How much does industrial downtime cost per hour?

Published estimates vary widely by industry and facility type. ABB reported that the typical industrial business loses close to $125,000 per hour during unplanned outages, while Siemens reported an automotive downtime estimate of $2.3 million per hour for large automotive operations.

Why do downtime cost estimates vary so much?

Downtime cost varies because every operation has different throughput, margin, labor cost, restart losses, customer penalties, inventory position, and asset criticality. A one-hour outage in a high-volume automotive plant can cost far more than a one-hour outage in a smaller discrete manufacturing operation.

What should be included in a plant-specific downtime cost calculation?

A plant-specific downtime cost model should include lost production, idle labor, scrap and rework, overtime, contractor costs, expedited freight, missed shipments, customer penalties, restart losses, quality checks, and safety or environmental exposure where applicable.

Are published downtime benchmarks exact numbers?

Published downtime benchmarks should be treated as directional estimates, not exact values for every plant. They are useful for framing downtime exposure, but each facility should build its own downtime model based on actual production, financial, and operational data.

Industrial Downtime Cost Benchmarks: What Published Studies Actually Show

Downtime costs get quoted like gospel. That’s risky.

A number that makes sense for an automotive assembly plant can look ridiculous inside a smaller food plant, a fabrication shop, or a regional packaging operation. Throughput, margin, labor model, inventory position, customer penalties, restart losses, and asset criticality all change the math.

So this benchmark should be read as a compiled review of published estimates, not a universal calculator.

The goal is simple: give maintenance, reliability, operations, and finance teams a more realistic starting point for discussing downtime exposure.

Published Downtime Cost Benchmarks

Reliable Confidence Score: How safely each figure can be used in industrial maintenance and reliability discussions, based on source quality, clarity, context, and plant-floor relevance.

Siemens, 2024

Automotive

$2.3 million per hour

Confidence: High, specific use

Strong source and clear hourly figure, but best used for large automotive operations.

Siemens, 2024

FMCG / CPG

$36,000 per hour

Confidence: High, specific use

Strong source and clear hourly figure, but specific to FMCG/CPG operations.

Siemens, 2024

Oil & Gas

Varies by year and oil price environment

Confidence: Medium

Useful directional source, but the economics change heavily with commodity prices and operating context.

Siemens, 2024

Heavy Industry

Annual losses, not hourly losses

Confidence: Medium

Valuable source, but should be cited carefully because the usable figure is annualized, not hourly.

ABB, 2023

Industrial businesses

About $125,000 per hour

Confidence: High, broad use

Best general-purpose benchmark because it comes from a large survey of 3,215 plant maintenance leaders.

Splunk / Oxford Economics, 2024

Global 2000 digital downtime

$400 billion annually

Confidence: Low for plant downtime

Useful business context, but it covers broader digital downtime, not plant-floor production downtime.

The Big Takeaway

The safest benchmark for general industrial discussion is ABB’s $125,000 per hour figure.

It’s broad enough to be useful across industrial businesses, and the survey base is large: 3,215 plant maintenance leaders. ABB also reported that more than two-thirds of industrial businesses experience unplanned outages at least monthly, while 21% still rely on run-to-failure maintenance.

The Siemens numbers are more industry-specific.

That’s what makes them valuable, but also easy to misuse. The $2.3 million per hour automotive figure should stay tied to large automotive operations. The $36,000 per hour FMCG figure should stay tied to fast-moving consumer goods. Oil and gas needs more care because the economics move with commodity prices, production rates, and refinery or upstream context.

Why Downtime Numbers Vary So Much

Downtime cost is rarely just the repair invoice.

A serious outage can include:

Lost production
Idle labor
Scrap and rework
Restart losses
Overtime
Contractor premiums
Expedited freight
Missed shipments
Customer penalties
Quality holds after restart
Safety and environmental exposure

That’s why the same failed bearing can be a maintenance nuisance in one plant and a seven-figure event in another.

The asset matters, but the process around that asset usually decides how expensive the failure becomes.

The asset matters. The process matters more.

A Practical Way to Use These Benchmarks

For most plants, the published numbers are useful as a conversation starter. They shouldn’t replace a plant-specific downtime model. A better internal calculation should include:

What to Include in a Downtime Cost Model

A practical starting point for building a plant-specific downtime estimate.

Lost production

Units not produced, margin per unit, missed throughput.

Labor

Operators, maintenance, supervision, overtime.

Materials

Scrap, off-spec product, wasted packaging, raw material loss.

Recovery costs

Expedite fees, contractors, rentals, premium shipping.

Customer impact

Chargebacks, penalties, late delivery risk.

Restart impact

Startup losses, quality checks, stabilization time.

Risk exposure

Safety, environmental, compliance, reputation.

Even a rough internal estimate will usually beat a generic industry number.

The real value comes when reliability teams can say, “Here’s what one hour of downtime costs on this line, with our product mix, our labor model, and our customer commitments.”

That changes the conversation.

Where Maintenance and Reliability Teams Should Be Careful

A few common mistakes make downtime articles look weak to experienced practitioners:

First, don’t compare a global enterprise IT outage number directly to a plant production outage. They’re related concepts, but they measure different things.

Second, don’t cite annual downtime losses as hourly losses. That mistake can turn a useful article into something reliability people won’t trust.

Third, don’t pretend every outage hour costs the same. The first hour of downtime may cost less than the sixth hour if customer shipments get missed, cold chain is affected, a batch is lost, or restart requires extensive validation.

Fourth, don’t make predictive maintenance sound like magic. PdM can reduce certain failure modes, but it won’t fix poor planning, weak lubrication practices, bad installation work, missing spares, unclear asset criticality, or a backlog nobody is willing to fund.

The strongest downtime argument isn’t the biggest number. It’s the number your plant can defend.

Methodology

This article reviewed published downtime cost estimates from Siemens, ABB, and Splunk/Oxford Economics.

Figures were included only when the source clearly described a cost estimate, survey result, or downtime impact. Industry-specific figures were kept tied to their original context.

Digital downtime estimates were separated from plant production downtime because IT outages, cybersecurity incidents, and industrial asset failures do not share the same cost structure.

The benchmark table should be treated as a directional reference, not a substitute for a plant-specific downtime cost model.

Bottom Line

Downtime cost is one of the strongest arguments reliability teams have.

But the argument weakens when the numbers are exaggerated, mixed across industries, or taken out of context.

Use the big numbers carefully. Then build your own. A plant-specific downtime model will do more for reliability funding than another generic claim about downtime being expensive.