Understanding Downtime
Production lines and complex plant operations are revenue and profit generating engines for asset-intensive industries.
Enterprise leaders, plant operation teams and production planning professionals constantly strive for maximizing the output and efficiency of these production units throughout the course of their lifecycle.
Recent studies from ABB, Siemens and EMA Research indicate that this number is steadily increasing, costs associated with downtime have nearly tripled in the last 5 years.
Increase in downtime costs over the last 5 years
Of unplanned downtime caused by equipment failure
Annual unplanned downtime cost in industrial manufacturing
The table below illustrates the staggering financial stakes, even a single hour of unplanned downtime in high-value sectors can erase days of operational profit.
| Sector | Scale | Average Cost Per Hour |
|---|---|---|
| Energy (Utilities / Power) | $2,480,000 | |
| Automotive Manufacturing | $2,300,000 | |
| Productos químicos | $700,000 - $1,200,000 | |
| Manufacturing (General) | $260,000 - $500,000 | |
| Water & Waste Treatment | $150,000 - $350,000 |
Companies are in dire need to overhaul both planned as well as unplanned downtime through a preventive, predictive as well as a corrective approach to asset management.
The Reasons for Downtime
A downtime instance can be linked to both a planned or an unplanned maintenance activity.
Unfortunately, despite best efforts, unplanned downtime is simply unavoidable. It can be minimized, but eliminating it is quite tricky and near-impossible.
In both the scenarios, the need for quick turnaround and remedy of the failure is of key importance.
The reasons for downtime that are related to asset-failure are detailed below;
Mechanical Technical Failure
One of the more popular reasons for asset failure purely has to do with the technical failure of the asset, that can be caused due to;
- Age of Asset: Instead of retiring the asset beyond its useful life, continuous usage results in a total technical failure due to breakages, faults in delicate parts
- Maintenance Mismanagement: The maintenance process and exact steps of the asset weren’t maintained timely OR they weren’t maintained in the way they were supposed to be as suggested by the OEM.
For example:
- A cheaper alternative part was used to save costs but compromised on quality
- A technical gap in process – Incorrectly installed a part, loose bolts etc
- Power & Utility Functions: This includes Surges, brownouts, or compressed air leaks that cause sensitive PLC (Programmable Logic Controller) systems to trip.
- Hazardous Exposure: Due to improper maintenance, the asset simply cannot be used in a production line without causing environmental damage or posing a health and safety risk to the operators.
Ejemplo:
- An oil leak causing severe environmental degradation.
- An Machine cover posing a serious risk for OSHA
Natural Events & Calamities
These are aspects leading to asset failures that are generally out of organizational control – think floods, storms, lighting strikes etc.
This also includes aspects that can somewhat be controlled like high humidity or heat leading to excess wear and tear. Operating the equipment in extreme cold, well before the asset is adequately “warmed up”
Ejemplo 1: Extremely high humidity causing excess wear and tear, thus requiring maintenance well before the prescribed intervals suggested by the OEM
Master Data & Inventory Friction
This is much less common as an occurrence. It occurs when the right spare part is simply unavailable at the site OR an incorrect spare part has been ordered due to poor data stewardship and/or employing substandard spare parts management processes.
Cataloging of the spare part materials and ensuring excellent inventory management processes can kill this challenge. We will cover more on this in the latter portion of this article.
Human Gaps
Human error and workforce-related gaps are a surprisingly underestimated contributor to unplanned downtime.
This can manifest in several ways, a technician using the wrong torque specification when reassembling a component, misreading a technical drawing, or bypassing a checklist step due to time pressure or fatigue.
The issue compounds when organizations experience high technician turnover. Institutional knowledge, the kind that lives in a senior engineer’s head after 20 years on the floor, walks out the door with them.
New hires, without access to documented failure histories or structured onboarding, are far more prone to errors.
2.1M
Manufacturing jobs expected to go unfilled through 2030 due to skills gap, directly translating to maintenance quality and asset uptime deterioration.
Skills mismatch is another common culprit. With the rapid adoption of digital and IIoT-enabled equipment, many maintenance teams find themselves operating assets that require competencies they simply haven’t been trained on.
The solution begins with structured workforce planning: mapping skill sets required for every asset class against the capabilities on staff, identifying gaps, and plugging them proactively, before the asset fails and a technician is standing in front of it unprepared.
