The Real Cost of Manufacturing Downtime (2026): Sector Impact & Resilience Outlook
Understanding the Downtime Challenge
Unplanned downtime is any period during which manufacturing output stops, slows, or becomes unreliable due to disruptions in equipment, systems, people, or external supply. It remains one of the most expensive challenges in the sector because the impact rarely ends when the line resumes moving.
Downtime is usually caused by a mix of:
- Equipment failure and wear (breakdowns, ageing assets, unplanned maintenance)
- Control system and software issues (PLC faults, sensor failures, configuration errors, patching problems)
- Outages and connectivity disruption (network instability, power events, communications failures)
- IT/OT complexity (more integration points, more dependencies, more ways for issues to escalate)
- Operational pressure (peak demand periods, rushed changeovers, deferred maintenance, staffing gaps)
The hidden cost is often in recovery: diagnosing what happened, isolating the problem, restarting safely, and restoring quality within tolerance. That is why resilience and visibility, especially across industrial networks, increasingly shape the true cost of downtime.
In 2026, manufacturers across the UK and Europe are projected to lose between £124 billion and £157 billion to unplanned downtime across the nine sectors analysed, according to IDS-INDATA modelling.
What This Report Covers (2026 Outlook)
This report provides a sector-by-sector view of manufacturing downtime and projects the likely cost impact for 2026. It combines recent downtime benchmarks with sector economics to estimate where disruption is most expensive, which industries are most exposed, and why recovery time is becoming a key cost multiplier.
Inside, you will find:
- A breakdown of the projected downtime impact by manufacturing sector for 2026
- Key patterns in downtime frequency, duration, and cost severity
- The operational pressure points where downtime risk tends to increase
- Why outages, network resilience, and OT security influence how long incidents last
- Practical actions manufacturers can take to reduce downtime impact
This analysis and modelling have been produced by IDS-INDATA, using a structured methodology and the best available public benchmarks to derive sector ranges.
Connecting Insights to Action
Downtime is often discussed as a single metric, but in practice, it behaves more like a chain reaction. A small disruption can become a significant loss depending on what happens next.
Three things usually determine the difference:
- Time to detect: how quickly teams know something is wrong
- Time to isolate: whether the issue can be contained to one cell, line, site, or supplier connection
- Time to restart: how quickly production can resume safely and return to quality tolerance
This is why two manufacturers can experience the same type of incident and end up with entirely different outcomes. The cost is not only driven by how often downtime occurs, but by how long it takes to recover and how far the disruption spreads.
From an operational perspective, the most practical levers are visibility, resilience, and control. That starts with the basics: reliable industrial networking, clear OT asset oversight, sensible segmentation, and recovery processes that have been tested rather than assumed. This is where IDS-INDATA typically supports manufacturers, particularly in environments where connected operations increase both opportunity and operational risk.
Downtime in Manufacturing: What We’ve Seen (2021-2025)
Downtime is still common, but the tail risk is the story
Most manufacturing sites can absorb short stoppages. The bigger risk is the long incident that forces a slow restart, triggers rework, or creates knock-on disruption across suppliers and logistics. Those events are less predictable, harder to contain, and more likely to appear in sectors with complex processes, compliance gates, or tightly coupled production lines.
Interconnected operations mean issues travel faster
As production environments become more connected, more of the downtime story sits between systems rather than inside a single machine. Integrations, remote access pathways, supplier links, shared data flows, and IT/OT dependencies increase the number of ways a small fault can escalate.
Recovery time is now a major cost multiplier
Even when the root cause is straightforward, recovery is not always fast. Diagnosis, isolation, safe restart, and return-to-quality can take longer than expected, particularly when visibility is limited or when environments rely on legacy assets that are difficult to monitor.
Resilience maturity is uneven
Some manufacturers have made real progress through monitoring, predictive maintenance, structured change control, and better asset oversight. Others remain exposed due to ageing infrastructure, flat networks, limited OT visibility, or processes that slow and make recovery more expensive. The result is a widening gap between sites that manage disruption quickly and those that suffer repeated or prolonged downtime events.
