The Hidden Cost Of Downtime In Manufacturing

Research conducted with Oxford Economics surveyed about 2,000 executives globally from Global 2000 companies. Within manufacturing, 115 companies were surveyed across roles including engineering, security, IT, finance, and marketing. The findings show a clear shift: unplanned outages and service degradations across IT and OT environments now carry financial and operational consequences at a much larger scale. For manufacturing teams, that shift matters.

Across industries, aggregate downtime costs have reached $600 billion annually, rising 50% compared with two years ago. That translates to $300 million per year per organization and $900,000 per hour. In Europe, the increase is even sharper, with downtime costs rising nearly 80%, from $198 million in 2024 to $354 million.

Why Manufacturing Feels Downtime So Acutely

Manufacturing stands out as one of the industries most affected by downtime costs. The annual cost for the sector reaches $280 million, an increase of about 10% compared with two years ago.

Across industries, lost revenue is the single largest cost category, rising from $49 million to nearly $100 million. Regulatory fines also doubled, increasing from $22 million to $51 million. Ransomware payouts grew the fastest, climbing from $11 million to $40 million over four years.

In manufacturing, the pattern is slightly different. Regulatory fines are the top downtime cost at $52 million, followed by ransomware payouts, then lost revenue. That ranking matters because it shows downtime is not only disrupting production. It is also creating compliance exposure and increasing the financial impact of cyber incidents.

Manufacturing also remains the industry most attacked by cyberattacks for five years in a row, which helps explain why ransomware costs are so significant.

The Operational Fallout Goes Beyond The Outage

The financial cost tells only part of the story. Downtime also creates recurring operational consequences that slow the business down long after the incident begins.

For manufacturing organizations, the key consequences are productivity loss and innovation loss. Downtime distorts innovation efforts, delays time to market, and forces teams to pull in additional personnel for remediation. Customer frustration is also a major consequence, and it happens frequently.

The survey data shows these effects are not isolated events:

Here’s what this means. Even when an outage is resolved, the broader cost continues through delayed work, diverted teams, and damaged customer experience. For manufacturers, resilience needs to account for those downstream effects, not only the initial disruption.

• Customer satisfaction is impacted more than five times per year
• Increased personnel for remediation happens almost four times per year
• Loss of productivity and innovation occurs close to three times per year

In Security, Human Error Still Leads The List Of Causes

Downtime can originate across IT, OT, networks, and security environments. But one finding stands out clearly in manufacturing: in security, human error is the number one cause of downtime by far, occurring almost seven times per year.

Cyber-related causes such as malware attacks and phishing attacks also rank high. In addition, third-party and supply chain issues are a significant factor. For manufacturing, 79% points to supply chain and third-party providers as an important source of downtime risk.

This combination makes the challenge harder to isolate. Outages can come from internal processes, external dependencies, or cyber threats, often crossing functional boundaries. That is why downtime cannot be managed as a narrow technical problem.

A more effective approach is to treat detection and root cause analysis as a shared responsibility across teams. Unified views, connected platforms, shared data sources, and shared context help organizations identify issues faster and reduce the time spent chasing disconnected signals.

The AI Paradox In Manufacturing Resilience

• Many manufacturing organizations are turning to AI to help reduce downtime. Executives are looking into: AI-driven security automation, AI-powered observability, and predictive analytics.

These capabilities are being used to improve detection, accelerate analysis, and support faster response. But the data also points to an important tension: AI itself is contributing to downtime.

That creates a double-edged dynamic. AI is becoming part of the solution, while also introducing new operational risk. Executives surveyed acknowledge that AI is causing some problems, and those problems are related to downtime.

For manufacturing leaders, the takeaway is practical. AI should help accelerate insight, but it should not replace human judgment. Analytical speed is valuable, but remediation decisions still need validation. Governance also matters, especially when managing shadow AI and reducing the risk of unapproved or poorly controlled AI use.

Three Practical Priorities For Manufacturing Leaders

The survey findings point to three clear strategies for reducing the cost and impact of downtime in manufacturing.

1. Treat Downtime As A Business Risk

Downtime should be translated into business language and elevated to a board-level discussion. The cost profile makes that necessary, especially when annual impact reaches $280 million in manufacturing and $354 million in Europe.

This helps teams align around resilience metrics that reflect business outcomes, not only technical performance.

2. Make Detection And Root Cause Analysis A Team Sport

Outages can come from anywhere, and the leading causes span human error, cyberattacks, and third-party dependencies. Manufacturing teams need unified visibility and shared context across functions.

Practical focus areas include:

• Leveraging Unified views
• Connecting platforms
• Establishing a shared data source with shared context
• Designing an additional coverage layer with a data fabric architecture

This helps teams move faster from detection to understanding, and from understanding to coordinated action.

3. Use AI To Accelerate Insight, Not Replace Judgment

AI has an important role in reducing downtime, especially in security automation, observability, and predictive analytics. But manufacturing teams need controls around how AI is used.

Key practices include:

• Pairing AI speed with expert human judgment
• Requiring validation before remediation or other ultimate actions are taken
• Implementing robust governance frameworks to manage shadow AI

This helps organizations gain the benefits of AI without introducing unnecessary new risk.

Building Resilience Against Prolonged Disruption

Downtime is difficult to avoid entirely. Prolonged disruption is not.

For manufacturing organizations, the path forward starts with recognizing downtime as a strategic issue that crosses business, operations, security, and technology teams. The data shows why. Costs are rising, customer expectations remain high, cyber threats continue to grow, and the causes of downtime are increasingly distributed across people, platforms, and partners.

Resilience depends on aligning technology with business outcomes. That means treating downtime as a measurable business risk, improving cross-team detection and analysis, and applying AI in ways that are fast, governed, and validated.

To go deeper, join our upcoming webinar or explore The Hidden Cost of Downtime report and The Hidden Cost of Downtime for Manufacturing. You can also learn more on our dedicated Splunk manufacturing page.