Loss Avoidance in Downtime Management
The goal with downtime management includes;
- Preventing Asset & Production Downtime in the first place:
- Ensuring RCA to understand the reasons for Failure and Future-proofing the issue
- Minimal downtime and faster turnaround during both planned and unplanned downtime
Companies typically arrive at a notional cost per hour of downtime. This is the output that the given piece of machinery or the production-line would generate, had the failure not occurred in the first place. These are losses that would otherwise have been clean revenue.
In effect, every minute of downtime represents a direct loss, not just a cost. The difference between a 2-hour repair and a 2-day shutdown is not always technical skill.
More often, it is a matter of preparation: having the right parts, the right people, and the right process documentation ready before the wrench is picked up.
Downtime Management Strategies
The strategies to prevent asset failure and reducing the downtime itself through effective diagnosis, and corrective action are detailed below;
Inventory Strategy & Processes
Both Asset failure and production downtime are inevitable.
Even the most mature predictive and preventive maintenance approaches consider the fact that this cannot be killed in its entirety.
One of the biggest opportunities for loss prevention OR revenue generation (depending on how one looks at it) is directly linked to managing critical inventories and ensuring they’re made available at the right plant location at the right time.
This is typically done through an advanced criticality assessment, not just at an asset but also at a spare part level.
Typically, companies review criticality only at an asset level and assume that IPSO FACTO all the spare parts linked to a given critical fixed asset are all critical.
This assumption itself is a logical fallacy, and leads to overstocking of parts that will likely remain unused for years together.
This is precisely where a spare parts management system can support the turnaround operation.
Some Studies and Research below to Showcase the Scale of the Challenge
A 2024 Siemens research, “True Cost of Downtime“, supported by recent ABB y Aberdeen updates, highlights that equipment failure causes 42% of all unplanned downtime.
Within that, the “Spare Parts Gap” is a primary driver of extended MTTR (Mean Time to Repair)
The Siemens 2024 data suggests that the absence of a critical spare part can turn a 2-hour mechanical repair into a 2-day facility shutdown (a 24x increase in duration) while waiting for international shipping or procurement cycles.
of global manufacturers have experienced at least one total line stoppage specifically due to a lack of a spare part, rather than a lack of technical skill to fix it. Within the 42% of downtime categorized as "Equipment Failure," an estimated 30-40% of the MTTR is dead time spent searching for, identifying, or waiting for the correct part.
Hidden Attribution: Within the 42% of downtime categorized as “Equipment Failure,” an estimated 30-40% of the MTTR (Mean Time To Repair) is “dead time” spent searching for, identifying, or waiting for the correct part.
MRO360, Verdantis’s flagship solution for Spare Parts & MRO inventory management solves this exact challenge.
If you’re not a fan of reading, the video above should be helpful in understanding what the software does and how it does it.
How to Solve it?
Trained on industry data pertaining to asset failures, inventories and Asset BOM documents, it’s possible to now assess criticality at a spare part level.
MRO360 does exactly this and assigns a score to every single part ranging from 1-10, with 10 being the most critical.
Among other things, the criticality score also accounts for the failure likelihood of that part, the consequence of that failure and even calculates the historical average lead-time for delivery that part to the production site.
Maintenance teams also undertake material demand planning exercises and apply those learnings to several different plant locations.
MRO360’s mathematical models with purpose trained AI forecasts material requirements based on legacy consumption and production volumes.
At an individual plant-level, this translates to accurate insight into the consumption volumes.
When both criticality & demand forecasts are considered in tandem, this ensures that Critical Spare Parts linked to both Critical as well as Non-Critical assets are adequately stocked, thus eliminating prolonged downtime or Maverick Spending linked to unavailability of the right spares.
The above analysis at a spare-part level is made possible due to linkages between Asset BOMs - linking a spare part with its dependent asset is critical for excellence in downtime management.
Just like Asset BOMs, work orders are complex documents as well - they detail extremely technical information about the repair work an asset is undergoing, the nature of failure, the likely parts and technician skills that will be required for undertaking the work order job itself.
Work orders can be both planned and unplanned, and they reside systematically in specific locations within EAM, ERP, CMMS or even specific folders on a drive!
With specially trained AI models, these documents can be scanned, interpreted and structured to feed into the asset management strategy.
For example; if 10 work orders in a specific plant location, all require 5 pieces each of a specific part, this spurt in demand can be passed along to the spare parts management software for further optimization of inventory.
Mantenimiento predictivo
Predictive maintenance implementation has become fairly common in pretty much all industries.
What was once a discipline restricted to large enterprise accounts with massive output is now a fairly common adopted practice.
Improvement in sensor technologies, Vibration Analysis, Spectrometry and the ability to make sense of the data has improved the ability of asset management teams to detect failures and the reasons for the failure.
