Overview
Organizations across manufacturing, oil and gas, energy, utilities, mining and chemicals face mounting pressure to raise asset availability while holding down cost. Unplanned downtime sits at the center of that pressure, and it has become the single largest hidden cost in industrial operations.
Siemens, in its True Cost of Downtime 2024 report, sized the problem at about $1.4 trillion a year across the world's 500 largest companies. The striking part is the trend: the cost has risen 62 percent since 2019 even as the number of incidents has fallen. Stoppages are rarer, yet each one is more expensive, because plants run leaner and the hidden costs around each event have grown.
This report picks up where the macro number leaves off. It maps where the loss actually accumulates and, more usefully, how much of it is recoverable. The short answer from Verdantis analysis: most of the loss is not the mechanical failure itself but the response, the search for the right part and the data to use it, and a large share of that is recoverable without adding headcount.
Key findings at a glance
Report purpose
MRO and asset data are often dispersed across ERP, CMMS and spreadsheets, which makes downtime a source of recurring loss rather than a managed risk. This report quantifies the impact of connected parts and asset data on downtime cost, recovery, and working capital, and gives operations leaders evidence-based benchmarks to act on.
The report's primary objectives are to establish defensible benchmarks for the cost of unplanned downtime anchored to published research, to show where inside the macro number the recoverable money sits, to rank the levers that return the most downtime per unit of effort, and to support decisions on EAM, predictive maintenance, and MRO inventory strategy with data-backed estimates.
Methodology and scope
The figures in this report are not drawn from a fielded survey of respondents. They are modeled estimates, built by combining published research with Verdantis scenario modeling, and they are written throughout as "Verdantis analysis" or "Verdantis estimate" so the basis is never in doubt. The approach combines four methods.
Benchmark anchoring. The macro cost is taken directly from Siemens, True Cost of Downtime 2024. Independent per-hour downtime-cost benchmarks are drawn from Aberdeen. Wrench-time, MRO inventory and predictive-maintenance figures come from published industry studies (including Deloitte and McKinsey), and failure-pattern ratios from reliability literature (Nowlan and Heap).
Scenario-based modeling. Per-industry downtime-cost rates are applied to a representative asset-intensive facility to estimate annual loss and the share recoverable by each lever, using input ranges rather than single points.
Data triangulation. Modeled outputs are cross-checked against the published benchmarks above to keep them within defensible bounds.
Forward validation. A structured survey instrument has been designed to field these estimates against operator-reported data, and results will update this report when available.
Scope: industries and downtime-cost rates modeled
The model uses representative downtime-cost rates per hour for six asset-intensive sectors. These are inputs, not survey responses, and can be tuned to a specific operation.
| Industry | Modeled downtime cost per hour |
|---|---|
| Discrete manufacturing | $260,000 |
| Oil and gas | $250,000 |
| Produtos químicos | $190,000 |
| Mining and metals | $187,500 |
| Power and utilities | $160,000 |
| Food and beverage | $120,000 |
The anatomy of the $1.4 trillion
Downtime is usually attacked one cause at a time. Verdantis maps it across five connected domains, and because the domains are linked, a parts gap turns a contained failure into a long, expensive one. No single function owns the majority.
The spare-parts domain is only an eighth of failures, yet it is the largest driver of how long an asset stays down once one occurs. That is the recurring theme of this report, and the reason critical spares management earns more attention than its share of incidents alone would suggest.
Key research findings
1. The cost of unplanned downtime
2. Most failures are random
This is why condition-based work matters more than the calendar. The full picture sits in our companion data set on predictive maintenance statistics.
3. Parts and data drive repair time
4. What is recoverable
5. The inventory and procurement drag
Consolidated financial impact
The table below summarizes the anticipated annual recovery for a single representative asset-intensive facility, by lever. Figures are Verdantis scenario estimates expressed as ranges, calibrated to the benchmarks in the methodology. They are indicative and not strictly additive.
| Lever | What it recovers | Low | High |
|---|---|---|---|
| Predictive maintenance | Random failures caught earlier | $14M | $20M |
| Parts and data availability | Shorter repair time, fewer stock-outs | $4M | $7M |
| Workforce productivity | Higher wrench time, less search | $2M | $3M |
| Inventory and procurement | Lower carrying cost, fewer emergency buys | $2M | $4M |
| Total anticipated recovery per facility | $22M | $34M |
Verdantis analysis, per facility, anticipated ranges. Deployment outcomes vary with operational maturity and require validation against operator data.
The AI shift in maintenance
The recovery in this report is not captured by adding people. It is captured by AI moving out of back-office pilots and onto the plant floor, into the storeroom and the work-order queue. The market for AI in predictive maintenance is now one of the fastest-growing categories in industrial software, with analyst forecasts running from the mid-teens to nearly 40 percent compound annual growth, a signal that asset-intensive operators are shifting from reactive to proactive at scale.
