. The report provides both quantitative and qualitative analysis by manufacturers, regions and countries, types and applications. As the market is constantly changing, this report explores market competition, supply and demand trends, and key factors that are causing many market demand changes. The report also provides company profiles and product examples of some of the competitors, as well as market share estimates for some of the leading players in 2026.
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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 USD million by 2032 with a CAGR of % 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.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approval.
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 in terms of volume and value. 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
Major players covered: 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
The content of the study subjects, includes a total of 15 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 manufacturers of Predictive Maintenance In Manufacturing, with price, sales quantity, revenue, and global market share of Predictive Maintenance In Manufacturing from 2021 to 2026.
Chapter 3, the Predictive Maintenance In Manufacturing competitive situation, sales quantity, revenue, and global market share of top manufacturers are analyzed emphatically by landscape contrast.
Chapter 4, the Predictive Maintenance In Manufacturing breakdown data are shown at the regional level, to show the sales quantity, consumption value, and growth by regions, from 2021 to 2032.
Chapter 5 and 6, to segment Predictive Maintenance In Manufacturing the sales by Type and by Application, with sales market share and growth rate by Type, by Application, from 2021 to 2032.
Chapter 7, 8, 9, 10 and 11, to break the Predictive Maintenance In Manufacturing sales data at the country level, with sales quantity, consumption value, and market share for key countries in the world, from 2021 to 2025.and Predictive Maintenance In Manufacturing market forecast, by regions, by Type, and by Application, with sales and revenue, from 2026 to 2032.
Chapter 12, market dynamics, drivers, restraints, trends, and Porters Five Forces analysis.
Chapter 13, the key raw materials and key suppliers, and industry chain of Predictive Maintenance In Manufacturing.
Chapter 14 and 15, to describe Predictive Maintenance In Manufacturing sales channel, distributors, customers, research findings and conclusion.
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
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