The Strategy: In predictive maintenance, assets are monitored throughout their lifecycle through Iiot enabled sensor data, the lifecycle of the monitoring mechanism is expressed by way of a “PF curve”
P (Predictability) is simply the first time the issue with an asset is detected and F (Failure) is the moment the asset completely breaks down.
The idea is to detect the failure as early on as possible, flag potential reasons for the issue and provision for both manpower resources as well as materials for correcting the issue.
Given the sheer scale of the assets in an enterprise operational, prioritization is important.
Assets with a lower PF should be prioritized first, the forecasted demand should also consider the predictive maintenance data and provision for low PF assets and critical spares linked to those assets.
This integrates the 4Ms approach discussed above with powerful Predictive Maintenance data.
Using Agentic AI, MRO360 orchestrates these tasks autonomously, thanks to its ability to make sense of Predictive Maintenance data and ensure that resource planning for both planned and unplanned work orders are executed autonomously.
AIOps & Predictive Observability
Traditional Predictive Maintenance relies on static thresholds. Enterprises are now quickly moving towards AIOps
AIOps excels at multivariate analysis, which looks at how different variables interact.
It eliminates the “alert fatigue” of static thresholds by identifying specific failure signatures, which significantly reduces false alarms while catching actual issues much earlier on the P-F curve.
Furthermore, it creates a closed-loop system where detection automatically triggers work orders and spare parts logistics, slashing the lag time between identifying a problem and resolving it.
This transition to automated, precision-guided maintenance maximizes the operational lifespan of heavy equipment while nearly eliminating unplanned downtime.
AIOps excels at multivariate analysis, examining how variables interact rather than evaluating them in isolation. It reduces alert fatigue from static thresholds by identifying true failure signatures, lowering false alarms while detecting issues earlier on the P-F curve. Detection can automatically trigger work orders and spare parts logistics, shortening the time between identifying a problem and resolving it, and supporting longer equipment life with fewer unplanned disruptions.
Work Force Scheduling
Enterprises already leverage a Work Order Management system or a CMMS software to schedule both preventive maintenance jobs as well as corrective ones.
In fact, even mid-market + small & medium companies also make use of Work Order Schedules to correct these downtime-related constraints.
El desafío: Despite mature systems, it’s fairly common for a work order to be finalized and the repair work started only to find out that the necessary manpower skills are missing and a technician is missing.
The Strategy: As a practice, a work order should not be confirmed and repair work shouldn’t start unless the work order reaches a “Ready to Work Status”
The 4Ms: A “Ready to Work” Framework
As a discipline, a work order should not be confirmed and repair work should not start unless the work order reaches “Ready to Work” status. This is enforced through the 4Ms principle:
The necessary spare parts, tools and consumables are available to undertake the repair work
- Spare parts confirmed available
- Tools & consumables staged
- Correct part number verified
The technicians with the requisite Skill Sets for correcting this failure are available at the time duration for undertaking the repair work
- Technician assigned & available
- Required skill set confirmed
- Duration allocated in schedule
The SOP or the technical manual is printed or loaded onto a tablet
- SOP or OEM manual loaded
- Safety checklist printed/digital
- Permit to work issued
The production team has agreed to “hand over” the equipment at a specific timestamp
- Production team hand-over agreed
- Isolation & lockout confirmed
- Handover timestamp documented
These steps pretty much eliminates delay, where a 2-hour repair turns into a 6-hour ordeal because of mid-job trips to the MRO Storeroom.
We’ve already covered how Agentic AI can solve for the “Material Availability” challenge in the section above.
The “Work Order Planner” module can also assign specific technicians for executing a job, with perfect context of the nature of failure and the skill sets required for executing the job.
This information is made available through integration with employee master data tables that detail the skill sets and work history of technicians, consultants and third party vendors.
RCA Governance & Maturity
Root Cause Analysis (RCA) is a discipline through which maintenance and reliability professionals get down into the root cause of the failure, precisely to avoid these instances from repeating in the future.
Every Root Cause Analysis exercise has to be backed with rigorous technical analysis, documentation and action items for the future. Typically, this is captured in the work order.
Generally speaking, RCA is not performed for every minor inconvenience or a “glitch”.
Companies set specific thresholds as part of their overarching asset management strategy;
Some Triggers could be;
- HSE or Environmental Incidents: RCA requirement created automatically in the CMMS or EAM system
- Chronic Incidents: Actors that have repetitively failed 4+ times in a 12-month rolling period
- Operational Loss: Any machine or production line downtime exceeding a specific value - For example; $10,000 or more
In an AI-native EAM environment, however, RCA isn’t just a document you file; it’s a dynamic data point that shifts how the entire enterprise views its assets and inventory.