In MRO the value is agentic, not a chatbot. AI agents continuously score parts and asset criticality, forecast spare demand, set dynamic reorder points, and flag the work orders at risk of a missing part before they are released. Each agent hands the next a cleaner signal, so a planner acts on the handful of items that carry real risk instead of triaging thousands, with a human approving every change.
Predictive, condition-based maintenance is the frontier, because it is the only approach that addresses the roughly 83 percent of failures that are random rather than age-related. The payoff is already documented: up to 70 percent fewer breakdowns and about 25 percent lower maintenance cost (Deloitte, McKinsey). That is the logic behind an AI-native approach to enterprise asset management: connect the parts, asset and work-order signals first, then let agents surface the routine decisions with a recommended action so skilled people are freed for the ones that need judgment.
Challenges and opportunities
Fragmented parts and asset data. The same physical part exists under several descriptions and part numbers, bin locations are wrong, and asset manuals are out of date. This is the root of the scavenger hunt and of duplicate purchasing.
Manual, reactive workflows. Where work orders still run on spreadsheets and paper, jobs are released without confirming the part and the plan, which turns a short fix into a long outage.
Static stocking and reorder policies. Reorder points and safety stock are often set once at system configuration and never revisited, which produces both stock-outs on critical parts and surplus on the rest. The policy no longer reflects real consumption, lead times, or criticality.
Workforce drag. Skilled technicians spend most of a shift not turning wrenches. Returning that time is one of the largest sources of capacity hiding in plain sight.
Strategic benefits of connected EAM
| Area | Key impact | Basis |
|---|---|---|
| Predictive maintenance | Catches random failures before they stop the line | Up to 70% fewer breakdowns (Deloitte) |
| Parts and asset data | Shorter repair time and fewer stock-outs | Parts are the #1 driver of repair time (Verdantis) |
| Inventory optimization | Less capital locked in surplus and dead stock | 15-25% of MRO inventory is surplus (industry) |
| Strategic procurement | Fewer emergency buys at a premium | 30-60% emergency premium avoided (industry) |
| Workforce productivity | More hands-on time, less search | +20-30pts wrench time when work is planned (industry) |
Specific MRO360 deployment outcome ranges, covering working capital released, emergency spend reduced and downtime reduced, are available on request as anticipated ranges, pending product and legal sign-off.
A workable approach
Closing the gap takes a stepwise approach that combines criticality, inventory discipline and predictive technology. Verdantis recommends the following sequence.
Audit parts, asset and work-order records, and quantify how much downtime and working capital is tied to stock-outs, dead stock and the search for parts.
Set parts and asset criticality so stock, safety buffers and planner attention go where a failure actually hurts.
Link spares to the equipment and work orders they serve, so the right part and its location surface before the technician needs them.
Right-size stock with MRO inventory management and demand forecasting to cut both stock-outs and surplus, and make reorder points dynamic.
Deploy AI-native EAM to catch random failures earlier, when there is time to prepare the part and the plan.
Track downtime, inventory hit rate and recovery against the estimates here, and expand from high-impact assets outward.
Frequently asked questions
Common questions on the cost of unplanned downtime, what drives it, and how much asset-intensive operations can realistically recover.
What does unplanned downtime actually cost?
Siemens puts the figure at about **$1.4 trillion a year** across the world's 500 largest companies, roughly 11 percent of revenue. An independent Aberdeen benchmark lands near $260,000 an hour in manufacturing, and Verdantis modeling estimates around $56 million in annual loss at a single asset-intensive facility.
Why is downtime getting more expensive when incidents are falling?
Plants run leaner than they did in 2019, so each stoppage removes more output and the hidden costs around it have grown. Siemens records a 62 percent rise in downtime cost since 2019 even as incidents have dropped. The cost has shifted into the response: the search for the right part, the data to use it, and the time the line waits.
What is the single biggest driver of how long an asset stays down?
Parts unavailability. Once a failure occurs, the asset waits for the whole search, not just the repair. Fragmented and duplicated parts and asset records are what send technicians on a scavenger hunt, which is why connected data shortens outages more reliably than adding labour.
How much downtime is realistically recoverable?
Verdantis modeling puts total anticipated recovery at **$22 million to $34 million per facility per year** across all levers, with roughly $17 million attributable to predictive maintenance alone. Recovery starts by connecting the parts, asset and work-order data the business already owns, not by buying new sensors first.
Where does AI actually help in MRO?
The value is agentic rather than a chatbot. AI agents score parts and asset criticality, forecast spare demand, set dynamic reorder points, and flag work orders at risk of a missing part before release, with a human approving each change. That lets a planner act on the few items that carry real risk instead of triaging thousands.
Is predictive maintenance enough on its own?
It is the largest single lever, addressing around 30 percent of recoverable downtime, because roughly 83 percent of failures are random rather than age-related. But predictive models are only as good as the records they read, so connecting and cleaning the parts and asset data has to come first.
