Predictive & Reactive Maintenance Report Cover TrendFeedr

Predictive & Reactive Maintenance Report

: Analysis on the Market, Trends, and Technologies
54.0K
TOTAL COMPANIES
Widespread
Topic Size
Stagnant
ANNUAL GROWTH
Surging
trending indicator
64.2B
TOTAL FUNDING
Average
Topic Maturity
Hyped
TREND HYPE
N/A
Monthly Search Volume
Updated: November 28, 2025

The predictive and reactive maintenance market is growing fast and is projected by the internal trend data to reach USD 162.1 billion by 2033 with a 32.2% CAGR, signaling that asset-intensive firms that do not adopt predictive approaches risk falling behind on cost and uptime metrics.

The last time we updated this report was 12 days ago. If there’s something missing or off, your tips are welcome!

Topic Dominance Index of Predictive & Reactive Maintenance

The Dominance Index for Predictive & Reactive Maintenance merges timelines of published articles, newly founded companies, and global search data to provide a comprehensive perspective into the topic.

Dominance Index growth in the last 5 years: 53.74%
Growth per month: 0.731%

Key Activities and Applications

  • Condition-based sensing and continuous monitoring — deploying vibration, temperature, acoustic, oil and electrical sensors to create live health baselines that trigger interventions before breakdowns.
  • Remaining Useful Life (RUL) estimation — ML models forecast component life to plan maintenance windows and reduce emergency repairs.
  • Automated work-order and parts orchestration — connecting predictions to ERP/CMMS to auto-create tickets, reserve parts, and schedule crews so interventions occur in planned windows rather than ad hoc The Role of CMMS in Predictive vs Preventive Maintenance.
  • Edge anomaly detection and streaming analytics — lightweight models run on-device to reduce latency for critical assets and reduce data transport costs.
  • PdM-as-a-Service (subscription/MaaS) offerings — outcome-based contracts that shift CapEx to OpEx and accelerate deployments for mid-market adopters.

Technologies and Methodologies

  • IoT sensing (vibration, acoustic, thermal, oil chemistry) for multivariate condition capture — provides the raw signals for ML pipelines Sensors and IoT foundations for PdM.
  • Physics-informed ML and hybrid models — combining engineering constraints with data-driven learning reduces data hunger and improves rare-failure detection.
  • Digital twins used for synthetic data generation and "what-if" intervention simulation — speeds model validation and supports prescriptive testing under constrained operational scenarios.
  • Graph neural networks to map systemic dependency and cascading failure risk across subsystems.
  • Federated learning and privacy-preserving training to pool model improvements without sharing raw OT data Data Bridge Market Research - IoT/AI integration driving PdM.

Predictive & Reactive Maintenance Funding

A total of 4.3K Predictive & Reactive Maintenance companies have received funding.
Overall, Predictive & Reactive Maintenance companies have raised $64.2B.
Companies within the Predictive & Reactive Maintenance domain have secured capital from 9.6K funding rounds.
The chart shows the funding trendline of Predictive & Reactive Maintenance companies over the last 5 years

Funding growth in the last 5 years: 70.48%
Growth per month: 0.9236%

Predictive & Reactive Maintenance Companies

  • Nanoprecise — Nanoprecise focuses on high-resolution vibration and acoustic sensors combined with edge analytics for rotating machinery, enabling early bearing and gearbox fault detection on brownfield assets with minimal retrofitting; they target industrial customers who need low-disruption installs and measurable MTTR improvements.
  • ClickMaint — ClickMaint offers modular CMMS with native PdM connectors that translate sensor alerts into prioritized mobile work orders and parts lists, helping operations teams reduce reactive firefighting by aligning field workflows with predicted faults.
  • FormFour — FormFour supplies non-intrusive sensing overlays and simple edge gateways for legacy equipment in sectors where replacing assets is not feasible; their value is rapid digitization of brownfield fleets and quick ROI for mid-market operators.
  • Ascribo — Ascribo couples anomaly detection with natural-language summaries and knowledge extraction from maintenance logs, using generative models to present concise remediation steps to technicians, which reduces diagnostic time and increases trust in model outputs.
  • AssetMetricsHub — AssetMetricsHub integrates health scores into procurement and inventory modules to create dynamic spare-part buffers tied to predicted failure windows, cutting emergency procurement costs for long-lead components.

Delve into the corporate landscape of Predictive & Reactive Maintenance with TrendFeedr’s Companies tool

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54.0K Predictive & Reactive Maintenance Companies

Discover Predictive & Reactive Maintenance Companies, their Funding, Manpower, Revenues, Stages, and much more

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Predictive & Reactive Maintenance Investors

TrendFeedr’s Investors tool provides insights into 8.2K Predictive & Reactive Maintenance investors for you to keep ahead of the curve. This resource is critical for analyzing investment activities, funding trends, and market potential within the Predictive & Reactive Maintenance industry.

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8.2K Predictive & Reactive Maintenance Investors

Discover Predictive & Reactive Maintenance Investors, Funding Rounds, Invested Amounts, and Funding Growth

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Predictive & Reactive Maintenance News

TrendFeedr’s News feature offers you access to 14.6K articles on Predictive & Reactive Maintenance. Stay informed about the latest trends, technologies, and market shifts to enhance your strategic planning and decision-making.

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14.6K Predictive & Reactive Maintenance News Articles

Discover Latest Predictive & Reactive Maintenance Articles, News Magnitude, Publication Propagation, Yearly Growth, and Strongest Publications

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Executive Summary

Predictive and reactive maintenance has shifted from an experimental cost-saver to a core operational lever that links asset health with financial and supply-chain decisions. The data show large market opportunity and consistent business outcomes when PdM is implemented correctly: measurable downtime reduction, spare-parts optimization, and faster payback under service-based commercial models. Strategic winners will integrate: (1) edge-enabled sensing for near-term alerts, (2) hybrid physics-informed ML for rare-event accuracy, (3) explainable prescriptive outputs technicians trust, and (4) procurement workflows that convert health signals into inventory and contract decisions. Firms that combine those capabilities through subscription service models can accelerate adoption across mid-market and brownfield use cases and capture the highest near-term value from the market expansion indicated by the available forecasts.

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