Downtime by Sector: 2026 Projections
The table below summarises the projected downtime impact for 2026 across nine manufacturing sectors. Figures are presented as ranges to reflect variation by facility size, production intensity, incident severity, and the time required to recover safely.
| Sector | Typical downtime frequency | Typical duration | Estimated cost per hour | 2026 projected financial impact | Main vulnerability | Why it happens | Mitigation strategy |
| Automotive | 20-25 incidents/month | 3-6 hours | £1.6M-£2.0M | £14-17B (UK/EU) | Line-wide cascade risk | JIT sequencing, tightly coupled suppliers, IT/OT interlocks | Predictive maintenance, segmented OT networks, supplier and remote access controls |
| Food Processing | Weekly minor stoppages | 1-4 hours | £20k-£30k | £5.5-6.8B (UK) | High throughput micro-stops | Hygiene cycles, ageing kit, cold chain dependencies | Condition monitoring, rapid changeover playbooks, scheduling simulation via digital twins |
| Heavy Equipment | 2-3 major events/year | 6-12 hours | £170k-£350k | £74-89B (EU) | Slow restart of high-energy assets | Legacy PLC/SCADA, bespoke spares, high thermal inertia | SCADA visibility, asset health analytics, secure remote diagnostics |
| Pharmaceutical | 225-400 hrs/year | 10-24 hours | £1.1M-£5.2M | £0.8-1.5B (UK) | Batch loss plus compliance restart | Validation gates, cleanroom resets, traceability constraints | Secure data handling, anomaly detection, and tested incident runbooks |
| Chemicals | 400-600 hrs/year | 5-12 hours | £275k-£1.1M | £11.7-14.6B (EU) | Safety-constrained shutdown risk | Continuous processes, strict control loops, and hazardous environments | Resilience planning, predictive asset health, segmented control networks, and the importance of tested incident response plans (IRP) that include safety personnel, particularly due to the hazardous operating environment. |
| Electronics | Frequent short stops | 2-6 hours | £120k-£550k | £8.6-11.5B (EU) | Sensitive environments | Cleanroom constraints, precision alignment, recipe change complexity | Environmental monitoring, OT/IT convergence governance, and change control discipline |
| Textiles | 180-300 hrs/year | 1-3 hours | £12k-£55k | £2.7-4.1B (UK/EU) | Deferred maintenance exposure | Margin pressure, older automation, skills gaps | Modernisation roadmap, real-time alerting, targeted retrofits |
| Aerospace | Few major events/year | 5-10 hours | £275k-£1.1M | £2.9-5.8B (UK) | QA and certification bottlenecks | Long inspection cycles, complex testing, supplier sign-offs | Traceability systems, validation-aware scheduling, secure data integration |
| Packaging | Weekly short disruptions | 45 mins-3 hours | £15k-£35k | £4.1-6.8B (UK) | Micro-faults that compound | High-speed line sensitivity, quick-wear parts, and frequent changeovers | Lightweight predictive analytics, proactive maintenance windows, and line performance monitoring |
Automotive
High-cost minutes define this sector. Downtime can cascade fast across connected lines and supplier schedules. Faster fault isolation and resilient industrial networking reduce the extent of disruption.
Food Processing
Smaller stops are frequent and add up, with extra costs and risks from spoilage and restart checks. Tight monitoring and quicker changeover recovery can reduce lost hours without heavy capital spend.
Heavy Equipment
Incidents can be less frequent but longer, driven by complex restart requirements and legacy controls. Visibility, secure remote diagnostics, and resilience planning have an outsized impact here.
Pharmaceutical
Batch-loss risk and validation requirements amplify downtime costs. The fastest wins come from reducing uncertainty during incidents and standardising recovery steps.
Chemicals
Continuous processes and safety constraints make recovery slow and expensive. Redundancy, segmentation, and predictive asset health monitoring help contain faults and shorten stabilisation time.