El desafío: The root cause analysis documentation process suffers from a “Governance” problem. General lethargy and human tendencies result in RCA documentation being incomplete without any context as to what exactly was the root cause.
Por ejemplo – The reliability engineer may simply mention that the issue was caused due to a “Pump motor Failure” without adequate context on:
- What exactly caused the motor to fail
- How likely is it to fail again
- Approximately how long the machinery is expected to work without failure
- What steps need to be taken to prevent this in the future
- What resources (parts and manpower) will be required to address the likely failure in the future
These are critical data points for an EAM solution, as this context can make inventory and resource planning extremely powerful.
En MRO360, RCA governance workflows are critical and configurable with a few simple steps, and they enforce the field technician to update the necessary context of the breakdown as detailed above, and this context is added to the intelligence layer of the software, linking the failure to the exact equipment.
This feeds into the inventory strategy, thus killing downtime, in the event of a repeated failure.
Reliability Centric Maintenance
In Reliability Centric Maintenance, the goal shifts slightly from the above. They focus here shifts slightly from “Keep the Equipment Running“ to “Smooth Continuity of Operations”.
This is a subtle but important distinction, it recognizes that not all assets require the same level of attention, and that over-maintaining non-critical assets can be as damaging to efficiency as under-maintaining critical ones.
According to ARC Advisory Group, 80% of assets fail randomly, not based on age, which fundamentally undermines time-based maintenance schedules that assume wear follows a predictable curve.
FMEA & FMECA Studies
Generally, an FMEA study is performed to understand all the different possible failure modes (What can go wrong?) and effects (the consequences in case something goes wrong)
This is generally done in gruelling workshops that are more of an alignment exercise where technicians do the entire analysis manually.
This is both time consuming, prone to errors and it’s simply a very static estimate that doesn’t account for real-world changes and ground-reality of machine conditions.
La solución:
Using AI to ingest thousands of historical work orders and OEM manuals to automatically populate “Failure Modes” and “Failure Effects”.
Dynamically calculating the Risk Priority Number (RPN) by cross-referencing real-time sensor data with the business cost of a specific line going down.
In the previous section, we showcased how MRO360 builds on the best practices of FMEA & FMECA and how it further improves on it with Agentic AI that feeds off first party data.
Integration of “Run-to-Failure” (RTF) Groups
A key RCM tenet is that some assets should be allowed to fail if they aren’t critical.
Approach for Reducing Downtime: For assets tagged as Run-to-Failure in the software, the system suppresses proactive maintenance alerts but maintains a Just-in-Time inventory of replacement parts so that when the failure occurs, the MTTR is minutes, not days
| Acérquese a | When to Apply | Estrategia de inventario | Risk Level |
|---|---|---|---|
| Predictive / Condition-Based | Critical assets with high downtime cost | Demand-forecasted, criticality-scored stock | Alta |
| Preventive / Time-Based | Assets with known wear patterns | Scheduled replenishment by OEM interval | Medio |
| Run-to-Failure (RTF) | Non-critical, easily replaceable assets | Just-in-Time replacement stock only | Bajo |
Dynamic “Task Interval” Optimization
RCM is not “set it and forget it.” Software provides the feedback loop necessary for Continuous Improvement.
Mathematical Analysis: Software analyzes the “Age-Reliability” relationship. If RCM originally suggested a part replacement every 1,000 hours, but the software sees that failures actually only start at 1,500 hours, it automatically pushes the maintenance interval back, saving costs without increasing risk.
Using software to track “Functional Failure” points versus “Potential Failure” points (the P-F interval) to refine exactly when a technician should intervene.
For assets designated as Run-to-Failure, the system suppresses proactive maintenance alerts while maintaining a Just-in-Time inventory of replacement parts, ensuring failures are resolved in minutes rather than days.
Conclusión
Asset downtime is not a single problem with a single solution. It is the compounded result of aging assets, inventory gaps, workforce limitations, documentation failures and missed predictive signals, each feeding into the other.
The organizations that are winning on uptime are not necessarily the ones spending the most on maintenance. They are the ones that have invested in the right data infrastructure, closed the loop between failure events and inventory planning, and built the institutional discipline to act on what the data is telling them, before the breakdown, not after.
The shift from reactive to proactive asset management is no longer a long-term aspiration. With purpose-built AI, connected work order intelligence and spare parts optimization software, it is an operational reality available to asset-intensive organizations today.
