Predictive and preventive maintenance management are foundational pillars for driving plant productivity, cost efficiency, and overall profitability in modern industrial operations.
As global manufacturing becomes increasingly data-driven and competitive, leaders are rapidly shifting towards proactive maintenance strategies to minimize downtime, optimize resource utilization, and safeguard asset value.
The following statistics, drawn from recent industry research and market reports, underscore the importance of plant-level productivity. Each insight underscores how excellence in maintenance is directly linked to operational outcomes – from dramatic reductions in unplanned losses to tangible improvements in ROI, asset lifespan, and inventory management.
1. $1.4 Trillion is Lost Annually due to Unplanned Downtime
A 2024 Siemens report estimated that prolonged and unplanned downtime at the world’s top 500 companies directly result in $1.4 Trillion in annual losses.
A variety of contributing factors to this includes product shortages, equipment breakdowns, poor maintenance data management and unoptimized supply chains.
Downtime costs a lot. In fact, Deloitte estimates it costs industrial manufacturers about $50 billion per year.
2. Reactive Maintenance Costs can be 3-5 times Higher than Preventive Upkeep
GitNux, a company specializing in reporting and statistics has estimated that reactive maintenance practices cost 3-5 times more than preventive maintenance after considering lifetime damage value due to downtime
Preventive Maintenance ROI for Every Dollar Invested
A Jones La Salle Study [Research Link] made an attempt at calculating the ROI on preventive maintenance practices. The results show that for every dollar spent on preventive maintenance, companies can expect a more than 545% in return.
3. Extending Equipment Lifespan by 35-80%
Multiple studies, like this one published on MDPI, strongly indicate an extension in fixed asset lifespan attributed to a combination of predictive and preventive maintenance processes. In some cases, the lifespan can even double.
4. The Market for Predictive Maintenance
The predictive maintenance market is slated to grow at a CAGR of 26.5% over the course of the next 5 years as investments in novel maintenance approaches is gaining momentum.
The market in 2024 was valued at USD 10.93 Billion and is predicted to touch USD 70.73 Billion by 2032.
This includes the market for Predictive Maintenance hardware as well as software.
5. The Market for Preventive Maintenance
Research by DataIntelo, estimates the market for Preventive Maintenance software in 2023 at USD 3.5 billion and estimated to grow to USD 7.8 billion by 2032 at a CAGR of ~10.8%
Preventive maintenance approaches here span work order management, spare parts inventory management, asset tracking, and software that syncs with predictive maintenance approaches as well.
6. Industry-wise Market Shares
As per Modor Intelligence, Industrial Manufacturing holds the largest share in preventive maintenance market share at 23.4%
Energy and Utilities is the fastest growing segment, with a projected annual 35.5% growth rate through 2030.
7. Preventive Maintenance & Inventory Carrying Costs
Preventive Maintenance helps reduce spare parts inventory costs by 15-20% (if not more), as better planning avoids overstocking and emergency procurement.
Other studies also indicate similar increase thanks to decrease in spare part inventory levels, insights into equipment and clean, fully managed and integrated maintenance master data.
8. Spare Parts Data Quality
Global averages of slow-moving and obsolete spare parts in ERP material master systems are estimated to be anywhere around 10% of total inventories.
As per studies conducted by throughput, more than 66% of enterprises rely on outdated hardware and legacy systems, which are expensive to maintain and incompatible with modern digital solutions. Directly exacerbating downtime and procurement costs.
9. Work Order & Backlogs
Although a slightly dated report, a work order backlog in a typical maintenance operation span about 4 weeks. This means that if no new work orders are added, a typical maintenance team will take more than 4 weeks to clear the task. [Source]
An acceptable backlog for every maintenance professional / team is typically pegged at 2 weeks. Which means that 2 weeks is a helpful benchmark that allows flexibility without burdening maintenance professionals.
Digital Work Order Management systems can reduce planned downtime through shutdown and outage optimization, leading to cost reduction of 15% to 30%. [McKinsey]
10. Enterprise Maintenance Costs
As per research by McKinsey, maintenance and related tasks can constitute anywhere between 20 – 60% of overall operational expenditure, depending on Industry and asset-type.