Electronics
Precision, calibration, and clean environments make “return to quality” a significant part of the downtime cost. Strong change control, telemetry, and fast isolation matter as much as mechanical reliability.
Textiles
Recurring minor disruptions, older assets, and limited monitoring often drive costs. Targeted retrofits and better alerting can reduce downtime without complete system replacement.
Aerospace
Incidents are fewer but of high consequence due to quality gates and testing cycles. Traceability and controlled integration reduce rework and shorten restart overhead.
Packaging
High-speed lines are sensitive to small faults that cause repeated short stoppages. Line monitoring and preventive maintenance discipline reduce compounding disruption.
2026 Industry Downtime Forecast Highlights
Biggest projected impact
- Heavy Equipment remains the most significant projected loss driver due to long recovery windows and high-value production time
- Automotive continues to carry extreme cost per hour exposure, especially where disruption cascades across suppliers
- Chemicals shows persistent risk due to continuous operations, safety constraints, and stabilisation time
High-cost minutes vs high downtime hours
Some sectors lose the most because downtime is frequent. Others lose the most because downtime is brutally expensive the moment it happens. This distinction matters for action planning. If you have high-cost minutes, the priority is containment and recovery speed. If you have high downtime hours, the priority is prevention and repeatability.
2026 watchlist
- Long recovery risk: Heavy Equipment, Chemicals, Pharmaceuticals
- Cascade risk: Automotive, Aerospace
- Micro-stops that compound: Packaging, Food Processing, Textiles
2025 vs 2026: Year-on-Year Changes
This edition updates the sector modelling to reflect newer benchmarks and a more conservative view of recovery time. Across the nine sectors analysed, projected downtime impact increases year on year, with the most significant changes concentrated in high-value, complex restart environments.
Key changes observed
- Heavy Equipment shows the most significant increase, driven by long recovery windows and high-value production time
- Automotive remains the standout for high-cost minutes, where disruption cascades quickly
- Chemicals and Electronics increase next, reflecting stabilisation time and return-to-quality overhead
- Packaging and Food Processing rise steadily, with micro-stops compounding during operational pressure
- Pharmaceuticals remain the smallest in absolute terms, but still climb due to compliance-heavy recovery
Peak Downtime Periods and Operational Pressure Points
Downtime does not arrive evenly across the year. Certain periods increase risk because operational change is higher, tolerance for disruption is lower, and recovery time is harder to protect.
Planned shutdown windows: change creates risk
Planned maintenance periods are often treated as “controlled downtime”, but they can also increase unplanned risk when assets are restarted, configurations change, or production ramps back up too quickly. These windows are a common point where minor issues can escalate into longer incidents, especially when environments rely on legacy controls or undocumented dependencies.
Demand stress windows: thin margins for failure
When order volume spikes, the system has less slack. Changeovers happen faster, maintenance gets deferred, and any disruption can cause immediate backlog. In many manufacturing environments, this is when micro-stops become most damaging, because they compound across shifts and create hidden capacity loss.
External shock windows: longer incidents, slower recovery
The most expensive downtime events are often linked to external factors, such as outages, supply disruptions, extreme weather, supplier incidents, or cyber events. These are the situations where recovery time becomes the multiplier, because teams are responding to uncertainty rather than a known fault.
Practical takeaway: peak periods are less about a specific month and more about the combination of high change, high demand, and limited recovery headroom. That is why resilience planning and tested recovery processes matter as much as prevention.
Industrial Connectivity, Outages, and OT Security
When downtime lasts longer than it should, the root cause is often not the original fault. It is the time spent diagnosing what happened, understanding what is affected, and coordinating a safe restart.
This is where industrial connectivity and OT security have direct downtime impact on downtime.
