Predictive maintenance for heavy equipment market seen reaching 16.9% CAGR by 2030

The Business Research Company says the predictive maintenance market for heavy equipment will rise from $8.25 billion in 2025 to $18.1 billion by 2030, driven by Industry 4.0, construction spending and 5G connectivity. North America led the market in 2025, while Asia-Pacific is projected to grow fastest through 2030.

Why it matters: - Predictive maintenance can cut unplanned downtime, reduce repair costs and improve reliability for heavy equipment used in construction and industrial operations. - The market’s projected jump to $18.1 billion by 2030 signals faster adoption of connected monitoring, AI analytics and industrial automation.

What happened: - The Business Research Company projected the predictive maintenance for heavy equipment market will grow from $8.25 billion in 2025 to $9.68 billion in 2026. - The report said the market is then expected to reach $18.1 billion by 2030, implying a 16.9% CAGR from 2026 through 2030. - North America held the largest market share in 2025. - Asia-Pacific is expected to be the fastest-growing region during the forecast period. - The report covered Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, the Middle East and Africa.

The details: - Predictive maintenance for heavy equipment uses data-driven monitoring tools, sensor diagnostics and analytical models to anticipate machine failures before they happen. - The approach focuses on continuous evaluation of machine health, performance metrics and operational stress factors. - The report said the earlier growth phase was supported by reactive maintenance habits, frequent unexpected equipment failures, limited sensor deployment, high repair costs and weak real-time monitoring. - Expected growth drivers include wider use of IoT-enabled industrial equipment, tighter focus on operational efficiency, lower downtime, expansion of smart manufacturing and Industry 4.0, more connected machinery and higher investment in AI-based predictive analytics. - Anticipated trends include sensor-based condition monitoring, digital twin tools, edge analytics, cloud-based predictive maintenance platforms and stronger telematics for remote diagnostics. - The report also pointed to construction as a key demand driver because predictive maintenance helps monitor excavators, cranes and loaders in real time. - In July 2025, the UK Office for National Statistics reported a 2.2% increase in government infrastructure investment to $38.54 billion (£28.9 billion). - The report said 5G is improving predictive maintenance by speeding up the transfer of large sensor datasets to analytics platforms. - Ericsson projected that 5G subscriptions will reach 2.9 billion globally by the end of 2025, or about one-third of all mobile connections. - Ericsson said North America leads in 5G penetration at 79%, followed by North East Asia at 61%, with Western Europe and Gulf Cooperation Council countries each at 55%. - The report said smart manufacturing adoption is accelerating, citing Rockwell Automation’s March 2024 finding that 83% of manufacturers view AI as crucial for business impact. - Rockwell Automation also found that most manufacturers were either deploying or evaluating generative AI and smart manufacturing technologies.

Between the lines: - The forecast ties predictive maintenance demand to a broader shift from reactive repairs to data-led asset management. - Industry 4.0, 5G and AI are reinforcing each other, which could make predictive maintenance easier to deploy and more valuable across heavy industries. - The regional split suggests mature markets are leading adoption now, while infrastructure buildout and industrialization are creating the next wave of growth in Asia-Pacific.

What's next: - The Business Research Company said its 2026 reports now include market attractiveness scoring, TAM analysis, company scoring matrix graphics, Excel-based dashboards, market hotspots infographics and future trend analysis. - More adoption of digital twin, edge analytics and cloud-based platforms is likely as heavy-equipment operators look for faster fault detection and better uptime. - More information