11. Spare Parts Inventory Levels
As per PWC studies and anecdotal observations, Demand planning and forecasting accuracy can reduce stockouts by 10% and increase on-time deliveries by 5%.
An old 2016 study by Boston Consulting Group strongly suggests that robust inventory management practices can promote a 15% improvement in spare part inventories ensuring availability of critical spares and preventing instances of overstocking.
12. Cost of Unplanned Downtime by Industry
Unplanned downtime costs vary significantly by industry. A 2024 Siemens report found that in the automotive sector, downtime can cost over $2.3 million per hour, a twofold increase since 2019, while in heavy industry, costs have quadrupled in the last five years.
These staggering figures highlight the critical need for proactive maintenance to protect revenue and operational continuity.
13. Spare Parts Inventory Management
Effective spare parts inventory management is crucial for efficient maintenance. According to a study by Boston Consulting Group, robust inventory management practices can lead to a 15% improvement in spare parts inventories.
By avoiding overstocking and having critical parts available, businesses can reduce emergency procurement costs and improve asset availability.
14. Predictive vs. Preventive Maintenance Adoption
While preventive maintenance is still the most widely adopted strategy, predictive maintenance is rapidly gaining ground.
According to a 2025 Plant Engineering study, 88% of manufacturing companies use preventive maintenance, but 40% also apply predictive maintenance using analytics tools. This highlights a blend of strategies becoming the new standard for many facilities.
15. AI's Role in Optimizing Maintenance Workforce
The maintenance workforce is undergoing a transformation. A 2025 Deloitte report notes that by predicting and preventing failures, the maintenance workforce spends less time reacting to machine failures and more time on strategic, proactive tasks.
This shift allows for the best use of valuable human capital and helps companies retain skilled workers by empowering them with data-driven insights.
16. The Growth of Predictive Maintenance-as-a-Service
The Predictive Maintenance-as-a-Service (PdMaaS) model is gaining popularity as a way to circumvent the high initial costs of technology and the skilled labor shortage.
Market Research Future expects the global PdMaaS market to grow at a CAGR of 28% through 2025, as companies seek flexible, scalable solutions that provide access to cutting-edge technology without the need for extensive in-house expertise.
17. Optimizing Spare Parts Inventory with AI
The Predictive Maintenance-as-a-Service (PdMaaS) model is gaining popularity as a way to circumvent the high initial costs of technology and the skilled labor shortage.
Market Research Future expects the global PdMaaS market to grow at a CAGR of 28% through 2025, as companies seek flexible, scalable solutions that provide access to cutting-edge technology without the need for extensive in-house expertise.
18. The Shift from Reactive to Proactive Maintenance Culture
The ultimate goal of modern maintenance is a cultural shift. A Deloitte analysis highlights that effective maintenance is about more than just asset uptime.
By preventing failures, companies can improve quality control, manage environmental impact by more efficiently using materials, and ensure a safer working environment. This proactive approach elevates ROI and supports a more robust, resilient business model.
Verdantis’ research demonstrates that improving spare parts data quality is crucial for optimizing inventory, minimizing downtime, and controlling procurement costs.
Through advanced Master Data Management (MDM) methodologies and automated tools, Verdantis has proven that harmonized, enriched spare parts data eliminates duplicate inventory, prevents obsolete part ordering, and streamlines maintenance planning.
Clean, standardized spare parts master data can reduce unplanned downtime by as much as 50% through improved inventory optimization and part tracking.
Automated enrichment and categorization tools increase manufacturer name accuracy by 36% and part number accuracy by 16%, directly lowering operational costs and redundancy.
Companies using Verdantis’ solutions have reported up to 20% of components being superseded or obsolete before intervention, with data cleansing halting expensive, redundant orders and enhancing procurement control.
Structured, harmonized spare parts descriptions allow for precise search and retrieval, facilitating better demand planning and strategic sourcing.
Adopting Verdantis’ comprehensive MDM approaches for spare parts has delivered measurable ROI-including reduced inventory overstocking, fewer stockouts, lower downtime costs, and heightened responsiveness in procurement and maintenance operations.
Organizations with strong spare parts data stewardship gain visibility and efficiency that directly improve the bottom line and plant productivity.
