PUBLISHER: QYResearch | PRODUCT CODE: 1871976
PUBLISHER: QYResearch | PRODUCT CODE: 1871976
The global market for Predictive Maintenance In Manufacturing was estimated to be worth US$ 8020 million in 2024 and is forecast to a readjusted size of US$ 26597 million by 2031 with a CAGR of 18.6% during the forecast period 2025-2031.
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 aims to provide a comprehensive presentation of the global market for Predictive Maintenance In Manufacturing, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Predictive Maintenance In Manufacturing by region & country, by Type, and by Application.
The Predictive Maintenance In Manufacturing market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Predictive Maintenance In Manufacturing.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Predictive Maintenance In Manufacturing company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Predictive Maintenance In Manufacturing in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Predictive Maintenance In Manufacturing in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.