Why outages and weak visibility extend downtime
In many manufacturing environments, teams lose time because:
- Critical assets are not fully visible in real time
- Networks are flat, making isolation harder
- There is limited segmentation between critical systems and general traffic
- Remote access pathways are poorly controlled or inconsistent
- Fault diagnosis depends on manual checks rather than reliable telemetry
Even when the fix is simple, uncertainty slows recovery. The result is a longer incident, a longer restart window, and a higher chance of repeated stoppages after recovery.
OT security as downtime prevention
OT security is often framed as risk management, but it also reduces downtime by limiting the pathways that cause disruption. Better segmentation, access control, and monitoring reduce both the likelihood of incidents and the time spent investigating them.
Connectivity and network resilience as a recovery lever
Resilient industrial networking helps manufacturers recover faster by improving:
- Fault detection and alerting
- Isolation of affected systems
- Confidence during restart
- Stability of connected operations, including supplier and remote support dependencies
This is why industrial networking is not only a performance issue. It is a downtime and resilience issue.
IDS-INDATA supports this work through industrial networking and Industrial IoT programmes that improve resilience, visibility, and safe connectivity in operational environments.
What Manufacturers Can Do Next
Reducing downtime does not always start with a major transformation. The fastest gains usually come from improving visibility, strengthening recovery, and removing single points of failure.
Immediate (next 30 days)
- Map critical assets and single points of failure across OT systems and networks
- Baseline common downtime causes and average recovery time, not just incident count
- Prioritise segmentation around the most critical lines and control systems
- Review remote access routes, including third-party and supplier access pathways
Medium (next 90 days)
- Implement monitoring and alerting for key OT networks and production-critical assets, including visibility into traffic entering and leaving the OT environment to enable data-driven decisions, such as firewall rule changes and device reconfigurations.
- Test downtime response and recovery playbooks using realistic scenarios
- Tighten change control for OT environments to reduce restart and post-fix instability
- Introduce targeted condition monitoring where recurring failures are driving repeat stops
Longer-term (6-12 months)
- Build redundancy into critical industrial networking to reduce outage sensitivity
- Expand predictive maintenance coverage and standardise asset health reporting
- Develop a legacy upgrade roadmap, prioritising assets that increase recovery time risk
- Strengthen OT/IT governance so security and uptime goals support each other rather than compete
IDS-INDATA typically supports these programmes by helping manufacturers design resilient industrial networks, improve OT visibility, and deliver secure connectivity strategies that reduce both disruption risk and recovery time.
Methodology
This report combines best-available published downtime benchmarks with sector economics to estimate 2026 downtime impact ranges.
- Sector cost profiles are informed by established downtime cost benchmarks, with sector mapping applied where granular public benchmarks are not consistently available.
- Downtime impact is presented as ranges to reflect variation by facility size, production intensity, incident severity, and recovery time.
- 2026 projections include a conservative adjustment for more prolonged recovery and longer-duration incidents, reflecting recent evidence that a greater share of downtime events run beyond micro-stops.
Summary and Next Steps
Manufacturing downtime remains a significant cost driver across the UK and Europe, with impact concentrated in sectors where disruption cascades quickly, or recovery is slow and compliance-heavy.
For 2026, the priority is not only prevention, but recovery. The manufacturers that reduce downtime impact fastest will be those that can detect issues earlier, isolate faults confidently, and restart safely without repeated stoppages.
If improving downtime resilience is a priority, start with visibility and recovery: strengthen industrial networking, improve OT monitoring, and test recovery playbooks under realistic conditions.
References
1) Siemens (Senseye) – True Cost of Downtime 2024 (report/PDF)
2) Fluke – Unplanned Downtime research (Censuswide survey, Oct 2025 press release)
3) Siemens – Pharmaceutical manufacturing downtime benchmark (cost per hour range)
4) Dragos + Marsh McLennan – 2025 OT Security Financial Risk Report (OT cyber financial impact context)
6) ONS – UK Manufacturers’ Sales by Product (PRODCOM) bulletin (UK manufacturing baseline context)
7) Eurostat – Sold production / industrial production context (EU manufacturing baseline context)
